Economics from a Business Perspective

Study the concept of long run economic growth and convergence in economics from a business perspective. Analyze data, calculations, and box plots to understand the growth patterns of different countries. Explore the factors influencing economic growth and the challenges faced by poor economies. Gain insights into the importance of capital infusion and the role of government policies in achieving convergence.

1
MBAF 504:
ECONOMICS FROM A BUSINESS PERSPECTIVE

2
Contents
1.0 Introduction..........................................................................................................................3
2.0 Background..........................................................................................................................3
3.0 Data analysis........................................................................................................................5
Calculations of growth of GDP per capita.................................................................................6
Box Plot for the year 1970.........................................................................................................6
Box Plot for the year 1985.........................................................................................................7
Box Plot for the year 2007.........................................................................................................7
4.0 Explanation of economic growth and convergence.............................................................9
5.0 Conclusion..........................................................................................................................10
Reference..................................................................................................................................11
Appendix..................................................................................................................................12

3
1.0 Introduction
Long run economic growth is an important concept in the field of economics which denotes
the increase in the value of goods and services of an economy for a large period of time. This
is measure is important to monitor the performance of a particular economy as a whole.
Although, this measure does not provide insight regarding the distribution of the wealth in the
economy, it provides a rough well being of an economy (Bücher & Kojadinovic, 2018). The
data shows that short run economic growth rate in poor countries are more than that of their
richer counterparts. This is due to the fact that availability of capital in poor economies is
very low.
Thus, their growth momentum has the potential to increase subject to the infusion of capital
in the economy. Theoretically, economics of growth and development says that poor
economies will eventually “catch up” owing to their high growth rate. However, historical
data analysis shows different results, a part of which will be shown in the analysis of this
paper (Savoia & Sen, 2016). There are two different types of convergence in the field of
economic growth. The concept of conditional convergence points out that, the dispersion in
the income level among different economies would reduce over time. On the other hand,
unconditional convergence is when the economic growth reduces with the increase in the
national product of that economy.
2.0 Background
1 2 3
0
5000
10000
15000
20000
25000
30000
35000
40000
45000
Australia
Canada
United States
Figure 1: Increase in per capita income of rich countries

4
(Source: Developed by the learner)
1 2 3
0
100
200
300
400
500
600
700
Nigeria
Bangladesh
Pakistan
Figure 2: Increase in per capita income of poor countries
(Source: Developed by the learner)
One of the common factors that can be seen between the increases in the per capita income
among the chosen countries is that it has increased over time. While, the inflation level in the
world as a whole is a factor for the increase, a significant impact have also caused by the
historic and economic factors of the world economics. Technological advancement is one of
the factors that increased the production cost around the world. This is one of the major
reasons for the increase in per capita income in rich countries (Kinfemichael & Morshed,
2019). From figure 1 it can be seen that the increase in the per capita income among the
countries has been almost the same.
However, in case of poor countries, the increase in the per capita income is mainly fuelled by
capital infusion in the form of foreign direct investment and foreign portfolio investment.
Pakistan has shown a strong increase in the per capita income after 1990 and this was also the
same year when the government increased the investment cap in the private companies of the
country. Bangladesh on the other hand showed a decrease in the per capita income between
1970 and 1985 owing to the war it fought for its freedom during this time. Although, the
government adopted neo liberal policies in the year 1975, it failed to have any positive impact
on the growth rate due to the after effects of the war which also includes reparations and
security payments (Li, Zhou & Pan, 2016). Nigeria too shows the same pattern of per capita

5
income as Bangladesh, however, increase in per capita income was much lower than that of
Bangladesh. The civil war in Nigeria ended in the year 1970 and after that it took time to
boost the national growth just like Bangladesh.
3.0 Data analysis
0
20
40
60
80
100
120
140
0 10000 20000 30000 40000 50000 60000
Series: Y1970
Sample 1 188
Observations 188
Mean 3937.432
Median 1278.380
Maximum 66571.87
Minimum 27.28000
Std. Dev. 7344.305
Skewness 4.692026
Kurtosis 34.02394
Jarque-Bera 8229.269
Probability 0.000000
Figure 3: The histogram for the year 1970
(Source: Developed by the learner)
In this case, there is an outlier which is Saudi Arabia whose per capita income is way too
high compared to the other countries. This can be due to the high revenue of the country from
oil exportation in different parts of the world.
0
10
20
30
40
50
60
70
80
0 4000 8000 12000 16000 20000 24000 28000
Series: Y1985
Sample 1 188
Observations 188
Mean 4619.299
Median 1655.445
Maximum 29956.82
Minimum 17.39000
Std. Dev. 6671.279
Skewness 1.974798
Kurtosis 6.210214
Jarque-Bera 202.9208
Probability 0.000000
Figure 4: The histogram for the year 1985

