All Terrain Thinking

A Compendium of things I think are Important

Economics: It's not just whats' in your wallet

You have now decided on the appropriate time frame and the appropriate variables, now you must decide on which form of the data you want to use. Unfortunately, there is no way at this point that I can provide you with a thorough treatment of this crucial issue. All that I can do is briefly mention a few 'tricks of the trade'. It may help as we go through these 'tricks' to keep in mind that in most cases we are simply attempting to place individual numbers in some perspective. Any number by itself is meaningless at best, and at worst misleading.

In general there are two ways of providing some perspective. The first we would call cross-sectional perspective. Is our income higher, our unemployment rate lower, our growth faster than others? It allows us to know how we are doing relative to others. The second would be time-series perspective. How are we doing today relative to yesterday, to last month, last year? This provides us with some historical perspective.

In the remainder of this section we will examine some techniques for gaining perspective. We will begin with one of the most important techniques, one which will allow us to adjust our data for the distorting effects of inflation. This is a must if you are to ever make sense of both cross-section and time-series analysis when we are using data that are measured in currency. For example, what can we tell about the welfare of American workers by noting that in the 1970s average wages grew by 7 percent, substantially higher than the 4 percent in both the 1960s and 1980s? Or what can we tell from the data on average annual pay where Alaska ranks first with average pay nearly 25 percent higher than Rhode Island? It turns out we can say very little based on these data until we adjust them for prices / inflation.

In addition to adjusting data for prices, we also often need to adjust data for scale. For example, what can we tell about the safety of children in the state given that the number of reported cases of child abuse in Massachusetts outnumbers those in Rhode Island by nearly 4 to 1? What can we say about the fact that in 1994 there were approximately 60 times as many poor living in California as there were living in Rhode Island? And what about death rates in Rhode Island which are about 12 percent above the national average-does this suggest that we are at greater risk? It turns out that once again the numbers tell us little-but in this case it is because we need to take account of size, something we will examine in the bench marking section.

When using any variables measured in terms of dollars, income, earnings, sales, profit, GNP for example, care must be taken in interpreting changes in these variables over time. To avoid, or more accurately, to correct for this distortion caused by rising prices in a dollar denominated variable, economists construct a new variable known as the real, constant dollar, or inflation adjusted variable. The concept is straight forward enough, we want a measure of wages that will indicate no change in 'real' wages if both wages and prices double and will indicate a doubling of real earnings if wages double and the price level remains unchanged. But what do we use as a measure of the price level? In most instances the Consumer Price Index (CPI) is used as a measure for the price level. The CPI, published monthly by the Bureau of Labor Statistics, is simply a weighted average of the prices of goods and services that households purchase. If you tend to spend considerably more money on food than movies, you will see your cost of living decline more as a result of a 10 percent increase in the price of food than in the price of movies.

The procedure for adjusting the nominal quantity is quite simple, but extremely useful. In the following example you will see how nominal (actual) wage data is corrected to arrive at the real (inflation adjusted) figure. The formula for the adjustment is:

where R = real value (constant dollar)

N = nominal value (current dollar)

PI = price index

And the adjustment does matter. To see how much, I suggest you check out the analysis of the financial situation at University. Here we will start by returning to our Slippery Slope example. Let's assume that the president gets his way and that we decide to use his time frame. Has he done a good job? Are you ready to extend his contract? Before you get out your pen, consider the fact that during this period prices were rising in the economy (yes, there was a little inflation-but more about that later).

To incorporate into your analysis the effect of price inflation, you need to get information on the price level. In the third column I have included information on the price level, the Consumer Price Index (CPI) which you hear people talk about every month.

With these data, and using the formula above, we can create a new concept called 'inflation adjusted', or real revenues. The 'real' data, in 1996 prices, are included in the following table and graph.

What we see here is that real revenue in 1996 was virtually unchanged from what it had been in 1992, and substantially lower than 1991. In 1996 the university finds itself in about the same position it was in in 1991 since revenues increased at about the same rate as prices for this period.

The importance of the adjustment is also evident in the two graphs below entitled Average Weekly Earnings. In the top graph, the untrained eye sees continual improvement in average weekly earnings. From an average of roughly $80 a week in 1960, weekly earnings rose to $370 in 1993. Furthermore, given the fact that wage earnings increased at an average yearly rate of 4 percent in both the 1960s and 1980s, and 7 percent in the 1970s, one might be led to believe that the 1970s was a period of more rapid growth, an observation that I repeatedly see in my student's initial reports on economic growth in the post WW II period.

Anyone aware of that period knows that this is not an accurate portrayal of the period, a fact more than adequately reflected in the second weekly earnings graph in which you find the data for wages adjusted for inflation. Real earnings of American workers peaked in 1973 and by 1990 they had fallen to 1960 levels. Yes wages increased 7 percent per year in the 1970s, but prices increased nearly 8%giving us an average yearly 'decline' of 1% in wages.

