Calculating Economic Indices

Started ‎08-07-2023 by
Modified ‎08-01-2023 by
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This example illustrates the calculation of some widely known economic indices such as Laspeyre, Paasche, Bowley, Fisher, and more by defining them with PROC FCMP and then accessing the compiled functions by using the SAS global option CMPLIB in a DATA step. An economic index is a statistic about the economy that is used to study relative movements in prices or quantities over a period of time. They assist in decision-making to create a stable economy by predicting future performance.


Some of the popular ways of computing economic indices are given by Laspeyre’s index, Paasche’s index, Bowley’s index, and Fisher’s index formulas. Each of the index formulas can be used to compute both a price index and a quantity index. A price index measures the change of price over time for a fixed basket of products and services. A quantity index calculates the change in consumption over time for a basket of goods with a fixed value at a certain time. Some examples of price-related economic indices are the consumer price index (CPI), import and export price indices, producer price indices, and the employment cost index. The growth rate of gross domestic product (GDP) is an example of quantity-related change.The following indices are calculated in this example:


  • Laspeyre’s
  • Paasche’s
  • Bowley’s
  • Fisher’s
  • Marshall Edgeworth’s
  • Mitchell’s
  • Walsh’s
  • geometric mean
  • harmonic mean




An ideal index might be expected to have a fixed weight in the numerator and denominator. But with a price change, quantities purchased are rarely identical over two given periods. Although Laspeyre’s and Paasche’s index formulas are two widely used methods to calculate indices, these indices do not account for the fact that consumers typically react to price changes by changing the quantities they purchase. With an increase in price, the consumer would reduce the quantity; hence the weights added to Paasche’s index would be smaller than that of Laspeyre’s index. As a result, Paasche’s index systematically understates inflation while Laspeyre’s index overstates it.


To compensate for this discrepancy, different formulas were introduced. Bowley’s and Fisher’s formulas use the arithmetic mean and the geometric mean (of Laspeyre’s and Paasche’s index values), respectively. Marshall-Edgeworth’s formula uses an average of the base price and the current price as weights. The Fisher’s index is more appropriate when dealing with percentage changes. As their names suggest, the harmonic mean index computes the harmonic average and the geometric mean index computes the geometric mean.


Except for Laspeyre’s, the harmonic mean, and the geometric mean, all the other indices require revised knowledge of the current expenditure pattern. This is a disadvantage because collecting current information and updating weights require more time and effort. All the indices put together serve as an overall measure of the relative movements.


In the following formulas, let period Ο  denote the reference point in an earlier period, also sometimes known as the base period. Let period denote the current time of interest with which the base period is compared. Let ί = 1...n denote the items in the basket over which the summation is carried out. Let Ρο,i denote the price of the ίth item at the base period, and qt,i denote the price of the ίth item at the current period of interest t. Some of the fixed-weight price indices illustrated in this example and defined by using PROC FCMP are enumerated below. The function names defined in PROC FCMP are shown in parentheses. For each index type, the quantity index formula is defined, analogous to its corresponding price index formula.



  • price index (laspeyres_price_index)


  • quantity index (laspeyres_qty_index)



  • price index (paasche_price_index)


  • quantity index (paasche_qty_index)



  • price index (bowley_price_index)


  • quantity index (bowley_qty_index)



  • price index (fisher_price_index)


  • quantity index (fisher_qty_index)


geometric mean (GM)

  • price index (geometricmean_price_index)


  • quantity index (geometricmean_qty_index)


harmonic mean (HM)

  • price index (harmonicmean_price_index)


  • quantity index (harmonicmean_qty_index)



  • price index (marshall_edgeworth_price_index)


  • quantity index (marshall_edgeworth_qty_index)



  • price index (walsh_price_index)


  • quantity index (walsh_qty_index)



  • price index (mitchell_price_index)


  • quantity index (mitchell_qty_index)



