UOL International Business Effects of Global Warming in Economic Development Essay

User Generated

flfgeryyn

Mathematics

University of London

Description

All are attached below, max word count is 9 pages

Problem Description:

The role of temperature in economic development has been an important question. It is often documented that hot countries (or countries closer to equator) tend to be poor. In recent decades, with the warming at a global scale, scientists and policy makers are increasingly concerned about the economic consequence of warmer and drier weather.

In this assignment, you are given historical data on temperature, precipitation, and economic outcomes for a sample of 106 countries.You are required to analyse the impact of temperature, precipitation on income levels (measured by per capita GDP in natural logarithm), and more important, on income growth (measured by annual growth of per capita GDP).

Each question is described below (2 + 3 + 4 + 6 + 8 + 4 + 1+ 4 = 32 marks; professional report = 8 marks):

Locate the data file (IndividualBusStats.xls) on CANVAS.

  • Has the global been becoming warmer and drier over 1950-2000? Draw evidence on the descriptive statistics of relevant variables in the dataset.
  • Calculate the sample covariance and correlation for the two relationships in question 2 above using Data Analysis Tool Pack or Excel statistical functions. In addition, you are required to calculate the sample covariance and correlation using a second method (using basic Excel formulae without Data Analysis Tool Pack). The calculations by the second method should be carefully laid out in Excel and should NOT use any hard-wired Excel statistical functions e.g. COVARIANCE.S, CORREL, et al. You can use the Excel sort command, the sum command, and any other non-statistical excel commands. Carefully interpret your results.
  • Use simple regression to explore the relationship between (i) annual growth of per capita GDP over 1990-2000 (Y) and mean temperature over 1990-2000 (X); (ii) annual growth of per capita GDP over 1990-2000 (Y) and mean precipitation over 1990-2000 (X), respectively. You may use Data Analysis Tool Pack for this. Based on the excel regression output, first write down the estimated regression equations, then carry out any relevant two-tailed hypothesis tests using the critical value approach at the 5% significance level. Carefully interpret your hypothesis test results.
  • Now use multiple regression to explore the relationship of annual growth of per capita GDP over 1990-2000 (Y) with, mean temperature over 1990-2000 (X1), and mean precipitation over 1990-2000 (X2). You may use Data Analysis Tool Pack for this. Based on the excel regression output, first write down the estimated regression equation, then interpret the estimated coefficients on the mean temperature over 1990-2000 (X1), and mean precipitation over 1990-2000 (X2). Carry out any relevant two-tailed hypothesis tests using the p-value approach at the 5% significance level, and an overall significance test using the p-value approach. Carefully interpret your hypothesis test results.
  • Write down a paragraph or two to illustrate: a) why the hypothesis test is required in the above regression analysis (either simple or multiple); b) the underlying mechanism/intuition of a two-tailed hypothesis test in the context of regression analysis. Aid your illustration with diagrams that you think appropriate.
  • Carefully interpret the adjusted R-squared in the multiple regression analysis.
  • If you could request additional data to study the factors that influence the annual growth of per capita GDP, what extra variables would you request? Illustrate two such variables. Carefully explain why you choose these two variables (by drawing evidence from the literature such as journal articles, newspapers, et al), types of your proposed variables (e.g. numerical or categorical), and how each of your proposed variables will be measured in the regression model.

2.Do hot and dry countries tend to be poor (with lower per capita GDP)? Use appropriate graphs to interpret the relationship between relevant variables. You only need to present two relationships. Carefully interpret and explain.

