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January 28, 2008

Comments

Gerardo Zaragoza

I am particularly interested in the Mexican economy, so I decided to focus almost exclusively on it. The first thing I learned is that although life expectancy increased in Mexico by a substantial amount, its income per capita has not. For comparison purposes, the United States, on the other hand, had a larger increase in income per capita, but life expectancy did not increase as much. This is of course is most likely due to natural causes, as life expectancy in the US still surpasses that of Mexico. Another fact that I found fascinating was how rapidly the fertility rate in Mexico dropped (a difference of about 2.2) given that income per capita did not increase much, whereas in the US, the fertility rate has remained somewhat stable at around two. Lastly, I would like to comment on the women as a percent of the labor force data, which shows that the percentage of women in the labor force in Mexico has had an increase of about seven percent for the years shown. I found this fact the most interesting and of particular importance, because not only is it indicative of a “better economy” but it also depicts in numbers the culture and political changes that have taken place. Finally, women in Mexico are starting to escape the subordination brought on by machismo, and are gaining (although too slowly) more equal treatment under the law.

Tianqi Zhu

I looked at the Military budget in % with respect to the total GDP and I was surprised that singapore was the country with the highest percentage of military spending. I always assumed that singapore doesn't spend anything on its military, but I guess the island spends a lot of money to keep its soveriegnty. Secondly, I was alsos surprised that the US budget had such a large percent of military spending.
Something else interesting is that the countries that are in the Americas don't spend much on military spending except for america. Guatemala for example is only at 4%.
It also seems to be a general trend that south asian countries spend the most on military while the european countries are all bunched together in their expenditures. Developing countries seem to spend the most money on GDP in comparison to other countries.

Samuel Leiboff

Having babies is bad for your health?
Maybe not, but tracing number of physicians against the number of births per mother with GDP per capita represented in the size of the marker, you see that though the GDP per capita of many nations stays about the same, there is a negative relationship between number of physicians and number of births.

Which comes first? Better health care probably means more contraception, family planning, and the certainty that your little bundle of joy won't be plucked from you by Illness.

Nan Lu

x: women, % of labor force (call it labor rate)
y: children per woman
color: income groups

findings:
1. The general trend is a clear decrease in fertility rate in most countries. However, the %ages of labor force do not change much.
2. Different income groups change very differently.
3. The vast majority high income OECD countries have low fertitlity rate (1 to 2) through out the whole period. They also homogenously move to a high labor rate (40% to 50% by the end).
4. Two types of countries have the highest labor rates: the richest and the poorest. While women in developed countries have more opportunities, those in underdeveloped countries probably have more duties. I guess families can't really afford to have an idle person.
5. The labor rates do not change much among low income countries. During the first half, fertility rates do not change much. But things are very different during the second half. It probably becomes common sense that babies are more of burdens than treasures.
6. Other countries span a wide range in both directions. They are rushing to the lower left corner (low fertility, high labor rate)

Emre Mangir

Vertical Axis: Urban Population
Horizontal Axis: Income per Capita in International Dollars
Bubble Size: Population
Bubble Color: Fertility Rate (Births/Woman)

Watching world development based on these parameters was interesting, although finding systematic relationships among all countries is much more difficult, since no finite number of parameters can account for the changes in the world over the past 40 years. For this reason, I selected 8 countries (Ireland, Japan, Germany, Turkey, Bangladesh, Saudi Arabia, Korea, and the Philippines) and observed their changes on the graph based on these parameters, hoping to uncover some sort of relationship among nations with similar birth rates, for example.
First, the obvious: in all 8 countries, the number of people living in urban centers as a percentage of total population increased, as would be expected: as a nation's total population increases, more people will migrate to urban centers, which can handle larger populations and where opportunities far outnumber those in rural areas. Furthermore, the increased population density will necessitate the creation of new urban centers. The rate of migration looks to be correlated with the fertility rate, a fact that could again be due to the fact that rural areas do not, by definition, have the infrastructure to handle large populations. Secondly, there seems to be a general trend of decreasing fertility rate around the globe, converging on an equilibrium rate of around 1.5 or 2 children per female. The rate of convergence toward this number depends on the initial fertility rate: the higher the 1975 fertility rate, the faster the decline in it. As fertility rates have declined, GNP/capita has increased, although the amount of this improvement differs from country to country. There is clearly a correlation between low birth rates (<3/woman) and per capita income, the only exception to this pattern being Saudi Arabia, which due to its huge oil reserves, can "get away with" having a high fertility rate while also maintaining per capita income in the top half of all countries. Another interesting note is that no there are only two countries with <50% of the population living in urban centers with GNP/capita above 10,000 people.
Saudi Arabia notwithstanding, there seem to be two general trends in the relationship between GNP/capita and urban population. The first of these is an upward motion, in which urban population increases dramatically, but income per capita increases only marginally. This is the case in countries that lacked high levels of industrial infrastructure in 1975, such as Bangladesh, Turkey, and the Philippines. The second trend, seen in industrialized countries like South Korea, Germany, and Japan is the opposite, in which urban population increases marginally, while income soars, doubling, and in the case of Ireland, increasing by a factor of more than 3.5x. Overall, there seems to be a logarithmic trend, in which countries increase in urban population up to a plateau of between 60-80 percent. At this point, birth rate seems to drop, perhaps due to the constraints of urban living (work is in office and not farms), and living standards, measured by income per capita take off.

