FACTORS AFFECTING TRACTOR PURCHASES AND EXPENDITURES Thesis for the Degree of MS. MlCHiGAN STATE UNlVERSITY AR. Jones 1966 . .,..:.m;z.a:m?ud.r THESIS LIBRARY Michigan S tatc Umvcrsity W University of Alberta. Printing Department FACTORS AFFECTING TRACTOR PURCHASES AND EXPENDITURES by A. R. Jones A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Department of Agricultural Economics 1966 Approved ABSTRACT FACTORS AFFECTING TRACTOR PURCHASES AND EXPENDITURES By A. R. Jones Monetary variables form the hard core of factors used in the fore- casting of investment spending. These are measures of ability to invest. Recently, attention has been directed towards the improvement of demand forecasts through incorporating a measure of willingness to invest. This new forecasting approach, using intentions data, infers that purchases will only be made when the ability to invest is coupled with willingness to invest. Measurement of willingness to invest usually involves studying purchase intentions. Additionally, there is some evidence indicating that the probability of purchase (strength of intent) influences both purchases and expenditures. The objectives of this study were (1) to determine if the pur- chase intentions of farmers are significant indicators of actual tractor purchases and expenditures, (2) to ascertain which of the various physical and financial factors commonly included in fanm records have value in predicting tractor purchases, and (3) to identify the combina- tion(s) of factors most useful in the prediction of tractor purchases and expenditures. Mail questionnaires, completed by farmers enrolled in Michigan State Mail Account project for the years 1960 and 1961, provided the data on strength of intent and the intended expenditures. The ii respondents' farm.records provided information on tillahle acreage farmed, previous and current year‘s disposable income, change in dispos- able income, the value of the opening inventory machinery investment, the annual hired labor cost, tractor purchases and.the expenditure made. This research demonstrated that intentions data make a signifi- cant contribution to the explanation of buying behavior beyond that possible with financial variables alone. However, since the predictive ability of strength of intent and intended expenditure was not the same in both years, one must be careful not to fonm an exaggerated opinion of their accuracy and empirical stability. Strength of intent was found more valuable for the prediction of tractor purchases than for the prediction of tractor expenditures. Intended expenditures had greater predictive ability in expenditure equations than strength of intent. or the variates recorded in account books and tested, either previous year's disposable income or machinery investment may furniah information on buying behavior which is not provided by strength of intent or intended expenditure. However, for those respondents indica- ting a "no chance of buying" intent, current and change in disposable income are needed to explain their subsequent buying behavior. Strength of intent designations such as ”very certain”, "quite certain", "fair chance" and “slight chance" were found.more effective in predicting tractor purchases than the less specific intent cate- gories, i.e., "some chance" and "no chance". This research also indi- cates that "very certain" and "quite certain" categories can be com- bined without significantly altering their predictive value. iii Intended expenditure was a very effective predictor of the tractor expenditures of those who expected to purchase without a trade- in of their present tractor. It was a relatively ineffective predictor of the expenditures of those who expected to purchase with a trade-in. Annual hired labor cost emerged as a dominant factor influencing intended expenditures. More data might permit more accurate predictions of tractor pur- chases and expenditures. Company and/or dealer policy regarding length of time tractor parts are stocked for particular models, tractor operating hours per year, total tractor hours operated and total tax deductions for depreciation of farm machinery probably warrant inclusion in future prediction models of tractor purchases and expenditures. iv FACTORS AFFECTING TRACTOR PURCHASES AND EXPENDITURES by A. R. Jones A THESIS Submitted to; Michigan State University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Department of Agricultural Economics 1966 ACKNOWLEDGMENTS Appreciation is expressed to Dr. L. Manderscheid, my thesis director, for his extra efforts, suggestions, and encouragement; to Dr. K. Wright for his suggestions and for permitting the use of the data which he and Dr. Vincent had gathered for their investiga- tions; and especially to the staffs of the Department of Agricultural Economics and the Computer Center for giving me the timely assistance necessary to complete this study. A special word of thanks is due my wife, Mary; my former director, Mr. S. S. Graham; and my present director, Dr. G. Purnell; who encouraged.my post graduate training and the completion of this thesis. A. R. Jones vi TABLE OF CONTENTS Page AMMCT O O O O 0 O O O O O O O O O 0 O O O O O O I O 0 O O 11 AcmommSe O O O O O O O O O O O O O 0 O O O O 0 O O 0 v1 I'm OF ms 0 O O 0 O O O O O O O O O O O O O O O O O 0 O 2x Chapter I INTRODUCTION 0 l O O O O O O O O O O O O O O O O O l The Problem ' - 3 Objectives of the Investigation h ii REVIEW OF PREVIOUS DEMAND STUDIES INVOLVING WENT IONS DATA 0 O O O I O I 0 O O O - _ O O ' O O O _ I O 5 Early History of ReSearch Utilizing Intentions 5 Modern Work on Censuler Spending Intentions 6 Modern Work on Business Expenditure Intentions 10 Modern Studies on the Demand for Farm Machinery 12 Conclusions from Literature Review 16 III METHODOLOGY AND HYPOTHESES . . . ... . . . . . . . 