Web Assignment 3

Visit:

The Gapminder World 2006

Explore the dataset therein.

Then write a paragraph or two as a comment on this webpost telling your fellow students something interesting you have learned from Gapminder.

Comments

[Tim Wang]

Y-axis: Economic Growth %
X-axis: Carbon dioxide emission, tons per capita
Bubble size: Income per capita

It is commonly believed that in order for economic growth to take place, it will be at the cost of the environment, that in order for a country to produce more goods more carbon dioxide and other pollutants must be emitted. However, according to this chart, a positive correlation between economic growth and carbon dioxide emissions does not exist. Also it is interesting to note that per capita carbon dioxide emissions have not noticeably increased over the years but of course, the global population has increased so more pollutants are being poured into the atmosphere. However, based on the size of the bubbles, it can be seen that there is a correlation between income per capita and CO2 emissions meaning citizens of wealthier countries pollute which is reasonable given that they have access to cars and etc.

[Dena Fehrenbacher]

Y-axis: Life expectancy
X-axis: Women, % of labor force
*click on the majority of countries to track their course over time for the best effect.

This graphic arrangement was very fascinating because it did produce one universal trend among nations over time, but instead produced several very distinct trends among certain similar nations.

There were three distinct clusters of countries on the graph.

1)Middle Eastern countries (and other countries with large Islamic populations like Pakistan)generally all experienced an increase of women in the workforce as well as an increase in life expectancy over time. However, these countries all began at lower levels of female workers, and thus never reached the employment levels of American/European/Asian countries or African countries.

2)European, N. and S. American, and Asian countries generally experienced the positive growth of both female employment and life expectancy over time.

3) The third cluster of African countries most surprised me. The majority of the countries were equal with or surpassed "western" countries in % of women in the work force throughout the time span. Thus, all these countries oscillated around 45% in work force. However, their life expectancy did not oscillate as well, but in half of the countries, life expectancy rose in the 1980s and then plunged sharply in the 1990's and 2000's.

A conclusion from this experiment was that countries of long-term extreme poverty have had women in the work force through necessity rather than through the social advancement that occurred in many industrialized countries with high life expectancy. The % of females in the workforce should not be analyzed solely as a variable of industrialization and improved standards of livings.

[Michael Leung]

Plotting carbon dioxide emissions against data sets that measure some sort of standard of living shows that human welfare gains have come at the cost of the environment. The most drastic example is plotting emissions against income per capita. A country like Congo, which had an income per capita of $619 had 0.034 tons per capita of emissions, whereas the U.S., which had an income per capita of $34,567, had 20 tons per capita of emissions. While plotting against economic growth doesn't show a positive correlation, total emissions have increased due to population growth. Child mortality, fertility rates, internet users, life expectancy, phones and physicians per 1000 people, and urban population all show positive correlations.

[Yanik Jayaram]