6
(Source: Developed by the learner)
0
20
40
60
80
100
120
0 10000 20000 30000 40000 50000
Series: Y2007
Sample 1 188
Observations 188
Mean 7460.105
Median 2259.140
Maximum 51924.06
Minimum 105.1800
Std. Dev. 10844.90
Skewness 1.876412
Kurtosis 5.802772
Jarque-Bera 171.8572
Probability 0.000000
Figure 5: The histogram for the year 2007
(Source: Developed by the learner)
So, over the years the number of countries with medium income has increased. Between the
histogram of 1985 and 2007, it needs to be noted that distribution has worsened (Bleischwitz,
Welfens & Zhang, 2017). That means the poor countries failed to catch up with the
developed economies of the world.
Calculations of growth of GDP per capita
Refer to the appendix

7
Box Plot for the year 1970
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
y1970
Figure 6: Box plot for the year 1970
(Source: Developed by the learner)
Box Plot for the year 1985
0
4,000
8,000
12,000
16,000
20,000
24,000
28,000
32,000
y1985
Figure 7: Box plot for the year 1985
(Source: Developed by the learner)

8
Box Plot for the year 2007
0
10,000
20,000
30,000
40,000
50,000
60,000
y2007
Figure 8: Box plot for the year 2007
(Source: Developed by the learner)
In an ideal scenario as put forward by the theories of development economics, the box plot
would move towards the centre of the space. However, the sign of convergence is not much
significant. Under conditional convergence the box plot should have been in the middle of the
graph. The growth rate of poor economies have reduces over time resulting in an
unconditional convergence.

9
Figure 9: Box plot of the poor countries for each year
(Source: Developed by the learner)
Figure 10: Box plot of the rich countries for each year
(Source: Developed by the learner)
From the figure 9 and 10 it is clear there is no evidence of convergence as the growth rate
poor economies have reduced before that could actually match with the developed one
(Asongu, 2017). However, it needs to be noted that, distribution among the rich countries
have improved between the year 1970 to 2007. On the other hand, the distribution has
worsened among the poor countries between the same times.
4.0 Explanation of economic growth and convergence
The data provided in the question has a specific structure which characterizes the country
based on the income per capita. Based on this income per capita, countries are either poor,
developing or developed countries (Lee, 2016). Now, based on the data and the analyses, I
believe poor countries cannot catch up to the prosperity level of the developed economy. First
and the foremost reason for this is the presence of liberal policies which increases the local
monopoly and reduces the potential gains available to the poor countries. In addition to that,
the political will of the existing government can also be blamed for this. In order to have a

10
conditional convergence, capital infusion in the economy needs to continue over the years.
However, most of the poor countries listed in the data set ranks higher in the corruption
perception index which becomes detrimental to the growth and development of the economy
(Yaya, Ling, Furuoka, Ezeoke & Jacob, 2019).
5.0 Conclusion
Therefore, on paper the different rate of economic growth will eventually make the poor
economies converge. However, an unconditional convergence set in where the growth of
economy reaches a saturation point. The data analysis done in this paper shows that, poor
countries, over the years have failed to show any sign of convergence. The distributions
worsened in all the parts of the world since the year 1970. In order to the theory to work,
individual interest or the operation of the corrupt government needs to be checked.

11
Reference
Asongu, S. A. (2017). Knowledge economy gaps, policy syndromes, and catch-up strategies:
Fresh South Korean lessons to Africa. Journal of the Knowledge Economy, 8(1), 211-
253.
Bleischwitz, R., Welfens, P., & Zhang, Z. (Eds.). (2017). Sustainable growth and resource
productivity: economic and global policy issues. Routledge.
Bücher, A., & Kojadinovic, I. (2018). A note on conditional versus joint unconditional weak
convergence in bootstrap consistency results. Journal of Theoretical Probability, 1-
21.
Kinfemichael, B., & Morshed, A. M. (2019). Unconditional convergence of labor
productivity in the service sector. Journal of Macroeconomics, 59, 217-229.
Lee, J. W. (2016). Korea's economic growth and catch‐up: Implications for China. China &
World Economy, 24(5), 71-97.
Li, K. W., Zhou, X., & Pan, Z. (2016). Cross-country output convergence and growth:
Evidence from varying coefficient nonparametric method. Economic Modelling, 55,
32-41.
Savoia, A., & Sen, K. (2016). Do we see convergence in institutions? A cross-country
analysis. The Journal of Development Studies, 52(2), 166-185.
Yaya, O. S., Ling, P. K., Furuoka, F., Ezeoke, C. M. R., & Jacob, R. I. (2019). Can West
African countries catch up with Nigeria? Evidence from smooth nonlinearity method
in fractional unit root framework. International Economics. 32(2), 135-142.