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> Arial (18 points, Boldface) Executive Summary Here you present a one- or two-paragraph summary of the report. This summary should stand alone (no reference to figures or tables in the text) and provides a clear overview of the essential information comprising a report: context to the issue/topic, scope of the investigation, methodology used, and key findings and recommendations. XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX Introduction In the “Introduction,” make sure that you orient the audience with sufficient background to understand what the problem is and why the problem is important (engage with audience). You also provide: the aims and objectives of the report, a description of research methods, and overall structure of the report. XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX The body of the report presents the analysis and interpretations. XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX Conclusions This section summarizes the document and provides closure. The difference between this summary and the executive summary is that the summary in the “Conclusion” is for someone who has read the report. XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX References XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX Other notes: a professional report also needs • • • • • Page numbering Informative heading and sub-headings Numbered sections Labelled graphs and tables Nice overall formatting and presentation 1 Data sources: The dataset consists of a sample of 106 countries. The historical weather data are taken from the Terrestrial Air Temperature and Precipitation: 1900–2006 Gridded Monthly Tim Data on the level and annual growth of per capita GDP are drawn from the World Development Indicators (WDI). variables: per_cap_GDP2000 growth1990_2000 tem1950_1960 pre1950_1960 tem1990_2000 pre1990_2000 per capita GDP in year 2000 (US$), expressed in natural logarithm annual growth of per capita GDP over 1990-2000, expressed in percentage (%) mean temperature over 1950-1960 (measured in degrees Celsius per year) mean precipation over 1950-1960 (measuerd in units of 100 mm per year) mean temperature over 1990-2000 (measured in degrees Celsius per year) mean precipation over 1990-2000 (measuerd in units of 100 mm per year) 0–2006 Gridded Monthly Time Series, Version 1.01 (Matsuuraand Willmott 2007). country Nigeria Panama United Arab Emirates Spain Austria Canada Vanuatu Australia Sierra Leone Brunei Ghana Fiji Egypt Turkey Belgium Cote d'Ivoire Iran India Suriname Senegal Togo Rwanda Peru Mauritius Philippines Mali Germany Chile Oman Honduras Argentina Uganda France Lesotho Laos Haiti Paraguay Costa Rica Mauritania United States Botswana Brazil Trinidad and Tobago Korea Ireland Colombia per_cap_GDP2000 growth1990_2000 tem1950_1960 pre1950_1960 6.341999468 0.017646708 26.52577 13.88756 8.309018174 3.343584973 24.68316 28.04099 10.41305497 0.351553339 26.84328 1.631635 9.596491202 2.487687259 13.48391 6.651904 10.10905589 2.300388065 7.549002 9.950752 10.09370492 0.582056958 4.841475 9.364386 7.2934069 1.483790792 24.15511 27.481 9.984110761 2.135885605 15.86288 9.355159 4.932303899 -2.469349752 25.70401 28.29842 9.798824364 -0.428450843 27.20606 30.216 5.554783345 1.456127111 26.31561 14.2106 7.638472188 1.673783596 23.89741 31.08469 7.279647225 2.512322923 21.03608 0.3591493 8.375048385 2.602163169 12.03383 5.847577 10.04505372 2.029001451 9.626793 7.704759 6.478979435 -1.23001107 26.24332 15.25539 7.420584576 2.423024302 13.79623 3.894315 6.094278759 3.585117687 25.23642 12.74809 7.607024507 -1.087064319 26.61093 22.14153 6.404656503 0.055498592 26.9184 9.227755 5.711110209 -0.37610378 26.07221 12.79692 5.563669533 1.252000313 19.12939 10.76035 7.578446198 1.279218094 13.26135 8.056942 8.276159434 4.281374685 24.04545 16.54273 6.978035781 0.530769128 25.38977 24.72692 5.597944577 0.967916696 