A C&EN Feature
Consumer spending foretells plastics demand Walter S. Fedor, Senior Editor, New York City
P
ersonal consumption expenditures grew 5.6% annually during the past 10 years, reaching $495 billion (in current dollars) this year. Through 1972, these expenditures will grow 7% a year, bringing personal outlays to around $660 billion. This is an encouraging prediction—but what does it mean to the chemical industry? The answer: much more than is apparent. For one thing, the growth in personal consumption expeditures indicates that sales of the chemical and allied products industry will reach $73 billion in 1972, about 50% more than this year. This is substantial growth when compared to that computed for all manufacturing industries and will certainly keep chemicals in the forefront of sales growth. The major reason for this betterthan-average performance lies in polymers, the acknowledged pacesetters of future chemical industry progress. Polymers accounted for $8 billion of the industry's sales this year and will likely account for $15 billion in 1972. Polymers brought the chemical industry from the back room to the display window, where these highly consumer-oriented products are exposed to the examination of the buying public. The most widely displayed polymers are the thermoplastics, which are used in a wide variety of consumer durable and nondurable goods, such as home furnishings, housewares, apparel, toys, appliances, automobiles, and packages. Polyethylene, polyvinyl chloride, and polystyrene are the most widely used thermoplastics. Combined production of these plastics will reach 8 billion pounds this year; output could reach 14 billion pounds in 1972—a volume nearly equal to this year's total production of all plastics and resins. Specifically, output of polyethylene (all types) will increase 70% by 1972. Polyvinyl chloride production will rise 7 5 % ; polystyrene will grow 7 5 % . 84 C&EN NOV. 20, 1967
Correlation and regression analysis applied to data on consumer spending yields production forecasts for thermoplastics
These are impressive growth forecasts, but they are not based upon traditional end-use market analysis. Rather, they are made by examining the relationships and variations of the demands of the people ultimately using the products—the consuming public. These relationships and variations were developed by regression and correlation analysis, fundamental techniques used by statisticians and economists, but rarely applied by market research people in the chemical industry to forecast future markets. Market analysts haven't used these techniques for a variety of reasons that vary from disbelief of a somewhat abstract method, to a lack of understanding, to lack of time to perform simple, but rather tedious, calculations. If the calculations are done by hand, a multiple regression problem can take from 10 to 20 hours to complete. Computers have cut the time considerably, however; by computer more than 100 analyses can be completed in that time. Since computers remove the drudgery of routine mathematics and free people to think more in depth, some market research and planning people in the chemical industry have turned to regression and correlation analysis. This tool, then, adds another dimension to a company's effort to understand itself, its markets, and the ultimate users of its products—the buying public. The chemical industry never really impressed anyone that it was concerned about events taking place in consumer markets. The industry did have a link with the buying public through products such as paints, drugs, cosmetics, and toiletries. These were usually small operations, however, compared to the much larger basic chemical products. Until 5 to 10 years ago, depending on the company, the chemical industry was chiefly a manufacturing industry, making products for other manufacturers. For the most part, the chemical in-
dustry was not concerned with what its customers did with the products it sold them. Thus the chemical industry was not oriented to the retail consumer market. In the past 5 to 10 years, however, competition and polymer products have caused the chemical industry to begin to take cognizance of the retail consumer market. The progress that has been made was aided by the emergence of inputoutput analysis as a marketing tool, the evolution of management by systems to direct companies with an eye to both internal and external environments, and the use of computers to use heretofore burdensome mathematical techniques more often to project, forecast, and plan. Today, any proposition to expand a plastics plant based on expected changes in the retail market is more apt to draw a suggestion that the proposer see a psychiatrist than to get serious attention from many executives. In due time, however, such suggestions will be commonplace. Consumer market research firms survey home economics students to find out what kinds of home furnishings, appliances, and the like tomorrow's housewives will demand. Spending plans of consumers are a good indicator of future economic activity, too. Much of the consumer demand involves thermoplastics in one way or another. Women have no qualms about buying bread wrapped in polyethylene or a vacuum cleaner with a polystyrene housing. Men accept polyvinyl chloride seats in automobiles, and parents do not hesitate to buy a toy made from a thermoplastic. Consumers will buy plastics products, and as their incomes increase their demands for these products will intensify. This spending relates directly to the production of various thermoplastics. This effect can be measured several ways; measuring retail sales is a common method. Knowing consumer spending patterns and how