12
Appendix
1) The calculation of growth of per capita income
obs
y197
0
y198
5
y200
7
growth rate of GDP per
capita 1970-1985
growth rate of gdp per
capita 1985-2007
Afghanistan 162.5
169.9
8
211.9
4 4.603076923 24.68525709
Albania
1278.
17
1452.
49
2275.
92 13.63824843 56.69092386
Algeria
1379.
76
1975.
03
2291.
11 43.14301038 16.00380754
Angola
925.3
8
895.6
4
1790.
86 -3.213814865 99.95310616
Argentina
6621.
31
6085.
8
9006.
42 -8.087674493 47.99073252
Armenia
607.6
6
716.7
3
1285.
33 17.94918211 79.3325241
Australia
1190
5.44
1488
6.28
2368
2.51 25.03762986 59.08951061
Austria
1127
2.37
1706
0.16
2647
3.34 51.34492569 55.17638756
Azerbaijan
1295.
15
1589.
64
1766.
71 22.73790681 11.13900003
Bahamas
1351
5.1
1550
0.03
1829
3.74 14.68675777 18.0239006
Bahrain
1482
4.64
8748.
96
1660
4.11 -40.98365964 89.7838143
Bangladesh
270.5
6 237.8
441.8
8 -12.10821999 85.82001682
Barbados
6311.
98
7394.
03
1034
8.16 17.1427983 39.95290795
Belarus
853.9
4
1219.
54
2132.
79 42.81331241 74.88479263
Belgium
1119
0.76
1600
6.21
2458
7.88 43.03058952 53.61462832
Benin
317.6
2
339.9
3
384.2
1 7.024116869 13.02621128
Bermuda
2275
2.91
2995
6.82
4099
1.63 31.66148857 36.83571888
Bhutan 63.12
125.5
3
337.0
2 98.87515843 168.4776547
Bolivia
868.1
8
864.2
5
1139.
91 -0.452671105 31.89586347
BosniaHerzegovina
144.0
8
197.0
7
1417.
8 36.77817879 619.439793
Botswana
508.7
1
1799.
19
4573.
41 253.6769476 154.1927201
Brazil
1986.
82
3291.
3
4188.
57 65.6566775 27.26187221
Brunei
2385
9.87
2141
3.43
1721
7.42 -10.25336685 -19.59522599
Bulgaria
1138.
38
1559.
03
2399.
77 36.95163302 53.92712135
Burkina
165.4
3
206.9
9
257.5
2 25.12240827 24.41180733
Burma 79.2 124.8 309.6 57.62626263 148.0134572

13
4 2
Burundi
117.8
3
137.0
5
105.1
8 16.31163541 -23.25428676
Cambodia 97.8
193.7
8
419.0
5 98.1390593 116.250387
Cameroon
441.8
1
856.4
5
654.9
8 93.85029764 -23.52384844
Canada
1271
6.56
1816
2.95
2589
9.21 42.82911416 42.59363154
CapeVerdeIslands
357.4
3
797.9
5
1807.
23 123.2465098 126.4841155
Central African
Republic 349.7
298.4
3 224.6 -14.66113812 -24.73946989
Colombia
1256.
67
1733.
64
2529.
88 37.95507174 45.92879721
Comoros Islands
541.1
8
499.0
1
342.0
9 -7.79223179 -31.4462636
Congo Brazzaville
5298.
69
3968.
04
3380.
85 -25.1128109 -14.79798591
Costa Rica
2509.
09
2891.
55
5218.
96 15.24297654 80.49004859
Cote Divoire
833.2
1
773.4
7
602.6
5 -7.169861139 -22.08489017
Croatia
2420.
84
4140.
44
5480.
74 71.03319509 32.37095574
Cuba 546.3
872.8
4
1778.
45 59.77301849 103.7544109
Cyprus
2237.
01
6491.
53
1448
6.82 190.1877953 123.1649549
Czech Republic
3018.
45
4528.
53
7253.
83 50.0283258 60.18067673
Denmark
1851
8.21
2371
3.02
3284
2.01 28.05244135 38.49779573
Djibouti 27.28 17.39
1372.
67 -36.25366569 7793.444508
Dominica
1501.
71
2303.
99
4260.
94 53.42442948 84.93743462
Dominican Rep
995.9
6
1511.
3
2832.
69 51.74304189 87.43399722
Ecuador 772.8
1320.
81
1578.
34 70.91226708 19.49788387
Egypt
606.9
5
1068.
02
1745.
18 75.96507126 63.40330705
El Salvador
1948.
69
1636.
66
2242.
36 -16.01229544 37.00829739
Equatorial Guinea
684.8
2
715.4
2
4544.
68 4.468327444 535.2464287
Eritrea
163.7
1 171.7
154.5
8 4.880581516 -9.970879441
Estonia
2818.
81
3891.
02 7169 38.03768257 84.24474816
Ethiopia
121.7
6 85.52
124.4
4 -29.76346912 45.50982226
Fiji
1399.
8
1683.
09
2194.
89 20.23789113 30.40835606
Finland
1100
3.11
1698
9
2763
1.23 54.40180095 62.64188593
France
1209
5.58
1676
5.32
2454
2.49 38.60699528 46.38843756