This diagram also helps explain the proliferation of reports indicating the plight of Generation X, those who are moving into the labor force in the early 1990's. The forecasts that this generation may be the first to not achieve a standard of living higher than that of their parents is simply the result of an extrapolation of existing trends.

When converting from nominal to real variables there is one additional important consideration: In what dollars should the real variables be expressed? 1994? 1987? 1967? In most cases the best solution would be to express the real variables in current dollars, the dollars that your audience is likely to be familiar with. For example, consider the data below on average weekly earnings for the period 1980 to 1994. If we followed the procedure outlined above we would generate the Real variable, something virtually impossible to explain. Two alternative constructs appear in the following columns. In the Real(80$) column we have the value of real wages expressed in terms of 1980 dollars which is derived by taking the Real column and multiplying it by the CPI in 1980 [Ex. in 1985 $229.04 = $299.09/107.6*82.4]. The Real(94$) column, meanwhile, expresses real wages in terms of 1994 dollars and is derived by multiplying the Real column by the CPI in 1994 [Ex. in 1987 $226.67 = $312.50/113.6*146.7].

Does it matter which variable you decide to use? In one sense, no. All three of these columns provide equivalent measures of real wage growth. If you do the math you will find that for all three measures, wages decline by 10 percent during the period. Where they differ is in the interpretation. One possibility would be to describe the fact that workers who earned $235.10 in 1980 are currently earning the equivalent of $211.03 1980 dollars in 1994. A second option would be to say that the workers earning $375.70 a week in 1994 have seen their earnings fall from $418.56 since 1980. Which you choose depends on which you think your audience would best be able to relate to.

A similar problem exists when we examine interest rates or other rate of return variables. Consider the position of a money lender who must determine the appropriate interest rate to charge. Certainly one of the considerations will be the rate of inflation, the rate of increase in the price level (CPI). If the inflation rate is 6 percent, a lender must receive 6 percent interest just to maintain the buying power of the money. Stated somewhat differently, if the cost of living increases 6 percent this year, then what you can buy this year for $100 will cost $106 next year. In this situation you must charge 6 percent so as to receive the $106 in one year. If on the other hand, the lender wanted a 2 percent return on money, then the interest rate would need to be 8 percent-6 percent simply accounting for inflation.

The realization that inflation rates are a common denominator in interest rates has prompted economists to develop a concept called 'real interest rates'. The unobserved 'real' rate which is what 'really' matters to decision makers, is defined as the actual rates minus the expected inflation rate. The relationships between real and nominal rates is captured in the equations:

where:

• r_n = actual interest rate (what you see in the news)
• r_r = real interest rate
• i = inflation rate

As with wage earnings, there is a significant difference between the movement in real and nominal interest rates. In the 1980s for example, nominal short term rates on government securities fell sharply from 11.5 percent in 1980 to 6 percent in 1986 before rising to 7.5 percent in 1990. Real interest rates, meanwhile, moved in the opposite direction. After actually being negative in 1980, real rates rose to 4.1 percent in 1986 and then fell back towards 2 percent in 1990.

Scale adjustments: Bench marking

Let's assume that are interested the performance of Rhode Island's economy. I can tell you that in the decade ending in 1993, wage and salary income in the State increased 75 percent, employment increased 4 percent, and the average price of a home in Minnesota rose over 100 percent. That and 25 cents might get you a phone call. There is no benchmark against which to evaluate these numbers. A 4 percent employment increase sounds low, but what if employment elsewhere were actually falling? On the other hand, the 75 percent increase in wage and salary income sounds impressive, but what if the average for the other 49 states was 120 percent?

To avoid being misled by the absolute numbers, In this section we will look at two 'bench marking' techniques which will be used to provide some perspective for construction employment data in the table below.

The first bench marking technique is particularly useful when comparing two variables that are of very different orders of magnitude. Consider the problem of analyzing the performance of employment in Rhode Island relative to that in the US. The problem with the raw employment data is that it is impossible to construct a meaningful graph of Rhode Island and the US employment because of the large difference in scales. The line for construction employment in Rhode Island appears as a straight line near the axis because the scale is set to accommodate both the numbers for the US, which are in the hundreds of millions, and for RI, where the figures are in thousands.

One frequently used solution would be the construction of a double Y-axis graph similar to the one below. This graph does allow us to see variations in both variables by using two scales, but there is one significant problem with the technique. The computer generated double-Y graphs set the scales for the variables so that the graphs will fill the page rather than provide the best comparison of the data. For example, in the accompanying double Y graph, the range for US employment is 100 percent of the initial value (3000 to 6000), while the range on Rhode Island employment is 150 percent of the initial value (8 to 20). Without carefully reading the scales, the reader is led to believe that employment in Rhode Island and the US expanded by roughly the same rate in the 1983-1988 period, something very far from the truth.

