Game Theory - How I Would Define It
Game theory considers economic scenarios with the following properties:
•Two or more 'economic actors' - that is, two or more firms, individuals, political parties, etc.
•Each individual economic actor has a set of decisions they can make - what price to charge, how much to save, whether to move right or left, etc.
•Each individual economic actor has a 'goal' or payoff, such as profit maximization, maximize happiness, minimize loss, etc.
•The decisions made by one economic actor not only affects her payoff, but the payoff of one or more other economic actors
Game theory is a particularly useful tool for understanding why firms and individuals make the decisions they do, and how the decisions made by one individual affects others.
Game Theory - Some Alternative Points of View
Here are a few definitions that should help you answer the question "What are Game Theory and Bargaining Theory?":
The best definition of Game Theory I've seen is contained in the Economists "Dictionary of Economics". In part it states: "Much of economic theory is concerned with the process and conditions under which individuals or firms maximize their own benefits or minimize their own costs in markets in which their individual actions do not materially influence others (perfect competition). There are, however, many cases in which economic decisions are made in situations of conflict, where one party's actions induces a reaction from others. An example is wage bargaining between employers and unions. A more simple case is the of duopoly, in which the price set by one seller will be based on his view of that set by the other in reply. The mathematical theory of games has been applied to economics to help elucidate problems of this kind."
The Game Theory Society
A group of academic game theorists called the Game Theory Society defines game theory as: "Game theory studies strategic interaction in competitive and cooperative environments. Only fifty years old, it has already revolutionized economics, and is spreading rapidly to a wide variety of fields. It develops general mathematical formulas and algorithms to identify optimal strategies and to predict the outcome of interactions."
Bargaining Theory and Evolutionary Game Theory
Bargaining Theory deals with a more narrow class of problems than Game Theory, but many economists tend to use the terms interchangably. Both bargaining theory and game theory can be broken into various subfields, most notably evolutionary game theory. The Economics Glossary gives the following definition for Evolutionary Game Theory: "Evolutionary game theory describes game models in which players choose their strategies through a trial-and-error process in which they learn over time that some strategies work better than others."
Saturday, December 12, 2009
What Is Experimental Economics?
Most applied work in economics is used by collecting 'real world' data from decisions made outside of the economist's control. Suppose we wanted to know how changes in the minimum wage impacts the unemployment rate for young workers. We would look for states, provinces or countries where there was a change in the minimum wage and then examine unemployment data both prior to the minimum wage law and after the minimum wage law. However, there is no possible way to be absolutely certain that any change in the unemployment rate was caused by the change in the minimum wage. We cannot be sure of the causal relationship because there are so many outside factors going on in an economy. Perhaps the unemployment rate rose, not due to the minimum wage cut, but because the Federal Reserve cut interest rates or because of a spike in oil prices.
Experimental economics attempts to eliminate these outside factors by running controlled experiments inside of a laboratory setting. While it is impossible to run a controlled experiment of one million minimum wage workers, experimental economics can give us a better idea of how consumers and firms respond to incentives, which helps economists strengthen their models.
Experimental Economics - Alternative Perspectives
The Economist's "Dictionary of Economics" when it defines "empirical testing" as follows:
"In contrast to the physical sciences, it is rarely possible to conduct controlled experiments in economics, for example to see what would happen to exports if the exchange rate were reduced and all other variables in the international economy remained unchanged. It is possible, however, to test hypotheses with facts, for example to verify historically the extent to which changes in the exchange rate have been associated with changes in exports. In fact, experiments can be and are carried out in economics, for example labratory simulations of market behaviour, and there is a growing interest in experimental economics as an adjunct to historical empiricism and theoretical work."
Experimental Economics - Conducting Economic Experiments
One such group conducting experimental economics is E-Economics. They define what they do as follows: "We conduct online experiments into economic decision making. How do people bid in auctions? What better way than to invite them to participate in a controlled auction with real money and observe their behavior? How do people bargain? We can watch real bargaining to arrive at an answer. Experimental Economics is a field of study that requires economists to confront reality - and that reality is represented by the actual decisions made by our subjects."
Experimental Economics - In Conclusion
While experimental economists study similar phenomena as traditional economists, they differ in methodology. Instead of looking at what has happened in the past, experimental economists take a laboratory approach similar to that used in many of the other social sciences.
Experimental economics attempts to eliminate these outside factors by running controlled experiments inside of a laboratory setting. While it is impossible to run a controlled experiment of one million minimum wage workers, experimental economics can give us a better idea of how consumers and firms respond to incentives, which helps economists strengthen their models.
Experimental Economics - Alternative Perspectives
The Economist's "Dictionary of Economics" when it defines "empirical testing" as follows:
"In contrast to the physical sciences, it is rarely possible to conduct controlled experiments in economics, for example to see what would happen to exports if the exchange rate were reduced and all other variables in the international economy remained unchanged. It is possible, however, to test hypotheses with facts, for example to verify historically the extent to which changes in the exchange rate have been associated with changes in exports. In fact, experiments can be and are carried out in economics, for example labratory simulations of market behaviour, and there is a growing interest in experimental economics as an adjunct to historical empiricism and theoretical work."