While exploring the various plots of countries from all over the world, I decided to finally focus on comparing the U.S., the world's wealthiest country, to Sub-Saharan Africa, home of the worlds poorest countries. In addition, I made comparisons between countries in Sub-Saharan Africa. Rather than plot certain statistics against each other, I plotted each statistic against time:
Looking at Phones per 1,000 people vs. time, the U.S in 1975 already has around 40 times more phones than the average SSA (Sub-Saharan African) country. However, while the U.S. is superior in numbers, almost all SSA countries (with the exception of the Dem. Republic of Congo, show rapid growth in phone use starting in 1998. This may suggest the idea that poorer countries can "catch-up" to richer countries in the sense that they will develop quicker if given the technology. While South Africa leads the SSA countries in 1975 by a significant margin, Botswana clearly has the most impressive growth rate over time.
Looking at the child mortality rate over time, we see similar trends as before. The U.S., from the initial data point of 1975, already has a margin of 20% longer life expectancy then the average SSA country. To make things worse, where the U.S. has a constantly declinin mortality rate, some countries in SSA have an increasing rate (ex, Zambia, Swaziland, Kenya, Botswana- all these countries show an upward trend in child mortality rates starting around 1990). South Africa once again leads the SSA countries with the lowest child mortality rate of 70 per 1,000.
Looking at GNP per capita, the U.S. has roughly 10 times as much GNP as the average SSA country, a gap which only widens over time. Souther Africa yet again leads the SSA countries starting in the mid 70's with an income per person of $10,000. Botswana, however, has had the most remarkable development, which starts at $2,000 per person and catches up to South Africa's $10,000 per person by 2004.
It would be very interesting to look at government policies and events in both South Africa and Botswana to try to explain why South Africa, despite it's advantageous start, has stagnated over time, and how Botswana, one of the poorer countries in SSA, has seen such significant growth.
A final statistic I focused on was contraceptive use among adult women. Comparing the U.S. w/ SSA, it is interesting to note that, although the U.S. starts out with higher percentage contraceptive use, the level stays relatively constant around 70% starting in 1970 and then begins to decline in 1996. In SSA, percentages start as low as 3% Zambia while ranging up to around 50% in South Africa. Most of the SSA countries show a relatively rapid increase in contraceptive use, in contrast to the U.S. This makes me wonder If economic prosperity leads to a lack of contraceptive use, and if so, why this would be the cause,

[Anela Chan]

I decided to look at the role of women’s education in a nation’s economic welfare. Regardless of effects on economic growth, education seems to improve the quality of life for women, taking them out of the home, informal sector, or unskilled work towards more equal positions in skilled labor. I am appalled that many girls in poor countries don’t attend school while boys do. Indeed, a plot of % Girls compared to boys in school vs. Income per capita shows a positively correlated curve, with poor African nations having low proportions of girls in school and rich Western nations having high proportions. It seems obvious that female education is a necessary prerequisite for economic success, as educated females will ultimately lead to increased Efficiency of labor and the “g” parameter. However, female education is not a sufficient condition for material success, as the spread of countries w/ proportions near 100% is large, with countries earning from $4000 to $40,000 per capita. Still, the trend towards more countries equalizing their education over time seems to be a step in the right direction.

Where do these young women go if they’re not in school? For one thing, they tend to not use contraceptives and to have more children. Indeed, a plot of Contraceptive use vs. % Girls compared to boys in school (best numbers from 1999) shows a negative correlation, though a variety of causal factors could exist—girls not in school could be uneducated about birth control, or they simply might live in areas w/o access to contraceptives, or some other reason. A plot of Fertility rates vs. % Girls compared to boys in school shows an obvious negative correlation, as one might suppose that girls quit school when pregnant or are more likely to get pregnant when not in school. And we’ve all seen that high fertility rates lead to too much labor and a lower income per capita. Do these women enter the workforce when not in school, or do they simply stay in the home? A plot of % Women in the workforce vs. % Girls compared to boys in school exhibits little correlation, but some interesting clusters develop. Poor African countries with low proportions of females compared to males in school seem to have relatively high numbers working, which indicates that girls may quit school to provide for their families. Middle Eastern countries have high numbers in school but low numbers of women in the workforce, which implies that girls attend school but end up in the home. Asian, American, and European countries, however, have high numbers in school and high numbers working, which hints that girls are educated and enter the workforce as skilled laborers, as is the aim of education (though we can’t tell exactly what work they go into).

Though having girls attend school with boys instead of staying home will not necessarily lead directly to G8-style economic success, it seems that the opposite will lead to a host of trends that can hurt economic welfare, such as decreased contraceptive use and increased fertility rates.

[Ronald Park]

Y-axis: Economic growth, %
X-axis: Carbon dioxide emission, tons per capita
Bubble size: Income per capita

The first poster made the observation that economic growth and carbon dioxide emission do not appear to have positive correlation and thereby concludes the commonly held notion that economic prosperity comes at the cost of the environment doesn't hold water. I must protest. As was observed before, income per capita and carbon dioxide emissions are positively correlated. The object of economic growth for all countries is to improve their standard of living and become wealthier, which is imperfectly but adequately captured by the income per capita statistic. The only way for countries to achieve higher income per capita is through economic growth, and as seems clear from the data there is positive correlation between carbon dioxide emissions and income per capita. If you follow some of the positive economic growth stories of the past quarter century such as India, China, and South Korea, you find that for each country income per capita and carbon dioxide emissions increased hand-in-hand. As such there still appears to be an environmental cost to economic growth.