much consumers have to spend becomes formidable planning information, as any company that deals directly with the buying public knows full well. This can be valuable planning information for industrially oriented companies also—particularly for thermoplastic resin producers who somehow have created a romance with the buying public. Retail sales When consumers buy, a chain reaction begins that reverberates throughout the whole economy and ultimately leads to events such as expansion by industry. The starting point for this reaction is often the retail store, the most common point of purchase for consumers. A thorough understanding of consumer spending patterns at the retail store level is bound to provide a keen insight into future economic activity and, through closer observation, into the future of specific products. On the retail level, consumers spend chiefly for food, with such outlays accounting for 3 1 % of their buying dollars. Automobile sales, services, and accessories take 2 7 % , while general merchandise store purchases account for 14%. The remainder is distributed among apparel, appliance, and furniture stores. Nearly 70% of spending is for nondurable goods; the balance is for durable goods. This year, consumers will spend $315 billion in retail stores, compared to $304 billion in 1966. This gain was slightly below the medium growth rate for retail sales for 1960-66, 5.9% a year. The Department of Commerce keeps monthly and annual tabs on consumer spending through a series of Retail Trade Reports. These reports provide seasonally adjusted and unadjusted sales figures. These data are easily obtained and provide an excellent starting point to study the broad relationships and variations that NOV. 20, 1967 C&EN
85
Correlation and regression analysis Correlation and regression analysis is a powerful statistical method to help unravel the various relationships among variables that cannot be measured directly or are hidden by other things. The technique is a prime tool of the social scientist, who must work in the abstract to obtain results; quite unlike the physical scientist who has elaborate instruments at his command to make measurements. The mathematical theory behind correlation and regression analysis can be rather burdensome; however, it is not necessary to completely understand the tool to use it in daily business practice. The case is analogous t o an automobile: Must one understand the design of an internal combustion engine to drive a car? Correlation involves three types of measurements: • An estimating or regression equation that allows measurement of one variable from another or other variables. • A measure of divergence of actual values from computed values. This is called a standard error or estimate and is similar to a standard deviation. • A measure of the degree of relationship, or correlation between the variables, independent of the terms in which the variables were expressed. (This is called the coefficient of correlation, r. The square of r is called the coefficient of determination, or index of determination, and in theory explains the relative amount of variation in the estimating equation.) In a simple time-series correlation, the dependent variable would be X and the independent variable, Y. In use, X is multiplied by Y, X is squared, Y is squared, and then the values of X, Y, XY, X8, and Y2 are summarized for the period under study and the correlation coefficient obtained with this equation: NSXY f
"~ v t N S X 2 -
(SY) 2 ]
The coefficient of determination is simply the square of the coefficient of correlation. When r is zero, there is no correlation. Perfect correlation is plus or minus 1. Assuming the correlation is high, a simple estimating equation can be developed using the equation of the straight line: Y = a + bx. X, Y, and XY are simply substituted and the equation solved using:
and N = number of years in time series However, not all correlation problems are that simple. A reliable estimating equation developed through correlation and regression analysis should contain at least three independent variables but no more than six variables. This brings in the concept of multiple and partial correlation.
86 C&EN NOV. 20, 1967
• A variation in variables may be caused directly or indirectly by variations in other variables. • Covariation of variables may be due to a common cause or causes affecting each variable in the same way or in opposite ways. • A casual correlation may be the result of interdependent relationships. • The correlation may be due to chance.
(SX) (SY)
(SX) 2 ] [NSY2 -
Actually, multiple correlation involves the same principles as simple correlation, but the procedure gets more laborious and more symbols are needed, for example, bt, bit bs, etc. Partial correlation is a somewhat different concept in that this is a method that tries to explain the increase in the proportion of variation (r2) of the dependent variable that results from the introduction of another independent variable to the proportion of the variation that had not been explained before the introduction of the new variable. Partial correlation is useful in stepwise (variable elimination) analysis before a multiple regression calculation to determine the most useful combination of variables to develop an estimating equation. Most reliable forecasting equations based on these methods contain several independent variables; the mathematics becomes messy. Fortunately we have computers today. Thus, all that is needed is a statistician, a programed and a computer. Once the mathematics is programed, only a set of simple instructions is needed to make correlation and regression a useful tool in business forecasting. There are cautions to remember, however, when using this technique. Correlation must be thought of not as something that proves causation, but only as a measure of covariation. Many situations can occur, such as:
Such cautions should not diminish the true value of this statistical tool. One should use common sense when studying the correlation between variables. Polystyrene in radio cabinets, or polyvinyl chloride in upholstery, or polyethylene in toys makes sense. A high covariate relationship means that variables exist that can make a useful regression equation to forecast the future. As with any forecasting method, the correlation and regression technique does not provide all the answers all the time. There are instances where this tool will work well; there are some cases where still more sophistication is needed to provide better understanding. The important point today is simply that, with a computer, only a few minutes are needed to find out whether the correlation and regression technique is useful for a specific problem. For more detailed explanation of correlation and regression analysis these books are particularly helpful: "Applied General Statistics" by F. E. Croxton and D. J. Cowden, Prentice-Hall, Inc., and "Methods of Correlation and Regression Analysis" by M. Ezekiel and K. A. Fox, John Wiley & Sons, Inc.