18 Limitation of Study 19 Treatment of Data 20 General Hypotheses 21 IV THE INFLUENCE OF INDIVIDUAL FACTORS ON TRACTOR PURCHASES. O I O O O 0 O O O O O O O O 0 O 0 r. O O 22 Buyers and NOn-Buyers of Farm.Tractors in 1960 and 1961 “ ‘ 22 Tillable Acreage as a Factor Affecting Tractor Purchases ' 26 Net Cash Available Previous Year as a Factor Affecting Tractor Purchases 28 Net Cash Available Current Year as a Factor . Affecting Tractor Purchases 3O Chance in Net Cash Available as a Factor ' Affecting Tractor Purchases 33 Intentions as a Psychological Factor Affecting Tractor Purchases 36 Factors Expected to Influence the Purchases of the "No Chance" Group ho vii Chapter TABLE OF CONTENTS-=Continued IV (cont.) The Effect of Net Cash Available in the VI Previous Year, Given a "No Chance" Intent, on Tractor Purchases The Effect of Net Cash Available in the Current Year and a "No Chance" Intent on Tractor Purchases The Effect of a Change-in Net Cash Available, Given a "No Chance" Intent, on Tractor Purchases ‘ The Effect of Tillable Acreage, Given a "No Chance" Intent on Tractor Purchases The Effect of a "Some Chance" Intent and Vari- able Physical and Financial Factors on Tractor Purchases' ' Strength of Intent as a Factor Affecting Tractor Purchases The Effects of a Given Strength of Intent and Variable Financial Factors on Tractor Purchases ' The Effect of Varying Previous Year's Net Cash Available and a "Fair Chance" Intent on Tractor Purchases The Effect of varying Current Year's Net Cash Available and a "Fair Chance" Intent on Tractor Purchases The Effect of a Change-in Net Cash Available and a "Fair Chance" Intent on Tractor Purchases The Effect of Previous Year's Net Cash Avail- able on the Proportion of Tractor Purchases, Given a "Slight Chance" Intent The Effect of Current Year's Net Cash Avail- able on the Proportion of Tractor Purchases, Given a "Slight Chance" Intent ' ” The Effect of Varying Change-in Nat Cash Avail- able on the Proportion of Purchases, Given a "Slight Chance" Intent MULTIPIE smasssron ANALYSES or morons manner so mummmcroammcmsss............ Statistical Analysis and Results .MULTIPLE LINEAR REGRESSION.ANALYSIS OF FACTORS THOUGHT TO BE IMPORTANT IN EXPLAINING TRACTOR mmms I O O O O O O I O O O I I O O I O O I Statistical Analysis_and Results viii Page Al #3 A5 A7 50 53 57 58 6O 55 57 69 73 78 79 TABLE OF CONTENTS-~Continued Chapter VI (cont.) Factors Affecting Actual Tractor Expenditures by Those Who Indicated from "Very Certain" to "Slight Chance" Strength of Intent Factors Affecting Tractor Expenditures by Those Indicating from "Very Certain" to "Slight Chance" Intents to buy "Without a Trade-In" Compared to "With a Trade-In" VII SUMMARY AND CONCLUSIONS. . . . . . . . . . . . . mmm O O O O O O O I O O O O O O O O O O O O O O O O O B ELIWRAPEY C O O O O O O O I O O O O O O O O O O O O O 0 ix Page 85 9O 95 100 111 Table 10 11 LIST OF TABLES Actual Frequencies of Buyers and Non—Buyers of Farm Tractors in Michigan State University's Farm Machinery Survey in 1960 and 1961 Relation of Tractor Purchases to Tillable Acreage-- 1960,1961 Relation of Tractor Purchases to Previous Year's Net.Cash Available-~1960, 1961 . Relation of Tractor Purchases to Current Year's Net Cash Availabler-l960, 1961 Relation of Tractor Purchases to Change in Net Cash Available-51960, 1961 Relation of Tractor Purchases to a "Some Chance" or a "No Chance" Intentf-l960, 1961 Relation of Tractor Purchases to Previous Year' 3 Net Cash Available and a "No Chance" Intent-- 1960,1961 Relation of Tractor Purchases to Current Year's Net Cash Available and a "No Chance” Intent-- 1960. 1961 Relation of Tractor Purchases to Change-in Net ngh Available and a "No Chance" Intent--1960, 1 1 Relation of Tractor Purchases to Tillable Acreage and a "No Chance" Intent--1960, 1961 Relation of Tractor Purchases to Current Year's Net Cash Available and a "Some Chance" Intent-- 1960, 1961. Relation of Tractor Purchases to Strength of Intent--1960, 1961 Page 23 27 29 3h 37 Ah A6 A9 51 55 Table 13 1A 15 16 17 18 19 2O 21 22 23 2h LIST OF TABLES-~Continued Relation of Tractor Purchases to Previous Year's Net Cash Available and a "Fair Chance" Intent-- 1960, 1961 Relation of Tractor Purchases to Current Year's Net Cash Available and a "Fair Chance" Inten ~- 1960, 1961 Relation of Tractor Purchases to Change-in Net Cash Available and a "Fair Chance" Intent--1960, 1961 Relation of Tractor Purchases to Previous Year's Net Cash Available and a "Slight Chance" Intent-- 1960, 1961 Relation of Tractor Purchases to Current Year's Net Cash Available and a "Slight Chance" Intent-- 1960, 1961 Relation of Tractor Purchases to Change-in Previous Year's Net Cash Available and a "Slight Chance" Intent-~1960, 1961 Multiple Linear Regression Analysis of Tractor Purchases with Eight Factors for the Year 1960 Multiple Linear Regression Analysis of Tractor Purchases with Eight Factors for the Year 1961 Multiple Linear Regressions with Seven and Four Factors in 1960 Multiple Regressions with Seven and Four Factors in 1961 Statistical Significance of the Factors Affecting Actual Tractor Expenditures by Only Those Who Indicated from "Very Certain" to "Slight Chance" Intents of Buying Statistical Significance of Factors Affecting Actual Tractor Expenditures by Those Who Indicated from "Very Certain" to "Slight Chance" Intents of Buying Without a Trade-In xi Page 59 61 6h 66 68 75 76 81 83 87 91 LIST OF TABLES--Continued Table Page 25 Statistical Significance of Factors Affecting Actual Tractor Expenditures by Those Who Indicated from "Very Certain" to."Slight Chance" Intents of Buying With a Trade-In 92 x11 CHAPTER I INTRODUCTION The continual and most pressing managerial problems of farm machinery manufacturers involve planning what to do in the next few months. Management must predict the likely demand for tractors over some future time period. Which factors to consider and how to predict with an acceptable degree of accuracy continues to be a baffling pro— blem for those who have the responsibility of seeing that their firm's resources are used.most effectively. Because of expediency and mental limitations in.handling a large number of variables, management must usually resort to the consideration of only a few variables. Even when it is economically feasible to use computers to handle several variables there is still the problem of selecting relevant variables for the farm.machinery business. Because of these limitations it is likely that the bulk of the forecasts will continue to be made on the basis of consideration of only a few factors. The factors which will likely enter into a prediction are those which management considers most likely to influence the final results. The factors