27.92813 8.587226 10.07052326 1.841511218 8.429167 7.853553 8.532062419 4.485345735 11.73466 6.047864 9.05966537 1.877228526 25.61425 1.243683 6.985144126 0.306134294 23.70358 15.80122 8.950027132 2.272210933 16.78562 8.500537 5.56784437 3.261433402 22.20274 11.86919 10.01520912 1.726098494 10.32313 7.900137 6.07876169 2.485163739 12.33063 7.941034 5.784400396 3.828208018 23.5511 18.59604 6.698926252 -1.597174052 23.80118 15.15912 7.416742365 0.217254068 22.6103 13.86094 8.235591267 2.253617492 21.69974 37.24714 6.517032455 -0.871080318 28.07941 3.883507 10.50053423 2.055704447 13.06245 9.107277 8.166871928 2.465008425 20.15629 5.036779 8.229445317 0.469748127 21.38649 13.77444 8.769532711 4.960545686 26.27126 20.02921 9.413851957 6.467544334 11.18752 11.73753 10.17509225 6.376785862 9.004849 10.22423 7.832205071 1.012220862 21.26738 20.56923 Indonesia Belize Algeria Cuba Uruguay Cabo Verde Norway Ecuador Denmark Dominican Republic Nicaragua Sri Lanka China Japan Gambia, The Guinea-Bissau Hungary Guatemala United Kingdom Nepal Jordan Samoa Venezuela Bolivia Iceland Greece New Zealand Cameroon Italy Burkina Faso Guinea Sweden Morocco Madagascar Netherlands Gabon Kenya Comoros Switzerland Congo, Dem. Rep. Jamaica Kuwait Benin Mexico Niger Malawi Romania 6.659537737 8.120984986 7.475918546 7.91830057 8.835650025 7.138330075 10.54879496 7.276057886 10.33343542 7.961755713 6.915228712 6.768144319 6.866279408 10.5592454 6.387134578 5.730567593 8.43907632 7.417159268 10.24529802 5.435860707 7.409513382 7.340878832 8.485090821 6.905334812 10.37405551 9.396235014 9.520842773 6.476959817 9.907857589 5.540155202 5.895731095 10.29637093 7.196643912 5.682242675 10.17158195 8.32499502 5.985151307 6.4705188 10.54187024 6.004420883 8.127019037 9.822298024 6.237152507 8.875959976 5.284935254 5.052325511 7.414517241 2.747891283 3.752138039 -0.164740644 -1.786452329 2.5076454 7.958015454 2.958794983 0.011939686 2.201612231 3.15293866 1.10224032 4.438963593 8.655843283 1.356308293 -0.034229888 -0.437058263 1.899129864 1.460227831 2.050550258 2.508794623 0.486085514 1.171336005 0.422032291 1.75394906 1.648187168 1.596620334 1.404064547 -2.15358778 1.671002216 1.986588691 1.245459329 1.620467187 1.514975138 -1.187089028 2.743167206 -0.533867964 -0.940087436 -0.405154735 0.728696009 -8.474498001 1.126682665 -2.867917947 1.702369936 1.928195582 -2.038653695 1.968176058 -0.771896213 25.50005 25.42072 16.80155 25.31185 16.99368 23.36591 3.29523 19.90497 7.6489 24.47031 24.78792 26.605 13.42423 12.96264 25.8047 26.46866 10.43776 19.84146 9.041043 20.44924 18.24715 26.06212 24.99508 18.62292 2.96588 14.95799 11.98894 24.23516 11.81764 27.41963 25.03104 5.282156 17.04815 20.67844 9.666837 24.77283 20.31933 24.80303 4.975078 24.07286 23.81385 25.5101 27.02115 18.61772 27.78783 21.42636 8.93429 24.98619 23.68984 5.642025 13.27607 11.09725 7.898182 9.886539 13.73285 6.644507 17.84686 16.98472 23.26587 9.934293 17.9546 13.73073 18.698 6.082292 22.9342 8.130628 15.23243 2.726088 30.906 11.94165 10.56473 8.354614 7.117124 12.29694 20.22872 10.35821 9.59704 22.86032 6.289688 4.353302 15.35144 7.447047 22.07469 11.0526 22.98245 13.79524 14.23098 19.84624 0.939177 11.75305 9.262138 5.96913 11.63722 5.974701 Central African Republic Tanzania Chad Bhutan Syrian Arab Republic Luxembourg Tunisia Mozambique Poland El Salvador Burundi Saudi Arabia Cambodia 5.526276812 6.018477217 5.110558976 6.576731138 7.071258601 10.79417316 7.701574067 5.766317693 8.41215575 7.601671169 4.91606063 9.123837609 5.712336331 -1.60894537 0.462086634 -1.433425935 4.340952013 2.494926336 3.626749501 3.286240995 2.790436047 3.769896707 2.334291939 -3.022205969 1.22463352 -0.932081399 24.71314 22.36172 27.7491 11.55096 17.28383 9.165152 18.06059 23.66864 7.640728 22.56823 19.28739 24.68902 27.06396 14.97238 9.373319 8.173254 34.60533 3.931275 7.408273 4.862517 10.34521 5.830766 18.02399 10.87615 1.22218 16.69377 tem1990_2000 pre1990_2000 26.85172 