[Xueyao Liu]

I played around with:
1) Y-axis: Life expectancy
X-axis: Income per Capita
2) Y-axis: Physicians per 1000 people
X-axis: Income per Capita
The interesting thing is that: Life expectancy of China hasn’t improved all that much from 1990 to 2004, if the change is significant enough to be even considered. Income per capita in China has risen from 1596 to 5419 (more than tripled) in international dollars over the past 14 years since 1990. However, life expectancy only increased from 69 to 71 years. It is very weird because one (at least I did) would think that people live longer and healthier when they are wealthier because health care would be more affordable. Especially after Chairman Deng’s foreign policies were in effect, Chinese economy was booming like crazy each year. So, I changed the axis into the second pair and realized that “physicians per 100 people” hasn’t actually grown since 1990, at least not in a statistically significant amount. This explains why life expectancy hasn’t grown much, but I am confused still. In the case of India, life expectancy grew from 59 to 63 when income per capita grew from 1686 to 2885, far from even double. Numbers for “physicians per 100 people” also grew from 0.46 to 0.6. Do Chinese just don’t care enough to get a doctor. It is unbelievable. Yet, I guess it is a good thing that people are dying off at a reasonable rate. It is very inhumane to say something like that, but it helps with the social security problem (Yeah, I don’t even think China has social security, but there could be similar problems that is saved by people not live longer…).

[Kevin Hong]

X-axis: Income per capita
Y-axis: Children per woman (fertility rate)

I examined the relationship between fertility rate and income per capital. Before looking at this model, I had the assumption that countries with a higher income per capita would have a lower fertility rate, because families from those countries would need less work from their children to support the family. The model seemed to loosely agree with my assumption, with some interesting patterns over time.

First, the South African countries were clustered near the top-left of this model with not much movement over the span of 29 years. This group had the highest level of fertility rate, on average of about 6 children per woman and the lowest income per capital. Conversely, Luxembourg and the United States had the first and second highest income per capital in 2004, respectively. The U.S. also had a 2.04 fertility rate and Luxembourg, 1.7. Countries of Europe and Central Asia had the lowest fertility as a group with many clustered at approximately 1.3 fertility rate in 2004.

Second, India showed an inverse relationship between fertility rate and income per capital over time. It started off in 1975 with a fertility rate of 5.35 and income per capita of 1,139, and gradually fell in fertility rate all the way to 2.88 and grew in income per capita to 2,885 in 2004. I think a case could be made that a combination of urbanization, technological and economic growth contributed to this trend. To observe this trend, I changed the x-axis to urban population, physicians per 1000 people, and phone users per 1000 people, and each time there was an inverse relationship with fertility rate.

Third, the effect of the one child policy of 1979 in China was interesting. In 1975, China had a fertility rate of 3.39 and a income per capital of 595. From 1992 to 2004, China has had a steady fertility rate of about 1.88, while increasing in income per capita. According to this model, the one child policy appears to have effectively curbed the fertility rate and population (as seen when the x-axis is changed to population).

[Michael Beckman]

Someone previously posted that economic growth did not necessarily correlate with an increase in carbon dioxide emissions. I decided to isolate the countries from the Soviet Bloc to see if this was true. However, it appears in this case that greater economic growth was associated with an increase in carbon dioxide emissions. But on when looking at individual countries, an interesting thing happened. For countries like Ukraine, Belarus and Estonia, carbon dioxide emissions decreased as economic growth decreased, but they continued to decrease when economic growth started to increase. However, Poland and Czech Republic experienced a growth in CO2 emissions as economic growth increased. It looks like countries that were closer to the West adopted more of the high emissions practices than countries closer to Russia.
Next, I looked at some of the "Asian Tigers". For most of these countries, CO2 emissions increased rapidly no matter what the economic conditions were. This probably occurred because more and more of the population was using gas-powered transportation.
I suppose what this teaches us is that developing countries don't necessarily have to increase CO2 emissions significantly to achieve economic growth, but the people that benefit from this growth will demand Western-style consumption, which can lead to an increase in CO2. Countries implementing development policies should keep this in mind as time goes on.

