Empirical investigation is observation and recording of specific happenings in the world. It is convenient when the happenings of interest can be adequately described in terms of numerical data. However, useful empirical investigation of a specific item of interest may also be represented in words or images.
When the observations take the form of showing how a numerical economic variable changes over time, we call them time series data. You will see many examples of time series data as you study economics—for gross domestic product (GDP) growth, employment, and other economic variables.
It is tempting to think that if two economic variables have an empirical relationship with each other, that there must be some kind of underlying relation between the two—or, in particular, that changes in one variable must be causing changes in the other.
Sometimes this is true. In the case of the upward trends over time that have occurred in both global production and carbon dioxide (CO2levels, there is causality: Growing industrial production has led, over time, to increasing accumulations of CO2, one of the primary gases involved in global climate change. There are good scientific reasons to believe that the rise in accumulated carbon dioxide is a direct result of years of fossil fuels-intensive economic growth.
But two variables may be related empirically (or be “correlated” with each other, to use the statistical term) without there being a well-defined causal relationship between them. In the case of unemployment rates and inflation, the two economic variables display a very strong empirical inverse relation for the period 1963-1969. Many economists during this period came to believe that this association was based on an underlying causal relationship. They thought that the government could “trade off” inflation and unemployment, suffering a little more inflation in order to get more people working. That is, it was thought that the government could make unemployment rates fall by allowing some inflation.
We can see why this sort of thinking had to be modified when we add data points for later years. In 1970 inflation continued to rise slightly, to 5.3 percent, while the unemployment rate unexpectedly also rose, to 4.9%. As you can see in Figure 1, the idea that there was a clear, causal relationship between these two variables became far less plausible as the nation moved into the 1970s and 1980s.
Empirical investigation creates the foundation for relevant macroeconomic analysis. Looking at the puzzle presented by the data on unemployment and inflation, we can see, however, that more tools are clearly needed if economists are to try to explain, rather than simply describe, macroeconomic phenomenon.
The adjective “empirical” is usually contrasted with “theoretical,” where the latter refers to statements that are made on the basis of mental constructs and processes, such as assumptions and logical deductions.
As you will see, the theories we introduce are based on “thought experiments.” Rarely having access to controlled laboratory experiments, as in the physical sciences, economists create theories based on assumptions about the economic agents and institutions, from which, with careful reasoning, they draw out potential implications for economic behavior.
In the mid-1960s, for example, economists created theories that plausibly (that is, believably) explained how the downward-sloping Phillips curve might have come about. They made assumptions about how workers and investors would respond to monetary and fiscal policies and other economic conditions. They created plausible stories about a chain of events that would connect higher inflation to more people wanting to offer or accept jobs.
In order to make it possible to build a theory, it is sometimes useful temporarily to isolate certain aspects of economic behavior from their larger historical and environmental context, in order to examine more closely the complex elements involved. A model is an analytical tool that highlights some aspects of reality while ignoring others. It can take the form of a simplified story, an image, a figure, a graph, or a set of equations, and it always involves simplifying assumptions. We’ll take a look at a couple of examples of economic models later in this reading when we examine the Production Possibility Frontier and the basic neoclassical model.
An important part of many models is the ceteris paribus assumption. This Latin phrase means “other things equal” or “all else constant.” In the models built around the Phillips curve relation in the mid-1960s, for example, one of the things “held constant” was people’s expectations about future inflation. The models assumed that even though inflation was rising steadily, people essentially wouldn’t notice. This assumption seemed to hold reasonably well for the period 1963-1969. Most economists now believe, however, that one of the main reasons for the jump in unemployment in 1970 was that people started to expect inflation, and to build inflation adjustments (such as cost-of living raises) into the contracts they made for employment. The theory built around the Phillips curve assumed that something (expectations) would stay constant, and the theory provided a plausible description of reality only along as this ceteris paribus assumption held. When it ceased to hold, new theories—now including an additional factor of expectations—were created.
Theorizing takes place in economists’ heads—hence the term “thought experiment.” “Is the resulting theory true?” you may rightly wonder. Generally, that is not a question that can be strictly answered “yes” or “no,” since our theories reflect only some selected aspects of the real world. Better questions to ask about economic theories include “Is the theory helpful in giving insight?” “Does it focus on things that we consider important?” Models can be useful—even though they require temporarily setting aside many complications and much of the larger context—when they are understood simply as tools to understanding, and when they remain open to revision as history evolves and new evidence is acquired.
Throughout your study of economics, you will notice the importance of knowledge of historical events—observations of happenings in the near or distant past, within the context of what went before and what came after, that are broader than the more narrowly focused empirical investigation. The Great Depression of the 1930’s, World War II, the Bretton Woods monetary agreement of 1947, the oil crisis of 1973, the invention of computers, the entry of women into market work, and the growing concern about environmental issues—all are examples of historical events that have had significant macroeconomic impact.
- Global Development And Environment Institute, Tufts University
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