HOUSEHOLD EXPENDITURE PATTERNS IN CANADA*

61

HOUSEHOLD EXPENDITURE PATTERNS IN CANADA*

Zuhair A. Hassan Cntroduction

The main purpose of this study is to analyze the Canadian household’s’ regional and sectoral expenditure patterns for major groups of commodities and services using cross-sectional data. Specifically, the objectives are: (1) To put forth a set of consumption functions and Engel’s curve parameters which take advantage of the most current data. Reliable estimates of these parameters would enable policymakers to forecast2 reactions of consumers (or households) to changes in factors which are likely to influence their expenditure patterns. (2) To test the hypotheses of differential expenditure patterns among (a ) urbanization groups (urban, rural non-farm and rural farm families), and (b ) among various regions (Atlantic, Quebec, Ontario, Prairie, and British Columbia). Such information may be instrumental in identifying factors affecting economic growth and development, and are a necessary input for the formulation of rational regional economic policy.

The discussion proceeds as follows. Part 1 provides a brief description of the model on which the results are based. Part 2 contains a discussion of the data source. The main empirical results are presented in Part 3. Finally, some concluding observations with regard to both the empirical results and the methods employed in obtaining them are presented.

The Model In estimating demand relationships from family expenditure data it is

customary to treat either current income or total expenditure as an ex- planatory variable. Summer [19] has shown that the use of total expenditure as an independent variable in ordinary least squares analysis yields biased and inconsistent estimates since expenditures on individual items (dependent variables) and total expenditure (the independent variable) are jointly determined. To complicate matters further, Friedman [8] has shown that the ordinary least squares estimates of consumption function parameters will be biased and inconsistent if current income is used as an independent variable. He argues that spending decisions are based on permanent income3 which is not observable. Therefore, the divergence between current income and permanent income becomes a measurement error in the independent variable which implies biased and inconsistent estimates of the parameters

I wish to express my thanks to 0. Al-Zand. 1. F. ,Furniss, S. R. Johnson, D. A. West and U. Zohar for their comments on an earlier draft of this paper. I am indebted to MI. H. Champion and to MI. U. Nevraument of Family Expenditure Section, Household Statistics Branch, Statistics Canada, who were most helpful in providing the data for the analysis. I am also indebted to D. Dau of Data Processing. Agriculture Canada for computational assistance.

1 Household and family are synonymous in this study. The family or spending unit is defined as a group of persons dependent on a common or pooled income for the major items of expense and living in the same dwelling or one financially independent individual living alone [17. p. 71.

2 Considering, of course, the proper qualification is embodied in results from cross-sectional analysis. 3 Three general theories currently exist on the determinants of total consumer spending: the absolute

income hypothesis. the relative income hypothesis, and the permanent income hypothesis. The permanent income hypothesis assumes first. that a consumer unit’s measured income and consumption in a certain period of time, say, in a given year, has two components, “transitory” and “permanent”. The second assumption is that permanent consumption is proportional to permanent income. Finally, the transitory and permanent income are assumed to be uncorrclatod, a s are transitory and permanent consumption. and transitory consumption and transitory income.

ZUHAIR A. HASSAN is an Economist with the Research Division, Economics Branch, Agriculture Canada, Ottawa.

62

if Friedman’s thesis is correct. Addressing the “errors in variables” problem suggested by Friedman, Liviaton [14] has shown that using current income as an instrumental variable for permanent income will yield consistent estimates of Engel’s curve parameters.

The basic model employed in this study is similar to the one proposed by Liviaton and used by others [I and 131. For n commodity groups the model is represented by the following system of equations:

El = a,, + a l l y * + a,,N + v, (1 1 E = a, + a,Y* + a,N + u (2 ) E = X E I (3 1

( i = l , . . . Y n)

i where E, is expenditure on the (i th) commodity, E is total expenditure, Y* represents permanent income, N is household size, a,,, a,,, a,,, a, and a2 are parameters to be estimated, v, and u are disturbance terms. I t is assumed that the disturbance terms v , and u have zero means and constant variances and are independent of Y* and N.

To estimate demand parameters, the above system of equations is rewritten so that the first equation is expressed in terms of the observed variable E rather than the unobservable variable Y *. By substitution, the following system of equations is obtained:

E, = b,, + b,iE + b2iN + W I (4) E = a, + a,Y* + a,N + u (5) E = Z E , ( 6 )

( i = l , . . . , n )

i where: b,, = a,, - a,, a,/a,, b,, = a,,/a,, b,, - a , , a2/al, and: w, = v, - ua,,/a,. E and u in equation (5) are correlated, and since: w, = v, - ua,,/a,, it follows that E and w, in (4 ) are correlated. The consequence of this dependence is that the straightforward application of ordinary least squares to equation (4) would yield biased and inconsistent estimates of demand parameters [ 11, p. 2811.

A number of approaches to the estimation problem presented by the association of E and w, in equation (4 ) are available. Probably the most popular, because of the work of Friedman, is the classical approach in which fairly strong assumptions are made about the probability distribution of the error terms [ l l , pp. 281-2911. A second is the “error in the variables”- type model, an approach suggested by Zellner [20] which involves regression relationships containing unobservable independent variables. A third is the instrumental variables approach which requires variables to be contem- poraneously uncorrelated with the error terms while being highly correlated with independent variables [16]. In this study, the last approach will be used to estimate demand parameters.‘ Disposable income, Y, will serve as an instrumental variable for permanent income, Y*, and family size, N, will be used as both the exogenous and instrumental variable.

Consistent estimates of the parameters in equation (4) can be obtained alternatively by using two-stage least squares.G Thus, for the model used here, in the first stage, the ordinary least squares estimators of a,, a, and a, in ( 5 ) are derived by regressing P n E on LnY and 1nN.O In the 4 Kmenta [12. p. 3121 states that when i t comes to models with ,“errors in variables”, we have no

method available to us better than the instrumental variables technique. 5 Two-stage least squares is a special case of instrumental variable estimation 19, p. 3321 d i m e the

first stage estimates of the endogenous variables satisfy the conditions defining the instruments. 6 The InE denotes natural lognrith of E.