The qa in the Mitchell’s index denotes the relative importance of the items. Mitchell has advocated using the average of quantities bought and sold over a period of time to be used as weights (Kenney and Keeping 1962). In this example, the Mitchell’s price index function is calculated with the weight "wt" (defined as qa in the preceding formula) as the average of q0 and qn. You can supply your own weights depending on how you rank the items in their relative order of importance. Instead of using the preceding Mitchell’s formula, some users might have a better idea of the "wt" given by value, va = qa x p0 of their products. In that case, Mitchell’s price index formula is given by:



Similarly, Mitchell’s quantity index formula is given by:


where in this case,va is defined as va = pa x q0


Note that the price indices defined by using PROC FCMP can be used to compute the quantity indices as well, by interchanging the price and quantity values. To avoid confusion, the quantity index formulas are defined separately. For example, the Laspeyre’s price index needs input arguments of laspeyres_price_index (p0,q0,pn). You can obtain Laspeyre’s quantity index by changing the input arguments to laspeyres_price_index (q0,p0,qn), replacing the p's with q's and vice versa. This yields the same result as using the laspeyres_qty_index (p0,q0,qn).




You can define the index functions as subroutines by using the FCMP procedure. In this example, the functions are stored in "sasuser.ecoidx.economic_indicators" by using the OUTLIB option in the PROC FCMP statement. The laspeyres_price_index created by PROC FCMP is shown as follows:


%let OUTLIB = sasuser.ecoidx.economic_indicators;

   /*-------- create functions with PROC FCMP --------*/

   proc fcmp outlib= &OUTLIB;
 function laspeyres_price_index( p0[*], q0[*], pn[*] ) label= "Laspeyres Price Index";
    * ENTRY:     laspeyres_price_index
    * PURPOSE:   Computes Laspeyres Price index.
    * USAGE:     idxl_p = laspeyres_price_index( p0, q0, pn );
    *            p0 denotes price vector for items at time 0/base;
    *            q0 denotes quantity vector for items at time 0/base;
    *            pn denotes price vector for items at time n;
    *            In the following example for 2 items,
    *            the Laspeyres Price index has been calculated.
    * NOTE:      Missing values as arguments to the function return missing value.
    * EXAMPLES:  idxl_p = laspeyres_price_index( p0, q0, pn );
   if  dim(p0) ~= dim(q0) | dim(p0) ~= dim(pn) then do;
      Put "ERROR: Arguments to laspeyres_price_index do not have the same dimensions";
      return( . );
   do i=1 to dim(p0);
       num=num + (pn[i]*q0[i]);
       den=den + (p0[i]*q0[i]);
   return( idxl_p );

The variable IDXL_P holds the computed Laspeyre’s price index. The RETURN statement in a subroutine enables the computed result to be returned when the specified function is invoked. Note that the IDXL_P result is used later in a DATA step given below. You need the base price p0, base quantity q0, and current price pn to calculate the Laspeyre’s price index. The input arguments for the laspeyres_price_index function are p0[*], q0[*], and pn [*]. The [*] in p0[*] denotes an array that contains the base prices for all the items in the basket. The other subroutines that define the other index functions have been created similarly. The complete statements can be found in the accompanying SAS source file (sas.html). The data set contains wine and cheese products over two time periods T1 and T2. The observations are as follows:





















The library catalog 'sasuser.ecoidx' in the SAS global option CMPLIB= specifies where to look for the previously compiled functions created in PROC FCMP. For more information about the FCMP procedure, see the Base SAS Procedures Guide, Version 9. The following DATA step reads the data and calculates the various price indices.