14
French Polynesia
8384.
55
1246
3.5
1442
3.7 48.64840689 15.72752437
Gabon
3040.
71
4428.
28
3934.
71 45.63309227 -11.1458625
Gambia
273.2
3
315.3
6
337.7
5 15.41924386 7.099822425
Georgia
1634.
78
3284.
37
1081.
54 100.9059323 -67.07009259
Germany
1257
1.26
1769
3.74
2518
2.88 40.74754639 42.32649513
Ghana
290.0
9 195.3
311.8
5 -32.67606605 59.67741935
Greece
6135.
62
8522.
85
1368
0.02 38.90772245 60.50992332
Grenada
1282.
94
2254.
81
5031.
96 75.75334778 123.1655882
Guatemala
1278.
59
1454.
1
1842.
88 13.72683972 26.73681315
Guinea
297.3
6
322.6
9
395.9
6 8.518294323 22.70600267
Guinea Bissau
167.8
1
174.9
4
154.2
3 4.248852869 -11.83834458
Guyana 758.1
652.4
6 999.3 -13.93483709 53.15881433
Haiti
598.0
4
722.4
7
468.6
4 20.80630058 -35.13363877
Honduras
728.8
8
909.5
7
1019.
03 24.7900889 12.03425795
Hong Kong
5925.
91
1370
0.24
3274
6.84 131.1921713 139.0238419
Hungary
2310.
56
3866.
22
6095.
47 67.32826674 57.65967793
Chad
228.0
3
201.8
8
289.5
1 -11.46778933 43.40697444
Chile
2241.
97
2411.
87
6406.
94 7.57815671 165.6420122
China
121.9
8
287.7
7
1754.
59 135.9157239 509.7195677
Iceland
1298
3.36
2275
3.33
3341
2.87 75.2499353 46.84826353
India 205.1
260.8
8
664.2
4 27.19648952 154.6151487
Indonesia
225.5
4
465.6
5
992.8
9 106.4600514 113.2266724
Iran
1939.
78
1403.
42
2229.
58 -27.65055831 58.86762338
Iraq
2289.
35 1660
2142.
89 -27.49033568 29.08975904
Ireland
7052.
58
1058
6.66
3241
7.82 50.11045603 206.2138578
Isle of Man
5655.
19
1059
5.99
3052
0.49 87.36753319 188.0381163
Israel
9425.
19
1351
1.57
2182
6.48 43.35594296 61.53918457
Italy
9596.
75
1405
6.4
1994
4.81 46.47041967 41.8913093
Jamaica and
Dependencies
2974.
44
2313.
73
3231.
23 -22.21292075 39.65458372
Japan 1712 2692 4080 57.20255385 51.54630637

