Macropolicy
Autor: evan92 • April 26, 2015 • Coursework • 1,825 Words (8 Pages) • 626 Views
Introduction
The hypothesis tested in this assignment is whether developing countries that showed a considerable drop in population growth are richer now than countries that have a prevailing population. Real GDP per capita (adjusted for PPP) , is used in this hypothesis to measure how rich a country is.
Process
For the purpose of testing the hypothesis, a total of 40 countries were selected; which consists of 20 countries that had a high population growth in 1960 which showed a declining trend in 2010 and 20 other countries which had a high population growth in 1960 which raised even further in 2010. Counties which showed a high population growth in 1960 and declined in 2010 are Afghanistan, Comoros, Timor-Leste, Benin, Burkina Faso, Burundi, Cameroon, Chad, Congo Rep, Cyprus, Iraq, Liberia, Mali, Madagascar, Malawi, Nigeria, Oman, Sierra Leone, Togo, Zambia and countries which showed a relatively high population growth in 1960 and increased even further in 2010 are Bahamas, Bangladesh, Belize, Chile, China, Colombia, Cost Rica, Czech Republic, Dominican Republic, Ecuador, Fiji, Honduras, India , Korea Rep, Lesotho, Malaysia, Maldives, Turkey and Uruguay.
For the process of selecting countries for these two criteria’s population growth percentages were compared against 2010 and 1960 and the counties which gave a positive figure was classified as countries that had an increasing population growth and countries which showed a negative figure was classified as counties that had a decline in population growth.
Figure 1 below shows a scatter plot diagram for countries that had a high population growth in 1960 which showed a further increasing trend in 2010.
Figure 1
[pic 1]
The countries and data’s illustrated in figure 1 is showed in the table below.
Table1
Country Name | Country Code | Population growth (annual %)1960 | Population growth (annual %)2010 | % change in population growth | GDP per capita, PPP (constant 2005 international $) |
Afghanistan | AFG | 1.958993663 | 2.458417085 | 0.499423423 | 1183.045192 |
Comoros | COM | 1.738612427 | 2.517814139 | 0.779201712 | 1057.709871 |
Timor-Leste | TMP | 1.704726645 | 2.879661709 | 1.174935064 | 1367.256757 |
Benin | BEN | 1.304979864 | 2.869601321 | 1.564621457 | 1321.271334 |
Burkina Faso | BFA | 1.359522204 | 2.907424319 | 1.547902115 | 1204.503648 |
Burundi | BDI | 1.945425218 | 3.371194115 | 1.425768897 | 475.3842531 |
Cameroon | CMR | 2.020835241 | 2.55560127 | 0.53476603 | 1957.219013 |
Chad | TCD | 1.901535214 | 3.026858444 | 1.12532323 | 1822.868995 |
Congo, Rep. | COG | 2.512806973 | 2.876009218 | 0.363202245 | 3748.223075 |
Cyprus | CYP | 1.044306126 | 1.196967402 | 0.152661276 | 25198.03189 |
Iraq | IRQ | 2.44887611 | 2.649523348 | 0.200647238 | 3270.348463 |
Liberia | LBR | 2.151489847 | 3.510900571 | 1.359410724 | 492.5599045 |
Mali | MLI | 1.035537335 | 3.098167701 | 2.062630366 | 1095.119233 |
Madagascar | MDG | 2.373168625 | 2.80876582 | 0.435597195 | 849.1192449 |
Malawi | MWI | 2.24527923 | 2.976902311 | 0.73162308 | 657.7296981 |
Nigeria | NGA | 1.989401905 | 2.746547633 | 0.757145727 | 2170.057238 |
Oman | OMN | 2.274941747 | 5.107008134 | 2.832066387 | 24753.12022 |
Sierra Leone | SLE | 1.27998982 | 1.944982993 | 0.664993173 | 996.548259 |
Togo | TGO | 1.326944703 | 2.595340664 | 1.268395961 | 862.041038 |
Zambia | ZMB | 2.851176681 | 3.010393667 | 0.159216986 | 1370.47282 |
Source: http://databank.worldbank.org/data/views/variableselection/selectvariables.aspx?source=world-development-indicators
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