63

second stage, E, is regressed on.L?nE (estimated in the first stage) and on 1nN. The resulting coefficients are the two-stage least squares estimators of b,,, b, , and b?, . The semi-logarithmic function was used in the second stage instead of the double-logarithmic function because zero observations on the dependent variable occurred rather frequently.'

The Data

The analysis is based on data obtained from the 1969 Family Expenditure Survey [17; 181. This survey departs from previous surveys of this type undertaken by Statistics Canada as it is the first family expenditure survey designed to provide information on families and individuals living in private dwellings in all areas of Canada, both urban and rural.* A total of 21,887 households were included in the sample. Useable schedules were tabulated for 15,140 families and unattached individuals. This particular analysis is based on 14,965 households.g The number of observations for each group of families is listed in Table 2.

Table 1 shows average family income, average current consumption and average family size. It is readily seen that these averages vary widely among regions and between urban and rural families in each region. Average disposable income (all households) in the five regions ranged from a low of $5,526 in the Atlantic region to a high of $7,657 in Ontario. Incomes also varied widely by degree of urbanization. At the national level, for example, average disposable incomes for urban, rural non-farm and rural farm households were $7,265, $5,437 and $5,323, respectively.

Expenditure shares also differed widely among regions and urbanizations (Table 2 ) . Food (consumed at home plus away from home) was the largest single item in the consumption category. The expenditure shares for total food ranged from 22 percent (urban Ontario) to 30.7 percent (rural Atlantic) and, in general, shelter, travel, and transportation were next in importance.

It should be noted that, unlike the data used in other major studies of patterns of household consumption [ 1 ; 21, the observations in this study relate to individual households rather than to groups of households.1°

Empirical Results

In this section the empirical findings obtained by applying the theory and methods outlined in the preceding section to eleven groups of commodities and services are reported and discussed. These groups are: I. Total Food; 11. Food at Home; 111. Shelter; IV. Household Operation; V. Furnishings and Equipment; VI. Clothing; VII. Personal Care; VIII. Medical and Health Care; IX. Tobacco and Alcoholic Beverages; X. Travel and 7 The chicf disadvantage of both functions (double-logarithmic and semi-logarithmic) is that they'fail to satisfy

the adding-up criterion. i.e., in both cases 1 E, + E. 8 We Yukon and Northwest Territories were excluded. 9 All units from the special areas and units with zero income were eliminated.

10 The sampling bariances of regression coefficients conlputed from grouped data can be greater than the sampling variances of the coefficients for the individual data. Similarly, the R' calculated from grouped data can bc Sreater than the R' for the individual data and may be il misleading indicator of the latter [ I ] , p. 430).

64

Transportation; and XI. Recreation, Reading, Education and Miscellaneous. Before turning to the main results, however, estimates of the consumption function for all goods and services are presented.

The Consumption Function Referring to the results of the estimated consumption functions (Table

3 ) , it can be observed that the statistical fit (RZ) of the consumption functions is generally good considering, of course, the results are based on cross-sectional data. Furthermore, all of the coefficient estimates are significant at the 1 percent probability level.

TABLE 1 Averoge Income, Averoge Consumption, Fomily Size ond Number of Families

Income Income Total Before After Current Average Number Taxes Taxes Consumption Family of

$ $ $ Size Families

Canada Atlantic Quebec Ontario Prairies B.C.

Urban - Canada Atlantic Quebec Ontario Prairies B.C.

Rural Non-Farm - Canada Atlantic Quebec Ontario Prairies B.C.

Rural Farm -Canada Atlantic Quebec Ontario Prairies B.C.

8026.5 6155.4 7789.6 8987.5 7 190.6 8059.4

8487.7 6819.1 8061.1 9354.1 7887.8 8226.9

6073.8 5196.0 5969.7 7014.9 5302.0 6962.6

5785.3 4861.7 6994.3 6335.7 4603.8 7344.6

6922.3 5526.0 6782.3 7656.7 6214.8 6930.1

7265.3 6042.2 6965.9 7925.2 6755.7 7053.0

5436.6 4766.2 5386.2 6204.0 4727.8 6098.6

5323.3 4592.0 6578.6 5745.8 4222.9 6502.0

6485.4 5268.6 6434.2 7129.1 583 1.7 6366.6

6744.6 5738.4 6572.0 7321.7 6220.6 6418.4

53 19.0 4578.2 5391.8 6139.9 4468.7 5943.8

5347.8 4415.1 6229.2 5682.7 4606.1 6352.3

3.28 3.71 3.54 3.18 3.09 2.93

3.14 3.50 3.36 3.10 2.97 2.85

3.66 3.96 4.01 3.56 3.10 3.29

4.24 4.37 5.45 4.00 3.71 3 92

15140 3686 2959 3469 3557 1469

11133 2099 2385 2799 2637 1213

2752 1404 426 459 255 208

109 1 161 140 171 575 44

a Units from the special areas arc included.

Source : Statistics Canada, Family Expenditwe in Cunudu, 1969, Vol. I, Catalogue 62-535

Statistics Canada, Family Expenditure in Canadu, 1969, Vol. 11, Catalogue 62-536 (Ottawa: Statistics Canada, January 1973).

(Ottawa: Statistics Canada, March 1973).