15
7.05 4.16 2.57
Jordon
670.8
5
2049.
54
2071.
41 205.5139003 1.067068708
Kazakhstan
1093.
55
1433.
42
2306.
95 31.07951168 60.94026873
Kenya
242.2
3
337.2
9
444.8
5 39.24369401 31.88947197
Kiribati 896.8
516.9
9 491.8 -42.35169492 -4.87243467
Kuwait
4270
0.35
1370
7.16
2010
2.15 -67.89918584 46.65437625
Kyrgyzstan 336.1
392.7
2
331.5
2 16.84617673 -15.58362192
Laos
190.2
9
206.3
3
407.9
4 8.429239582 97.71240246
Latvia
2154.
88
3743.
59
6001.
2 73.72614716 60.30601642
Lebanon
2745.
88
3345.
99
5468.
36 21.85492447 63.43025532
Lesotho
184.1
7
305.1
5
506.0
6 65.68930879 65.83975094
Liberia
837.1
3 596.5
161.9
5 -28.74463942 -72.84995809
Libya
6186.
74
2609.
69
8173.
61 -57.81801078 213.2023344
Lithuania
2768.
14
3823.
57
5227.
61 38.1277681 36.72065635
Luxembourg
1581
8.73
2135
5.42
5192
4.06 35.00085026 143.142303
Macau
4843.
29
1029
6.97
2339
2.78 112.6027969 127.1811999
Macedonia
1664.
92
1923.
9
1987.
47 15.55510175 3.304225791
Madagascar
414.7
7 284.3
253.4
2 -31.45598766 -10.86176574
Malawi
119.3
6
148.1
9
160.8
7 24.15382038 8.556582765
Malaysia
1097.
05
2098.
9
5035.
57 91.32218222 139.9147172
Maldives Islands 407.5
1000.
37
2407.
6 145.4895706 140.6709517
Mali
187.2
7
210.6
6
303.9
8 12.48998772 44.29887022
Malta
1831.
98
5046.
41
1018
0.46 175.4620684 101.7366801
Marshall Islands
2109.
64
1776.
65
1844.
04 -15.78420963 3.793093744
Mauritania 350.7
318.5
8
556.1
4 -9.158825207 74.56839726
Mauritius
1020.
99
1842.
86
4597.
84 80.49736041 149.494807
Mexico
3430.
14
4956.
94
6329.
31 44.51130275 27.68583037
Micronesia,
Federated States
of
3302.
45
2441.
55
1112.
93 -26.06852488 -54.41707112
Moldova
574.9
5
786.7
5
460.3
1 36.83798591 -41.49221481
Mongolia 313.4 412.3 481 31.57206216 16.64564943

16
1 6
Morocco 693.1 984.9
1333.
37 42.10070697 35.38125698
Mozambique 91.02
144.1
4
275.1
1 58.36079982 90.86304981
Namibia
2375.
61
1650.
89
2219.
97 -30.50669091 34.47110347
Nepal
145.0
5
158.2
9
229.3
6 9.127886936 44.89860383
Netherlands
1282
3.45
1637
6.05
2438
2.04 27.70393303 48.88840716
New Caledonia
1089
2.15
9219.
07
1731
7.4 -15.36042012 87.84324232
New Zealand
9722.
85
1161
0
1589
2.09 19.40943242 36.88277347
Nicaragua
1548.
92
1065.
18
872.8
6 -31.23079307 -18.05516439
Niger
277.5
9
191.0
5
182.1
5 -31.17547462 -4.65846637
Nigeria
359.0
3
321.6
4
466.1
8 -10.41417152 44.93844049
Norway
1590
6.14
2737
0.49
4137
7.57 72.07499745 51.17584669
Oman
4040.
3
7360.
19
8061.
83 82.16939336 9.53290608
Pakistan
260.8
7 379.5
622.5
4 45.47475754 64.04216074
Palau
5663.
88
6669.
29
6669.
38 17.75125885 0.001349469
Panama
2790.
48
3505.
8
4897.
04 25.63429948 39.68395231
Papua New Guinea
671.8
3
623.5
2
667.4
5 -7.190807198 7.045483705
Paraguay
833.9
2
1431.
09
1361.
86 71.60998657 -4.837571362
Peru
2079.
67
2058.
07
2496.
04 -1.038626321 21.28061728
Philippines
693.5
7
778.8
6
1138.
59 12.29724469 46.18673446
Poland
1945.
36
2684.
17
5665.
61 37.97806062 111.0749319
Portugal
4111.
09
6239.
19
1048
5.88 51.76486041 68.06476482
Puerto Rico
6977.
18
9201.
3
1832
9.95 31.87706208 99.21043766
Republic of South
Africa
3014.
89
2982.
11
3930.
87 -1.087270182 31.81505712
Romania
1309.
19
2286.
51
2478.
34 74.65073824 8.389641856
Russia
1712.
36
2446.
84
2819.
42 42.89284963 15.22698664
Rwanda 228.8
277.0
2
247.3
6 21.07517483 -10.70680817
Samoa
1250.
13
1113.
15
1607.
46 -10.95726044 44.40641423
Sao Tome and
Principe
370.0
1
334.6
1
322.6
6 -9.567308992 -3.571321837
Saudi Arabia
8614.
28
8984.
73
9095.
77 4.300417446 1.235874645