occur among retail sales and specific products. Through correlation and regression analysis, forecasting equations (models) can be built, based on the significant relationships. Relationships between the major thermoplastics—polyethylene (all types), polyvinyl chloride, and polystyrene—can be uncovered in many consumer products. What is not readily apparent is the indirect use of these plastics (for example, vinylcoated wires in appliances and automobiles), or the seemingly unrelated event of more plastics being required in manufacturing operations because of the demand for consumer products (for instance, more parts arrive in plastic packages, or more tote boxes to move parts about in a plant). By its very nature, correlation and regression analysis tends to focus attention on both the direct and indirect reasons for performance by identifying the degree of association or relationship (coefficient of correlation, r) and by explaining variation (coefficient of determination or index of determination, A simple time series correlation (1955-66) was done among the three major plastics and important segments of retail trade sales. Correlation of production of these plastics and sales of home furnishings, apparel, automobiles, drug and proprietary products, food, and general merchandise is nearly perfect. Appliance store sales are an exception, but even here there is reasonable correlation. However, this times series correlation was done without regard to fluctuation or trend. Many statisticians prefer to eliminate fluctuations and correlate on a percentages-of-trend for the various time series. Polyethylene Polyethylene (all types) is the big one in the plastics business. This year, nearly 3.9 billion pounds will be produced—about 2.8 billion pounds will be low-density polyethylene, and 1.1 billion pounds will be high density. The markets for the two overlap in some cases and are substantially different in other cases. Film and sheeting uses are the largest outlet for low-density polyethylene, accounting for 4 3 % of consumption this year. Injection and blow molding take 16%; extrusioncoating uses take another 12%. Exports are 1 1 % of consumption. Blow molding is the largest single outlet for high-density polyethylene, taking 4 5 % of consumption. Injection-molding uses take 2 3 % . Regardless of the way low- and high-density polyethylene are examined, packaging is the major final
Production of major thermoplastics correlates well with consumer spending Polyethylene
Important economic or consumer variables Gross national product Personal consumption expenditures Durable goods expenditures Nondurable goods expenditures Service expenditures Personal income Disposable personal income Discretionary purchasing power Personal savings Employment Consumer price index New housing starts FRB production indexes Industrial Durable goods Nondurable goods Consumer goods Retail sales Total Durable goods Nondurable goods Home furnishings Appliances Apparel Automobiles Drugs and proprietary products General merchandise Food
Fluctuation Corrected and trend for trend
| Polyvinyl chloride Fluctuation Corrected and trend for trend
Polystyrene Fluctuation and Corrected trend for trend
0.999 0.998 0.974 0.997 0.997 0.998 0.998 0.987 0.773 0.971 0.967 0.109
0.685 0.687 0.753 0.525 0.768 0.521 0.515 0.759 0.099 0.367 -0.400 0.412
0.996 0.993 0.989 0.999 0.985 0.992 0.993 0.996 0.793 0.986 0.941 0.087
0.923 0.895 0.927 j 0.761 0.907 0.819 0.812 0.941 0.354 0.741 -0.437 0.201
0.993 0.991 0.987 0.984 0.984 0.989 0.989 0.995 0.785 0.978 0.934 0.133
0.845 0.831 0.884 0.659 0.885 0.721 0.716 0.907 0.280 0.637 -0.530 0.314
0.978 0.940 0.998 0.996
0.652 0.643 0.055 0.680
0.993 0.971 0.995 0.990
0.919 0.917 0.927 0.911
0.990 0.964 0.994 0.992
0.846 0.843 0.868 0.859
0.992 0.942 0.994 0.948 0.683 0.983 0.882 0.980 0.984 0.986
0.506 0.606 0.329 0.282 0.308 -0.237 0.698 -0.316 0.715 0.143
0.996 0.969 0.988 0.975 0.761 0.971 0.976 0.963 0.994 0.973
0.790 0.866 0.602 0.705 0.681 0.089 0.894 -0.135 0.907 0.303
0.990 0.963 0.982 0.965 0.739 0.962 0.915 0.954 0.990 0.966
0.671 0.773 0.475 0.564 0.557 -0.094 0.842 -0.285 0.868 0.177
use. This year nearly 2 billion pounds of polyethylene will be used in food and other packaging applications. This year 850 million pounds of polyethylene were used to package food. With such heavy use, one might easily expect a high correlation between polyethylene and food sales. Such is not the case when compared on a percentages-of-trend over the past 12 years. The correlation is quite poor and for a somewhat obvious reason: Food sales lead polyethylene consumption in various food-packaging applications. Also, polyethylene penetration in food packaging is growing at a rapid rate—use has doubled in just two years. However, as an augur of the future, food sales would be a good prophet because of the wider use of polyethylene to package various food products. In fact, by pushing food sales ahead two or three years, the correlation starts to improve.
Packaging and molded products applications team up to explain the good correlation between polyethylene production and general merchandise retail sales. At this level, polyethylene shows up in many ways—housewares, toys, bottles, and packages for nonfood items. General merchandise is a virtual catchall for polyethylene products. Many items sold here are inexpensive and would account for many impulse sales. This market is the largest single outlet for general-purpose polyethylene. Since general merchandise sales correlate well, a reasonably good relationship might exist between polyethylene and sales at drug and proprietary product stores. Today, a typical large drugstore not only fills prescriptions, but also contains many self-service departments that may feature home furnishings, novelties, appliances, hardware, and personal-care products. As a final-demand material, NOV. 20, 1967 C&EN
87
Building a regression model The emergence of operations research as a business tool has led to much use of the term "model" to forecast events. To the uninitiated, model usually means a series of complicated mathematical expressions. Sometimes this is true and indeed necessary. But many models are really based on simpler mathematics such as correlation and regression analysis. With computers, such models can be built with relative ease. Before computers, they were a tedious, timeconsuming exercise that greatly limited the application of correlation to business problems. Even a simple correlation between two variables could take several hours to complete by the time one adds, multiplies, squares, and takes square roots. With a time-shared computer, this takes one second of calculation time and one to two minutes of input and output statements. Even a complicated six-variable multiple-regression analysis takes only 12 seconds and about two or three minutes of printing time. To C&EN, correlations are now a series of computer programs called BIXCOR, TRNCOR, MULCOR, FACFIG, MULTRY, MULREG and XFRCST. Translated they mean simple correlation, trend correlation, multiple correlation, long-term projection, variable elimination, multiple-regression analysis and extended forecast. MULTRY and MULREG are programs available to subscribers of the General Electric 265 time-sharing system; the others are C&EN programs. Building a forecasting model becomes a fairly