# Resources

## Excel walk-throughs

- Part 1.1: Excel walk-through 1.1: Drawing a line chart of temperature and time
- Part 1.1: Excel walk-through 1.2: Plotting a line chart and adding a horizontal line
- Part 1.2: Excel walk-through 1.3: Creating a frequency table
- Part 1.2: Excel walk-through 1.4: Calculating percentiles
- Part 1.2: Excel walk-through 1.5: Using Excel’s COUNTIF function
- Part 1.2: Excel walk-through 1.6: Calculating and understanding the variance
- Part 1.3: Excel walk-through 1.7: Calculating correlation and drawing a scatterplot
- Part 2.1: Excel walk-through 2.1: Reformatting a table
- Part 2.1: Excel walk-through 2.2: Drawing a line chart with multiple variables
- Part 2.2: Excel walk-through 2.3: Drawing a column chart to compare two groups
- Part 2.2: Excel walk-through 2.4: Calculating the standard deviation
- Part 2.2: Excel walk-through 2.5: Finding the minimum, maximum, and range of a variable
- Part 2.3: Excel walk-through 2.6: Using Excel’s T.TEST function
- Part 3.1: Excel walk-through 3.1: Making a frequency table using Excel’s PivotTable option
- Part 3.1: Excel walk-through 3.2: Making a PivotTable with more than two variables
- Part 3.1: Excel walk-through 3.3: Making a column chart to compare two groups
- Part 4.1: Excel walk-through 4.1: Making a frequency table
- Part 4.1: Excel walk-through 4.2: Adding data labels to a chart
- Part 4.2: Excel walk-through 4.3: Calculating the HDI
- Part 4.2: Excel walk-through 4.4: Ranking data
- Part 5.1: Excel walk-through 5.1: Creating a table showing cumulative shares
- Part 5.1: Excel walk-through 5.2: Drawing the perfect equality line
- Part 5.2: Excel walk-through 5.3: Drawing a column chart with sorted values
- Part 6.1: Excel walk-through 6.1: Using Excel’s IF function
- Part 6.1: Excel walk-through 6.2: Overlaying one column chart over another
- Part 6.1: Excel walk-through 6.3: Drawing box and whisker plots
- Part 6.2: Excel walk-through 6.4: Creating confidence intervals and adding them to a chart
- Part 6.3: Excel walk-through 6.5: Using Excel’s IF function
- Part 8.1: Excel walk-through 8.1: Cleaning data and splitting variables
- Part 8.1: Excel walk-through 8.2: Dropping observations that satisfy particular conditions
- Part 8.1: Excel walk-through 8.3: Calculating percentiles from actual values
- Part 9.2: Excel walk-through 9.1: Creating and formatting time variables
- Part 12.2: Excel walk-through 12.1: Using SUBSTITUTE to clean text in cells