13.39307 25.11478 27.48667 26.91433 1.263214 14.41149 6.297207 7.952092 9.221741 5.296148 9.385453 24.62386 25.28607 15.80989 7.964385 26.19973 25.13283 27.06742 26.36298 26.67841 12.75037 24.03207 25.13651 21.2031 0.6710021 12.12031 6.115713 10.52248 7.875272 26.26308 12.65963 14.32119 3.533688 25.26296 11.98164 26.69649 21.5842 27.44442 6.163564 26.32782 11.59565 20.2585 10.81682 14.05118 8.142179 23.99242 14.99573 25.64058 23.83464 28.47546 6.926775 9.261319 7.223675 11.39667 5.756211 25.76736 1.718448 24.79869 12.35854 17.18774 9.177779 22.31762 11.9865 11.22639 7.400362 12.00661 6.443765 23.60845 17.78357 24.65671 10.86323 22.1425 16.53286 22.8697 39.86776 28.8736 2.743884 13.30472 8.762918 21.53338 4.100937 22.30956 13.2029 26.05961 19.53647 11.56427 13.40523 9.577958 10.37711 21.22947 19.03087 25.76939 25.85874 17.13557 25.33854 17.37455 23.60606 4.282211 20.40689 8.391273 25.66094 26.53195 26.87928 13.91455 13.55762 26.40894 26.88505 10.61292 21.82512 9.619326 20.37417 17.66171 25.95227 25.74873 18.49841 2.467539 14.64826 11.90126 24.34234 12.33237 27.9637 25.37372 6.023034 17.19479 21.05817 10.35187 24.69613 20.06081 26.02197 5.804756 24.33597 24.99295 25.97743 27.12502 18.99939 28.34429 23.08399 9.209678 18.55051 23.32534 4.614381 10.69392 11.01161 8.581819 9.770261 16.58907 3.57791 14.14335 14.15275 22.05306 10.40647 16.39544 9.141487 14.93492 5.907116 18.68205 7.720697 14.7353 2.621536 15.19527 11.70615 9.714385 8.012055 5.710767 10.24473 17.56387 11.61727 8.097651 19.88388 5.778014 4.088074 14.66957 7.761985 20.5727 12.29716 20.58245 11.75557 15.48968 17.55477 1.293124 10.76115 8.251323 4.570006 10.28716 3.596791 24.49559 22.63398 28.09092 11.37613 17.54745 9.602273 18.98573 24.48417 8.318139 24.23132 20.75322 25.52149 27.78295 14.09322 10.18079 6.980453 23.39394 3.316102 7.092182 4.316459 9.191278 6.146651 14.27574 10.58038 0.8200489 12.87399 ECON 1035 – BUSINESS STATISTICS 1: Individual Assignment Instructions: This is an individual assignment with a total of 40 marks. The allocation of marks is as follows: Statistical Analysis (including excel) Professional Report Total 32 8 40 The response to the assignment must be provided in the form of a professional report with no more than 10 pages (excluding cover page). The structure of your professional report must include: 1] A Title, 2] An Executive Summary, 3] An Introduction, 4] Analysis & Interpretation, and 5] Conclusions. You must submit an electronic copy of your assignment in Canvas. See the attached Template of your submission for more details. This assignment requires the use of Microsoft Excel. If you have Windows, you will need to use the Data Analysis Tool Pack. If you have a Mac with Excel 2011, you may need to use StatPlus:MAC LE. You will need to include your Excel output as an excel file submitted with your report. The excel file needs to be clear and carefully organised and must show relevant workings underlying the Professional report and associated statistical analysis. It will be treated as an appendix to your report, i.e. not included in the page count. Do not refer to the excel workbook within the Professional report. You will need to take the key results from your workbook and incorporate into your report. Presentation Instructions: Your written professional report should comply with the following presentation standards: 1. Typed using a standard professional font type (e.g. Times Roman), 12-point font size. 2. 1.5-line spacing, numbered pages, and clear use of titles and section headings. 3. Delivered as a Word (.doc or .docx) or PDF (.pdf) file. 4. Checked for spelling, typographical and grammatical errors. Where relevant, round to 3 decimal places. 