simple task. The key to a successful equation is the selection of meaningful independent variables to correlate with the dependent variable or product in question. Unfortunately, correlation analysis has received a bbck eye in the past by correlations of meaningless variables, for example the height of a hemline on a woman's skirt and economic conditions. Such novel correlations are good for humor, but hardly would have much meaning in decision making in business. One approach would be to correlate meaningful products and applications such as polyethylene and food packaging. A simple correlation analysis would show relationship and variation quickly and determine whether the variable should or should not be used in a forecasting equation. Another approach would be to take several meaningful variables and do a variable elimination analysis that would separate the useful from the useless variables. Another consideration is whether independent variables lead or lag events in the dependent variable's (product) history. Once the variables are selected, a multiple-regression analysis determines regression coefficient's and beta coefficient's means, standard deviations, degree of error, and multiple fit to the trend line. The coefficients are really weighing factors that distribute relative importance of independent variables to other independent variables in relation to the dependent variable. For example, a beta coefficient of 0.40 compared to a beta value of 0.10 would mean that the first variable is four times more important to the dependent variable than the second independent variable. The results of the multiple-regression analysis are placed in a multiple-regression formula to forecast the future. As a general rule, an equation should not have fewer than three or more than six independent variables. Since future growth would have three paths—low, medium, and high—a six-variable equation could mean 189 possible combinations to forecast the future of a product. This is rather clumsy so one would do well to choose several periods of history from the independent variables and assume these as indicative of low, medium, or high growth, then forecast the product accordingly. In this article, several forecasts were made for major thermoplastics based on changes in retail sales in one series and changes in known economic indicators in another series. Both series produced forecasts with reasonable agreement over the short and long term. 88 C&EN NOV. 20, 1967
polyethylene most commonly appears in drugstores as an injection- or blowmolded item or as a package for some product. Despite similarities in marketing polyethylene products through drugstores and general merchandise stores, there is no similarity in correlation. In fact, the relationship is weak and negative. There is no really good explanation for this behavior other than the fact that the growth rate in sales of drug and proprietary product stores was static during most of the 12 years in which production of polyethylene grew dynamically. Since drug and proprietary product sales were static several years ago, such stores were apparently slow in developing as an outlet for polyethylene products. Now that the growth rate of drug and proprietary product store sales has started to improve (mainly through growth of personal care products), sales at these stores should start to establish a good relationship with polyethylene. Using drugstores as a lead indicator does produce a better correlation that may become even more significant in the year ahead. Another low and negative correlation is with apparel sales. This is partly expected since there is no appreciable use for polyethylene as an apparel product except as rain covers for hats or as rain bonnets. However, at the present time about 150 million pounds of polyethylene is used to package soft goods and various rack and counter items. Another 75 million pounds is used in garment bags. No significant markets for polyethylene as a fiber could ever develop in the apparel field other than for indirect applications, but there is some promise in nonwoven goods. Polyethylene's use in appliance products is mainly indirect. About 30 million pounds of polyethylene will be used in appliances this year and these mostly inside the product. No strong correlation should be expected and any use of appliance sales to forecast polyethylene would be due to underlying factors in the consumer durable goods markets—for example, strong economy, tax reductions, price cuts, and the like. Such underlying factors also apply to automobile sales, which correlated rather well with polyethylene. The use of polyethylene on a poundage basis in automobiles is small; applications include rug backing, trim liners, and cable covers. Future applications may be more promising—for example, use of polyethylene to make gasoline tanks. Probably some 25 million pounds were used in automobiles last year, but the good correlation is most likely explained by the great impor-
tance of the automobile to the U.S. economy, which in turn creates a heavy indirect need for polyethylene and many other products. A final point in correlation of polyethylene lies with home-furnishings items. Polyethylene is used here in lower priced items such as patio, recreation room, and children's furniture. Indirect applications include use of film to protect higher priced items during display and delivery. Correlation on a percentages-of-trend basis was not good but does improve, as shown by home furnishings, which are a leading indicator. Polyethylene forecasting from retail sales apparently depends mainly on using appliance, home furnishings, drug and proprietary, and food sales as leading indicators, and general merchandise sales and automobile sales, which show a good relationship for the past 12 years. Polyethylene grew quite dynamically during the past 12 years, growing at 2 1 % annually, considerably ahead of growth of the various retail sales markets. As growth rate of polyethylene slows, a closer relationship will develop, which should add more reliability to any forecast developed from retail sales. This point is emphasized by comparing the high correlation that exists when fluctuations in demand are taken into account. A forecasting equation for polyethylene developed from retail sales of general merchandise, automobile, appliance, home furnishings, food, and drug and proprietary product sales predicts polyethylene production will reach 4 billion pounds in 1967 based on a medium growth rate, with the range extending from 3.8 to 4.2 billion pounds. For 1972, the model forecast is 7.3 billion pounds if retail sales follow a medium growth rate. If a low growth rate is followed, production would be 6.6 billion pounds; if a high growth rate takes place, it would be 8.3 billion pounds. Polyvinyl
Polyethylene dominates the plastics business
Domestic polyethylene consumption, all types, 1967 Million pounds Contemporary pattern Retail purchases
Classical end-use pattern Injection molding Blow molding Film and sheet Extrusion coating Wire and cable Pipe and conduit Other extrusions All other uses
650 450 1200 360 330 120 50 290
General merchandise Food Automobiles Appliances Home furnishings Apparel" Miscellaneous retail outlets Subtotal Industrial uses
950 850 25 30 175 225 245 2500 950
Total
3450
Total
3450
" Includes garment bags.