TABLE 2 Expenditure Shares& and Number of Families, Canada, by Regions and by Urbanization

Canada Atlantic Region Quebec

All Rural Rural All Rural Rural All Rural Rural Commodity House- Urban Non-Farm Farm House- Urban Non-Farm Farm House- Urban Non-Farm Farm

holds holds holds

Total Food Food At Home Shelter Household Operation Furnishing and

Equipment Clothing Personal Care Medical and

Health Care

,2428 .207 1 .I848 .0508

.233,8

.1967

.1937

.05 16

.2835

.25 17

.1530

.0487

.2578

.2301 ,1492 ,0469

.274 I

.2427

.1695

.05 I9

.2559

.2238

.I855

.0544

.3067

.2760

.1427

.0473

,2872 .2597 .1401 ,0526

.2615

.2205 ,1794 .0485

.2551

.2 133

.1879

.0493

.2990

.2597

.1428

.0459

,2800 .2469 .1221 .0404

.0507

.1103

.0266

.0572

.lo41

.0266

,0575 .I043 .0274

.0562

.lo04

.0240

.0565

.11 I9

.0229

.0509 ,1117 .028 1

.05 12

.lo79 .024 1

.04 17

.lo83

.0244

.0517

.1127

.0279

.0520

.1122

.0282

.048 1

.I087

.0265

.0573

.1322 ,0252

.0404 .0403 .0374 .0495 .0302 ,0300 .0296 .0438 .0436 .0404 .0454 .0439 Smoking and

Alcoholic Beverages .0476 .0471 .0527 ,0406 .0521 .0516 .0545 ,0391 .0548 .0533 .0629 .0596 Travel and

Transportation .1657 .1608 .1802 .1939 .1653 .I561 .1783 .2049 .1502 .1462 .I663 .1798 Recreation, Reading,

Education and Miscellaneous .0799 .0837 .0639 .0708 .0692 .0758 .0578 .06 1 3 .0695 .0722 .0545 .0594

Number of Families 14,965 11,126 2,749 1,090 3,663 2,099 1,403 161 2,949 2,383 426 140

TABLE 2 (Continued) Expenditure Shoresa and Number of Families, Conodo, by Regions and by Urbonizotion

Prairie Region British Columbia Ontario

All Rural Rural All Rural Rural All Rural Rural Commodity House- Urban Non-Farm Farm House- Urban Non-Farm Farm House- Urban Non-Farm Farm

holds holds holds

Total Food .2230 Food At Home .1851 Shelter .I939 Household Operation .0528 Furnishing and

Equipment .06 13 Clothing .0990 Personal Care ,0275 Medical and

Health Care ,0460 Smoking and

Alcoholic Beverages .0447 Travel and

Transportation .I685 Recreation, Reading,

Education and Miscellaneous .0832

.2 189

.1794

.1993

.0532

.2457

.2158 ,1693 .052 1

.2426

.2 134

.I542

.0465

.2272

.1935

.I900

.0492

.22 12

.I861

.I959

.0497

.2586

.2262

.1783

.0472

.2502

.2246

.1595

.047 1

.2238

.1918

. I929

.05 19

.2205

.I875

.1990

.05 14

.2411

.2136

.I662

.0548

.2407

.2140

.1410

.053 1

.06 10 .loo8 .0282

.0644

.0875

.024 1

.0596 ,0950 .0225

,0608 .lo24 .0252

.0607

.I012 ,026 1

.0664

.0950

.0208

.0584

.1131

.02 19

.0637

.0908

.0247

.0628

.09 I9

.0253

.0695

.0807

.02 14

.06 19

.lo67

.0224

.0456 .0455 ,0566 .04 18 .0399 .0473 .0512 .0373 .0371 .0362 .0474

.0452 .045 1 .0328 .0417 .04 18 .0475 .0384 .0433 .0433 .0462 .0333

.I620 .I972 .2205 .1725 ,171 1 ,1631 .1850 .1768 .1734 .I929 .ZOO7

.0758 .0752 .0948 .0954 .09 10 .0929 .0859 .069 1 ,0697 .0892 .0925

Number of Families 3,425 2,795 459 171 3,464 2,636 254 574 1,464 1,213 207 44

a Based on unwcighted sample wunts “sample size.”

67

TABLE 3 Consumption Functions, Canoda, by Regions and by Urbanization

Marginal Propensity

Constant P n Y k n N R? to Consume

CANADA A11 Households 3.936

Urban 3.104

Rural Non-Farm 3.205

Rural Farm 7.020

(.0539)

(.04 13)

(.0830)

(.0883) ATLANTIC

All Households 2.893

Urban 2.669


(.0708)

(.0948)

(.1143)

(.4069) Rural Farm 3.201

QUEBEC All Households 2.296

Urban 2.412


Rural Farm 1.900

(.0768)

(.0860)

(.2 126)

(.4265)

,5080 (.0043 )

,6133 (.0050) S857

(.0106) .0879

(.0112)

,6283 (.0088 ) .6608

(.0116) 3358

(.0148) S698 (.0535)

.7 155 (.0094) .7029

(.O 105) .7092

(.0267) 7.423

(.0548 )

,2627 (.0053) .2 120

(.0057) .2332

(.O 120) .5224

(.0233)

.I957 (.0096) .1848

(.0118) .2591

( .O 170) .2625

(.0533)

.I3 10 (.0096) .I355

(.0110) ,1737

(.0247) .1799

(.0524)

.6356

.7176

,6896

.4056

,7024

.7240

.6744

,6536

,7652

.7659

.7513

.7680

.6767 (.004O)

.6700 (.0046) .7037

(.0103) .4652

(.0196)

,7241 (.0083) .7087

.7 194 (.0138) 5908

(.0503)

.1130 (.0096) .7080

(.0108) .6973

(.0282) .6958

(.0552)

(.0111)

For the purpose of interpreting these results in a more useful context, it is of interest to examine the marginal propensities to consume (Table 3).” The marginal propensity to consume is the additional consumption resulting from a one unit increase in disposable income. The marginal propensities to consume (for all households, Canada) are .6700, .7037, and .4652 for urban, rural non-farm and rural farm families, respectively. These values appear to have the relative magnitude which a priori reasoning would lead one to expect. That is, one would expect marginal propensity to consume to be lower for farm families than for urban and rural non-farm families because farm families tend to save and reinvest large proportions of their income in the farm business. The marginal propensity to consume for farm families (.4652) may appear too low. However, it has been shown [ 5 ] that money income is an inadequate measure of the true economic position of farm I I Estirnaiing equation is: E = a,, + a , Y + a.N + U.