ignored are those which, they hope, will exert only a small change in the results or else will tend to balance out over the time period covered in their prediction. Monetary variables form.the hard core of the acceptable variables used in forecasts of investment spending. Their use has sometimes led 1 2 to reasonably accurate forecasts but at other times end results have been poor. There is interest, therefore, in trying new approaches which offer some hope of correcting some of the weaknesses which may be inherent in any method which relies on financial variables exclusively. It has been suggested that a person's stated intention to invest or not to invest foretells to some extent the actual investment made. This is a relatively new idea which may be valuable to management for prediction purposes. It must be afforded ample opportunity to demon- strate whether or not it will help increase the accuracy of demand analyses and forecasts and at the same time provide a,method.which is economically feasible. This approach to forecasting infers that investments depend on two main factors--the ability to buy and the willingness to buy. sAn individual may have control of sufficient resources to make aniinvebte ment but if he is not willing to invest he will not make the investment. Similarly, a person may be willing to buy but be unable to get control of adequate financial resources. The end result is the same--no invest- ment will be made. An investment will be made only when the ability to invest is coupled with the willingness to invest. To use intentions alone, to predict the demand for tractors, it is necessary to make some broad and basic assumptions. First, it presumes that those who state their intentions have sufficient knowledge on which to act and will be able to carry out their intended action. Secondly, the individual can actually foretell all the changes which will develop during the ensuing time period and has made all the necessary allowances before stating his intentions. If stated intentions to purchase only partially foretell subsequent 3 behavior then it would be wise to study this determinant to see if it can be used with presently recognized objective variables to improve the accuracy of demand forecasts. The Problem Manufacturers of farm.machinery do not know if they can improve their own production and marketing planning by finding out from farmers the number of tractors the latter intend to buy. Nor do they know how reliable farmers' stated intentions are, i.e., how completely farmers fulfill their tractor purchase intentions. This reliability needs to be established before the intention to purchase method is accepted. It is thought that intentions are held with various degrees or strengths of certainty and that these have a measurable influence on whether or not a purchase is made. It is conceivable that farmers who state, for instance, that they are "quite certain" that they will buy a machine are more likely to purchase than those who state there is only a "slight chance" that they will purchase. Whether strength of intention is more valuable as an indicator of actual purchase than an intention made without any qualification also needs to be ascertained. The agricultural sector of the economy has been virtually neglected insofar as the testing of the usefulness of intentions and strength of intent approaches because of the high unit cost in checking whether or not farmers carry out their intended investment plans. The availability of mail-in farm.account records to check on whether or not intended investments are made, presented an opportunity to test the usefulness of the approach. Strength of intent plus certain other variables will be examined h to determine their worthiness in explaining and predicting actual tractor purchases. These, then, will form the centre of the investigation re- ported herein. Objectives of the Investigation This study was undertaken to measure (1) subjective opinions, i.e., purchase intentions and strength of intent as indicated by farmers, to see if they are of any significant value as indicators of actual tractor purchases, (2) selected financial and physical variables so as to evaluate each factor's significance in explaining the purchases of tractors, and (3) combinations of accessible variables so as to deter- mine how much of the variance in tractor purchases and expenditures can be explained by each combination. More specifically, the Objectives of this investigation can be stated in the form of questions as follows: 1. Do farmers' stated intentions assist in the prediction of tractor purchases? 2. Is there any explanatory advantage in asking an individual how certain he is of making a tractor purchase as compared to asking him simply, do you intend to buy? 3. Does a knowledge of a respondent's disposable income (previous, current or change-in net cash available) plus the size of tillable acreage farmed give any indication of his subsequent tractor pur- chases and/or expenditures? A. Which combination of the accessible variables gave the "best" explanation of tractor purchases and expenditures? CHAPTER II REVIEW OF PREVIOUS DEMAND STUDIES INVOLVING INTENSIONS DATA The purpose of this chapter is to review the literature on inten- tions studies data and thereby provide the necessary background for a study of the intended and actual tractor buying expenditures of farmers. This review begins with the early work utilizing intentions data in the preparation of forecasts, and follows with the modern research findings involving intentions data in the consumer and business sectors of the economy. It concludes with an examination of the results Obtained from recent demand studies involving agricultural machinery. Early History of Research Utilizing;Intentions Data The earliest efforts in the regular gatherings of intentions data can be traced back to the early 1920's when the federal Department of Agriculture conducted sample surveys to determine what farmers planned to produce in the coming year.1 Their purpose was to inform producers what other farmers were doing so that they could make any adjustment that appeared warranted. There is no evidence that the Department of Agriculture checked with each respondent to see if production plans ‘were carried out. However, the U.S.D.A. estimation procedure itself :FFrahco Modigliono, and J. Cohen}.,"The Role of Anticipations and Plans in Economic Behavior and Their Use in Economic Analysis and Fore- casting",(Bureau of Economic and Business Research, University of Illinois, Bulletin No. A, 1961) pp. 1h8-1h9. 