Source: C&EN estimates
Forecasting model for production of polyethylene, all types Million pounds From Personal consumption expenditures Discretionary purchasing power FRB index, consumer goods production Retail sales, total
Year
1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972
Forecast Low growth rate
Medium growth rate
3800 4200 4700 5200 5700 6200
410 551 697 855 1211 1416 1634 1920 2233 2654 3075 3544 3900 4400 4900 5420 6000 6600
From Retail sales: general merchandise, automobiles, home furnishings, food, appliances, drug and proprietary products
Forecast
Actual High growth rate
Low growth rate
Medium growth rate
High growth rate
3800 4300 4800 5400 6000 6600
355 640 718 855 1141 1389 1606 2037 2216 2598 3070 3562 4100 4500 5100 5800 6400 7300
4300 4800 5600 6500 7400 8300
402 566 708 865 1195 1338 1607 2017 2270 2613 3048 3557 4000 4600 5200 5700 6400 7200
chloride
Polyvinyl chloride has long had the reputation of being the most versatile resin ever produced. This year, 2.2 billion pounds of the plastic will be made. Applications range through the spectrum of retail products from baby pants and shower curtains to wall covering and upholstery. Comparison of retail sales with polyvinyl chloride should produce several valid correlations. Compared with polyethylene, the percentages-of-trend correlations for polyvinyl chloride were indeed much stronger, particularly with home furnishings, appliance, automobile, and general merchandise sales.
This year, about 570 million pounds of polyvinyl chloride will be used in various home-furnishings applications, with floor covering, wall covering, and upholstery being the most important. The good correlation of polyvinyl
chloride with home furnishings reflects not only the readily identified consumer outlets, but also the fact that polyvinyl chloride has penetrated the consumer markets for a much longer period than have other thermoplastics. NOV. 20, 1967 C&EN 89
Home furnishings will continue to provide an important indicator for the future of polyvinyl chloride. The same can be said for correlations with general merchandise sales. Here, polyvinyl chloride is found in many products such as place mats, records, and toys. These outlets will take at least 435 million pounds of the plastic this year. Polyvinyl chloride products sold in general merchandise stores are not particularly expensive thus, as with polyethylene, these products are susceptible to considerable impulse buying. It is not surprising that high correlation exists between polyvinyl chloride and general merchandise sales. Another strong correlation exists between polyvinyl chloride sales and automobile sales. At least 210 million pounds of the thermoplastic will be used in automobiles this year, for items such as upholstery, seat covers, and floor mats. Hidden applications include electrical insulation. Besides the general updraft provided to the economy, the auto market provides an indirect effect on polyvinyl chloride, although most of the resin gain with automobiles is in the car itself. Appliance sales correlations are quite interesting in that most use of polyvinyl chloride in appliances is inside the product—for example, as vinyl-coated wires. The appliance market only takes about 30 million pounds of polyvinyl chloride annually, so that a high correlation is really a measure of other forces in the economy. Relationships with the apparel market were quite poor for the time series. Apparel is a growing outlet for polyvinyl chloride, particularly in outerwear such as raincoats, hats, and skirts, as well as for shoe soles and boots. The low correlation means appreciable growth could develop here and that there was really little penetration of the apparel market over the past 12 years. As in the apparel market, there was little penetration of polyvinyl chloride in the drug and proprietary products market six to 12 years ago. Vinyls were depressed, as was polyethylene, through slow development of markets in these stores. Too, the trend toward more sales of personal-care products enhances the outlook for vinyl packaging—hair tonic bottles, for example. The market for vinyl bottles is about 15 million pounds a year now with some predictions of this outlet reaching 100 million pounds in a few years. Sales of drug and proprietary products could become a 90 C&EN NOV. 20, 1967
More polyvinyl chloride is produced than polystyrene, but currently
Domestic polyvinyl chloride consumption, 1967 Million pounds Contemporary pattern Retail purchases
Classical end-use pattern 410 250 60 110 80 210 95 325 105 60
Calendering, except flooring Flooring, calendered Flooring, coated Paper and textile coatings Protective coatings and adhesives Wire and cable Extruded film and sheet Other extruded products Phonograph records Injection and blow molding Plastisol formulating and molding All other domestic uses Total
General merchandise Food Automobiles Appliances Home furnishings Apparel Miscellaneous retail outlets Subtotal Industrial uses
435 50 210 30 570 180 125 1600 525
Total
2125
95 325 2125
Source: C&EN estimates
Forecasting model for production of polyvinyl chloride Million pounds From Personal consumption expenditures Discretionary purchasing power FRB index, consumer goods production Retail sales, total Year
1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972
Actual
Forecast Low growth rate
Medium growth rate
2200 2400 2700 2900 3200 3400
543 607 669 670 922 956 989 1205 1368 1610 1896 2138 2350 2600 2900 3200 3500 3900
From Retail sales: general merchandise, automobiles, food, apparel, home furnishings, drug and proprietary products
High growth rate
Forecast Low growth rate
Medium growth rate
High growth rate
2200 2400 2600 2900 3100 3400
506 638 729 664 888 928 1001 1223 1349 1603 1819 2208 2400 2700 2900 3300 3700 4000
2500 2900 3200 3700 4200 4700
527 637 690 657 905 936 977 1215 1386 1637 1820 2178 2500 2800 3100 3500 3900 4300
nearly equal amounts are used for general merchandise
Domestic polystyrene consumption, 1967 Million pounds Contemporary pattern Retail purchases
Classical end-use pattern Molding Extrusion All other uses
990 325 180
Total
1495
General merchandise Food Automobiles Appliances Home furnishings Miscellaneous retail outlets Subtotal Industrial uses