## R walk-throughs

- Getting started in R: Installing R and RStudio
- Getting started in R: RStudio orientation
- Part 1.1: R walk-through 1.1: Importing the datafile into R
- Part 1.1: R walk-through 1.2: Drawing a line chart of temperature and time
- Part 1.1: R walk-through 1.3: Producing a line chart for the annual temperature anomalies
- Part 1.2: R walk-through 1.4: Creating frequency tables and histograms
- Part 1.2: R walk-through 1.5: Using the quantile function
- Part 1.2: R walk-through 1.6: Using the mean function
- Part 1.2: R walk-through 1.7: Calculating and understanding mean and variance
- Part 1.3: R walk-through 1.8: Scatterplots and the correlation coefficient
- Part 2.1: R walk-through 2.1: Plotting a line chart with multiple variables
- Part 2.2: R walk-through 2.2: Importing the datafile into R
- Part 2.2: R walk-through 2.3: Calculating the mean using a loop or the apply function
- Part 2.2: R walk-through 2.4: Drawing a column chart to compare two groups
- Part 2.2: R walk-through 2.5: Calculating and understanding the standard deviation
- Part 2.2: R walk-through 2.6: Finding the minimum, maximum, and range of a variable
- Part 2.2: R walk-through 2.7: Creating a table of summary statistics
- Part 2.3: R walk-through 2.8: Calculating a t-test for means
- Part 3.1: R walk-through 3.1: Importing the datafile into R
- Part 3.1: R walk-through 3.2: Counting the number of unique elements in a variable
- Part 3.1: R walk-through 3.3: Creating frequency tables
- Part 3.1: R walk-through 3.4: Calculating conditional means
- Part 3.1: R walk-through 3.5: Making a column chart to compare two groups
- Part 3.1: R walk-through 3.6: Testing for significant differences in price changes
- Part 3.2: R walk-through 3.7: Importing data from a Stata file and plotting a line chart
- Part 4.1: R walk-through 4.1: Importing the Excel file (.xlsx or .xls format) into R
- Part 4.1: R walk-through 4.2: Making a frequency table
- Part 4.1: R walk-through 4.3: Creating new variables
- Part 4.1: R walk-through 4.4: Plotting and annotating time series data
- Part 4.1: R walk-through 4.5: Calculating new variables and plotting time series data
- Part 4.1: R walk-through 4.6: Creating stacked bar charts
- Part 4.2: R walk-through 4.7: Calculating the HDI
- Part 4.2: R walk-through 4.8: Creating your own HDI
- Part 5.1: R walk-through 5.1: Importing an Excel file (either .xlsx or .xls format) into R
- Part 5.1: R walk-through 5.2: Calculating cumulative shares using the cumsum function
- Part 5.1: R walk-through 5.3: Drawing Lorenz curves
- Part 5.1: R walk-through 5.4: Calculating Gini coefficients
- Part 5.1: R walk-through 5.5: Calculating Gini coefficients for all countries and all years using a loop
- Part 5.1: R walk-through 5.6: Plotting time-series of Gini coefficients, using ggplot
- Part 5.2: R walk-through 5.7: Importing .csv files into R
- Part 5.2: R walk-through 5.8: Creating line graphs with ggplot
- Part 5.2: R walk-through 5.9: Drawing a column chart with sorted values
- Part 5.2: R walk-through 5.10: Drawing a column chart with sorted values
- Part 5.2: R walk-through 5.11: Using line and bar charts to illustrate changes in time
- Part 6.1: R walk-through 6.1: Importing data into R and creating tables and charts
- Part 6.1: R walk-through 6.2: Obtaining frequency counts and plotting overlapping histograms
- Part 6.1: R walk-through 6.3: Creating box and whisker plots
- Part 6.2: R walk-through 6.4: Calculating confidence intervals and adding them to a chart
- Part 6.3: R walk-through 6.5: Calculating and adding conditional summary statistics and confidence intervals to a chart
- Part 7.1: R walk-through 7.1: Importing data into R and creating tables and charts
- Part 8.1: R walk-through 8.1: Importing the data into R
- Part 8.1: R walk-through 8.2: Cleaning data and splitting variables
- Part 8.1: R walk-through 8.3: Dropping specific observations
- Part 8.1: R walk-through 8.4: Calculating averages and percentiles
- Part 8.1: R walk-through 8.5: Calculating summary statistics
- Part 8.2: R walk-through 8.6: Calculating frequencies and percentages
- Part 8.2: R walk-through 8.7: Plotting multiple lines on a chart
- Part 8.2: R walk-through 8.8: Creating dummy variables and calculating correlation coefficients
- Part 8.2: R walk-through 8.9: Calculating group means
- Part 8.3: R walk-through 8.10: Calculating confidence intervals and adding error bars
- Part 9.1: R walk-through 9.1: Importing data into R
- Part 9.1: R walk-through 9.2: Creating summary tables
- Part 9.1: R walk-through 9.3: Making frequency tables for loan applications and outcomes
- Part 9.1: R walk-through 9.4: Creating variables to classify households
- Part 9.1: R walk-through 9.5: Making frequency tables to compare proportions
- Part 9.1: R walk-through 9.6: Calculating differences in household characteristics
- Part 9.1: R walk-through 9.7: Calculating confidence intervals and adding them to a chart
- Part 9.1: R walk-through 9.8: Calculating conditional means
- Part 9.2: R walk-through 9.9: Data cleaning and summarizing loan characteristics
- Part 9.2: R walk-through 9.10: Making summary tables and calculating correlations
- Part 9.2: R walk-through 9.11: Creating summary tables of means
- Part 10.1: R walk-through 10.1: Calculating correlation coefficients
- Part 10.1: R walk-through 10.2: Making box and whisker plots
- Part 10.1: R walk-through 10.3: Tabulating and visualizing time trends
- Part 10.1: R walk-through 10.4: Creating weighted averages
- Part 10.1: R walk-through 10.5: Dealing with extreme values
- Part 10.2: R walk-through 10.6: Calculating confidence intervals
- Part 10.2: R walk-through 10.7: Plotting column charts with error bars
- Part 11.1: R walk-through 11.1: Importing data and re-coding variables
- Part 11.1: R walk-through 11.2: Creating indices
- Part 11.1: R walk-through 11.3: Calculating correlation coefficients
- Part 11.1: R walk-through 11.4: Using loops to obtain summary statistics
- Part 11.1: R walk-through 11.5: Calculating summary statistics
- Part 11.2: R walk-through 11.6: Summarizing willingness to pay variables
- Part 11.2: R walk-through 11.7: Summarizing Dichotomous Choice (DC) variables
- Part 11.2: R walk-through 11.8: Calculating confidence intervals for differences in means
- Part 12.1: R walk-through 12.1: Importing a specified range of data from a spreadsheet
- Part 12.1: R walk-through 12.2: Calculating cumulative income shares and plotting a Lorenz curve
- Part 12.1: R walk-through 12.3: Generating Gini coefficients
- Part 12.1: R walk-through 12.4: Converting nominal incomes to real incomes
- Part 12.2: R walk-through 12.5: Importing data directly from a website
- Part 12.2: R walk-through 12.6: Cleaning imported data
- Part 12.2: R walk-through 12.7: Cleaning data and setting dates