Jonathan Ong

In political science we were taught that in order for economic growth to occur, urbanization had to occur. Thus I graphed urban population % against economic growth with constant bubble size. The results were intriguing. Economic growth seems to be independent of urban population %. Even overtime, the results were about the same except for times when countries had higher rates of growth and negative rates of growth. However these anomalies were roughly distributed over all urban population percentages. Perhaps this shows how political science could be deficient. I looked at the anomalies, and they were mostly African countries such as Zimbabwe and unstables countries like Iraq. Perhaps it isn't realistically true that urbanization leads to economic growth.

Dmitri Krupnov

When you plot % of women in labor force against income per capita, it is interesting to note that most countries hover around 40-50%, except predominantly Muslim countries in the Middle East and North Africa. This is probably due to cultural and religious differences, however Indonesia and many African countries that also have large Muslim populations do not show the shame numbers. Also, in the Middle East, there is a negative correlation between income per capita and % of women in labor force. Maybe this is because financial security means less women need to provide for the family?

Amanda Bradshaw

For my first run, with life expectancy on the y-axis and income per capita on the x-axis, I selected the size of the bubble to represent the fertility rate. I wanted to see whether I could observe the demographic shifts that countries experience as a rise in material standards bring an initial increase then decline in birth rates. I limited my focus to China, Thailand and the United States, and the results paralleled our already accumulated knowledge of modern economic growth.

Upon running the model, all three countries climbed the chart as the years progressed. Because of its highly industrialized state, it is unsurprising that the United States had no significant change in the areas of its representative bubbles; however, from 1975 to 2004, the circular areas signifying the fertiltiy rates of China and Thailand decreased at decreasing rates. Although Thailand started with 4.36 of children to a woman in 1975 compared to China's 3.32, by 2004 both converged to roughly 1.90 children to a woman. Most notably, Thailand experienced a jump backwards in its GNP from 1997-1998, almost wholly explained by the coinciding financial crisis.

Controlling for population, similar results express the demographic transitions of China and Thailand.

Rina(Seung Eun) Park

I've chosen the most interesting and funny graph among the options.
chosen x axis ; economic growth
y axis ; population
bubbles; life expectancy
1. first thing the most interesting part was that the chart, was moving sideways back and forth toward 0 line of economic growth. Eventually, for 35 years, no such country's economic growth rate has increased over long time. Of course there were some difference during certain years but eventually, the moved country came back to origin. Middle east and north africa tend to have huge difference in economic growth rate.
2. Life expectancy tend to grow in a slow rate for most of the countries but such as Russia or Green land or Canada suddenly appear with a huge rate of life expectancy and starting from 1990, life expecatancy tend to fall in a slow rate wereas countries that suddenly grew tend to disappear suddenly too.

Joe Cummins

Haiti and the Dominican Republic (DR) share an island about 600 miles from the United States. They share a similar reserve of minerals and, at some point in history, a similar share of other natural resources, particularly in the form of forests and high quality soil. They have similar population sizes, the same geographic location (and thus technological access), essentially many of the same parameters the Solow model takes into account in predicting future growth. Studying these two countries reveals how historical relationships and foreign aid can shape the developing world.
In just about every category of development, Haiti lags behind DR, and the gap is widening. Haiti has shown consistently lower growth rates over the last three decades, with per capita income dropping from about 75% of the DR’s to about 20%. It has 1/10 as many phone users. The urban population in Haiti, a measure of industrialization, is ½ of that of the DR and CO2 emissions are today about 1/10th, Fertility rates are much higher in Haiti, and contraception use is significantly lower. Haiti does not even report educational statistics with any regularity.
The point of this comparison was to try and demonstrate that potential for economic growth does not lead to economic growth, something the Solow model cannot necessarily account for. Historical factors, foreign relations (Haiti has been ostracized from much of the west since a slave rebellion in 1804 – there is clearly a question of historic fear and racism in the case of Haiti), and governance-quality all contribute greatly to long-term prosperity. Macro-Economic indicators can explain where a country stands, but without historical interpretation, they will provide little insight as to where it is headed. A model that cannot account for historical and political differences has significant limitations.