450 25 30 275 125 115 1020 475
Total
1495
Source: C&EN estimates
Forecasting model for production of polystyrene Million pounds From Personal consumption expenditures Discretionary purchasing power FRB index, consumer goods production Retail sales, total Year
1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972
Forecast Low growth rate
Medium growth rate
1700 1900 2100 2400 2600 2800
428 462 452 474 641 691 782 907 1045 1246 1429 1599 1780 2000 2200 2500 2800 3100
From Retail sales: home furnishings, appliances, automobiles, general merchandise, food, drug and proprietary products Actual
High growth rate
Forecast Low growth rate
Medium growth rate
High growth rate
1700 1900 2100 2100 2600 2900
372 515 470 482 619 674 733 945 1063 1243 1378 1627 1900 2100 2400 2700 2900 3400
1910 2100 2500 2900 3200 3600
414 476 460 474 627 686 744 909 1079 1276 1369 1614 1800 2100 2300 2600 2900 3300
leading indicator of polyvinyl chloride production, particularly, as with polyethylene, since correlation improved, as drug sales lead vinyl production. Food sales could also become a leading indicator for polyvinyl chloride. Only something like 50 million pounds of polyvinyl chloride is used in food packaging now, and this mostly for produce and meat wrap. The market is promising as implied by a low correlation for the past 12 years and a better correlation when food is used as a leading indicator. Several forecasting equations for polyvinyl chloride have been prepared. One equation is based on home furnishings, appliance, apparel, automobile, drug and proprietary, general merchandise, and food sales. Another equation involves only retail sales of general merchandise, automobiles, food, and apparel. Both equations make essentially the same forecast: 2.3 billion pounds of polyvinyl chloride produced in 1967 with the range 2.2 to 2.6 billion pounds. For 1972, the equations predict 4 billion pounds, assuming a medium growth rate for the retail sales segments, 3.4 billion pounds if the growth rate is low, and 4.7 billion pounds if the growth rate is high.
Polystyrene Pound for pound and dollar for dollar, polystyrene is probably the best molding material made. It is getting considerable competition from newer engineering and tailor-made plastics, but will still hold a considerable portion of present markets for many years in the future. This year about 1.8 billion pounds of polystyrene will be produced. About 45% of this will be straight polystyrene; the balance will be various rubber grades modified to improve impact strength. About 66% of production will be used in molding, extruding, or forming applications. As a general-purpose molding and extrusion resin, polystyrene has had the bulk of consumer applications in the appliance and general merchandise areas. Probably the best-known appliance uses are refrigerator liners, radio cabinets, air conditioner cabinets, and housings for many small appliances such as vacuum cleaners, electric can openers, and electric knives. About 275 million pounds of high-impact and general-purpose polystyrene were used in appliances this year. However, the correlation between polystyrene and appliances NOV. 20, 1967 C&EN
91
Consumer spending and economic barometers point to intensified demands for thermoplastics
Economic indicators and retail sales Gross national product Personal consumption expenditures Durable goods expenditures Nondurable goods expenditures Service expenditures
Billions of actual dollars, unless noted Actual Projected0 1966 1967 deviation 1972 $740 465 69 206 189
$776 486 75 212 199
=fc$12 8 3 4 2
$1072 660 115 275 275
Personal income Disposable personal income Discretionary purchasing power Personal savings
580 505 220 27
605 529 237 30
9 8 4 2
825 720 345 42
Employment, million people Consumer price index, 1957-59 = 100 New housing starts, million units
73 113 1.25
73 114 1.49
1 1 0.2
80 122 1.55
FRB production indexes Industrial Durable goods Nondurable goods Consumer goods
156 165 150 147
161 170 156 153
4 7 2 2
220 245 205 195
Retail sales Total Durable goods Nondurable goods Home furnishings Appliances Apparel Automobiles Drug and proprietary products General merchandise Food
304 98 206 9.1 4.9 17.0 54.0 10.1 40.0 71.0
315 104 212 9.5 4.7 17.2 60.0 10.2 42.0 74.0
8 4 5 0.3 0.2 0.3 2.1 0.3 1.3 2.0
420 140 280 12.5 5.5 21.0 95.0 13.0 65.0 95.0
° Based upon exponent al trend projection, medium growth rate. Sources: Department of Commerce; C&EN computer projections
was only moderate. This can be explained partly by a slowing down of penetration because of competitive materials such as ABS resins encroaching upon polystyrene outlets. Too, an important indirect use of polystyrene is as foam in packaging various small appliances and other items. This market has really developed during the past 10 years, thus tending to lower correlation since the indirect uses make appliances a leading indicator of polystyrene's future. Home-furnishings correlations are only moderately good. This is a newer outlet for the resin and will improve considerably over the next few years as polystyrene penetrates 92 C&EN NOV. 20, 1967
the furniture market for frames, trim, and panels. Particular progress is being made in developing sophisticated simulated wood grains from polystyrene. Correlations between the resin and the home-furnishing markets will become more important over the next few years and thus a significant indicator of polystyrene production. The strong relationship between polystyrene and general merchandise sales is readily apparent. Probably 450 million pounds of polystyrene end up in various general items such as toys, novelties, combs, brushes, housewares, eyeglass frames, and insulated cups. Also, these are mostly inex-