[Yusuf Amir-Ebrahimi]

I wanted to test the income per capita, a measure of prosperity, against other measures of the standard of living. First I checked it against the number of phone users per 1000 people. Not surprisingly, the data almost fit a straight positively sloped line: as Income per capita increases, the number of phone users rise. Conversely, it could be interpreted that as the use of phones rise, the economy runs more efficiently and income per capita rises. Either way, there is a very solid correlation between the two factors. Furthermore, almost the exact same trend appears when looking at internet users per 1000 people. Next I compared the income per capita to the number of physicians per 1000 people. The data shows no outstanding correlation. For example, the richer countries lead with the number of physicians, but countries with lower income per capita, such as China and Russia have just as many doctors. Only in countries with income per capital less than $4000 do you see a trend where more doctors go hand in hand with a high income per capita. Finally, I looked at the child-mortality rate and saw a very prominent trend. With very few exceptions, nations with a higher income per capita have lower child-mortality rates. The poorest nation, Sierra Leon, unsurprisingly had the highest mortality rate. Though this all makes sense, it’s strange that this dataset doesn’t match up with the number of doctors in those nations. Even poorer nations with lots of doctors, like China and Russia, have relatively high infant mortality rates.

[Ying (Cindy) Wu]

I looked at the affect that the percentage of women in the workforce had on the income per capita, which is a measurement of the wealth of a nation. From the graph, there does not seem to be a very big correlation between these two variables, as the dots are scattered throughout the plot. However, three groups do seem to formulate with a somewhat hazy boundary. They are the 1) Africa; 2) the Middle East; 3) Americas, Europe, and Asia.

The first group, Africa, had low per capita incomes and a high percentage of women in the workforce. Because of their lack of economic development, women are forced to work just to survive. Their income goes to helping feed their families, which is a basic necessity.

The second group, the Middle East, had a fairly high (average about $5000) per capita income, but very low percentage of women in the workforce. I think this is mostly due to the cultural ideas of the region. Women have limited rights in these areas and most are confined to the home, which limits their ability to earn an income.

The last group had high per capita incomes and a high percentage of women in the workforce. This has to do with the fact that the majority of these countries are developed (or in the case of the Asian countries, developing). Therefore, the wage rate is fairly high. I think these women work because they prefer to, as opposed to the women in Africa, who have to work.

In comparison to per capita income, I do not think the percentage of women has much to do with a country's prosperity. Rather, women work mostly because of cultural norms. Ultimately, a country's wealth depends on other resources.

[Deviyani Gurung]

In first trial my x-axis was children per women (fertility rate) the y-axis was income per capita. Between these two variables there is a relatively clear correlation. What seems to be the trend is that countries with low fertility rates have high income per capita. Before 1980 countries such as the US and many countries in Europe had a low fertility rate and high income per capita. By 1980, many peripheral countries such as India and China are moving towards lower fertility rates and greater incomes. However, many countries in Africa remain in the region with high fertility rates and low incomes. Countries in the Middle East as well as South America are first spread out across during the 1980s but by 2004 all move towards the trend of lower fertility rates and increased income. I suppose this makes sense because in many of the countries with high income the living expenses are high therefore there are fewer children. Furthermore, the countries with lower fertility rates are also more educated and are informed about the use of contraceptives.

