LIMIT PRICING: The strategic behavior process in which a firm with market control sets its price and output so that there is not enough demand left for another firm to enter the market and earn profits. The firm expands its output causing the price to fall, which discourages potential entrants to this market. This practice is most commonly undertaken by oligopoly firms seeking to expand their market shares and gain greater market control.
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Observations or measurements that quantify or otherwise identify some aspect of the real world. Data are used to track economic performance, quantify economic characteristics, and test economic hypotheses. Data collection is often the most challenging part of undertaking an empirical analysis. Data are the key to acquiring knowledge about the world, especially through the scientific method. While theoretical speculation might indicate what someone "thinks" the world is like, no one knows for sure until the hypothesized view is compared with the real world itself. Data are what add the term "empirical" to empirical economic analysis.
For empirical economic analysis, data usually take the form of quantified, numerical measurements of economic variables, for example, the price of gold ($400 per ounce), the quantity of automobiles sold (8 million cars), or the unemployment rate (5 percent).
Much of this data come through official government sources. The Federal Reserve System, for example, provides abundant data on money, bank deposits, and assorted financial activities. The Department of Labor provides key employment and unemployment data. The Department of Commerce maintains a database of production and income data used to calculate gross domestic product, national income, and associated measures.
Other data come from private sources. The Conference Board, for example, compiles the leading, lagging, and coincident economic indicators used to track business cycles. The Dow Jones Company maintains and reports the popular Dow Jones Industrial Average (The Dow).
Four TypesEconomic data are usually one of four types--total, average, percentage, or index.
- Total: A study of the aggregate macroeconomy is full of total data. Measuring a total is relatively straightforward--just count everything. This is the technique used for calculating gross domestic product--total production in the economy.
- Average: A second type is an average, usually the arithmetic mean of several observations. A common average is per capita income--the average income for each person in the economy.
- Percentage: A third type falls under the heading percentage, the relative proportion of a value compared to a total. The unemployment rate--the percent of the labor force unemployed--is a notable and important percentage.
- Index: The fourth and last type is index. An index is a composite number that marks changes relative to some given time period or value. A useful index is the index leading economic indicators, which combines assorted measurements like the money supply, stock prices, and unemployment compensation claims.
Objective and SubjectiveData used to track the economy, evaluate market performance, or test hypotheses fall into one of two categories--objective and subjective.
The difference between objective and subjective data can be difficult to discern at times. For example, tracking the market price of a good, like hot fudge sundaes, would seem to involve objective data. Tracking this price is independent of who does the tracking.
- Objective: Objective data are based on cardinal measures that are largely free of subjective values or beliefs. Cardinal measures are based on a benchmark scale. Linear measures of distance provide an example. A mile is 5,280 feet, regardless of who does the measuring. Likewise, if the total labor force of the economy is 100 million people and 5 million people are unemployment, then the unemployment rate is 5 percent. Such objective data is ideally suited for testing scientific hypotheses.
- Subjective: Subjective data are based on personal beliefs, opinions, and judgments. This data likely depends on who does the collecting or possibly who is being studied. A common example of subjective data comes from opinion surveys, such as that used to compile the Consumer Confidence Index. Is the economy in good shape or bad shape? The answer, the data collected, depends on who is asked.
However, the price of hot fudge sundaes is itself subjective. Duncan Thurly might be willing to pay $3 for a hot fudge sundae because he really likes hot fudge sundaes and has plenty of income to make the purchase. Lisa Quirkenstone, however, is only willing to pay $1 for a hot fudge sundae because she is allergic to chocolate and has very little available income.
Because a great deal of economic analysis involves subjective valuations, objective and subjective data are often intertwined. What might appear to be completely objective, very likely has subjective undertones.
Data CollectionMost economic data are collected in one of two ways--total tabulation and sampling.
- The first is total tabulation. Much as the name indicates, total tabulation identifies every item. This is the method used to derive the Census of Population. Count everyone. Total tabulation is also the primary method for deriving gross domestic product.
- The second way is sampling. At times, counting everything is not possible, the time or resources to accomplish the task are not available. In such cases, statistical sampling is generally used. Data for a small, representative number (a sample) from the larger group is collected. When done correctly, this sample can indicate important characteristics of the larger group.
Tracking PerformanceAn important use of data is to track or document economic activity, both macroeconomic and microeconomic.
- At the macroeconomic level, data such as gross domestic product, unemployment rate, and inflation rate indicate whether the economy is expanding or contracting, doing better or doing worse.
- At the microeconomic level, data such as prices, production quantities, and profit levels indicate the performance of a particular industry or the activity in a given market.
Hypothesis VerificationData are also extremely important for hypothesis verification step of the scientific method. Without data, the scientific method is not able to determine the validity of theoretical implications. A hypothesis might sound correct, but without empirical validation, there is no way to know for certain.
Professor Grumpinkston, for example, has a hypothesis that students seated closer to the front of a classroom learn more and earn higher grades. The professor has two alternatives for this hypothesis.
- One, he could treat the hypothesis "as fact." The connection "sounds right" and seems to "make sense." He could pass along this relation "as fact" to his colleagues, who might then pass it along to others. Before long everyone accepts this hypothesis "as fact."
- Two, he could test this hypothesis with real world data. He could compare grades and seating positions for thousands of students in hundreds of classes. He could collect data, calculate percentages and indexes, and conduct study after study, each time comparing his hypothesis with real world observations. In so doing, he might be able to scientifically document this hypothesis as fact, or he might refute it. In either case, he has learned something.
DATA, AmosWEB Encyclonomic WEB*pedia, http://www.AmosWEB.com, AmosWEB LLC, 2000-2024. [Accessed: March 4, 2024].
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