pensive items so their overall popularity with customers should not change much in the foreseeable future. As with polyvinyl chloride and polyethylene, correlation of polystyrene production and drug and proprietary store sales is poor. Again, this reflects the same effects as cited earlier: a slower growth and penetration of the drugstore outlets than the growth of polystyrene itself. This could become an important correlation in the future, particularly as polystyrene packaging outlets continue to expand. A similar situation exists with polystyrene in the food-sales field. Again, food sales are now leading polystyrene growth as was the case with polyethylene and polyvinyl chloride. Considerable progress has been made using polystyrene trays for meat packaging. Although correlation is weak now, it will become better in the years ahead. There is little prospect that polystyrene will ever become important in the apparel area. Thus, the poor correlation is entirely expected and probably won't change in the future. Correlation with the automobile market is as much a mystery with polystyrene as with polyethylene. The correlation is high and thus reflects more the indirect use than the direct use in automobile manufacture. Only 30 million pounds of polystyrene is currently used in automobiles. The vast strength of automobile sales to durable goods sales and to the economy in general shows up in the correlation of polystyrene to sales in retail stores as it does with correlations between polyethylene and polyvinyl chloride to retail store sales. A forecasting equation for polystyrene based on home furnishings, appliances, automobiles, drug and proprietary products, general merchandise, and food sales indicates production of polystyrene will total 1.8 billion pounds this year, based on medium growth of retail sales. The low-tohigh possibilities are 1.7 and 1.9 billion pounds. For a longer term forecast, production in 1972 will reach 3.4 billion pounds, assuming a medium growth of retail sales. A lower growth rate would mean 2.9 billion pounds and a high one, 3.6 billion pounds. The overall advantage of forecast equations based on retail sales is that once a correlation is detected, a change in the particular retail segment can be measured against a possible change in the market for the thermoplastics. As with all forecasting methods, direction is the important point and magnitude of secondary importance. A change in correlation is
Economic indicators and retail sales are meaningful to major thermoplastics Economic indicators and retail sales Gross national product Personal consumption expenditures Durable goods expenditures Nondurable goods expenditures Service expenditures Personal income Disposable personal income Discretionary purchasing power Personal savings Employment Consumer price index New housing starts FRB production indexes Industrial Durable goods Nondurable goods Consumer goods Retail sales Total Durable goods Nondurable goods Home furnishings Appliances Apparel Automobiles Drugs and proprietary products General merchandise Food Chemical and allied products sales Polyethylene, all types Polyvinyl chloride Polystyrene 1
Annual growth rates 1960-66
1963-66
5.9% 5.7 6.7 4.5 6.6 5.7 5.6 6.4 3.3 1.3 1.4 1.2
6.7% 6.3 8.4 5.2 6.6 6.3 6.4 7.3 7.2 1.7 1.5 0.7
7.8% 7.5 8.8 6.8 7.5 7.6 7.6 8.4 9.9 2.4 1.9 -8.1
5.1 3.0 5.0 4.9
6.3 7.5 5.3 5.0
8.0 7.4 6.3 5.6
4.7 4.5 4.8 3.5 1.8 3.4 6.8 4.9 7.3 4.4 7.3 19.3 14.1 15.5
5.9 6.5 5.6 5.6 3.6 3.8 9.4 4.9 9.1 5.1 11.1 17.3 15.7 15.9
7.5 7.5 7.5 6.5 5.5 5.2 9.7 7.3 11.4 6.8 9.0 16.2 15.7 13.6
1955-66
Compounded annual rate based on exponential trend.
a symptom of change in the consumers' purchasing plans, which in turn could be good or bad for the thermoplastic in question. General economic model Specific segments of retail sales provide one link between the consumer and major thermoplastics. Another link is obtained by correlation of broad economic indicators, specific to consumer demand, with the major resins. The broadest of all indicators is the gross national product, the sum total of goods and services produced, and the key measure of economic ac-
tivity. Personal consumption expenditures are 6 5 % of GNP; The balance is government, business, and foreign spending. Many economists and statisticians believe that GNP is much too broad to correlate with a single product. Also, I am only concerned with the relationship and variation with consumer spending, not total economic activity. A simple correlation analysis covering the 12-year time series (195566) of polyethylene, polyvinyl chloride, and polystyrene with personal consumption expenditures and the three main components, durable goods, nondurable goods, and service expenditures, produces a nearly per-
fect final correlation. Again, this is the effect of correlating without regard to fluctuation and trend. Correlations on a percentages-oftrend basis lead to an appreciable reduction in correlation and variation with polyethylene, but not as much of a reduction as with polyvinyl chloride or polystyrene. In general, the correlations are much better than the correlations noted among various retail store sales. Correlation with service expenditures is quite strong with all three resins. The most likely reason is affluence. As people spend heavily for durable goods, such as automobiles and appliances, they are more likely to spend for leisure activities and vacations. Too, there is a parallel trend in growth rates among these plastics and service outlays that is not desirable in correlation analysis. Unexpectedly, the relationship with durable goods is higher than with nondurable goods—and it is stronger with polyvinyl chloride and polystyrene than for polyethylene. The chemical industry is classified as a nondurable goods industry. Logically, a strong correlation would be expected. However, in final form, plastics are sold in both durable and nondurable goods. Most plastics products, except packaging applications, last more than one year. This durability partly explains a lower relationship for polyethylene and a higher relationship for polyvinyl chloride and polystyrene. A forecasting equation for these plastics could contain durablegoods expenditures as an independent variable. However, the correlation with personal consumption expenditures is rather good and somewhat better for polyvinyl chloride and polystyrene than for polyethylene. Since personal outlays would cover correlations and variations that exist in durable and nondurable goods, as well as what may be hidden in the service expenditures, the total personal spending could be included in a forecasting model. Whether personal consumption expenditures or durable-goods expenditures are used in the final model makes no significant difference in the final forecast. Another point to consider in a general economic model is the consumer's income. His income can be studied several ways: personal income (total income before taxes), disposable personal income (income after taxes), discretionary purchasing power (income after deduction of taxes and necessities), and personal savings (interest or dividends). On a trend basis, personal savings correlates poorly with production of NOV. 20, 1967 C&EN