[Max Zheng]

I modeled internet users per 1000 people (linear, y-axis) versus life expectancy (linear, x-axis). The interesting thing about tracking these variables through time was the explosion of internet use. Prior to 1990, there were roughly 6 distinguishable dots on the graph, all close to 0 on the y axis. At 1990, the rest of the dots appeared on the graph, and from then on, the countries on the right hand side of the axis moved up on the graph, indicating more internet use. Not surprising is that almost all the African countries fell below the 60 year life expectancy line, as well as falling below 100 internet users per 1000. Also, from 1990 on, countries with life expectancies above 65 started to increase in internet access. At 2004, the country with the highest number of users, New Zealand, had internet penetration of nearly 800 users per 1000 people. Surprisingly, however, the number of internet users was spread out evenly across countries in this life expectancy range. This seems to indicate that past a certain level, standard of living is not strongly correlated with internet access.

[Seung Eun(Rina) Park]

Through out many trials and errors, I've chosen to change the annual rate of growth of the labor force(n) initial values. U.S senate has shown that from 1950s to 1970s, the rate has increased. (i.e. 1950s-1.1%, 1960s 1.7%, 1970s 2.7%) seeign how the change in the annual rate of growth of the labor force effected other values is my efficient goal of the experiment.
Graphically, increased in growth rate has made the output per worker's slope more bigger, closer to balanced-growth path output per worker line.
nummeratically, effected efficiency of labor (E), Capital-OUtput Ratio, balanced-growth path capital-output ratio, capital per worker but did not effect output per worker, balanced groth path output per worker, and output per worker along the continuatin of the pre-shock balanced growth path. Efficiency of labor E increased while n grow, capital output ratio decreased, balance-growth path capital output ratio also decreased and capital per worker decreased.

[Daniel Yeghiazarian]

I looked at how income per capita affects the standard of living for people in their respective countries. All the bubbles were set to population, so that I can tell the difference. It was no surprise that African countries had less than one physician per 1000 people. What was surprising was the many countries that had more physicians than the U.S. were many of the Western European countries such as France, Germany, Spain, and Switzerland. These countries have better rated health care than the U.S. and also have universal health care. Another correlation that comes with a better standard of living is less child mortality. Countries with incomes at least above 10,000, had no less than 10 child mortalities per 1000 children. Looking at the countries with the lower incomes made had shocking results as a growing country like India has 1 out of every 10 child die and African countries where conflict occurs like Rwanda or the Ivory Coast has 1 out of every 5 child die. Finally, checking out the life expectancy, the graph tends to fit most of my assumptions with countries with less income tending to have lower life expectancies in the end. There are some surprises when checking graphs. A country like Russia had some of the most physicians, but they have a shorter life expectancy which does not make sense. The reason is they also have a higher income than China, but China has 6 more years of life expectancy than Russia. It is also no surprise that the countries with universal health care have a higher life expectancy than the U.S. One more country that stands out is South Africa who has a high income per capita compared to its African counterparts, but its life expectancy is only 46 years for its residents. This is probably due to the high number of people with AIDS in the country.

[Valerie Cheung]

On the Y-axis, I plotted number of girls compared to boys in school, %; while on the X-axis, I plotted internet users per 1000 people.
In 2004, most of the East Asians female went to school and most of the East Asians were internet users. The lowest ratio of girls going to school compared to the boys is in the sub-Saharan Africa region and they also had the lowest number of internet users. There is one city in which it had relatively high percentage of girls compared to boys in school, but the lowest internet users per 1000 people, and it was Tajikistan in the Europe and East Asian region. The United States is somewhere in the middle in both axis.
Interestingly, when I looked at the same comparisons between the regions in 1994, the East Asia and Pacific had lower number of girls compared to boys in school than in 2004 and also were almost the lowest (0.01) internet users per 1000 people. The highest ratio of number of girls in school and the number of internet users per 1000 were the United States.

[Zhihui Zhang]

for this exercise, i took a look at the various trends between countries of Asia (represented by China, Japan, Korea Republic, and the Democratic Republic of Korea) and Europe (represented by France, Germany, and the United Kingdom) using the Snited States as a basis for comparison from 1960 to present.