5 6 involves an adjustment of aggregate intentions on the basis of their past experience before they publish their "intentions" report.2 In the late 1920's the American Railroad's Regional Boards pre- pared forecasts of anticipated freight car requirements by commodity groups and included in its report actual car loadings. In 1938 Sweden commenced sample surveying to collect information on business plans for capital investment and in a relatively short period had not only demonstrated that the collection of data was feasible but that the information was useful in the preparation of short term forecasts.3 Major research efforts, using the new intentions approach did not get underway until after World war II. Modern Work on Consumer Spending Intentions Sample surveys recording factors assumed to influence consumer actions are now extensively employed for short-term.forecasting pur- poses. The most important contributions in the consumer research field have been.made by G. Katona and co-workersh and are aimed at supplement- ing the traditional analysis (income, spending, saving, investment, prices) with the factors underlying the behavior of consumers. In three reinterview studies in 19h8, 19h9, and 1952, plans to buy were found to be highly correlated with actual purchases by Lansing and Withey. This correlation was supported by the multivariate studies of Klein and 2L. V. Handerscheid, Personal communication, January, 1966. 3Modigliani and then, op. cit., p. 1A9. "G. Katona and E. Mueller, Consumer ExpectatiOns 1953-1956, (Survey Research Center, Institute of Social Research, University of Michigan), pp. 1-hh. 7 Lansing involving 1,036 cases. The factors contributing to the carrying out of buying intentions were purchase expectation, respondents' feeling of economic well-being and price expectation. Price-income interactions, income expectations, appraisal of buying conditions and past income changes were not found to be useful in distinguishing between buyers and non-buyers of automdbiles and household goods. They also found little relation between asset holdings and purchase decisions. In dealing with aggregate variables they found their data could be substantially imp proved by combining these with the actual course of the variables representing initial conditions, even though the effect of these vari- ables should already be reflected in the initial expectations. In the 1953 Survey of Consumer Finances (conducted for the Board of Governors of the Federal Reserve System by the Survey Research Center of the university of Michigan) 1,036 of the spending units interviewed were spending units which had also been interviewed in the 1952 Survey (the same sample used by Klein and Lansing reported above). K’reinin5 found that 52 percent of those who early in 1952 expected to buy a used car actually bought one, while only 15 percent of those who did not express any intention to buy actually bought. He found thl. the socio- economic variables most related to used car purchases are income, liquid assets and life cycle. But in addition to these factors, changes in the individual's economic position, his subjective evaluation of market con- ditions and the age of the car he owns are important explanatory vari- ables. 5M. Kreinin, "Analysis of Used Car Purchases", 1mm of Econanics and Statistics, Vol. XLI (Feb., 1959). pp. has-has. ""'—"_""""""" 8 In subsequent studies by the Survey Research Center, Katona and Mueller report that there were logical explanations for the consumers who expressed no intention to buy and for the failure to purchase by those who expressed an intention to buy.§- Unexpected developments, such as income increases or decreases, suddenly" arising needs, high trade-in allowance, an oversupply of cars, good buys and a host of other reasons helped to account for the discrepancy between intended and actual pur- chases. 7 James Tobin concluded that buying intentions are not an adequate substitute for objective variables. Buying intentions are to be con- sidered but as complementary to objective variables, not substitutes for them. Katona8 has presented the psychological thesis that expressed intentions reflect current attitudes rather than indications of things to come and that these attitudes represent useful information for those who wish to make predictions about consumer demand in conjunction with relevant information on changes in financial variables. He further expresses the view that intentions and other attitudinal variables are most useful in determining the direction rather than the magnitude of forthcoming developments. The predictive value of the data is contingent 6G. Katona and E. Mueller, Consumer Expectations 1953—1956, (Survey Research Center, Institute of Social Research, university of Michigan), p. 67. 7James Tabin, "0n the Predictive Value of Consumer Intentions and Attitude," Review of Economics and Statistics, Vol. XLI, No. 1 (Feb., 1959). pp. 1-10. 8G. Katona and E. Mueller, Consumer Expectations 1953-1956, (Survey Research Center, Institute of Social Research, University of Michigan). 9 upon the absence of important external developments which are not foreseen by the consumer. 9 Juster, using unusually large samples of a product testing organiza- tion called the Consumers Union of the United States, found that about 95 percent of the total yearly variation over the period 19h9 to 1957 can be explained (statistically) by the two variables--changes in dis- posable income and the buying plans of the Consumers Uhion members. After allowing for the effects of changes in disposable income on pur- chases, some 80 percent of the residual year-to-year variation in aggregate purchases is associated (statistically) with variations in the buying plan of Consumer Union members. From the survey carried out between OctOber 19h? and April 1958, Juster noted that income changes of around 20 percent or less had little effect on durable good purchases or buying intentions. He comments that this result may be due to the sharp business contraction which occurred during the survey and which would influence the plans and purchases during the period. In examining expectation variables, Juster found that expectations about general business condition, income expectations for the one year future period and household attitudes about current buying conditions were strongly related to buying plans and recent purchases. Recent research (1963) by Eva Mueller10 indicates that the predic- tive performance of buying intentions is not consistent from one test to another. She also reports that similar conclusions regarding 9F. Thomas Juster, Consumer Expectations, Plans, and _Purchases: A Progress Report, Occasional Paper5 5(National Bureau of Economic Research, Inc., 1959). 