/*-------- test functions with datastep --------*/
                   /***calculate price index***/
   options  CMPLIB= (sasuser.ecoidx);
   data indices(drop=i);
   array p0[2] _temporary_ (15 22);
   array q0[2] _temporary_ (100 25);
   array pn[2] _temporary_ (18 16);
   array qn[2] _temporary_ (108 38);
   array wt_q[2] _temporary_;
   array wt_p[2] _temporary_;
   do i =1 to dim(p0);
       wt_q[i] = .5 * (q0[i]+ qn[i]);
       wt_p[i] = .5 * (p0[i]+ pn[i]);
   idxl_p = laspeyres_price_index( p0, q0, pn );
   idxp_p = paasche_price_index( p0, pn, qn );
   idxb_p = bowley_price_index( p0, q0, pn, qn );
   idxf_p = fisher_price_index( p0, q0, pn, qn );
   idxgm_p = geometricmean_price_index( p0, q0, pn );
   idxhm_p = harmonicmean_price_index( p0, q0, pn );
   idxme_p = marshall_edgeworth_price_index( p0, q0, pn, qn );
   idxw_p = walsh_price_index( p0, q0, pn, qn );
   idxm_p = mitchell_price_index( p0, wt_q, pn, 1 ); /*<----user supplied _type_ */
   idxl_q = laspeyres_qty_index( p0, q0, qn );
   idxp_q = paasche_qty_index( q0, pn, qn );
   idxb_q = bowley_qty_index( p0, q0, pn, qn );
   idxf_q = fisher_qty_index( p0, q0, pn, qn );
   idxgm_q = geometricmean_qty_index( p0, q0, qn );
   idxhm_q = harmonicmean_qty_index( p0, q0, qn );
   idxme_q = marshall_edgeworth_qty_index( p0, q0, pn, qn );
   idxw_q = walsh_qty_index( p0, q0, pn, qn );
   idxm_q = mitchell_qty_index( q0, wt_p, qn, 1 ); /*<----user supplied _type_ */

Figure 3.1 shows the price indices computed with the Laspeyre’s, Paasche’s, Bowley’s, Fisher’s, geometric-mean, harmonic-mean, Marshall-Edgeworth’s, Walsh’s, and Mitchell’s index formulas.



Obs idxl_p idxp_p idxb_p idxf_p idxgm_p idxhm_p idxme_p idxw_p idxm_p
1 1.07317 1.03909 1.05613 1.05599 1.04914 1.02181 1.03259 1.05670 1.05459

Figure 3.1: Price Indices


Note that the relative importance of the items denoted by "wt" (or qa in the Mitchell’s price index formula) is calculated as an average of the base and the current quantities while computing price indices. You can supply your own weights "wt" based on your judgment of the relative importance of the items.


As pointed out earlier, if you are required to calculate the Laspeyre’s quantity index, you could enter laspeyres_price_index (q0, p0, qn)  with p’s replaced by q’s and vice versa or laspeyres_price_index (p0, q0, qn). Note that during the quantity index calculation, the "wt" in Mitchell’s formula is calculated as an average of the base and the current prices. Again you might use the va weighted Mitchell’s formula. In that case, you would enter mitchell_price_index(p0, wt, pn, 2) instead of "1" for _type_. For the same dataset, the quantity indices are computed.


Figure 3.2 shows the various quantity indices computed for the same data with the Laspeyre’s, Paasche’s, Bowley’s, Fisher’s, geometric-mean, harmonic-mean, an Marshall-Edgeworth’s, Walsh’s, and Mitchell’s index formulas.



Obs idxl_q idxp_q idxb_q idxf_q idxgm_q idxhm_q idxme_q idxw_q idxm_q
1 1.19805 1.16 1.17902 1.17887 1.18371 1.17094 1.08822 1.17771 1.17835

Figure 3.2: Quantity Indices


When a price increases, the price index increases; when the price falls, the index falls as well. The same is true for the quantity index. For the small data set presented here, you can guess what the results would be. Over the two time periods, there is an increase in the price of cheese and a decrease in the price of wine. However, the quantities purchased for both cheese and wine show an increase over the two periods. If you compare the two results above, the quantity index values are slightly larger than the price index values; this is expected if you compare the relative changes by observation.




SAS Institute Inc. (2003), The FCMP Procedure, Version 9, Cary, NC: SAS Institute Inc.

Kenney, J. F., and Keeping, E. S. (1962), Mathematics of Statistics, Pt.1, 3rd Edition, Princeton, NJ: Van Nostrand, 64–74.



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Last update:
‎08-01-2023 02:19 PM
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