I found that although these countries differ in their political and geographic conditions, overall these countries all faced similar general trends

Here is a summary of some of the things I looked at:

life expectancy: all experienced a steady rate of growth with China experiencing sudden growth during the 60's and then following the same trend as the other countries, most likely explained by the the cultural revolution

child mortality: all the countries I examined faced a decrease in child mortality at a similar rate

economic growth:ll the countries I examined faced a similar economic growth rate with china experiencing a sudden increase in the early 2000's and then staying relatively stable (possibly due to the return of HK to the Chinese government)

fertility rate: a somewhat similar trend occurred for all countries with rapid decrease in China and the Korea Republics, possibly explained by the institution of the 1-child policy

internet user per 1000 - a similar trend occurred for all the countries I looked at with China's growth slightly steeper then the rest

income per capita: a somewhat steady growth for most of the "westernized" countries with the Korea Republic and China facing lower income per capita but experiencing high rates of growth

military budget: interestingly enough, all the countries had either unchanging military spending or decreases in military spending (very steeply in the case of the Korea Republic) except in the case of the United States, whose spending has steadily been increasing since 2000, possibly due to the Iraq war.

girl to boy ratio in school: looks to be at a steady 100% for all the nations examined(where there is data) expect for the case of the United Kingdom which has been increasing and increasing with the current ratio at 115%. Maybe the girls have been getting smarter over there ;-)

[Elizabeth Creed]

Since I was born in 1975, Gapminder’s data is particularly interesting to me: the motion charted on the site’s x axis, y axis, bubble size and color illustrate the changes in global well being and distribution of well being since my birth. Gapminder suggests a strong correlation with income and the well being indicators of life expectancy, phones per 1000 people, low child mortality rates, low fertility rates and low dependents to working population ratios. A looser correlation is also suggested between gender ratio in schools and percent urban population. No correlation is apparent with the gender makeup of the workforce and per capita wealth.

Across time, the general global trend has been for nations to increase in population size, per capita wealth, life expectancy and other indicators of well being. There appears to be a converging trend in global wealth and life expectancy as well. Curious whether this was just a trick of the eye, I watched closely to national patterns that strayed from a steady up and rightward slide. Considerable back and forth motion in wealth was experienced by many Middle East nations and the Philippines since 1975 and considerable up and down motion in life expectancy was experience in Sub Saharan nations in Africa. Sharp increases and decreases in life expectancy in Sub Saharan Africa were particularly chilling to observe. Life expectancy in Rwanda, for example, was 45 in 1975, dropped to 24 in 1992 and has “recovered” to 44 in 2004. Zimbabwe and Botswana’s life expectancy was three fifths in 2004 (37 years of age and 35 years of age, respectively) of what it was in their life expectancy peaks in the mid 80s (62 years in Zimbabwe and 65 years in Botswana). As the most of the global population experienced an increase in wealth and life expectancy, other non-trend experiences were South Africa, who shadowed the SSA trend in increasing and decreasing life expectancy with roughly a constant national income during the entire period and the United Arab Emirates, who experience an increase in life expectancy while experiencing a decrease in per capita GNP.

All nations I have traveled in during the past year (the US, the UK, Italy, Spain, Egypt and the Dominican Republic), the general global trends were followed: increase in per capita wealth, life expectancy, population size, urban migration and communication technology use and decrease in child mortality rate and births per 1000 people. These trends illustrate increasing well being over time, but one must question the tactics of measuring well being as well as the assumption that increased wealth and life expectancy results in greater happiness. Despite economic indicators of increasing well being in Italy, for example, I overheard a lot of barstool discussion during my travels about a growing pessimism among the Italian people. The nation’s chart topping pop song attempts to establish that Italians have “nothing to fear” (watch how the video encourages the disgruntled youth of Italy to shed their hoodies) and recent Pew Research Center surveys found seven times as many Italians believe the next generation will be worse off than believe that conditions for the next generation will improve. Additional indicators that could perhaps add insight to global well being could be measures of corruption, social institutions, unemployment (in particular, unemployment and perhaps income among young people in the work force), energy use, birth weight, over/under nurishment and change in renewable and non-renewable resources. I’m also curious about distribution of property rights (and rights in general). Early lessons in economics tout an importance for property rights, but what does that actually relate to change in wealth and well being over time, within economies and between nations?