68

TABLE 3 (Continued) Consumption Functions, Canada, by Regions and by Urbanization

Marginal Propensity

Constant ZnY 2nN R' to Consume

ONTARIO All Households 4.624

Urban 4.05 1


Rural Farm 7.491

(.0742)

(.0846)

(.2126)

(.1911) PRAIRIES

All Households 5.164

Urban 3.162


Rural Farm 7.272

(.067 1 )

(.0843)

(.2429)

(.1064)

BRITISH COLUMBIA All Households 3.052

Urban 3.070


Rural Farm 3.729

(.1178)

(.1292)

(.3398)

(.6766)

.4344 (.0089) so50

(.0102) .6445

(.0265) .0654

(.0230)

.3472 (.0083) .6012

(.0104) .4109

(.0320) .0499

(.0134)

.6145 (.0144) .6129

(.0158) .6237

(.0419) S257

(.0816)

.2982 (.0120) .269 1

(.0127) .la67

(.03 11) .4098

(.0612)

A054 (.0122) .2464

(.0123) .4244

(.0426) .552 1

(.03 17)

.259 1 (.0160) .2598

(.O 178 ) .2670

(.0437) .3088

(.0963)

.59 17

.6535

.6952

.2800

S789

.7396

.7131

.3933

.7333

.743 1

.6920

.6268

.6694 (.0081) .6685

(.0089) .7267

(.0263 ) .3786

(.0445)

.6221 (.0084) .6346

(.0096) 5758

(.0343) .3682

(.0297)

5467 (.0141) 5409

(.0157) .6576

(.0754) 5175

(.0385) ____ ~~~ ~

Note: Standard errors are in parentheses.

people. Therefore, one should consider net worth along with money income when analyzing the economic situation of farm families. Unfortunately, net worth information was unavailable in the survey data, so that net worth could not be combined with money income and introduced in the relationship.

It is significant to note that the results for the Prairie region (.6346, .5758, and .3682 for urban, rural non-farm and rural farm families, respectively) and for all households in Ontario (.6694) are rather similar in magnitude to those reported by other studies. The marginal propensities to consume obtained by Bollman and MacMillan [4, p. 461 for the Interlake Area in Manitoba were .4012, .3322, .0246, ,1565, and .1722 for urban, rural non-farm, large farm, medium fann and small farm families, respectively. Haronitis [9, p. 341 obtained marginal propensities for Ontario ranging from S874 to .6086, depending upon the method of estimation.

69

The Elasticities: Canada All of the income elasticities in Table 4 are significant at the 1 percent

probability level.’? In addition, all have the expected sign (positive), which a priori theory would lead one to expect.

With regard to the size and sign of the estimated elasticities, several inferences can be drawn from Table 4. First, expenditure elasticities for all food, food at home, shelter, clothing, personal care, tobacco and alcoholic beverages, and travel and transportation are smaller for the urban and rural non-farm households than for the farm households. This suggests that in response to income increases, the farm households would expand expenditures on the above group of commodities and services relatively more than would urban and rural non-farm households. Second, although a rise in income tends to increase expenditure on furnishing and equipment,travel and transportation, and recreation more than proportionally, an increase in household size tends to decrease expenditure on these items (i.e., an increase in household size tends to induce a shift away from luxury goods to the more essential ones). Third, for total food, food at home and clothing, the household’s response of expenditure is low relative to an increase in family size. Fourth, the expenditure elasticities for total food and shelter are worth noting since Engel’s and Schwabe’s lawsI3 concerning the decreasing share of expenditure on food and housing are confirmed. Finally, one notes that total food has a much higher income elasticity than food at home. This implies that the income elasticity for food away from home is more elastic than it is for food at home. Thus, one may argue that as families become wealthier and purchase more food away from home, the income elasticity for total food tends to be more elastic.

The Elasticities: All Regions Since agriculture is of much greater importance in some regions (Prairies)

than in others (British Columbia), it is interesting to examine regional differences separately for farm and non-farm families. In reviewing Tables 4 to 9, one’s attention is first directed to the pattern of income elasticities of demand. I t can be seen that the income elasticities for urban and rural non-farm families in all regions are positive and significant at the 1 percent probability level. However, income elasticities obtained for a number of commodities purchased by rural farm families (e.g., Ontario and Prairies) were not statistically significant at the above level. This may imply that income is not a major factor influencing a rural farm household’s expenditure decision. This result may also suggest that other factors (e.g., age of head of household, income stability, net worth) may be important in determining the expenditure of farm families.

The results reported in Tables 5 to 9 also indicate that the estimated elasticities (income and family size) differ among regions. This suggests that there is a difference in expenditure patterns among households in different regions. To test the hypothesis of a differential expenditure pattern between urbanization groups and among various regions or to test 12 The standard errors of the elasticities were obtained from a formula derived by Bartcn, ct al.

1 3 Engel’s law states that the poorer a family is, the greater the proportion of total expenditures which i t will use to procure food. Schwabe’s law states that the poorer anyone is, the greater the amount relative to his income that he will 5pend for housing.

13. P. 5641

TABLE 4 Income (Tot01 Expenditure) ond Fomily Size Elosticities, Canada, by Urbanization

All Households Urban Rural Non-Farm Rural Farm

Income Family Size Income Family Size Income Family Size Income Family Size Commodity

Elasticity Elasticity Elasticity Elasticity Elasticity Elasticity Elasticity Elasticity

4 0

Total Food

Food at Home

Shelter

Household Operation

Furnishing and Equipment

Clothing

Personal Care

Medical and Health Care

Smoking and Alcoholic

Travel and Transportation

Recreation, Reading, Education

Beverages

and Miscellaneous

.4610 (.0072) .2905

(.0074) 3249

(.0134) .9556

1.1615 (.0320) .9613

(.016 1 ) .9152

(.014 I ) .6424

(.0195) .92 18

(.0240) 1.2103 (.0245) 1.2972 (.0234)

(.0220)

.2922 (.0056) ,4488

(.0059) -.2163

(.0101)

(.O 164) -.2 174

(.0233) 1.2250 (.0120)

-.0690 ( .O 105) .0035

(.0149)

(.0179)

(.0178)

(.0169)

-. 1807

-.1439

-.270 1

-.3321

,4460 (.0078) .2562

( . O O S O ) .7 194

(.0138) .9476

(.0254) 1.1713 (.0358) .9811

(.0182) 3413

(.0151) .6 I78

(.0214) ,8846

(.026 I ) 1.2837 (.0275) 1.2748 (.0254)