17
Senegal
450.2
2 395.8
492.1
2 -12.08742393 24.33552299
Serbia 557.7
727.6
9
1245.
18 30.4805451 71.1140733
Seychelles
2526.
8
4234.
39
7276.
95 67.5791515 71.85356096
Sierra Leone
232.3
3 220.3
165.6
2 -5.177979598 -24.82069905
Singapore
4414.
54
1065
2.19
2671
9.65 141.2978476 150.8371518
Slovakia
2546.
02
3548.
16
5317.
51 39.36104194 49.86669147
Slovenia
4580.
39 6332
1216
6.25 38.24150345 92.13913455
Solomon Islands
535.4
1
642.6
7
603.0
4 20.03324555 -6.166461792
South Korea
1885.
75
4439.
99
1432
2.13 135.4495559 222.571222
Spain
6744.
33
9053.
6
1778
6.94 34.24016915 96.4626226
Sri Lanka
335.0
8
511.5
9
1050.
5 52.67697266 105.3402138
St Kitts and Nevis
1503.
98
3409.
82
1059
2.81 126.7197702 210.6559877
St Lucia
1966.
2
2324.
67
4576.
54 18.23161428 96.86837272
St Vincent and the
Grenadines
1056.
93
1703.
86
3484.
77 61.20840548 104.5220851
Sudan
273.0
3
239.7
1 445.6 -12.20378713 85.8912853
Suriname
1675.
6
1751.
57
2513.
24 4.533898305 43.48498775
Swaziland
674.7
4
875.2
2
1419.
72 29.71218543 62.21292932
Sweden
1582
6.3
2027
3.85
3142
7.01 28.1022728 55.01254079
Switzerland
2535
7.78
2938
2.72
3575
6.38 15.87260399 21.69186515
Syria
545.7
2
969.7
5
1190.
76 77.70101884 22.7904099
Taiwan
2318.
72
5727.
36
1766
7.65 147.0052443 208.4780772
Tajikistan
604.1
6
526.1
2
242.1
3 -12.91710805 -53.97817988
Tanzania
255.7
9
256.4
8
372.2
1 0.269752531 45.1224267
Thailand
517.7
7
946.5
2
2633.
15 82.80703787 178.1927482
Togo
325.1
1 323.9
282.1
7 -0.372181723 -12.88360605
Tonga 955.7
1255.
49
1545.
42 31.36863032 23.09297565
Trinidad and
Tobago
3245.
3
5520.
02
1085
2.22 70.09274951 96.5974761
Tunisia
831.9
1
1440.
17
2628.
6 73.11608227 82.52011915
Turkey
1575.
7
2129.
34
3866.
62 35.13612997 81.58772202
Turkmenistan 954.1 1014. 1644. 6.298081962 62.16882438

18
19 7
Uganda
189.6
1
166.6
8
290.2
7 -12.09324403 74.14806815
Ukraine
909.8
5
1332.
2
1066.
37 46.41973952 -19.95421108
United Arab
Emirates
6657
1.87
1987
6.14
4390
5.5 -70.14333532 120.8955059
United Kingdom
1292
5.58
1707
7.13
2829
9.86 32.11886817 65.71789288
United States
1839
4.85
2538
5.98
3834
0.27 38.00590926 51.0293083
Uruguay
3922.
23
4086.
89
6889.
77 4.198121987 68.58222267
Uzbekistan
732.8
3
648.8
7
729.6
9 -11.45695455 12.45549956
Vanuatu New
Hebrides
1319.
43
1997.
26
3147.
94 51.37294135 57.61292971
Venezuela
6481.
05
5108.
04
5992.
36 -21.18499317 17.31231549
Vietnam
191.9
9
201.2
8
607.0
6 4.838793687 201.5997615
West Bank
4191.
66
3219.
91
1462.
98 -23.18293946 -54.56456857
Yemen United
296.4
9
297.7
7
158.7
5 0.431717765 -46.68704033
Zaire
352.2
1
315.2
9
159.8
9 -10.48238267 -49.28795712
Zambia 559.4
411.1
2
396.5
3 -26.50697176 -3.548842187
Zimbabwe
582.6
2
629.2
1
422.3
6 7.996635886 -32.87455698