Pablo Barrientos

I first analyzed the relationship between percentage of urban population and total population size and discovered that over the past fifty years, the percentage of people living in urban locations has grown faster than the rate at which total population has increased. This conclusion suggests considering if there is a relationship between income per capita and percentage of urban population. Given that total population has not increased as fast as the percentage of urban population, we might assume that more people has had the opportunity to engage in profitable businesses and thus income per capita and percentage of urban population be positively correlated. Indeed, after plotting these two variables I confirmed my assumption and thus showed the impact of urbanization on material well being.

Andrew Nhieu

Y-Axis: Life Expectancy
X-Axis: Phone Users per 1000 people

There was a definite correlation between these two variables. The correlation is roughly one, with a couple of outliers. Mostly the African countries, for they tend to all have about the same life expectancy, yet some of them are more phone-savvy than the others. The rest of the graph though follows the rule that a higher life expectancy is related to higher phone use. They are not caused by each other though. For phone use does not make you live longer. This trend is just one of the examples that show that more affluent countries generally have a higher life expectancy, and due to their affluence, happen to use and own more phones.

Y-Axis : Number of girls compared to boys in school
X-Axis: Fertility Rate

This graph looked almost the opposite of my first one, with a negative sloping line of best fit. It was interesting to me to see that overall there are at least 50 percent more girls as compared to boys in school. When looking at the U.S, that percentage is around 100. It was also interesting to see that Switzerland women on average bear almost five children. The generally trend was there are more girls in school as compared to boys when there is a lower fertility rate. There could be a variety of reasons for this, like the countries with high fertility rates are usually poor, so they usually send the boys to school and leave the girls at home doing domestic work.

Dingjiao Xu

I investigated the relationships between many variables but I was most intrigued by the link between women’s opportunities in education (Number of girls compared to boys in school) and workplace (women, % of labor force), and the material wealth of the country (income per capita).
There is a positive correlation between women’s opportunities in education (x-axis) and the wealth of the country, with wealthier countries having a more equal boys-to-girls ratio in school. This may suggest that education for women plays an imperative role in economic growth, or that women have greater opportunities to be educated in more economically developed countries. However, closer inspection seems to prove otherwise. Most American, European and Asian countries have a comparable proportion of girls and boys in schools, but their GDP per capita differ greatly. The sub-Saharan African region, on the other hand, are mostly poor but have varying levels of girls-to-boys ratio in schools.
The link between female participation in the workforce and the material wealth of a country appears even weaker. A rich country like United Arab Emirates has a female labor force of 13% while a poor one like Rwanda has a 52% female workforce.
These observations seem to suggest that female economic opportunities are have deeper cultural (e.g. Middle East) and historical (e.g. Rwanda, with prolonged years of war that deplete the male working population) influences rather than economic ones.

Konrad Knusel

X-axis: Urban Population % (lin)
Y-axis: Physicians per 1000 people (log)
Bubble size: GDP per capita

There seems to be a positive linear relationship between all three variables. The relationship which is not as straightforward as the others (ie is more interesting) is that between Urban % and Physicians per capita. A high Urban % would normally be a sign of a well-off country (more profitable industries are generally centered in cities as there are more information sharing and attaining opportunities) so the positive relationship is logical (more wealth to pay for the historically expensive service of health care).

What may diminish this positive effect of urban living is that physicians in cities are closer to more potential patients so hypothetically less physicians are needed per capita. Whereas a rural physician may not be occupied with patients at all times because of the lack of people requiring attention, a urban physician is more likely to maximize his/her usefulness by being nearby more people. If wealth were not required to attain health care, it would follow from this logic (assuming that there are not more factors than these two) that countries with a low urban population (more rural/ suburban) would have more physicians per capita. To loosely confirm this I narrowed the chart to only those countries with universal health care. In 2002 the results support my hypothesis. The relationship is slightly negative at best and at least not positive at worst.