.3063 (.0064) .49 I2

(.0067)

(.0110)

(.0198)

(.0273 ) .09 10

(.0141)

(.0119) .0365*

(.0172)

-.1352

-.1677

-.2069

--.0257

-. 1298 (.0205)

(.0208)

(.O 192)

-.3 189

-.3181

.3843 (.O 162) .2557

(.O 166) A391

(.0374) .9333

(.0360) 1.2129 (.0742) .8104

(.0340) 3984

(.0334) .7327

(.0510) 1.0061 (.0573 ) 1.422 1 (.0548) 1.3733 (.0564)

.3462 (.0132) .4402

(.0136)

(.0292) -. 1695

(.0278) -.3447

(.0558) .29 17

(.0267) -.0125*

(.0258) -.I191

(.0401)

(.0438)

(.0406)

(.0417)

-.2394

-. 1682

-.5013

-.3868

.7689 (.1079) S988

(.1104) A686

(. 1905) 3274

(. 1841 ) 1.5718 (.3358) 1.0295 (. 1736) 1.1726 (. 1725) S348

(.1998) 1.663 1 (.3594) 1.5158 (.2809) .9135

(.2682)

.0608* (.065 1 ) .1576*

(.0667) -. I877 4

(.1149) -.1752*

(.I 11 1) -.5229

(.2016) .1657*

(.1046) --.1376*

(.1037) .0552*

(. 1208)

(.2157) -.4141*

(. 1685)

(. 1617)

-.6095

--.0192*

Asymptotic standard errors arc in parentheses. Coefficient is not significant at 1 percent by two-tailed t-test.

TABLE 5 Income (Total Expenditure) and Family Size Elasticities, Atlantic Region, by Urbanization


Commodity Income Family Size Income Family Size Income Family Size Income Family Size Elasticity Elasticity Elasticity Elasticity Elasticity Elasticity Elasticity Elasticity

Total Food

Food at Home

Shelter

Household Operation


Clothing

Personal Care





Beverages

and Miscellaneous

.3835 (.0125)

.2687 (.0132) .8862

(.0243 ) .9607

(.03 13 ) 1.1261 (.0540) .9309

(.0280) .9309

(.0247) .6824

(.0426) A992

(.0485) 1.3604 (.0483 ) 1.3263 (.0436)

.3479 (.0099) .443 1

(.0107) -.2969

(.O 184) -. 1858

(.0234) -.262 1

(.0397 .1942

(.0210)

(.0185) --.0773 *

(.0327)

(.0364)

(-0347)

L O 3 12)

-.0637

-.0395*

-.3900

--.2856

.3810 (.0158) .2526

(.0171) ,761 1

(.0274) .9262

( .04 12) 1.1555 (.0711) .9583

(.0374) .8633

(.0310) .6628

(.0542) ,7679

(.0630) 1.4580 (.0652) 1.2833 (-0544)

.3553 (.0123) .4790

(.0135)

(.0205 )

(.0304)

( .05 12) .1496

(.0274)

(.0229 )

(.0408) .0493 *

(.0470)

(-0455)

(.0385)

--.1966

-.I410

-.2733

--.0265*

-.0567*

--.4379

-.2388

.3809 (.0247) .2853

(.0251) 3952

(.0546) .9515

(.0543) 1.1520 (. 1007) .8220

(.0492 ) .9228

(-0470) .7201

(.0879) 1.1229 (.0923) 1.5184 (.0872) 1.2545 (.0789)

.3479 (.0194) .40 19

(.0198) --.3079

(.0413)

(.0407)

(.0742) ,3317

(.0375) --.0208 *

(.0352) -.1185*

(.0669) -. 1945

(-0680)

(.0627)

(.0575)

-. 1960

-.2829

--.5476

--.2583

.4571 (-0935) .3211

(J049) .7982

(. 1770) ,7520

(.1696) .7386+

(.2890) .7821

(.1515) .8036

(.1426) .5811'

(.2377) 1.5144 (.35633 1.5398 (.25 17) 1.7530 (3443)

.2460 (.0701) ,3408

(-0793)

(.13 10)

(.1252)

--.2884+

-.1040*

.025 1 * (.2134) .3795

(.1126) .0685 *

(. 1050) ,034 1 *

(. 1768) --.4812*

(.2522) -.3895*

(. 1768) -.3334*

(.2362)

Asymptotic standard errors are in parentheses. * Coefficient is not significant a1 1 percent by two-tailed 1-test.

TABLE 6 Income (Total Expenditure) and Family Size Elasticities, Quebec, by Urbanization

~ ~~



Total Food

Food at Home

Shelter

Household Operation


Clothing

Personal Care



Travel and Transportation Beverages

Recreation, Reading, Education and Miscellaneous

.4723 (.0136) .2702

(.O 139) .7611

(.0249) .9777

(.0406) 1.2159 (.0838 ) .9776

(.0317) 3533

(.0294) .6102

(.0462) ,9628

(.0437) 1.3098 (.0534) 1.42 14 (.057 1 )

.290 I ( .O I09 ) .4692

(.0114) -.2028

(.0194) -.2023

(.0309)

(.0620) .1140

(.0240)

(.0226)

(.0364)

(.0333)

(.0390)

(.0415)

-.2550

--.0427*

-.O 199*

-.1434

-.2627

-.4278

.4824 (.0153) .2748

(.0156) .684 1

(.0267) .980 1

(.0468) 1.2897 (.0996) .9700

(.0368) .793 I

(.0327) .6 162

(.0531) ,9443

(.0489) 1.3457 (.0608) 1.3776 ( .063 6)

.2837 (.O 124) .4769

(.0129)

(.02 12)

(.0362) -.2958

(.0745) .1147

(.0284) -.0050*

(.0257)

(.0424)

(.0380)

(.0452)

(.0474)