## Solution figures

### Empirical Project 1

**Solution figure 1.1**: An example of a line chart with average temperature anomaly for January on the vertical axis and time (1880–2016) on the horizontal axis.**Solution figure 1.2**: A line chart showing average temperature anomaly for spring.**Solution figure 1.3**: A line chart showing average temperature anomaly for summer.**Solution figure 1.4**: A line chart showing average temperature anomaly for autumn.**Solution figure 1.5**: A line chart showing average temperature anomaly for winter.**Solution figure 1.6**: A line chart with annual average temperature anomaly on the vertical axis and time (1880–2016) on the horizontal axis.**Solution figure 1.7**: A frequency table for 1951–1980.**Solution figure 1.8**: A frequency table for 1981–2010.**Solution figure 1.9**: A column chart for 1951–1980.**Solution figure 1.10**: A column chart for 1981–2010.**Solution figure 1.11**: Mean and variance per season for periods 1921–1950, 1951–1980, and 1981–2010.**Solution figure 1.12**: Trend and interpolated monthly mean CO_{2}(mole fraction).**Solution figure 1.13**: Scatterplot CO_{2}vs temperature (June).**Solution figure 1.14**: A scatterplot showing CO_{2}levels and temperature anomaly for January.**Solution figure 1.15**: A scatterplot showing CO_{2}levels and temperature anomaly for December.

### Empirical Project 2

**Solution figure 2.1**: Average contribution over time.**Solution figure 2.2**: Mean contributions by period, with and without punishment.**Solution figure 2.3**: Comparison of mean contributions over time.**Solution figure 2.4**: Average contributions in Periods 1 and 10, with and without punishment.**Solution figure 2.5**: Standard deviations in both experiments.**Solution figure 2.6**: Minimum and maximum values for both experiments.**Solution figure 2.7**: Summary tables for contributions in both experiments.**Solution figure 2.8**: Example data from two coin-toss experiments.

### Empirical Project 3

**Solution figure 3.1**: Frequency table: All stores in December 2014 and June 2015.**Solution figure 3.2**: Numbers of taxed and untaxed beverages by store type, December 2014.**Solution figure 3.3**: Numbers of taxed and untaxed beverages by store type, June 2015.**Solution figure 3.4**: Products types available, December 2014 and June 2015.**Solution figure 3.5**: Average price per ounce of taxed and non-taxed beverages, by time period and store type.**Solution figure 3.6**: Change in the average price per oz ounce for taxed and non-taxed beverages, by store type.**Solution figure 3.7**: Mean change in price per oz for taxed and non-taxed beverages, by store type.**Solution figure 3.8**: Average prices of taxed and non-taxed beverages in Berkeley vs non-Berkeley stores.**Solution figure 3.9**: Average prices of taxed and non-taxed beverages in Berkeley vs non-Berkeley stores.