This is was actually a fun program because I got to make a hypothesis and then test it (sort of).

Eugene Kur

The first interesting thing I noticed is that if you graph economic growth against any of the other parameters (except itself) there is no general trend among countries. All countries have roughly the same economic growth (minus a few outliers) and economic growth does not seem to be affected much by any other parameter.

I then graphed Children Mortality per 1000 born on the Y-axis and Contraceptive use amongst adult women (%) on the X-axis. As expected, there is an inverse (mostly linear) relationship. Presumably this is due to women giving birth to children that they are ready to care for instead of giving birth more often, and not having the means to support the children in infancy.

Kevin Ahn

In the Gapminder World chart I looked specifically at the military budget over time of various countries in the world. Not so surprisingly I found the United States near the top with other countries such as Russia, China, Pakistan, and India. What I found interesting was that most of the nations in Europe spent very little in terms of their military including Germany (around 5%), France (around 7%), and the UK (around 8%). Although it may be expected that many of the countries that have contentious diplomatic relations with other countries to have larger military spendings than those who are at peace, I found it questionable as to why countries such as Singapore, Chile, and Jordan would have high military spending.

The next two variables that I compared were X=time and Y=Life Expectancy, and I closely analyzed the countries in Sub-Saharan Africa (specifically Lesotho, Botswana, Zambia, and Zimbabwe). The countries were steadily increasing in life expectancy until it sharply dropped in the early 1990s due to the AIDS outbreak that ravaged the population of many African countries. If you compare the life expectancy in 2004 to 1960, it's shocking to see that that current life expectancy dips below that of the 1960s.

Lisa Sweeney

I decided to look at the relationship between technology and urbanization. I set the y-axis to “Urban population, %” and the x-axis to “Internet users per 1000 people.” No surprisingly, in 1990 very few countries had any Internet access at all, those countries being limited to Northern American and European countries, Japan, Israel, Australia, and the Republic of Korea. All of these countries had relatively high urbanization (above 60%). In the next few years, a boom of internet use swept the world, leaving very few countries behind. Those not on the Internet bandwagon mostly included very, very small countries and very, very poor or authoritarian countries. A clearly positive and linear relationship between urbanization and Internet use can be seen. As the 1990s progressed, so did Internet access regardless of urbanization. By 1997 only Comoros and the Democratic Republic of Korea were left out. In 1998 the Internet came to Comoros, and North Korea maintained its web-free status until the end of the collected statistical data. This suggests several things to me. First, either North Korea has no Internet access despite being a nuclear power, or the data at Gapminder should be double-checked. Second, the increase in Internet use across all levels of urbanization is an encouraging example of how technology can spread globally, despite disparate levels of technological infrastructure and wealth.

Jerry Wang

The Y axis I chose economic growth percentage and for the x-axis I chose carbon dioxide emission, tons per capita. As time went on, the bubble size, income per capita, increased in size. As I can see from the chart the all time disputed debated that as society grows, the environment is damaged in the process. However from the chart this is not true. As a country produces more, there is not a positive connection with more carbon dioxide that is emitted. Therefore we can not say that as an economy grows, there is more emission in the air. However there is a correlation between the size of the bubble and the income per capital correlate with the amount of CO2. This means that the wealthier the citizens are the more access to materials that pollute like cars. Also the carbon dioxide emissions per capita have remained relatively constant throughout the years. However this means there has been an increase in carbon dioxide because the population of the world has increased.

Stuart Jaffe

X: Urban Population
Y: Physicians per 1000 people
Bubble Size: Income per capita

This graph yielded not so surprising results. As urban population increased, so did the number of physicians relative to the population. One interesting aspect however, was that as urban population decreased among European nations, the number of relative physicians stayed fairly constant. This is probably attributed to the the universal health care systems in those particular countries. Also, there were apparent disparities between some countries with similar urbanized populations and relative number of physicians. For example, Saudi Arabia and Germany have a similar proportion of their population urbanized. But Germany has more than twice the number of physicians (3.3) per 1000 people than the Saudis have (1.4). The only country in the middle to be on par with the Europeans in both urbanized population and physicians per 1000 people is Israel, and Uruguay (in South America).

