Economics is a very interesting subject. The scope of the economic domain is vast. Economics deals with market structure, consumer behavior, investment, growth, fiscal policy, monetary policy, the roles of the bank, and so forth. The list can go on for quite some time. It also predicts how economic agents behave in response to changes in economic and noneconomic factors such as price, income, political party, stability, and so on. Economic theory, however, is not specific. For example, the theory proves that when the price of a good increases, the quantity supplied increases, provided all the other pertinent factors remain constant, which is also known as ceteris paribus. What the theory does not and cannot state is how much the quantity increases for a given increase in price. The answer to this question seems to be more interesting to most people than the fact that quantity will increase as a result of an increase in price. The truth is that the theory that explains the above relationship is important for economists. For the rest of the population, knowledge of that relationship is worthless if the magnitude is unknown. Assume for a 10% increase in price, the quantity increases by 1%. This has many different consequences if the quantity increases by 10%, and totally different consequences if the quantity increases by 20%. The knowledge of the magnitude of change is as important, if not more important, than the knowledge of the direction of change. In other words, predictions are valuable when they are specific.
Statistics is the science that can answer specific issues raised above. The science of statistics provides necessary theories that can provide the foundation for answering such specific questions. Statistics theory indicates the necessary conditions to set up the study and collect data. It provides the means to analyze and clarify the meaning of the findings. It also provides the foundation to explain the meaning of the findings using statistical inference.
In order to make an economic decision, it is necessary to know the economic conditions. This is true for all economic agents, from the smallest to the largest. The smallest economic agent might be an individual with little earning and disposable income, while the largest can be a multinational corporation with thousands of employees, or government. Briefly, we will discuss some of the main needs and uses of statistics in economics, and then present some uses of regression analysis in economics.
The first step in making any economic decision is to gain knowledge of the state of the economy. Economic condition is always in a state of flux. Sometimes it seems that we are not very concerned with mundane economic basics. For example, we may not try to forecast what the price of a loaf of bread is or a pound of meat. We know the average prices for these items; we consume them on a regular basis and will continue doing so as long as nothing drastic happens. However, if you were to buy a new car you would most likely call around and check some showrooms to learn about available features and prices because we tend not to have up to date information on big-ticket items or goods and services that we do not purchase regularly. The process described previously is a kind of sampling, and the information that you obtain is called sample statistics, which are used to make an informed decision about the average price of an automobile. When the process is performed according to strict and formal statistical methods, it is called statistical inference. The specific sample statistics is called sample mean. The mean is one of numerous statistical measures at the disposal of modern economists.
Another useful measure is the median. The median is a value that divides observations into two equal halves, one with values less than the median and the other with values more than median. Statistics explains when each measure should be used and what determines which one is the appropriate measure. Median is the appropriate measure when dealing with home prices or income. Applications of statistical analysis in economics are vast and sometimes reach to other disciplines that need economics for assistance. For example, when we need to build a bridge to meet economic, social, and even cultural needs of a community, it is important to find a reliable estimate of the necessary capacity of the bridge. Statistics indicates the appropriate measure to be used by teaching us whether we should use the median or the mode. It also provides insight on the role that variance plays in this problem. In addition to identifying the appropriate tools for the task at hand, statistics also provides the methods of obtaining suitable data and procedure for performing analysis to deliver the necessary inference.
One cannot imagine an economic problem that does not depend on statistical analysis. Every year, the Government Printing Office compiles the Economic Report for the President. The majority of the statistics in the report are fact-based information about different aspects of economics, however, many of the statistics are based on some statistical analysis, albeit descriptive statistics. Descriptive statistics provide simple, yet powerful insight to economic agents and enable them to make more informed decisions.
Another component of statistical analysis is inferential statistics. Inferential statistics allows the economist and political leaders to test hypotheses about economic conditions. For example, in the presence of inflation, the Federal Reserve Board of Governors may choose to reduce money supply to cool down the economy and slow down the pace of inflation. The knowledge of how much to reduce the supply of money is not only based on economic theory, but also depends on proper estimation of the final outcome.
Another widely used application of statistical analysis is in policy decision. We hear a lot about the erosion of the middle class or that the middle class pays a larger percentage of its income in taxes than do lower and upper classes. How do we know who the middle class is? A set dollar amount of income would be inadequate because of inflation although, we must admit, even a single dollar amount must also be obtained using statistics. However, statistical analysis has a much more meaningful and more elegant solution. The concept of interquartile range identifies the middle 50% of the population or income. Interquartile range was not designed to identify the middle 50%, and it is not explained in these terms; nevertheless, the combination of economics and statistics is used to identify the middle 50% for economics and policy decision purposes.
Knowledge of statistics can also help identify and comprehend daily news events. Recently, a report indicated that the chance of accident for teenage drivers increases by 40% when there are passengers in the car who are under 21 years of age. This is a meaningless report. Few teenagers drive alone or have passengers over 21 years of age. Total miles driven by teenagers when there are passengers less than 21 years of age far exceeds any other types of teenage driving. Other things equal, the more you drive, the higher the probability of an accident. This example indicates that knowledge of statistics is helpful in understanding everyday events and in making sound analyses.
One of the most important aspects of statistics is the discovery of rules that allow the use of a sample to draw inferences about population parameters. Inferential statistics allows us to make decisions about the possibility of an outcome based on its probability, not dissimilar to what we do in real life anyway. Life experience is private and is based on an individual. A friend is usually late, and based on that, we estimate his approximate arrival time. In statistics the process is formal. We take random samples, and based on statistical theories of sampling distribution and the probabilities of outcomes, we make inferences and predictions about the outcomes. In essence, statistics formalizes the human experience of estimation and makes predictions more formal and provides theoretical proofs for anticipated outcomes.
This book focuses on a few introductory topics in statistics and provides examples from economics. It takes a different orientation for covering the material than most other books. Chapters 1 and 2 cover descriptive statistics from tabular, graphical, and numeric points of view. A summary table of all the tools introduced in these chapters is provided in Chapter 1 to help you see the big picture of what belongs where. This grouping helps relate topics to each other. Chapter 3 provides some applications of these basic tools in different areas of economics. The purpose of Chapter 3 is to demonstrate that even simple statistics, when used properly, can be very useful and beneficial. Interestingly, some, if not most, of descriptive statistics are either intuitive or commonly utilized in everyday life. However, as the first three chapters reveal, it is useful to demonstrate their power using examples from economics.
Chapter 4 introduces some commonly used distribution functions. These will most likely be new for you. These distribution functions are used as yardsticks to measure different statistics to determine if they behave as expected, or they should be considered unusual outcomes. Interestingly, when we sample, the resulting sample statistics such as sample mean, follow certain distribution functions. These important properties are discussed in Chapter 5, titled Sampling Distribution of Sample Statistics. Chapter 6 formally discusses estimation. Point estimation uses sample statistics directly, while confidence interval provides a range that covers population parameter with a desired level of confidence. Finally, Chapter 7 combines materials from Chapters 4 through 6 to perform statistical inference. Statistical inference is a probabilistic statement about the expected outcome of a study.
A volume like the present work is not sufficient to do justice to the subject. Every aspect of science is touched by statistics, to some extent. Therefore, specialty books about applications of statistics in different fields abound.