Reflation First

Reflation First

Reflation, the first phase in the recovery of an economy from a slump before the stage of full employment and inflation with rising prices is reached. A slump is characterized by a widespread surplus of unused resources idle machines, large stocks of unsold goods and high .php�>unemployment. Recovery from this situation usually requires an increase in total expenditure on goods and services. Once started, it tends to grow, extra spending creating extra employment and incomes, which in turn lead to more spending. Previously unemployed resources are drawn into production to meet the rising demand. At first, because each round of extra spending is matched by an increased supply of goods and services, there is little rise in prices. Eventually, when all resources are fully employed, no further increases in output will be possible. If demand continues to increase, prices will rise, thus marking the end of the reflation phase and the beginning of inflation.

Regression Analysis, a statistical technique used by economists to deduce a general tendency or 'pattern' from a number of individual observations of economic behaviour or relationships. An example is the increase in family or national expenditure on food as personal or national income rises from year to year. This relationship can be shown as a series of points on a 'scatter' diagram. Income is the major or 'explanatory variable' and expenditure on food the 'dependent variable'. The clustering of the points will indicate whether they seem to form a straight line or a curve. The 'best fit' is the line or curve which takes in the most points on or near it and leaves the least far from it. The line or curve then indicates the general or average relationship between expenditure on food and incomes. Readings can be 'interpolated' between any two points or 'extrapolated' if the line or curve is extended at both ends; if the line or curve is carried forward in time it enables predictions to be made about the probable expenditure on food in a given year as income rises in the future. If the observed points are scattered widely around the line or curve the predictions can be made with less certainty and within wider margins of error (which can be calculated) than if they 'fit' more closely. Regression analyses can also be used for more than two quantities ('variables') that seem to be related to one another.

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