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Key to Penn World Data Variables

Why not print out the following list for reference when using Penn World Data?

Variable List

1. pop - Population ('000s in General Variables)
2. xrat - Exchange Rate (US=1 in General Variables)
3. cgdp - Real Gross Domestic Product per Capita ($ in Current Prices)
4. cc - Consumption Share of CGPD (% in Current Prices)
5. ci - Investment Share of CGDP (% in Current Prices)
6. cg - Government Share of CGDP (% in Current Prices)
7. p - Price Level of Gross Domestic Product (US=100 in Current Prices)
8. pc - Price Level of Consumption (PPP/Exchange rate in Current Prices)
9. pg - Price Level of Government (PPP/Exchange rate in Current Prices)
10. pi - Price Level of Investment (PPP/Exchange rate in Current Prices)
11. openc - Openness in Current Prices (% in Current Prices)
12. cgnp - Ratio of GNP to GDP (% in Current Prices)
13. csave - Current Savings (% in Current Prices)
14. y - CGDP Relative to the United States (US=100 in Current Prices)
15. rgdpl - Real GDP per capita (Constant Prices: Laspeyres) ($ in 1996 Constant Prices)
16. rgdpch - Real GDP per capita (Constant Prices: Chain series) ($ in 1996 Constant Prices: Chain series)
17. rgdpeqa - Real GDP Chain per equivalent adult ($ eq. adult in 1996 Constant Prices)
18. rgdpwok - Real GDP Chain per worker ($ worker in 1996 Constant Prices)
19. rgdptt - Real Gross Domestic Income (RGDPL adjusted for Terms of Trade changes) ($ terms of trade in 1996 Constant Prices)
20. openk - Openness in Constant Prices (% in 1996 Constant Prices)
21. kc - Consumption Share of RGDPL (% in 1996 Constant Prices)
22. kg - Government Share of RGDPL (% in 1996 Constant Prices)
23. ki - Investment Share of RGDPL (% in 1996 Constant Prices)

Supporting information

Current Prices and Constant Prices:

Comparing data between different years can give misleading results if we confuse the quantities being produced with the value of the output. For example, if GDP rises by £10 billion from one year to the next, is that because we are producing more goods or is it the case that we are producing the same amount of goods but the price of those goods has risen?

The use of current and constant prices helps us to clarify this distinction. Current prices express the value of output in terms of the prices that exist at the time of measurement. For example, if we were measuring output in terms of beer, in year 1 the output might be 10,000 litres of beer each selling at £2 per litre giving a total value of output of £20,000. In year 2, output and prices might have risen. Output may have risen by 2,000 litres but prices increase by 10p. The value of output in year 2 therefore would be 12,000 x £2.10 = £25,200.

To identify the real change in output levels (i.e. the change in output when the effects of price rises (inflation) have been taken into account) we adopt a system whereby we express the price in terms of those existing at a point in time - the base year. In the above example, if we expressed the year 2 output level in constant prices, we would use the price existing in year 1 - £2.00 and multiply that by the new output level in year 2. The output level in year 2 is thus 12,000 x £2.00 = £24,000.

The use of constant prices helps us to be able to strip out the effects of price changes on output levels and is seen as being a more accurate indication of the changes in data like GDP.

Index Numbers:

Indexes are used as a means of comparing the average movements in data variables over a period of time. With inflation for example, some prices will be rising, some falling and some staying the same. Those prices rising and falling will be doing so at different rates. The use of an Index helps to give a measure of the average change in those prices. It makes use of a base year in which the index is always 100. If in year two, the index has risen to 105, it tells us that prices (for example) have risen by 5% on average. If the Index fell in year 3 to 99, it would tell us that prices had fallen on average by 6% on the previous year and by 1% compared to the base year.

Laspeyres:

This is a statistical device used to measure changes in index numbers over a period of time. The Laspeyres index measures the change in an identical 'basket' of (for example) goods and services over a period. The index is weighted in terms of the relative importance in the index of the data. In inflation measures, for example, the prices of staple foods might be considered to be more important in consumer spending patterns than a box of matches. Changes in the price of those items, which feature heavily in everyday spending, therefore can be more accurately reflected in the resulting index. The Laspeyres index uses weights determined in the base year. A Paasche index uses weights determined in the current year. A Laspeyres index therefore is likely to give different results to a Paasche index. This is because spending patterns and consumer tastes change from year to year, video cassettes, for example, played a more important part in spending habits ten years ago than they do today. They have now been superseded by the availability of DVDs. Weightings therefore have to be changed periodically to reflect these changes. A Paasche index will take this into account every year but in so doing, the cost of compiling the statistics is much higher than a Laspeyres index.

Chain Linked Measures:

Chain linking refers to the fact that measures of changes to indexes over a period have some link, for example, if the weighting of the prices in the Consumer Price Index (CPI) are changed every five years then there is some link between the data over the last (say) 15 years but the links are relatively widely spread (i.e. every 5 years). Chain linked volume measures take account of this by updating the weights every year therefore reducing the time span of the link and making the data more accurate over periods.