-. I368

-.1980

--.0074*

-.I612

-.3025

-.4099

.405 1 (.0401) .I688

(.0412) .6792

(.07 19) .9030

(.0979) ,9209

(.1756) 1 .oooo (.08 15) 1.0178 (.0991) .4648

(.1263) 1.3535 (.1328) 1.6002 (. 1555) 1.6015 (.1731)

.3652 (.0306) .5477

(.0323)

(.0534)

(.07 12) -. 1659*

(. 1278) .0955*

(.0587)

(.0713)

(.0950)

(.0928)

(. 1062)

(.1192)

-.1332*

-.0616*

-.088 1 *

-.0279*

-.3523

-.4876

-.5695

.4550 ( .0825) .3092

(.0878) .8670

(. 1885) .6525

(.1810) .7199

(.2722) .9995

(. 1366) 1.2045 (.1552) .6602

(.2563) 1.2905 (.2809) 1.3973 (.268 1 ) 1.3016 (.2735)

.2565 (.0690) .32 16

(.0739) -.2418*

(.1538) --.288 1 *

(.1501) -.0255*

(.2236) .0786*

(. 1099)

( . I 232)

(.2113) -.2063 *

(.2206)

(.2093 )

(.2 140)

--.2881*

-.0380*

-.3463*

-.03 1 1 *

Asymptotic standard errors are in parentheses. * Coefficient is not significant at 1 percent by two-tailed 1-test.

TABLE 7 Income (Totol Expenditure) and Family Size Elasticities, Ontario, by Urbanization



Total Food .4744 .27 17 .4495 .294 1 .4 130 .275 1 .8807 .0966* (.0167) (.0126) (.0177) (.0140) (.0375) (.0324) (.2983) (.1438)

Food at Home .2666 .46 19 .2245 3071 .2682 .3854 .7177 .1938* (.0171) (.0132) (.0180) (.0146) (.0377) (.0329) (.3127) (.1510)

(.0342) (.0253) (.0356) (.0277) (.0830) (.0697) (5279) (.2540)

(.0678) (.0494) (-0761) (.0577) (.0765) (.0634) (3251) (.2528)

(.0712) (.0512) (.0744) (.0558) (.1948) (.1536) (1.0834) (.5208) Clothing I .0785 .0318* 1.0828 .0072* .8179 .3229 1.0515* .249 1 *

(.0415) (.0300) (.0441) (.0332) (.0924) (.0774) (5026) (.2422) Personal Care .8712 --.0382* ,8137 -.0098* .767 1 .1085* 1.1746* --.0046*

(.0319) (.0234) (.0332) (.0256) (.0724) (.0604) (.4684) (.2252)

(.0378) (.0285) (.0400) (.0315) (.0973) (.0826) (.4699) (.2270)

Beverages (.0540) (.0395) (.0570) (.0436) (.1217) (.1016) (.9811) (.4707) Travel and Transportation 1.0744 -.2056 I . 1539 --.2503 1.4002 --.0575* ,5625' .0294*

(.0513) (.0373) (.0552) (.0416) ( . I 104) (.0850) (.8217) (.3969) Recreation, Reading, Education 1.2280 -.2849 1.2193 --.2844 1.3337 -.3570 .0197* .4673 *

and Miscellaneous (.0532) (.0382) (.0566) (.0423) (.1234) (.0971) (.7209) (.3501)

Shelter .7957 -.1727 .7320 -.1296 ,6937 -.0842* 1.3308* -.3570*

Household Operation 1,0502 -.23 12 1.0727 --.2529 .8346 --.0580* 1.2553* -.3621*

Furnishing and Equipment 1.1807 -.2143 1.1591 -.1938 1.3264 -.3772* 2.2448* -.9121*

Medical and Health Care SO93 -1 129 .4910 .1359 S541 .0763 * .6356* -.1139*

Smoking and Alcoholic .9920 --.2509 .9696 --.2362 .7726 -.1268* 2.1953* -.8224*

Asymptotic standard errors are in parentheses. Coefficient is not significant at I percent by two-tailed t-test.

4 W

TABLE 8 Income (Total Expenditure) and Family Size Elasticities, Prairie Region, by Urbanization

4 P


Commodity ~~

Income Family Size Income Family Size Income Family Size Income Family Size Elasticity Elasticity Elasticity Elasticity Elasticity Elasticity Elasticity Elasticity

Total Food

Food at Home

Shelter

Household Operation

Furnishings and Equipment

Clothing

Personal Care





Beverages

and Miscellaneous

S396 (.0199) .3649

(.0209) ,7923

(.0302) .8600

(.0395) 1 .0885

(.0689) .9817

(.0388) 1.01 10 (.0358) .4263

(.0375) .9465

(.063 1 ) 1.3007 (.0651) 1.3158 (.0530)

.2 I34 (.0152) .3914

(.0162) -.I485

(.0227) -. I028

(.0295)

( .0508) .0719*

(.0289) -. 1426

(.0266) ,1977

(.0288) -.2578

(.0472)

(.0474)

(.0385)

-.I 178

-.3 167

-.3278

.4768 (.0179) ,2867

(.O 187) .6540

(.0259) .8206

(.0368) 1.0455 (.0641) 1.0296 (.0372) ,9002

(.0324) .452 1

(.0356) 3699

(.0575) 1.3592 (.0617) 1.2782 (.0484)

.2739 (.0155) .48 I4

(.0165) --.0447*

(.0220) -.0643 *

(.0309)

( .0525) .0205*

(.0305)

(.0269) 1.8307 (.0308)

(.048 I )

( ,049 1 )

(.0388 )

-.0747*

-.0659*

-. 1724

-.3736

-.305 1

,4348 (.0697) .2268

(.0724) ,575 1

( . lo3 I ) ,8380

(.1126) .9895

(.2697) 3143

(.1234) 1.0855 ( . I 185) .4797

(. I391 ) 1.1771 (.2583) 1.3937 (.2325) 1.3892 (.207Y)

.3231 (.0603) .5 I46

(.063S) -.02 1 1 *

(.0882) -. 1526*

(.0949 )

(.2249) .1840*

(.1043) -.2426*

(.0983) .1237*

( . I 195) --.4870*

(.2147) --.4476*

(. 1890) -.4028'