I also was interested in watching a change in the gender makeup of the labor force in nations that have experienced a rise in fundamentalism since 1975, but data for nations like Afghanistan and Iraq are not available. Has the US experienced a significant change in fundamentalism since 1975? I dunno. How about an indicator for fundamentalism.

[Elizabeth Creed]

The links to the video and NY times article didn't come through...

Here's the Italian pop song about nothing to fear:

http://www.youtube.com/watch?v=3MusLT1PSLw

Here's the Pew Center survey mentioning pessimism in Italy (and elsewhere):
http://www.nytimes.com/2007/07/24/world/24cnd-ihtpoll.html

[Coleman Maher]

I looked at the fertility rate versus income per capita over time.

There appears to be the general trend towards lower fertility in non-African countries. This decrease in fertility is accomapanied by an increase in income per capita. The two giants India and China see a dramatic decrease in fertility and a dramatic increase in income per capita.

Sub-Saharan African countries do not see a drop in fertility and as a result stay in the upper lefthand corner of the chart. Their income never increases even though their population has exploded and 30 years have passed. This seems to suggest their economy is growing enough to support a larger population, but not more wealth. This is reminiscent of the growth in pre-1800 societies Professor DeLong talked about in class. Societies grew larger but not wealthier in terms of individuals.

[Seng Tat Chua]

A very interesting exercise indeed. I modeled carbon dioxide against income per capita. For the developed nations such as USA and Japan, as income per capita increases, carbon dioxide emissions remain high but constant. On the other hand, in the case of developing countries like China and India, as their income per capita increases, the amount of carbon dioxide emissions per capita increases as well, and looks set to overtake that of the developed world. No wonder there is so much dispute over who should bear responsibility for the current situation of global warming.

Then, I investigated the dependence of number of internet users on income per capita and discovered that even though income per capita remained constant, the number of internet users surged rapidly. This is so for less developed countries like Rwanda as well. Being a relatively new phenomenon (since 1990), the internet as a tool for communication has indeed revolutionized the way humans function.

[Sanjay Nimbark Sugarek]

Y Axis: Child mortality (per 1000 born)
X Axis: Children per woman (fertility rate)

There is a relatively strong positive correlation between these two factors, which may imply that families in poorer countries may deliberately have more children as a way to offset the increased chances of child mortality. After all, such families often depend on their children to aid their general subsistence, as well as to take care of them financially and emotionally when the parents grow old.
However, I must leave my conclusion tentative, as there is the potential for the presence of a confounding variable: Wealthier countries tend to have lower fertility rates, and also experience lower child mortality rates; therefore, a country's wealth could be a confounding variable. However, one reason that wealthier countries have less children on average could be for the very reason that I mentioned: that they need not worry about child mortality to the same degree as must families in developing countries.
Interestingly, both child mortality and fertility rates have declined substantially over time in many, if not most, of the included countries. This trend seems genuinely reassuring.

[Chen Xu]

The first graph that I studied was a plot of number of girls vs. boys in school (percentage) versus the percentage of women in the labor force. One would think that the more girls attend school, the higher percentage of women would be in the work place, but that is not always the case. Though it is true for the upper right extremity of the graph, there are countries that have a high percentage of girls in school and low percentage of women in the workplace and also countries that have a low percentage of girls in school but a high percentage of girls in the workplace. To clarify these results, I plotted percentage of girls in school against GDP per capita and found that in general the more girls in school, the higher GDP per capita. (The graph increased exponentially). When GDP was plotted against percentage of women in the workplace there was no such pattern. Perhaps it is the other way around, though. Perhaps the countries that educate women are those countries that can afford to - rather than to send them off to work. In many third world countries, despite the lack of education in women, there is a need for women to work where social norms would have dictated that they otherwise should not work. Upon closer examination, those countries that have a low education rate amongst women but a high employment rate are countries in Africa. Those with high education rate but low employment percentage tend to be those countries in the middle east that discourage women from working. The next thing to do was to examine these graphs over a period of time. Intuitively, one should think that as time progresses, the world's view of women's role in society should progress, and indeed that has been the case. Over a period of 20 years, all of the bubbles in the graph have moved towards the top right. That means more and more women are being educated vs men and more women are working vs men.

