AN ECONOMETRIC STUDY OF EXPENDITURE PATTERNS OF CONSUMERS IN SELECTED URBAN AREAS OF NIGERIA

Abstract

Optimum utilization of available statistical data is an objective which a country, particularly a developing one, should pursue. Adequate information about the structure of an economy is necessary for purposeful and meaningful planning, but a developing country lacks long statistical series that can make such information available. The objective of this study is to construct a simultaneous equation model of expenditure patterns of consumers based on the theory of consumer’s behavior; estimate parameters of the model; examine statistically various other factors, in addition to income, which affect consumption; and subsequently, with the help of certain assumptions on consumer preference orderings, derive income and price elasticities using Nigerian family budget data. The achievement of this objective depends on sound theoretical basis and appropriate data. These are covered in chapters 2, 3 and 4. The findings are discussed in chapters 5 and 6. These findings show: that different levels of factors like area and occupation affect average group expenditures (test between means) but the slopes of group expenditures with respect to total expenditures (the B’s) are not significantly affected by these factors; that areas effects are reasonably explained by occupational composition of areas; that the way estimates of B vary with family size confirms common indication of economics of scale particularly for commodities like food and transport: that because of broad classification of commodities, family composition cannot be meaningfully incorporated into the analysis: that two-way classification by income and family size is superior to one-way classification by income alone and; that with the aid of the leser-Frisch approach to assumptions concerning substitutability or complementarity between broadly classified commoditites, some kind of estimates of price elasticities is possible in addition to the usual income elasticities from family budget data.