### Empirical Project 4

**Solution figure 4.1**: Number of years of GDP data available for each country.**Solution figure 4.2**: US’s GDP components (expenditure approach).**Solution figure 4.3**: China’s GDP components (expenditure approach).**Solution figure 4.4**: China’s GDP components (expenditure approach), with annotations.**Solution figure 4.5**: Share of components of GDP in China.**Solution figure 4.6**: Share of components of GDP in US.**Solution figure 4.7**: Share of each component of GDP for a selection of countries in 2015.**Solution figure 4.8**: Composition of GDP in 2015.**Solution figure 4.9**: Minimum and maximum values of the chosen indicators.**Solution figure 4.10**: Comparing alternative HDI rank and HDI rank.**Solution figure 4.11**: Scatterplot of GDP per capita rank and HDI rank.**Solution figure 4.12**: Comparison of countries according to GDP per capita and HDI.

### Empirical Project 5

**Solution figure 5.1**: Table showing cumulative income shares for China (1980).**Solution figure 5.2**: Table showing cumulative income shares for China (2014).**Solution figure 5.3**: Table showing cumulative income shares for the US (1980).**Solution figure 5.4**: Table showing cumulative income shares for the US (2014).**Solution figure 5.5**: Lorenz curves for China.**Solution figure 5.6**: Lorenz curves for the US.**Solution figure 5.7**: Lorenz curves for China, with labelled Gini coefficients.**Solution figure 5.8**: Lorenz curves for the US, with labelled Gini coefficients.**Solution figure 5.9**: Mortality inequality Gini coefficients (1952–2002).**Solution figure 5.10**: Countries ranked according to mortality inequality Gini coefficients in 1952.**Solution figure 5.11**: Countries ranked according to mortality inequality Gini coefficients in 2002.**Solution figure 5.12**: Median availability of selected generic medicines in the private sector.**Solution figure 5.13**: Median availability of selected generic medicines in the public sector.**Solution figure 5.14**: Female pupils as a percentage of total enrolment in primary education.**Solution figure 5.15**: Change (%) in female pupils’ share of total enrolment in primary education.

### Empirical Project 6

**Solution figure 6.1**: Mean of management scores.**Solution figure 6.2**: Rank according to management scores.**Solution figure 6.3**: Management practices in manufacturing firms around the world.**Solution figure 6.4**: Frequency tables for the US and Chile.**Solution figure 6.5**: Comparing the distribution of management scores for the US and Chile.**Solution figure 6.6**: Box and whisker plots for the US and Chile.**Solution figure 6.7**: Mean scores for hospitals.**Solution figure 6.8**: Mean scores for schools.**Solution figure 6.9**: Bar chart of mean management score for hospitals.**Solution figure 6.10**: Bar chart of mean management score for schools.**Solution figure 6.11**: Mean management score in manufacturing firms for the US and Chile.**Solution figure 6.12**: Bar chart of mean management score in manufacturing firms for the US and Chile, with 95% confidence intervals.**Solution figure 6.13**: Mean management score and 95% confidence interval width for hospitals and schools.**Solution figure 6.14**: Bar chart of mean management score for hospitals, with 95% confidence intervals.**Solution figure 6.15**: Bar chart of mean management score for schools, with 95% confidence intervals.**Solution figure 6.16**: Mean management score and 95% confidence interval width for private and public hospitals.**Solution figure 6.17**: Mean management score and 95% confidence interval width for private and public schools.**Solution figure 6.18**: Bar chart of mean management score for public and private hospitals, with 95% confidence intervals.**Solution figure 6.19**: Bar chart of mean management score for public and private schools, with 95% confidence intervals.**Solution figure 6.20**: Table of mean management score and 95% confidence interval width, according to ownership type.**Solution figure 6.21**: Brazil: Bar chart of mean management score by ownership type, with 95% confidence intervals.**Solution figure 6.22**: Canada: Bar chart of mean management score by ownership type, with 95% confidence intervals.**Solution figure 6.23**: US: Bar chart of mean management score by ownership type, with 95% confidence intervals.

