Broadly speaking, two different approaches interpret ceteris paribus assumptions in economics. The first of these is known as substantive isolation, whereby certain possible causal and explanatory factors are disregarded because it is assumed they possess insignificant correlation; variables removed from substantive isolation are neither exogenous nor endogenous. The second is hypothetical isolation, where any certain variables may be assumed to be constant for a while to narrow down the list for theoretical simplicity.

This might at first seem confusing, but these terms should be made clear with a few examples. The distinction between substantive and hypothetical isolation is rarely a definitive one. That is to say, even though they have different logical roots, these two methods are often intermingled in ceteris paribus analysis.

Substantive Isolation

It is often held that for variable data to be a logical choice for substantive isolation, it must be sufficiently stable such that movements in the other data do not destroy or supersede it. Economists might refer to this as the "isolation principle," which is an important discovery in the history of economic thought.

Two important aspects of the isolation principle are time and causality. Temporal isolation is simple; time must pass for any one cause to produce its effects. According to British economist Alfred Marshall, temporal isolation is one of the most difficult aspects of general equilibrium economics.

Marshall meant that an equilibrium price is dynamic and ever-changing. The equilibrium price of bread in Colorado might be $3 one day, but in a week's time, it could be $4 or $40 depending on any huge number of variables.

An economic model is supposed to demonstrate a "tendency toward equilibrium." A model built to tend toward $3 equilibrium might not, if factors change too quickly, actually tend toward a new $40 equilibrium. In short, the change in equilibrium data must be slow enough for the changes in the variable data to be meaningful.

Causal isolation means the frozen portions of the data should not be influenced directly by the unfrozen portions. In statistics, this problem is often called endogeneity. Consider a sports analogy: Joe Montana and Jerry Rice won three Super Bowls together. Did Jerry Rice cause Joe Montana's success, or was it the other way around? The answer is clearly that they influenced each other; a loop of causality would make isolating either variable worthless.

Hypothetical Isolation

Hypothetical isolation is much less rigorous than substantive isolation. Stated simply, hypothetical isolation allows for any variables to be imagined into existence or imagined away. For example, an economist might imagine a barter economy where all money is taken away.

Superficially, it seems hypothetical isolation is of little use because anything and everything might be assumed or unassumed. This objection is actually theoretically misleading. Hypothetical isolation, when done correctly, is an extension of the logical sciences. Economic thinking by its nature is a process of imagination.

If thoughtful deductions can be shown to be necessarily true, the hypothetical isolation can reveal insight with more certainty than the empiricism of substantive isolation.

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