5. With all relevant tables and charts, the report should be no more than 10 pages long. Problem Description: The role of temperature in economic development has been an important question. It is often documented that hot countries (or countries closer to equator) tend to be poor. In recent decades, with the warming at a global scale, scientists and policy makers are increasingly concerned about the economic consequence of warmer and drier weather. In this assignment, you are given historical data on temperature, precipitation, and economic outcomes for a sample of 106 countries. You are required to analyse the impact of temperature, precipitation on income levels (measured by per capita GDP in natural logarithm), and more important, on income growth (measured by annual growth of per capita GDP). Each question is described below (2 + 3 + 4 + 6 + 8 + 4 + 1+ 4 = 32 marks; professional report = 8 marks): Locate the data file (IndividualBusStats.xls) on CANVAS. 1. Has the global been becoming warmer and drier over 1950-2000? Draw evidence on the descriptive statistics of relevant variables in the dataset. 2. Do hot and dry countries tend to be poor (with lower per capita GDP)? Use appropriate graphs to interpret the relationship between relevant variables. You only need to present two relationships. Carefully interpret and explain. 3. Calculate the sample covariance and correlation for the two relationships in question 2 above using Data Analysis Tool Pack or Excel statistical functions. In addition, you are required to calculate the sample covariance and correlation using a second method (using basic Excel formulae without Data Analysis Tool Pack). The calculations by the second method should be carefully laid out in Excel and should NOT use any hard-wired Excel statistical functions e.g. COVARIANCE.S, CORREL, et al. You can use the Excel sort command, the sum command, and any other non-statistical excel commands. Carefully interpret your results. 4. Use simple regression to explore the relationship between (i) annual growth of per capita GDP over 1990-2000 (Y) and mean temperature over 1990-2000 (X); (ii) annual growth of per capita GDP over 1990-2000 (Y) and mean precipitation over 1990-2000 (X), respectively. You may use Data Analysis Tool Pack for this. Based on the excel regression output, first write down the estimated regression equations, then carry out any relevant twotailed hypothesis tests using the critical value approach at the 5% significance level. Carefully interpret your hypothesis test results. 5. Now use multiple regression to explore the relationship of annual growth of per capita GDP over 1990-2000 (Y) with, mean temperature over 1990-2000 (X1), and mean precipitation over 1990-2000 (X2). You may use Data Analysis Tool Pack for this. Based on the excel regression output, first write down the estimated regression equation, then interpret the estimated coefficients on the mean temperature over 1990-2000 (X1), and mean precipitation over 1990-2000 (X2). Carry out any relevant two-tailed hypothesis tests using the p-value approach at the 5% significance level, and an overall significance test using the p-value approach. Carefully interpret your hypothesis test results. 6. Write down a paragraph or two to illustrate: a) why the hypothesis test is required in the above regression analysis (either simple or multiple); b) the underlying mechanism/intuition of a two-tailed hypothesis test in the context of regression analysis. Aid your illustration with diagrams that you think appropriate. 7. Carefully interpret the adjusted R-squared in the multiple regression analysis. 8. If you could request additional data to study the factors that influence the annual growth of per capita GDP, what extra variables would you request? Illustrate two such variables. Carefully explain why you choose these two variables (by drawing evidence from the literature such as journal articles, newspapers, et al), types of your proposed variables (e.g. numerical or categorical), and how each of your proposed variables will be measured in the regression model.
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Explanation & Answer