(.1687)

-.1223*

.7862 (.2303 ) .6088

(.23 13 ) .779 I *

(.4174) .4625*

(.3884) 1.7226* (.7195) .9058

(.3510) .9213

(.3399) .2872*

(.3533) 1.2991 * (.7057) 2.0952 (.6437) .9082*

(S652)

--.0125* (.1363) .0892*

(.1370) --.0867*

(.247 1 ) .1211*

(.230 I ) -.5529*

(.4246) .2177*

(.2077) -.0156*

(.2011) .2755*

(.2094) --.6647 *

(.4 179) -.7563*

(.3793 ) -.0584*

(.3344)

Asymplolic standard errors are in parentheses. Coefficient is no1 significant at 1 percent by two-tailed ¶-test

TABLE 9 Income (Totol Expenditures) and Family Size Elasticities, British Columbia, by Urbanization

Commodity


Income Family Size Income Family Size Income Family Size Income Family Size Elasticity Elasticity Elasticity Elasticity Elasticity Elasticity Elasticity Elasticity

Total Food

Food at Home

Shelter

Household Operation


Clothing

Personal Care



Travel and Transportation Beverages

Recreation, Reading, Education and Miscellaneous

.4460 (.0226) .2577

(.0236) .6500

(.0395) ,8520

(.OSO7 ) 1.0458 (.0897) .9648

(.0486) .8652

(.0446) ,5743

(.0537) 1.0968 (.0801) 1.0777 (.0712) 1.1484 (.0590)

.2829 (.O 192) .4664

(.0204) -.0685*

(.0330) -. 1300

(.04 16) -.2047

(.0722) ,069 1 *

(.0395)

(.0365) ,1850

(.045 I )

(.0649)

(.OS72)

(.047 1 )

-.0807 *

--.4070

-.1497

-. 1984

,454 1 (.0252) ,2536

(.0263) .5704

(.0376) .8444

(.0564) ,9933

( ,1024) ,907 1

(.0530) .8120

(.0486) .5 109

(.0603 ) 1.1699 ( ,0903 ) 1.1171 (.0819) 1.1261 (.064Y)

,2800 (.02 18) .4802

(.032 1 ) -.0242*

(.0320)

(.0472) .0528*

(.0844) ,1005 *

(.044 1 ) -.0439*

(.0407) .243 I

(.05 18) --.4806

(.0733) -. 1559*

(.066S) -.196l

(.OS29)

--.I257

,4000 (.0576) ,2926

(.0624) ,9038

(.1911) ,9034

(. 1388) 1.2278 I.2131) 1.0388 (. 1269) .8443

(.1171) 3 7 19

( . I3 16) ,7801

( . I 90 1 ) .9508

(.1570) 1.2439 (. 1669)

,3297 (.0471) ,4320

(.0516)

(.1497) -. 1380*

(. 1088) --.3455*

(. 1627) .1106

(.O 107 ) -.0113*

(.092 1 )

(. I033 ) -.0648*

(. 1505)

(.1230)

(. 1263 )

-.1055*

-.0600*

-.2614*

-. 1682

.4487 (. 162 1 ) .1704* (. 1623) ,9452

(.3087) 1.1367 (.3039) 1.7580 (.3975) 1.8012 (.3544) 1.7261 (.3553) .7736*

(.3603 ) 1 .02 84 *: (.6316) 1.3142 (.3756) 1.3774 (.4172)

.1115 C.1255) .27 l4*

(. 127 1 ) .1749+

(.2326) -.5589*

(.2298)

(.2844) -.4767*

(.2485) -.5838*

(2533 ) -. 1276*

(.2744) -.5689*

(.4824) -.1339*

(2739) -.4077*

(.3054)

--.673 1 *

Asymptotic standard errors are in parentheses. Coefficient is not significant at 1 percent by two-tailed I-test.

76

the equality between subsets of coefficients in two linear regressions, Chow’s test was used [6].” An examination of the values of Chow’s F-statistics in Table 10 indicates that there is a marked difference in consumption patterns between urban and rural non-farm families and between urban and rural farm families (except for tobacco and alcoholic beverages). However, only about one-half of the values of Chow’s F-statistics differ significantly between rural non-farm and rural farm families. This is to be expected since it can be argued that rural non-farm and rural farm families are relatively similar with respect to tastes and preferences. Referring to Table 11, it can be noted that, except for a few values (e.g., Atlantic vs. British Columbia and Prairies vs. British Columbia), the regressions differ significantly among regions.

An explanation of the differences among the elasticities by region is rather difficult. It is, however, conceivable that the variation among the regions is in part attributable to the degree of urbanization, e.g., the Prairie region is the most rural and British Columbia, the most urban. Another factor that could contribute to regional differences is price variation. Prices are assumed to be the same for all households in a budget survey. However, this is not always true. Prices differ among household groups because of discriminatory pricing, quality differences, availability of goods and differences in transportation costs.

TABLE 10 Values for Testing Equality of Regression Coefhcientr, Canada, by Urbanization

Commodity Urban vs. Urban vs. Rural Non-Farm Rural Non-Farm Rural Farm vs. Rural Farm

Total Fooo Food At Home Shelter Household Operation Furnishing & Equipment Clothing Personal Care Medical & Health Care Smoking & Alcoholic Beverages Travel & Transportation Recreation, Reading, Education

and Miscellaneous

12.71 9.33

146.76 25.55 24.84 67.87 84.03 32.69 11.41 94.15 77.14

52.20 107.80 79.78 2 1.77 6.85 9.7 1

49.45 8.17

76.17 23.33

-*

45.59 60.27 2.21*

11.22 -* -* -* 35.19 11.92 14.40

.60*

a The values are based on Chow’s lest. Not significant at the 1 percent level.

- Values are less than 2.

Concluding Remarks Several inferences ,can be drawn from the results presented in this study.

First, the empirical results presented are considered satisfactory. All the income elasticities have the anticipated sign and are quite plausible in terms of relative and absolute magnitudes. They are also more complete than those presented in other significant studies [ 1 ; 41 and would, therefore, 14 A more elegant exposition of the test is found in [7].