Kelland Chan

I first looked at the relationship between the fertility rate and the percentage of women in the work force. One would think that as women give birth to fewer babies, the percentage of women joining the work force would increase since women can now more easily go to work instead of taking care of their children. However, I found it interesting that while most countries over the years have more women join the work force as the fertility rate goes down, some countries have the opposite result. Countries such as India and Sudan have fewer women joining the workforce even though the number of children per women is going down. When I looked at the relationship between the percentage of women in the labor force and the ratio of girls to boys in school, I noticed a trend over the years for most countries, including India and Sudan, to have a more even number of girls compared to boys in school. Even though girls are in school as much as boys are, some of these girls either have a harder time getting a job than before or no longer wish to work in some countries. There must be another factor that the graphs do not show that would explain this strange occurrence.

Yikam Law

After several tries plotting different variables, I decided to investigate the relationship between urban population and life expectancy. I specifically selected a few countries from different geographic regions (U.S., France, Japan, China, India, Zimbabwe, South Africa, Ecuador, and Saudi Arabia). In most of the countries, there is a positive correlation between these variables. That is, the percentage of urban population increases as the year of life expectancy also increases. However, something interesting happened to countries in Sub-Saharan Africa. The rate of urban population and life expectancy move in the same direction until late 1980’s to early 1990’s. From there on, it dropped constantly. The years of life expectancy decreased as more people lived in the urban.

Chanwoo (Chad) Myung

I tried to measure to see if there was any correlation between income per capita and the number of physicians per 1000 people. On the horizontal axis, I selected the income per capita and on the vertical axis, I selected the number of physicians per 1000 people. Indeed there was a general correlation between the two values. As countries have higher income per capita, there was higher number of physicians per 1000 people. However, the graph showed that countries with low income per capita, such as Tajikistan and Uzbekistan had higher number of physicians per 100 people than the United States. Also, another thing that the graph showed was that China, even though with extremely high population, maintained high on the number of physicians per 1000 people. It would be interesting to know the causes of these abnormalities.

Ben Bednarz

Y-axis: Number of physicians/phone users/internet users per 1000 people
X-axis: GDP per capita

I noticed that while no countries existed with high GDP per capita and a low phsycian count, I noticed many countries fell into the catagory of high amounts of phsyicians and low GDP. The biggest example was China during the 1980s.

Both high amounts of phone users and internet users, however, directly correlate to high GDP. It appears that this is a much better indicator of a high GDP nation.

My guess is that doctors can exist in societies with a low GDP on the account that they just make a lot less money as opposed to doctors in richer countries and thus the GDP does not need to be high for many to exist. The doctors simply do not need the same sort of salary that their rich counterparts do if they are in a poor country. The same as not true of cell phones or computers though, because their cost is not so easily variable.

Ryan Helbert

x-axis: Income per capita (International dollars)
y-axis: Internet users per 1000 people
Size indicator: Fertility rate

I decided to experiment with the relationship between internet users per 1000 people and GNP. Not surprisingly, I found a strong positive correlation between internet usage and GNP, with a high rate of internet users corresponding to a larger GDP. Every country in the world has shown an increase in internet usage, from a level of 0-8 users per 1000 people in 1990 to between 0.72 users /1000 people (Tajikstan) and 788 users /1000 people (New Zealand) in 2004. Countries that started out with a higher GNP in 1990 have shown significantly greater growth in internet usage rates (Europe, Japan, US) than did countries that started with a lower GNP.

However, when I used fertility rate as a size indicator, I noticed some interesting trends. In general, there is a positive correlation between lower fertility rates and higher internet usage rate as well as higher GNP (there are a few outliers such as Israel, which has a fertility rate of 2.9, but also has a significantly higher GNP and internet usage rate than would be expected). What also surprised me is just how much fertility rates around the world have dropped in the last decade and a half. Virtually every country in the world has seen a drop in fertility rates (including Africa). The United States and France are unique among wealthy nations in that they have actually seen a slight increase in fertility rates in the last few years.

Anne Wu

Looking at the life expectancy in years and child mortality per 1000 infants born, I see that there is a clear negative correlation between the two factors. For the most part, throughout the years, the countries stayed in the same place relative to the other countries, but most of them did crawl towards the top left corner of the graph, meaning that as the years went by, in general life expectancy increased and child mortality decreased. I set the bubble colors as income per capita and the bubbles ended up resembling a rainbow slightly, indicating that there is a relationship between income per capita and the other two variables.

All these results pretty much made sense because the richer countries have higher life expectancies and lower child mortality rates, due to technology & medicine, money, and environment. The poorer countries have less technology & medicine, less money to buy what medicine is available, and worse living conditions.

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