Attached. Please let me know if you have any questions or need revisions.

Data sources:
The dataset consists of a sample of 106 countries.
The historical weather data are taken from the Terrestrial Air Temperature and Precipitation: 1900–2006 Gridded Monthly Tim
Data on the level and annual growth of per capita GDP are drawn from the World Development Indicators (WDI).

variables:
per_cap_GDP2000
growth1990_2000
tem1950_1960
pre1950_1960
tem1990_2000
pre1990_2000

per capita GDP in year 2000 (US$), expressed in natural logarithm
annual growth of per capita GDP over 1990-2000, expressed in percentage (%)
mean temperature over 1950-1960 (measured in degrees Celsius per year)
mean precipation over 1950-1960 (measuerd in units of 100 mm per year)
mean temperature over 1990-2000 (measured in degrees Celsius per year)
mean precipation over 1990-2000 (measuerd in units of 100 mm per year)

0–2006 Gridded Monthly Time Series, Version 1.01 (Matsuuraand Willmott 2007).

country
Nigeria
Panama
United Arab Emirates
Spain
Austria
Canada
Vanuatu
Australia
Sierra Leone
Brunei
Ghana
Fiji
Egypt
Turkey
Belgium
Cote d'Ivoire
Iran
India
Suriname
Senegal
Togo
Rwanda
Peru
Mauritius
Philippines
Mali
Germany
Chile
Oman
Honduras
Argentina
Uganda
France
Lesotho
Laos
Haiti
Paraguay
Costa Rica
Mauritania
United States
Botswana
Brazil
Trinidad and Tobago
Korea
Ireland

per_cap_GDP2000 growth1990_2000 tem1950_1960 pre1950_1960
6,341999468
0,017646708
26,52577
13,88756
8,309018174
3,343584973
24,68316
28,04099
10,41305497
0,351553339
26,84328
1,631635
9,596491202
2,487687259
13,48391
6,651904
10,10905589
2,300388065
7,549002
9,950752
10,09370492
0,582056958
4,841475
9,364386
7,2934069
1,483790792
24,15511
27,481
9,984110761
2,135885605
15,86288
9,355159
4,932303899
-2,469349752
25,70401
28,29842
9,798824364
-0,428450843
27,20606
30,216
5,554783345
1,456127111
26,31561
14,2106
7,638472188
1,673783596
23,89741
31,08469
7,279647225
2,512322923
21,03608
0,3591493
8,375048385
2,602163169
12,03383
5,847577
10,04505372
2,029001451
9,626793
7,704759
6,478979435
-1,23001107
26,24332
15,25539
7,420584576
2,423024302
13,79623
3,894315
6,094278759
3,585117687
25,23642
12,74809
7,607024507
-1,087064319
26,61093
22,14153
6,404656503
0,055498592
26,9184
9,227755
5,711110209
-0,37610378
26,07221
12,79692
5,563669533
1,252000313
19,12939
10,76035
7,578446198
1,279218094
13,26135
8,056942
8,276159434
4,281374685
24,04545
16,54273
6,978035781
0,530769128
25,38977
24,72692
5,597944577
0,967916696
27,92813
8,587226
10,07052326
1,841511218
8,429167
7,853553
8,532062419
4,485345735
11,73466
6,047864
9,05966537
1,877228526
25,61425
1,243683
6,985144126
0,306134294
23,70358
15,80122
8,950027132
2,272210933
16,78562
8,500537
5,56784437
3,261433402
22,20274
11,86919
10,01520912
1,726098494
10,32313
7,900137
6,07876169
2,485163739
12,33063
7,941034
5,784400396
3,828208018
23,5511
18,59604
6,698926252
-1,597174052
23,80118
15,15912
7,416742365
0,217254068
22,6103
13,86094
8,235591267
2,253617492
21,69974
37,24714
6,517032455
-0,871080318
28,07941
3,883507
10,50053423
2,055704447
13,06245
9,107277
8,166871928
2,465008425
20,15629
5,036779
8,229445317
0,469748127
21,38649
13,77444
8,769532711
4,960545686
26,27126
20,02921
9,413851957
6,467544334
11,18752
11,73753
10,17509225
6,376785862
9,004849
10,22423

Colombia
Indonesia
Belize
Algeria
Cuba
Uruguay
Cabo Verde
Norway
Ecuador
Denmark
Dominican Republic
Nicaragua
Sri Lanka
China
Japan
Gambia, The
Guinea-Bissau
Hungary
Guatemala
United Kingdom
Nepal
Jordan
Samoa
Venezuela
Bolivia
Iceland
Greece
New Zealand
Cameroon
Italy
Burkina Faso
Guinea
Sweden
Morocco
Madagascar
Netherlands
Gabon
Kenya
Comoros
Switzerland
Congo, Dem. Rep.
Jamaica
Kuwait
Benin
Mexico
Niger

7,832205071
6,659537737
8,120984986
7,475918546
7,91830057
8,835650025
7,138330075
10,54879496
7,276057886
10,33343542
7,961755713
6,915228712
6,768144319
6,866279408
10,5592454
6,387134578
5,730567593
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Malawi
Romania
Central African Republic
Tanzania
Chad
Bhutan
Syrian Arab Republic
Luxembourg
Tunisia
Mozambique
Poland
El Salvador
Burundi
Saudi Arabia
Cambodia

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Anonymous
Very useful material for studying!

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