### Empirical Project 7

**Solution figure 7.1**: Prices and quantities of watermelons (values rounded to two decimal places).**Solution figure 7.2**: Price of watermelons (USD per 1,000, 1931–1950).**Solution figure 7.3**: Quantity of watermelons planted (millions, 1931–1950).**Solution figure 7.4**: Calculated prices and quantities (in natural logs and base 10).**Solution figure 7.5**: Supply and demand diagram.**Solution figure 7.6**: New supply after the shock.**Solution figure 7.7**: Supply and demand after a negative supply shock.

### Empirical Project 8

**Solution figure 8.1**: Completed data dictionary.**Solution figure 8.2**: Self-reported employment status in each country (per cent of sample).**Solution figure 8.3**: A summary table for the EVS data.**Solution figure 8.4**: Frequency table for work ethic (Germany, Wave 3).**Solution figure 8.5**: Frequency table for work ethic (Germany, Wave 4).**Solution figure 8.6**: Distribution of work ethic score in Germany: Waves 3 and 4.**Solution figure 8.7**: Average wellbeing across countries and survey waves.**Solution figure 8.8**: Line chart of average wellbeing across countries and survey waves.**Solution figure 8.9**: Correlation between wellbeing, work ethic and other variables.**Solution figure 8.10**: Average wellbeing according to employment status and country.**Solution figure 8.11**: Difference in average wellbeing: full-time employed minus unemployed, and full-time employed minus retired.**Solution figure 8.12**: Difference in wellbeing between the full-time employed and the unemployed (sorted from lowest to highest average work ethic).**Solution figure 8.13**: Difference in wellbeing between the full-time employed and the retired (sorted from lowest to highest average work ethic).**Solution figure 8.14**: Summary table of wellbeing, by employment status.**Solution figure 8.15**: Calculated values for differences in wellbeing (full-time vs retired).**Solution figure 8.16**: Calculated values for differences in wellbeing (full-time vs unemployed).**Solution figure 8.17**: Calculated width of 95% confidence interval for differences in wellbeing (full-time vs retired).**Solution figure 8.18**: Calculated width of 95% confidence interval for differences in wellbeing (full-time vs unemployed).**Solution figure 8.19**: Difference in wellbeing (full-time and retired).**Solution figure 8.20**: Difference in wellbeing (full-time and unemployed).

### Empirical Project 9

**Solution figure 9.1**: Proportion of sample living in towns vs rural areas, by ‘region’. (Note that numbers may not add up to 1 due to rounding.)**Solution figure 9.2**: Summary table for household demographics (all households).**Solution figure 9.3**: Loan applications and approvals.**Solution figure 9.4**: Most important reason for not applying for a loan.**Solution figure 9.5**: Second most important reason for not applying for a loan.**Solution figure 9.6**: Loan purpose for households denied a loan (credit-excluded) and successful households.**Solution figure 9.7**: Comparison of household characteristics.**Solution figure 9.8**: Comparison of household characteristics, conditioning on the ‘rural’ variable.**Solution figure 9.9**: Calculating 95% confidence interval for difference in means.**Solution figure 9.10**: Differences in household characteristics, with 95% confidence intervals.**Solution figure 9.11**: Comparison of household characteristics.**Solution figure 9.12**: Summary measures of loan amount (principal) and total amount.**Solution figure 9.13**: Loan amounts and interest rates.**Solution figure 9.14**: Comparison of distribution of long-term and short-term loans.**Solution figure 9.15**: Correlations between interest rate and household characteristics.**Solution figure 9.16**: Source of loan, by variable ‘rural’.**Solution figure 9.17**: Source of loan, by variable ‘rural’ (‘Other’ category only).**Solution figure 9.18**: Duration of loan (rounded to nearest day).**Solution figure 9.19**: Loan amount (rounded to nearest whole number).**Solution figure 9.20**: Interest rate (rounded to two decimal places).**Solution figure 9.21**: Proportion of households with a female head, according to source of finance.