10E. Mueller, "Ten Years of Consumer Attitude Surveys: Their Forecasting Record," Journal of the American Statistical Association, Vol. 58, No. 38h (Dec., 1963). lO predictive value of buying intentions were recently arrived at by Friend and Jones. Consumer buying intentions made a contribution in only a few of their regression equations and these were largely equa- tions not containing their Index of Consumer Attitudes. Mueller found that discretionary spending by consumers is to a large extent deter- mined.by their income level and the state of consumer optimism and confidence. Modern Work on Business Expenditure Intentions Since;World War II there has been a strong effort directed towards finding out the capital expenditure intentions of business in order to appraise the business climate. One of the most important regular sample surveys revolving around the plans for plant and equipment expenditure is conducted by the U. S. Department of Commerce in cooperation with the Securities and Exchange Commission. This survey collects information on investment plans on a quarterly and annual basis for the purpose of pre- paring an aggregate forecast. A similar survey covering approximately 500 of the most important capital consuming industries is conducted by McGraw-Hill. George Katona11 has made some interesting generalizations from the McGrawaHill surveys, on the capital expenditure plans of companies: (1) The capital spending plans of some industries are more likely to be carried out than those of other industries. (2) Plans for the replacement of old equipment are more likely to be carried out than are * 11George Katona, Psycholggical Surveys in Busipess Forecastipg. Report of a seminar conducted by the Foundation for Research on Human Behavéor, Ann Arbor, Michigan, Jan. 22-23 and Feb. 5-6, 195A, ppe 1 -18e 11 plans for the replacement of newer equipment. (3) The capital spending plans of large companies are more likely to be carried out than are those of small companies. (A) There seems to be a consistent tendency for small firms to underestimate their investment outlay; whereas large firms, on the average, do not have this tendency. (5) Plans in the more distant future are very likely to be vague and incomplete, as compared with plans for the immediate future, and therefore have a downward bias. (6) Although differences between planned and actual investments have more or less cancelled out up to the present, there is no guarantee that they will do so in the future. (7) The surveys are not very reliable for predicting regional economic conditions. (8) The fact that the McGraw-Hill survey is a large company survey might well influence the results, particularly during major economic changes. Using data for 19h7, l9h8 and l9h9 from the Commerce-SEC data, Katona also reports that individual capital expenditure plans have been compared with actual expenditures. This analysis showed that only about one-fourth of the firms were within 20 percent of planned expenditures. A third of the firms went over planned expenditures by 100 percent or fell short by 50 percent or more. The largest firms were quite close to planned expenditures, while medium sized firms went over each year by a substantial amount and the smallest firms exceeded their intended expenditures by even more than the medium sized firms. Robert Eisner12 reports that forecasts based in investment surveys 12Robert Eisner, Forecasting;Investment Spending, Eleventh Annual Conference on Economic Outlook (university of Michigan, Mimeograph) p. h. _ 12 such as those of the Securities and Exchange Commission and the office of Business Economics, as well as the McGraw-Hill Department of Economics, have, in fact, an impressive record of accuracy. The accuracy is due to a considerable extent on the cancelling out of compensating errors among individual firms and industries. This same view is shared by Irwin Friend and Jean Bronfenbrenner13 in their study of the extent to which individual investment plans are fulfilled._ U. Lewis Bassielh warns that the results of surveys on intentions cannot be considered sure because plans do change rapidly with a change in the business climate. He calls attention to the instability noted in the recession of 1958 where the cutbacks in both inventory and fixed capital were drastic. Modern Studies on the Demand for Farm Machingpy Recently there has been some research on the demand for farm machinery. Fettig15 using secondary data found that changes in farm income are more closely associated with machinery purchases in contrac- tions than in expansions. He attributes this to the tendency of the farmer to be more careful in making expenditures when there is a general business decline and the difficulty of obtaining the necessary credit. Although it was expected that expenditures on machinery would fall as 13Irwin Friend and Jean Bronfenbrenner, "Business Investment Programs and Their Realization," Survey of Current Business (30th Dec., 1950), pp. 11-22. th. Lewis Bessie, Uncertainty in Forecastingand Policy Forma- tion, Bureau of Business Research (university of Texas, Austin, 1958? 1§59 Series), pp. 15~20. 15Lyle P. Fettig, "Purchases of New Farm Tractors and Machinery in Relation to Non-Farm Business Cycle", (unpublished Master of Science thesis, Michigan State University, 1958), p. 31. 