TABLE 1 1 F-Volues for Testing Equality of Regression Coefficients, 8 by Regions

~~ ~~ ~

Atlantic Atlantic Atlantic Atlantic Quebec Quebec Quebec Ontario Ontario Prairies

Quebec Ontario Prairies British Ontario Prairies British Prairies British British Commodity vs. vs. vs. vs. vs. vs. vs. vs. vs. vs.

Columbia Columbia Columbia Columbia

Total Food 78.34 18.51 56.81 2.35" 32.76 191.14 56.59 64.05 13.07 13.00 Food At Home 42.07 3.78 44.81 1.87* 20.98 144.70 23.01 55.73 3.64* 24.87 Shelter 23.57 83.12 45.16 40.69 29.79 5.34 7.13 3 1.42 11.26 1.63 * Household Operation 11.32 25.92 7.73 3.17* 11.04 14.12 2.97* 36.67 7.77 8.67 Furnishing & Equipment 17.78 57.88 26.82 39.19 10.25 15.27 9.00 22.37 4.67 5.89 Clothing 33.74 44.40 5.71 18.96 15.13 38.58 49.88 37.30 25.93 14.87 Personal Care 25.97 45.61 1.83* 3.38' 2.44* 26.76 20.32 47.72 29.78 .49* Medical & Health Care 132.40 292.22 178.68 72.87 14.98 26.95 16.28 58.05 40.35 9.49 Smoking & Alcoholic

Beverages 11.91 18.31 39.68 21.19 32.80 83.62 37.01 18.15 4.32 6.40 Travel & Transportation 36.97 22.04 6.70 9.75 1.77* 21.02 3.70* 10.66 1.49* 3.12 Recreation, Reading, Education

and Miscellaneous 34.94 61.82 74.26 77.51 15.37 49.02 35.82 17.24 9.58 3.60*

a The values are based on Chow's test. Nor significant at the 1 percent level.

4 4

78

appear to represent an improved empirical basis for policy decisions. Second, the analyses for urban and rural non-farm households appear to have been more successful than those for rural farm families. This result may suggest that other factors may be important in determining the expenditure of farm families. Third, in response to income increase, households’ demand for furnishing and equipment, travel and transportation, and recreation will increase proportionally more than the rest. Consequently, such industries will be more favoured by economic growth. Finally, one of the limitations of Chow’s test is that if two regressions are different, the test will show that they are different without specifying whether the difference is due to the intercept term, or to the slopes, or both. A more extensive investigation of these relationships would possibly contribute to improved interpretation of the data as a basis for policy decisions.

REFERENCES 1. Asimakopulos, A. “Analysis of Canadian Consumer Expenditure Surveys.” Carin-

d i m Journal of Economics and Polirical Science, XXXl (May, 1965), 2221-2241., 2. Auer, L. “Urban Consumer Incomes and Food Expenditures.” Paper presented at

the Canadian Economics Association Annual Meeting, June, 1970, Winnipeg, Man. 3. Barten, A. P., H. Theil, and C. T. Lunders. “Farmers’ Budgets in a Depression

Period.” Econometrica, XXX (July, 1962), 548-564. 4. Bollman, R. D., and J. A. MacMillan. Income Expenditure Relationships and

Level of Living in rhe Interlake Area of Maniroba. Research Bulletin No. 72-2. Winnipeg: Dept. of Agricultural Economics, University of Manitoba.

5. Carlin, T. A., and E. I. Reinsel. “Combining lncome and Wealth: An Analysis of Farm Family Well-Being.” Anrerican Jourtral of Agricultural Economics, 55 (February, 19731, 38-43.

6. Chow, G . C. “Tests of Equality Between Sets of Coefficients in Two Linear Regressions.” Econometrica, XXVIll (July, 1960), 59 1-605.

7. Fisher, F. M. “Tests of Equality Between Sets of Coefficients in Two Linear Regressions: An Expository Note.” Econotnerrica. XXXVl11 (March, 1970), 361- 366.

8 . Friedman, M. A Theory of the Co,rsuuipriori Function. Princeton, New Jersey: Princeton University Press, 1957.

9. Goldberger, A. S. Econometric Theory. New York: John Wiley and Sons, Inc., 1964. 10. Haronitis, D. “An Econometric Model for the Ontario Economy.” Onrario Ecorr-

ornic Review, Special Supplement, (March, 197 I ) . 1 1 . Johnston, J. Ecorromefric Methods. 2nd Edition; New York: McGraw-Hill, 1972. 12. Kmenta, J. Elements of Econometrics. New York: The MacMillan Company, 1971. 13 . Lee, Feng-Yao and K. E. Phillips. “Differences in Consumption Patterns of Farm

and Non-Farm Households in the United States.” Aniericnn Journal of Agricultural Economics, 5 3 (November, 1971), 573-582.

14. Liviaton, N. “Errors in Variables and Engel Curve Analysis.” Ecotiornerrica,

15. MacMillan, James A., Fu-Lai Tung and R. M. A. Loyns. “Differences in Regional Household Consumption Patterns by Urbanization: A Cross-Section Analysis.” Journal of Regional Science, XI1 (December, 1972).

16. Sargarn, J . D. “The Estimation of Economic Relationships Using Instrumental Variables.” Econonietrica, XXVI (July, 1958). 393-415.

17. Statistics Canada. Family Expenditure in Conada, 1969. Volume I, Catalogue No. 62-535. Ottawa: Statistics Canada, January, 1973.

18. Statistics Canada. Family Expenditure in Canada, 1969. Volume 11, Catalogue No. 62-536. Ottawa: Statistics Canada, March, 1973.

19. Summer, R. “A Note on Least Squares Bias in Household Expenditure Analysis.” Economcrrica, XXVII (January, 19591, 121-126.

20. Zellner, A. “Estimation of Regression Relationships Containing Unobservable Independent Variables.” lnreniatiorrnl Ecoriorrric Review, XI (October, 1970), 441- 454.

XXXIX (July. 1961), 336-362.

Documents

HOUSEHOLD EXPENDITURE PATTERNS IN CANADA*