### Empirical Project 10

**Solution figure 10.1**: Correlation table for Access indicators.**Solution figure 10.2**: Correlation table for Depth indicators.**Solution figure 10.3**: Correlation table for Stability indicators.**Solution figure 10.4**: Box and whisker plot: Private credit by deposit money banks to GDP (%).**Solution figure 10.5**: Box and whisker plot: Deposit money banks’ assets to GDP (%).**Solution figure 10.6**: Box and whisker plot: Bank accounts per 1,000 adults.**Solution figure 10.7**: Box and whisker plot: Bank branches per 100,000 adults.**Solution figure 10.8**: Box and whisker plot: Firms with a bank loan or line of credit (%).**Solution figure 10.9**: Box and whisker plot: Small firms with a bank loan or line of credit (%).**Solution figure 10.10**: Box and whisker plot: Bank Z-score.**Solution figure 10.11**: Box and whisker plot: Bank regulatory capital to risk-weighted assets (%).**Solution figure 10.12**: Deposit money banks’ assets to GDP (%), 2000–2014, by income group.**Solution figure 10.13**: Deposit money banks’ assets to GDP (%), 2000–2014, by region.**Solution figure 10.14**: Bank accounts per 1,000 adults, 2000–2014, by income group.**Solution figure 10.15**: Bank accounts per 1,000 adults, 2000–2014, by region.**Solution figure 10.16**: Deposit money banks’ assets to GDP (%), 2000–2014, by income group.**Solution figure 10.17**: Deposit money banks’ assets to GDP (%), 2000–2014, by region.**Solution figure 10.18**: Bank accounts per 1,000 adults, 2000–2014, by income group.**Solution figure 10.19**: Bank accounts per 1,000 adults, 2000–2014, by region.**Solution figure 10.20**: Population-weighted averages, 2004–2014.**Solution figure 10.21**: Bank accounts per 1,000 adults: Winsorized averages for 2010.**Solution figure 10.22**: Bank Z-score, by income group.**Solution figure 10.23**: Capital to asset ratio, by income group.**Solution figure 10.24**: Bank Z-score, by region.**Solution figure 10.25**: Capital to asset ratio, by region.**Solution figure 10.26**: Confidence intervals for Bank Z-score, by income group.**Solution figure 10.27**: Confidence intervals for Capital to asset ratio, by income group.**Solution figure 10.28**: Confidence intervals for Bank Z-score, by region.**Solution figure 10.29**: Confidence intervals for Capital to asset ratio, by region.

### Empirical Project 11

**Solution figure 11.1**: Correlation table for ‘climate change beliefs’ items.**Solution figure 11.2**: Correlation table for ‘preferences for government intervention’ items.**Solution figure 11.3**: Correlation table for ‘personal responsibility for the environment’ items.**Solution figure 11.4**: Gender of participants, by group.**Solution figure 11.5**: Age of participants, by group.**Solution figure 11.6**: Highest educational attainment, by group.**Solution figure 11.7**: Number of children, by group.**Solution figure 11.8**: Environmental organization membership, by group.**Solution figure 11.9**: Household net income per month in euros, by group.**Solution figure 11.10**: Summary table for ‘climate change beliefs’ index.**Solution figure 11.11**: Summary table for ‘preferences for government intervention’ index.**Solution figure 11.12**: Summary table for ‘personal responsibility for the environment’ index.**Solution figure 11.13**: Column charts of minimum WTP.**Solution figure 11.14**: Column charts of maximum WTP.**Solution figure 11.15**: Correlation table of average WTP and other variables.**Solution figure 11.16**: DC format: Responses for each amount.**Solution figure 11.17**: DC format: Reponses (in percentages), with ‘abstain’ counted as ‘no’.**Solution figure 11.18**: ‘Demand curve’ from DC respondents.**Solution figure 11.19**: DC format: Responses (in percentages), with ‘abstain’ responses excluded.**Solution figure 11.20**: ‘Demand curve’ from DC respondents, under different treatments for ‘abstain’ responses.**Solution figure 11.21**: Summary table for WTP.

### Empirical Project 12

**Solution figure 12.1**: 15th percentile of incomes.**Solution figure 12.2**: 25th percentile of incomes.**Solution figure 12.3**: 50th percentile of incomes.**Solution figure 12.4**: 75th percentile of incomes.**Solution figure 12.5**: 85th percentile of incomes.**Solution figure 12.6**: Cumulative share of income, for some percentiles of the population.**Solution figure 12.7**: Lorenz curves for 2011 and 2012.**Solution figure 12.8**: Incomes earned by each percentile of the population.**Solution figure 12.9**: Creating an index-based series from percentage increases.**Solution figure 12.10**: Overall satisfaction with the government (2006–2017).**Solution figure 12.11**: Satisfaction with government’s improvement of people’s prosperity (2006–2017).