13 income fell, the regression coefficients indicated that the expenditures reductions were more than proportionate to the income decreases. In a further examination of the relation between farm income and farmers' expenditures on tractors and machinery purchases he presents evidence that farmers reduce machinery expenditures rapidly (in the current year) as a result of income decreases but they only slowly increase expendi- tures when income increases, i.e., there is a lag of about one year. While this phenomenon did not occur in every instance of farm.income increases and decreases, the relation appeared to have a noticeable regularity. I Reiling16in reviewing Cromarty's statistical study on wheel-type tractors for the years 1926 - 1956 inclusive, points out the following interesting results using income as one of the determinants of demand: (1) A 10 percent increase in net fanm income for the previous year was accompanied by a 5 percent increase in machinery purchases. (2) The amount of machinery on farms appears to have no effect on the qpantity purchased during the year. (3) A.lO percent increase in net farm.cash receipts for the previous year resulted in a 2 to h percent increase in tractor purchases. Wright and Vincent17 conducted the initial research in Michigan in 1959 on intended and actual farm.machinery purchases using data ob- tained from farmers cooperating in the M.S.U. Farm Accounting ProJect. l6EldonA. Reiling, "Demand Analysis for Combines, Pickup Balers and Forage Harvestors," (unpublished.M. Sc. thesis, Department of Agricultural Economics, Michigan State university, East Lansing, 1962). 17K. T. Wright and W. H. Vincent, "Intended and Actual Tractor Purchases by Famers in Michigan, 1959.” Quarterly Bulletin of Michigan Agricultural Experiment Station, Vol. XLIV, (East Lansing: Michigan State University, Nov. 1961). l 1h They obtained intended machinery purchases data from a mail questionnaire and determined whether machinery purchases were as intended, by referring to each ccoperator's own farm records. Their objectives were to deter- mine the number intending to make major machinery investments, the time at which the investment would be made and the strength of the intention to make a machinery investment. On the basis of one year's results they reported that 50 percent of the farmers who indicated some prdbability of buying ("some chance") actually fulfilled their intentions of buying. Some 1h percent of the "no chance" farmers, who indicated on the questionnaire that they would not be buying a tractor, actually purchased tractors. When it came to the actual amount of the expenditures compared to the intended amount, it was found that soaperators spend considerably more than they had expected to spend. When they tried to relate strength of intent to actual tractor purchases they found that some 65 percent of the "very certain" farm.operators actually purchased tractors, 72 percent of the "quite certain", #9 percent of the "fair chance" and ho percent of the "slight chance" farmers purchased tractors. The relationship between the level of disposable income (current and previous year) appeared to influence more the purchase decisions of those cooperators who had expressed a "no chance" intention of buying than those who expressed a "some chance" intention of buying. They concluded that more than strength of intent to buy and income levels are needed to predict the percentage of farmers WhO‘Will buy tractors. Wrightla, using data from the same source and obtained at the same 18K. T. Wright, Purchases of Major Farm.Machinery. Research Report No. 3 (Michigan State university, Agr. Experimental Station, 1963). 15 time, but studying major agricultural machinery other than tractors, reached a similar conclusion to that reported for tractors. .Almost 80 percent of those indicating a "some chance" probability of buying other major machinery actually bought. Detracting somewhat from this 80 percent fulfillment rate of those who intended to buy and did buy was the fact that almost half of the "no chance" group did not fulfill their original intention of not buying, i.e., they bought major machin— ery other than tractors. Actual per fanm expenditures for major farm machinery was double that estimated by those who intended to buy and did buy. The total amount spentby those who did not express an inten- tion to buy was almost one-half that of all those who expressedan intention to buy. Total expenditure exceeded intended expenditure by some 80 percent. Considering only those who expressed an intention to buy, over 90 percent of the "very certain", 8% percent of the "quite certain", 7h percent of the "fair chance" and 7h percent of the "slight chance" actually made major machinery purchases. For these groups higher net income from the previous year was associated with a higher percentage fulfilment of intentions. Income level of the previous year was more closely correlated to actual expenditures than was income level in the current year. Wright concluded that strength of intent and income level do help explain some of the purchases but that these variables were not sufficient to explain a substantial proportion of the deviations between intended and actual purchases or the dollar amounts to be expended. 16 Conclusions from Literature Review A review of the literature on business and consumer demand fore- casting as it relates to intentions indicates that the following conclusions might be drawn. 1. No infallible way to predict future purchases and expenditures has yet been developed, i.e., there is no formula which will blueprint the expenditures which will be made. Forecasts'are still estimates about which no one can be sure. In spite of recent contributions in the uncertainty aspects of decision making, there is still no accepted frame- work for the analysis of choice under uncertainty. New techniques for analysis are needed. 2. Although it has been hoped that buying intentions would embody all the effects of relevant variables, the evidence shows this hope is far from being realized. Individuals and groups in both the business and consumer sector go over and under their intended expenditure by substantial amounts. Expressed intentions must be used along with other objective variables for predictive purposes, i.e., they canft stand alone. A host of variables have been used with and without expressed intentions to predict the future. 