93
thermoplastics. There are several reasons for this. People are saving in a different manner today, that is, more life insurance and stock investments and less in savings accounts. Also, personal savings is more an augur of future buying than present spending, and the poor correlation is quite valid. Personal income and disposable personal income correlate only modestly with production of thermoplastics, a surprise since these are indicators of present spending. However, discretionary purchasing power correlates strongly for all three plastics on a trend basis. This shows the difference between luxury and necessity spending, and the correlation of luxury spending to thermoplastics. This correlation is more acceptable when one thinks of a vinyl swimming pool, recreation room and patio furniture, an additional TV set, or a second automobile. Such spending may not be necessary, but these outlays have a pronounced effect on thermoplastics demand. An index of production is a desired variable in a forecasting model. Since the correlations of consumer demand to thermoplastics are being studied, the Federal Reserve Board index of consumer goods production is a logical starting point. All three plastics correlate well on a trend basis although the correlation of polyethylene is lower than those for polyvinyl chloride and polystyrene. Obviously, as consumer goods production rises, so too will the need for these three thermoplastics. Polyvinyl chloride and polystyrene correlate well with the FRB indexes of industrial production, durable goods production, and nondurable goods production, but slightly favor nondurable goods. This is a reflection of chemical products being treated as nondurable goods at the point of manufacture. A final point is the correlation of these three thermoplastics with total retail sales. There is some overlap here with personal consumption expenditures because of value-added extractions for GNP reporting. However, retail sales are the final movement point for consumer goods. Useful correlations appear again for polystyrene and polyvinyl chloride, which reflect the much wider direct use of these materials in consumer products. The polyethylene correlation is not particularly good and probably reflects the limited use of polyethylene in markets other than housewares, toys, novelties, and packaging applications. Additional correlations have been studied with employment, new housing starts, and the Bureau of Labor Statistics' consumer price index. Em94
C & E N N O V . 20, 1967
ployment does not contribute much to an explanation of consumer demand for thermoplastics, although it can be used in an equation for polyvinyl chloride. New housing starts correlate very poorly, as might be expected. Housing starts are classified as a leading indicator by the Bureau of Economic Research. The low correlation supports this leading status. The house must first be built before it can be filled with consumer goods. Correlations with the consumer price index support what anyone in the plastics industry has known for some time: Plastics prices have declined since 1955 and the price decline, in turn, led to increased use of thermoplastics. At the same time, the consumer price index was rising slowly. Thus, correlations are negative, as expected. Inclusion or exclusion of the consumer price index does not have any material effect on a forecast. My initial general economic model for the three thermoplastics contained six variables: personal consumption expenditures (or durable goods expenditures), discretionary purchasing power, FRB index of consumer goods production, total retail sales, consumer price index, and employment. Employment and consumer price index were eliminated in later models since these variables had no appreciable effect on the final forecast. For polyethylene, the equations forecast that 3.9 billion pounds will be produced this year, assuming the various economic indicators follow a medium growth rate. The low figure is 3.8 billion pounds and the high one 4.0 billion pounds. For polyvinyl chloride, the equations pick between 2.3 and 2.4 billion pounds for 1967 on a medium growth rate. The low-high figures are 2.2 and 2.5 billion pounds. For polystyrene, the medium forecast is 1.78 billion pounds this year, with low and high figures of 1.7 and 1.8 billion pounds. The 1972 forecasts for the three thermoplastics were optimistic and ran as follows: Plastic Polyethylene, all types
Growth rate High Medium Low
Billions of pounds 7.2 6.6 6.2
Polyvinyl chloride
High Medium Low
4.3 3.9 3.4
Polystyrene
High Medium Low
3.3 3.1 2.8
These forecasts are somewhat lower than those obtained by projections of
historical data. The particular advantage of a forecast from correlation and regression analysis is that variables can be manipulated to match expected growth rates, and the impact of change on the total output of the thermoplastics can be measured. A particular company may wish to go beyond the readily available economic and retail sales statistics and develop forecasts from variables more specific to the company's line of business. One example might be correlation between polyethylene and various parts of the packaging market. With computers available, such mathematical techniques are getting much more attention to relate and forecast products and sales by chemical companies as well as elsewhere—by investment and brokerage firms, for example. Correlation and regression analysis is no panacea, but then, neither is any other forecasting technique. However, if properly used, the method does add another dimension to market understanding, particularly the final demand sector-the point that really determines the future of the chemical and other industries.
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