Gapminder In-Class Essay

For this exercise I decided to choose 2 countries which I thought had very different life expectancies and income functions as time progressed: Bangladesh and the United Kingdom. Based on the data, the United Kingdom started out being one of the most affluent countries of the early time period (1800-1850s), with an average life expectancy of 40 and an average income of $3530. Between 1800-1900 both the average income and life-expectancy stayed relatively the same, causing many other countries to catch up. However, at the turn of 20th century, the country experienced exponential growth in both characteristics until 1925, followed by linear growth until 2020. Today, the average income is $46,700 and the average life expectancy is 81 years. On the other hand, Bangladesh started out being one of the poorest countries of the early time period (1800-1850),with an average life expectancy of 25 years and income of $979. In fact, between 1800-1930, the country experienced no growth in life expectancy and a little in income. Between 1930-1965, average income decreased whereas the life expectancy increased to around 45 years. However, in 1970, Bangladesh began to experience linear growth and to this day, this growth has not stopped. Today the average income is $4750 and the average life expectancy almost 75 years. One aspect of UK’s growth that surprised me was the stagnant growth between 1800-1900. I always viewed the UK as a sophisticated country with a superb economy that was always developing (increasing its life expectancy and income), but the data disproved my point. One aspect of Bangladesh’s growth I found surprising is just how far they’ve come in economic growth (as seen through increased life expectancy and income) since the 1800s. Prior to this exercise, I always thought Bangladesh’s life expectancy was very low in part because I assumed this country never really experienced much development. However, the data disproved my assumption, as it shows that the average life expectancy is 74.5 years. Through this exercise I learned an important lesson of not making assumptions when I’m discussing any subject, especially those that I am not familiar with. This idea connects with our reading on Hans Rosling because I made the exact mistake which Rosling discussed. He claims many people today come into data science with pre-conceived ideas, generalizing the data (i.e. claiming that all of Africa is poor). Moreover, he explains that coming into data science is dangerous because it prevents the data from speaking for itself and causes companies to make general and not specific solutions based on their interpretation of the data. Thus, it is important for companies to come into data science with an open-minded mentality and to interpret the data rooted in a deep understanding of the context of the audience in question. As they do so, companies can make impactful change with data.