3. The need for information on intentions to buy arises primarily from our inadequate knowledge of many important aspects of economic behavior. Intentions appear to have some explanatory value above that obtainable with the more traditional economic variables. ‘ 1.. There is a rapidly growing body of information available on the investment intentions of business and the buying intentions of con- sumers. The information on actual fulfilment of buying intentions is scarce. In the agricultural industry the fulfilment of farmers' 1? machinery investment intentions has apparently not been studied prior to the initiation of the project on "Intended and.Actual Machinery Purchases" by the Department of Agricultural Economics at Michigan State university. CHAPTER III METHODOLOGY AND HYPOTHESES Data for this thesis are primary data derived from mail question- naires completed by farmers enrolled in Michigan's mail accounting pro- ject plus available physical and financial information recorded in their respective accounts books. Survey questionnaires were mailed in December and returned within one month for the years 1960 and 1961. An average of approximately 85% of the farmers returned the question- naire. The samples for the analyses consisted of all those who had both completed the questionnaires and their account books. :In the questionnaire (see Appendix) the farmers were first asked whether there was "some chance" or "no chance" of their buying a tractor in the next twelve months. If they intended to buy a tractor they were asked to record how certain they were of buying. If they had already made or were making a deal on a tractor they recorded this strength of intent as "very certain". With a considerably better than a 50:50 chance of buying the "quite certain" designation was used. A 50:50 chance and a considerably less than 50:50 chance was to be indicated as "fair chance" and "slight chance", respectively. Then, they were asked to check (a) the quarter of the year in which they intended to buy, (b) whether they expected to buy new or used, (c) whether they expected to trade in a tractor and (d) how much they expected to pay. Data from each respondent's farm record provided information on 18 19 tillable acreage farmed, previous and current year's net cash income available, change in net cash available, the value of the machinery investment and the amount spent for hired labor. The same record was used to determine whether or not the individual had purchased a tractor and the amount he had paid. The data from the account book were then matched against the intentions recorded on the survey questionnaire. The intentions to buy major farm machinery survey was initiated by and the sample selected by the staff of the Department of Agricul- tural Economics. The collection of the data, analysis and publication of the research results of the 1959 survey and the collection and four original tabulations for the 1960 and 1961 data are also due to their efforts alone. The original project covered all major farm machinery but this thesis is concerned only with tractors and only with the data that had not been analyzed previously, i.e., for the years 1960 and 1961. Limitation of Study The project was designed to explore an area of methodology--to explore devices which might have commercial value. There is thus no claim that if, for instance, 70 percent of the farmers fulfilled their purchase intentions that this rate could be transferred to industry at large. The farm operators participating in this project are considered typical of those in the upper 30-h0 percent income levels of commercial farms and so the sample is biased upward.1 1K; Wright, Personal communication, Nov., 1963. 20 With regard to the application of the data, it is worth noting that farm machinery manufacturers were especially interested in the behavior of farmers of the same type as those enrolled in the mail accounting project. Also, they suggested that the results would be of more use to them in indicating the general buying behavior of commer- cial farmers than a probability sample cutting across all income levels.2 Treatment of Data Not all returned questionnaires were used. .Some operators com- pleted the questionnaire but not the account book. Some account books did not contain sufficient information so that previous year's net cash available (disposable income) and change in net cash available could be calculated.3 A few c00perators did not indicate the strength of their intent. For any of the above circumstances, the data were excluded from the analysis. . The data on tractor purchase intentions (from mail questionnaires) plus land and capital information (from the farm records) were key punched on I.B.M. cards and.machine sorted and four tabulations machine printed. The machine sorts were (1) tillable acreage, (2) previous year's net cash available, (3) current year‘s net cash available and (h) change in net cash available. Sorting was done from low to high for each variable and divided into quartiles. Additional hand tabulations were produced from the four basic machine tabulations. 2W.-Vincent,-Persona1 communication, Nbv., 1963. 3Net cash available a total cash receipts minus all cash expenses except those cash expenses made on machinery and improvements. Dispos- able income is used as a shorthand for the net cash available terminology. 21 The major portion of this thesis is devoted to the analysis of the effects of the subjective (intentions and strength of intent) and objective variables (tillable acreage and disposable income) on actual tractor purchased. The chi-square test of significance is used to measure the validity of these variables and the so-called contingency coefficient is used to measure the strength of the relationships. The final part of this investigation is concerned with the joint effects of each of the above variables plus three additional variables, i.e., intended expenditures on tractors, value of machinery inventory and the year's hired labor cost on tractor expenditures. To ascertain joint effects, linear multiple regression models are used. General Hypotheses l. Tillable acreage and disposable income (previous yearfs,,current year's and change in net cash available) are constraints on the number of tractor purchases and expenditures made. 2. An indicated intention to buy and/or the amount of the intended expenditure are variables which can be used to explain tractor purchases and expenditures. 