Unit 1

• The CORE Econ website uses essential cookies to make our website work. You may disable these using your browser settings but this may affect website functionality (such as your access to logged-in resources). We would also like to use analytics cookies to help us improve the functionality of our website and improve your user experience. These analytics cookies will be set only if you accept. We do not sell or otherwise transfer personal data or usage data to any third parties or use it for any other purpose. For more detailed information about the cookies we use, see our Privacy policy.
• This empirical project is related to material in Unit 1 of Economy, Society, and Public Policy and Unit 1 and Unit 20 of The Economy.
• Climate change is one of the effects of the rapid economic growth that has occurred in most countries since the Industrial Revolution. It is an important issue for policymaking, since governments need to assess how serious the problem is and then decide how to mitigate it.
• To find out more about climate change and its effects, visit the Met Office's webpage.
• Suppose you are a policy advisor for a small island nation. The government would like to know more about the extent of climate change and its possible causes.
• To answer the first question, we look at the behaviour of environmental variables over time to see whether there are general patterns in environmental conditions that could be indicative of climate change. In this project, we focus on temperature-related variables.
• To answer the second question, we examine the degree of association between temperature and another variable, CO2 emissions, and consider whether there is a plausible relationship between the two, or whether there are other explanations for what we observe.
• The CORE Econ website uses essential cookies to make our website work. You may disable these using your browser settings but this may affect website functionality (such as your access to logged-in resources). We would also like to use analytics cookies to help us improve the functionality of our website and improve your user experience. These analytics cookies will be set only if you accept. We do not sell or otherwise transfer personal data or usage data to any third parties or use it for any other purpose. For more detailed information about the cookies we use, see our Privacy policy.
• Temperature anomalies


  Differences from the average temperature from 1951 to 1980
• Plotting line charts


  1. Download data

  2. Understand how temperature is measured

  3. Create line charts using monthly, seasonal, and annual data

  4. Identify patterns over time
• Researchers have chosen to use temperature anomalies over absolute temperature to measure climate change
• Drawing a line chart of temperature and time


  1. Add data to chart

  2. Change horizontal axis labels to years

  3. Reposition horizontal axis

  4. Add titles
• Plotting a line chart and adding a horizontal line

  1. Draw line chart of mean temperature anomaly

  2. Add horizontal line showing 1951-1980 average

  3. Change horizontal axis labels to years

  4. Label data series

  5. Add titles and reposition horizontal axis
• The government is concerned about climate change leading to more frequent extreme weather events
• Frequency table


  A record of how many observations in a dataset have a particular value, range of values, or belong to a particular category
• Creating a frequency table

  1. Create table

  2. Filter data

  3. Use FREQUENCY function to fill in table
• The frequency table shows how many values belong to a particular group
• Plotting column charts to show temperature distribution

  1. Plot column charts for 1951-1980 and 1981-2010

  2. Describe similarities and differences between the distributions
• Mean


  Used to describe distributions
• Median


  Used to describe distributions
• Deciles


  Used to describe parts of distributions
• Variance


  Used to describe distributions
• FREQUENCY function


  Excel function used to fill in a frequency table based on the values in selected cells
• The values obtained will be slightly different from those shown, because the station temperature data is slightly different
• Figure 1.6 shows how to create a frequency table in Excel
• Using the frequency tables from Question 1

  Plot two separate column charts for 1951–1980 and 1981–2010 to show the distribution of temperatures, with frequency on the vertical axis and the range of temperature anomaly on the horizontal axis
• Variance


  A measure of dispersion in a frequency distribution, equal to the mean of the squares of the deviations from the arithmetic mean of the distribution. The variance is used to indicate how 'spread out' the data is. A higher variance means that the data is more spread out.
• Variance examples


  • The set of numbers 1, 1, 1 has zero variance (no variation), while the set of numbers 1, 1, 999 has a high variance of 221,334 (large spread)
• Decile


  Used to determine which observations are 'normal' and 'abnormal'. Temperatures in the 1st to 3rd decile are 'cold' and temperatures in the 7th to 10th decile or above are 'hot'
• Using Excel's PERCENTILE.INC function


  Determine the values that correspond to the 3rd and 7th decile across all months in 1951–1980
• Using Excel's COUNTIF function

  Count the number of anomalies that are considered 'hot' in 1981–2010, and express this as a percentage of all the temperature observations in that period
• The New York Times article considers the bottom third (the lowest or coldest one-third) of temperature anomalies in 1951–1980 as 'cold' and the top third (the highest or hottest one-third) of anomalies as 'hot'
• In decile terms, temperatures in the 1st to 3rd decile are 'cold' and temperatures in the 7th to 10th decile or above are 'hot' (rounded to the nearest decile)
• For each season ('DJF', 'MAM', 'JJA', and 'SON')

  Calculate the mean (average) and variance separately for the following time periods: 1921–1950, 1951–1980, and 1981–2010
• Correlation


  A measure of how closely related two variables are. Two variables are correlated if knowing the value of one variable provides information on the likely value of the other.
• Correlation coefficient


  A numerical measure, ranging between 1 and −1, of how closely associated two variables are—whether they tend to rise and fall together, or move in opposite directions.
• The government has heard that carbon emissions could be responsible for climate change, and has asked to investigate whether this is the case
• Trend and interpolated CO2 levels

  Similar but not identical measures of CO2 levels
• There may be seasonal variation in CO2 levels
• CO2 levels


  Relationship with time (as shown in the line chart)