[Hovhannes Harutyunyan]

Y-Axis: Internet users per 1000 people
X-Axis: Income per Capita in International Dollars
Bubble Size: Population

Up until 1990, the chart is bare of any data because the Internet had not yet been invented. However, upon the creation of the Internet, countries such as Belgium, Spain and Korea began internet use by averaging together at .123 users per 1000 people. The United States in 1990 had approximately 8 internet users per 1000 people. With the exception of Korea, Rep. at $9,792 GDP per capita, all internet-using countries had a GDP per capita above $10,000. By 1991, almost all of the bubbles climb up the chart to represent the gradual introduction of internet -use throughout the countries. At this point, only a handful of African countries, Nepal, and Thailand are not exhibiting any internet activity. While the use of the internet increases throughout the years, it is not until 1994 when Nepal begins to use the internet. Although Nepal's use of the internet has increased, in 2007 only an estimated .9% of the population was using the internet (Source: InternetWorldStats.com).

According to the chart on Gapminder, by mid-1997, all recorded countries were displaying an observable degree of Internet use. In 2005, the United States is shown as displaying the most use of the Internet with 635 users per 1000 people and with GDP per Capita at $36,465. Ever since 1990, when Internet use began, there has been a strong positive correlation between GDP per Capita and Internet use. To me, this does make sense. After all, those countries that display advances in technology experience an increase in total production. In turn, those counties with growing total output per capita more often than not display increases in their standards of living. For example, the positive correlation in the U.S. is very evident. In the past 10 years, the U.S. has seen Internet use climb from 307 to over 600 users per 1000 people. Computers and Internet service have gotten both more inexpensive and readily available. Our society is growing more dependent and friendly towards Internet use as more of our lives become electronically transpired. However, I am not sure which is the dependent and which is the independent variable. It seems the positive correlation revealed in the experiment can go both ways. Not only does increased output lead to more technology (such as Internet use) but more applications of technology inevitably increase labor productivity and thus output per worker, according to our textbook.

In conclusion, one of the main highlights from the simulation was the observation of the interaction between GDP per Capita and technology.

[Anshul Shah]

I looked at Internet users per 1,000 people vs. Income per capita (international dollars). This was a very interesting graph to watch from the late 1980s onwards. According to Wikipedia, the opening of the 'internet network' to commercial interests began in 1988. In 1990 the internet finally "popped up" all over the world.

What is very interesting from gapminder is that in 1989, only 7 countries show up with a significant amount of users per 1000 people. Out of these 7 countries, 3 are African countries (Burundi, Togo, Botswana), which is quite surprising considering that even in present day, these countries were and still are much behind developed economies such as those of the US and Britain (as seen on the income per capita vs. economic growth graph on gapminder)

In 1990 hundreds of other countries show up on the gapminder graph. Right away, the highest number of users per 1000 people are from the US and dozens of European countries. The original 3 African countries were way behind in terms of the number of users. It took them an average of 5 years to get to much higher user-levels of all the other countries (including many other African countries that started out with much higher levels in 1990).

Even more interesting is the fact that all the dots moved upwards much faster than they moved to the right. This shows that between about 1990 and 1998, internet usage increased much faster than did the income per capita in most of the countries throughout the world. Only after 1998 was there any significant lateral movement in the dots relative to their upward movement. This makes sense because the costs of internet access were very high initially and over the years they dropped by a lot. Another factor might be the high proliferation of internet cafes in the developing countries. Even today, there are many countries that have internet cafes because the majority of the population cannot afford a computer. This has caused usage to increase rapidly, even if income per capita doesn't increase.