The differences between the performance of the construction employment in Rhode Island and the US can be better represented by the creation of index numbers which provide us with a second solution to the problem of analyzing these data. The advantage of the index approach is that it provides a better visual representation of comparative growth while its disadvantage is that the index number is not readily understood by the lay reader without some guidance. The index numbers are constructed by taking all the numbers in a column and dividing them by the first number. In the US column for example, each of the numbers is divided by 3,877, while for Rhode Island each number was divided by 9.9. As you can see, all the columns of indexes begin at 1 which is what you would expect since you are dividing a number by itself. For the other years you should interpret the index numbers as giving you a measure of the changes in construction employment over a specific period of time.

The results of applying this index approach to employment in construction are dramatic For example, the 1.33 figure for the US in 1989 tells us that between 1983 and 1988, employment in the US has increased by 33 percent. During the same time period construction employment in Rhode Island increased 83 percent, evidence of the speculative nature of RI's construction boom in the mid 1980s that laid the groundwork for the collapse in the late 1980s.

A second approach to bench marking RI's performance would be to look at the State's relative performance. One of the most common measures of relative performance would be a ratio. In the table above, the figures in the last column were derived by dividing Rhode Island employment into the US total for the same year. For example, the .35% figure for Rhode Island in 1989 equals 17/4969*100.

How do we interpret the table of ratios? One must be quite careful. The ratio form allows us to look at how the state has performed relative to the nation, but we lose information about the actual performance of the state when we construct the ratio. It is impossible to tell what happened in Rhode Island from looking at only the ratio. Stated somewhat differently, a decline in the Rhode Island/US ratio could happen in each of the following cases:

1 employment rises slower in Ohio than the US

2 employment falls faster in Ohio than the US

3 employment falls in Ohio and rises in the US

4 employment falls in Ohioand remains unchanged in the US

5 employment remains unchanged in Ohio and rises in the US

The advantages of the relative variable can also be seen in the graphs for wages in Rhode Island. In the graph of Ohio's wages, there is every indication that wages were increasing throughout the post W.W.II period. A very different picture emerges, however, when we look at the graph of the ratio of Rhode Island to US wages. In the period 1950 to 1978, Rhode Island's wages fell from approximately 88 percent of the US average to approximately 73 percent of the US average. During this period wages in Rhode Island grew, but more slowly than they did in the US. This trend was reversed in the 1980s as wages in Rhode Island began to rise faster than wages in the rest of the nation. Unfortunately for those of you who are Rhode Islanders, the long term pattern of decline had been reestablished by the early 1990's.

Before we leave this section, let us ask the questions: What would employment in Rhode Island have been in 1989 if it had grown at the same rate as the US and What would employment in Rhode Island have been in 1989 if its share of national employment had remained unchanged? The fact is that they are the same question. If employment grew at the same rate in Rhode Island and the US, the share would have remained constant - so in answering one of the questions we are answering both. Let's approach it from the constant share side. Rhode Island construction employment in 1989 was 18,200 which represented .35 percent of the nation's total construction employment. This was an increase of .09 percentage points from the 1983 share which explained Rhode Island's faster than average growth. By 1989 employment in Rhode Island had increased 83.8 percent, substantially more than the 33.5 percent increase nationally. If the share had remained the same-.26 percent - then Rhode Island employment in 1989 would be .26 percent of the national total which is 13,455 [.0026*5175]. Stated somewhat differently, RI's above average growth was responsible for 4,745 jobs in the construction industry-jobs that could not be explained by the national growth. You will end up in the same place if you let employment in the state grow at 33.5 percent.

With this new information, what can we say about the President's relative performance? There are two techniques which we might use to answer this question. One would be to look at the ratio of Slippery Slope to the revenue for the entire group of universities. If we adopt the President's view that we should begin in 1992, he has been successful at increasing Slippery Slope's share of total revenue from 6.9 to 7.5 percent because revenue has increased faster at Slippery Slope (12 percent) than for the entire group (3 percent).

For those who like visuals, here is what the graph of Slippery Slopes's relative performance. Given that we are graphing a ratio (SS/Total), when this graph slopes downward it means that Slippery Slope is not doing as well as the total and when it has a positive slope, Slippery Slope's revenue is growing faster than the total.

Another way to look at it would be with the use of an index number. Below you will see a table containing the original plus the new indexes for Slippery Slope and the Total. The indexes were constructed by dividing each of the original columns by the corresponding 1992 figure.

These data tell us that from 1992 to 1996, Revenue at Slippery Slope increased 12 percent [we take away 1 from the index 1.12 and have .12 which equals 12 percent] while revenue for the entire group of universities increased 3 percent [we take away 1 from the index 1.03 and have .03 which equals 3 percent]. The graphical representation of these two are presented below.

What's the verdict? As I indicated at the outset, this was not going to be easy. The bad news for the President is that revenues have gone up, but not after we account for inflation. The university is about where it was in 1992 in terms of the buying power of its revenue. The good news is that the President's university seems to have done better than the other universities. The review is mixed-and the decision will be a difficult one-but I hope that you can see how a little data analysis can go a long way toward helping us make a more informed decision.

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