Study of the income elasticity of demand in Canada for selected food products.
Bibliographic record
Abstract
Analysis of income elasticity of demand was carried out for beef, pork, eggs, tea, and coffee. Prices for beef and pork were taken at the wholesale level, while those for eggs, tea and coffee were taken at the retail level. Prices and disposable income were obtained from the National Accounts and were deflated by the Implicit Price Deflator obtained from the same source. The effect of the time variable was eliminated, in the case of beef, pork, and eggs, by taking the first differences. Straight line regressions were fitted to the consumption data of all 5 commodities. From the multiple variable equation, estimated values of x₁ and the residuals for each year were computed. The residuals were plotted around the partial regression equation (obtained by substituting the arithmetic mean for the value of the ether variables) according to the sign of the residuals for the year. Free-hand lines were fitted to this scatter diagram and from these lines the degree of variation was calculated. The regression coefficients were used in calculating elasticities. The income elasticity of the demand for beef varied from +0.815 to +1.178 from 1929 till 1944. For the period 1945-52, the average increase in income was only 0.875 dollars. There was a decrease in the per capita consumption of beef as calculated from the partial regression equation (removing the effect of changes in price of beef and price of pork). Thus income elasticity was -0.199 ± 0.02 during 1945-52. When the years of extreme variation in beef consumption (1934,-35,-43.-44,-48) were omitted from the series, the income elasticity was + 0.349 for the period 1929-33 and 0.00, -0.0063 for the periods 1939-42 and 1946-52. Thus in recent years the income elasticity of demand for beef has been decreasing and has approached 0.00. However, in considering these results, one should remember that the multiple regression equation explained only 26 per cent of the total variation. The regression coefficient fitted to pork consumption explained only 19 per cent of the total variation. Even out of this total explained variation the price of pork explained 80 per cent. The income elasticity of demand for pork came out to be +0.053 to +0.090. In the analysis of the demand for eggs, the price coefficient was not statistically significant. The income elasticity of demand for eggs varied from +0.403 to +0.657. The consumption of tea has been decreasing and that of coffee Increasing since 1926. The results of the multiple regression analysis were influenced by this time trend. When income elasticity of demand for coffee was calculated over the whole period it decreased from +1.120 to +1.068. The income elasticity of demand for tea was found to vary from -0.530 ± 0.133 to -1.438 ± 0.362. When separate, regression lines were fitted for the periods 1926-41 and 1942-52, none of the regression coefficients was significant in the latter period and only the price of tea coefficient in the first period. As has already been pointed out, there has been a trend in the consumption of tea and coffee. It is possible that because of Increases in disposable Income and in coffee consumption the income elasticity of demand is positive for coffee and negative for tea. The income elasticity of demand has been decreasing in the case of both tea and coffee but the reason seems to be different in the two cases. The income elasticity of demand for tea declined at a faster rate than the Income elasticity of demand for coffee.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".