3. An intention made with some qualification (strength of intent) is more indicative of subsequent tractor purchases than an intention made without qualifications. M. An individual interprets all the relevant physical and financial variables and expresses these in his intention to buy and in his strength of intent. More specific and statistically testable hypotheses are formulated and tested in Chapters IV and V of this thesis. CHAPTER IV THE INFLUENCE OF INDIVIDUAL EACTORS ON TRACTOR.PURCHASES We are never sure which factors dominate purchase decisions. Undoubtedly many factors are involved in final buying decisions. Some of the important determinants may be those quantified in farm account books. The same and/or additional factors might be captured through "intentions" surveys. How much explanatory information can be squeezed from the data on each accessible variate insofar as tractor purchasers are concerned? The central task in this chapter is to isolate those variates that have explanatory value. The chi-square statistic (X?) is used to determine whether differences in buying proportions may be attributed to chance or to the variate under consideration. The strength of each bivariate relationship is measured‘with the contingency coefficient. ’Buyers and Non-Buyers of Farm Tractors in 1960 and 1961 One of the first questions that needs to be answered from the data available is whether there is a significant difference in the pro— portions of buyers from one year to the next. If there is no significant difference, data mightbe pooled for_the purpose of some analyses. . So that we know what to expect the hypothesis is stated in statistically testable form, i.e., H°--the null hypothesis or "no difference". In case the null hypothesis is rejected there is the 22 23 alternative hypothesis of some difference accepted HA. The first null hypothesis which we want to test is H01: The pro- portion of the farm operators who buy tractors is the same in.both years. The alternative hypothesis is that the respective buying pro- portions are significantly different. The survey samples yielded the results given in Table 1. TABLE 1 ACTUAL FREQUENCIES OF BUYERS AND NON-BUYERS 0F FARM TRACTORS IN MICHIGAN STATE UNIVERSITY'S FARM MACHINERY SURVEY IN 1960 AND 1961 1960 1961 Total Buyers 15? 12h 281 Non-Buyers #80 th 890 Sample Total 637 53k 1,171 Percentage Buyers 25 23 2h Denoting the actual proportions of buyers in the given two years as P and P 'we shall want to test the hypothesis. 2 Null hypothesis: P1 - Pé (-P) against the alternate hypothesis that the two P's are not the same. 1 Assuming that P is unknown we shall estimate it as the proportion of buyers observed in the two samples combined, namely as 1 + 12k 281 351—1 =- —— - 0.21: 37 + 53 1171 We can now ask for the number of buyers that we could have expected in each of the two samples if the null hypothesis were true and P equalled 281/1171 or (0.2h). In a sample of 637 we could have 2# expected 637 (O.2#) = 152.9 buyers, and in a sample of 53# we could have expected 53# (0.2#) = 128.1. Writing the expectedfrequencies, i.e., the expected number of buyers and non-buyers below the corresponding entries in Table 1, we have Year 1961 1962 Total Buyers 157 l2# 281 (152.9) (128.1) Non-Buyers #80 #10 890 (#8#.1) (105.9) Total . 637 531+ where the expected number of non-buyers was obtained by subtracting the expected number of buyers from the totals of each sample. In order to test the hypothesis formulated above, we now compare the expected frequencies shown in this table with the frequencies actually observed. It stands to reason that the null hypothesis should be accepted if these two frequencies are very much alike. After all, we would then have obtained almost exactly what we should haveiexpected if the null hypothesis were true. If the discrepancies between the two sets of frequencies are large, the observed frequencies do not agree with what we could have expected and we conclude that our expectations and, hence, the null hypothesis must be false. Our next step will be to test whether the discrepancies between the observed frequencies and the expected frequenciesare significant, or whether they may reasonably be attributed to chance. The criterion that is generally used for this purpose is based on the statistic X2 = Z (observed frequency - eggcted frequency)2 expected frequency which is called chi-square. 25 In other words, we must calculate the statistic for each cell of the 2 x 2 (r x k) or contingency table and then add the values obtained. Using X? we can new test the null hypothesis with the following criterion: Reject the null hypothesis if X2 > X2.05 where )8 is to be calculated as outlined above and the number of degrees of freedom equals (k - l)(r - 1). Returning to the data calculate X? for the observed and expected number'of buyers and non-buyers of farm tractors as follows: a (157 - 152.9 2 (112 - 128.1)2 "2 ""13279"'z '"““1287f“" (#80 - h8#.1)2 + (#10 - no .9 2 + #8#.1 _ 05. .11 + .13 + .03 + .O# = 0.31 Since this is less than 3.8#1 the value given for X? 05 with (2 - l) (2 - 1) = 1 degree of freedom, the null hypothesis cannot be rejected. The discrepancy between the proportion of buyers may be attributed to chance and we shall conclude that the actual proportion of buyers re- mained constant in the two years studied. Would we be justified in concluding that henceforth about 2# percent of the farmers in this sample could be expected to purchase each year? No, because economic theory and historical evidence indicate that we can expect some socio-economic factors to change and thereby significantly alter buying portions. The task now is to identify the subjective and objective factors which do have value in explaining differences in buy- ing proportions. 26 For the balance of the analyses the expected frequencies are denoted by a number enclosed by parenthesis. Chi-square (X2) is used for the purpose of testing the null hypothesis, i.e., that the buying proportion is the same for all groups. 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