Experimental Economics - Conducting Economic Experiments
One such group conducting experimental economics is E-Economics. They define what they do as follows: "We conduct online experiments into economic decision making. How do people bid in auctions? What better way than to invite them to participate in a controlled auction with real money and observe their behavior? How do people bargain? We can watch real bargaining to arrive at an answer. Experimental Economics is a field of study that requires economists to confront reality - and that reality is represented by the actual decisions made by our subjects."
Experimental Economics - In Conclusion
While experimental economists study similar phenomena as traditional economists, they differ in methodology. Instead of looking at what has happened in the past, experimental economists take a laboratory approach similar to that used in many of the other social sciences.
What Is Econometrics?
Econometrics is concerned with the tasks of developing and applying quantitative or statistical methods to the study and elucidation of economic principles.Econometrics combines economic theory with statistics to analyze and test economic relationships. Theoretical econometrics considers questions about the statistical properties of estimators and tests, while applied econometrics is concerned with the application of econometric methods to assess economic theories. Although the first known use of the term "econometrics" was by Paweł Ciompa in 1910, Ragnar Frisch is given credit for coining the term in the sense that it is used today.
Although many econometric methods represent applications of standard statistical models, there are some special features of economic data that distinguish econometrics from other branches of statistics. Economic data are generally observational, rather than being derived from controlled experiments. Because the individual units in an economy interact with each other, the observed data tend to reflect complex economic equilibrium conditions rather than simple behavioral relationships based on preferences or technology. Consequently, the field of econometrics has developed methods for identification and estimation of simultaneous equation models. These methods allow researchers to make causal inferences in the absence of controlled experiments. Early work in econometrics focused on time-series data, but now econometrics also fully covers cross-sectional and panel data.
Purpose
The two main purposes of econometrics are to give empirical content to economic theory and to subject economic theory to potentially falsifying tests.
For example, consider one of the basic relationships in economics: the relationship between the price of a commodity and the quantities of that commodity that people wish to purchase at each price (the demand relationship). According to economic theory, an increase in the price would lead to a decrease in the quantity demanded, holding other relevant variables constant to isolate the relationship of interest. A mathematical equation can be written that describes the relationship between quantity, price, other demand variables like income, and a random term ε to reflect simplification and imprecision of the theoretical model:
Regression analysis could be used to estimate the unknown parameters β0, β1, and β2 in the relationship, using data on price, income, and quantity. The model could then be tested for statistical significance as to whether an increase in price is associated with a decrease in the quantity, as hypothesized: β1 < 0.
There are complications even in this simple example, and it is often easy to mistake statistical significance with economic significance. Statistical significance is neither necessary nor sufficient for economic significance.In order to estimate the theoretical demand relationship, the observations in the data set must be price and quantity pairs that are collected along a demand schedule that is stable. If those assumptions are not satisfied, a more sophisticated model or econometric method may be necessary to derive reliable estimates and tests.
Methods
One of the fundamental statistical methods used by econometricians is regression analysis. For an overview of a linear implementation of this framework, see linear regression. Regression methods are important in econometrics because economists typically cannot use controlled experiments. Econometricians often seek illuminating natural experiments in the absence of evidence from controlled experiments. Observational data may be subject to omitted-variable bias and a list of other problems that must be addressed using causal analysis of simultaneous equation models.
Data sets to which econometric analyses are applied can be classified as time-series data, cross-sectional data, panel data, and multidimensional panel data. Time-series data sets contain observations over time; for example, inflation over the course of several years. Cross-sectional data sets contain observations at a single point in time; for example, many individuals' incomes in a given year. Panel data sets contain both time-series and cross-sectional observations. Multi-dimensional panel data sets contain observations across time, cross-sectionally, and across some third dimension. For example, the Survey of Professional Forecasters contains forecasts for many forecasters (cross-sectional observations), at many points in time (time series observations), and at multiple forecast horizons (a third dimension).
Econometric analysis may also be classified on the basis of the number of relationships modeled. Single equation methods model a single variable (the dependent variable) as a function of one or more explanatory (or independent) variables. In many econometric contexts, such single equation methods may not recover the effect desired, or may produce estimates with poor statistical properties. Simultaneous equation methods have been developed as one means of addressing these problems. Many of these methods use variants of instrumental variable to make estimates.
Example
A simple example of a relationship in econometrics from the field of labor economics is:
Economic theory says that the natural logarithm of a person's wage is a linear function of (among other things) the number of years of education that person has acquired. The parameter β1 measures the increase in the natural log of the wage attributable to one more year of education. It should be noted that by using the natural log we have moved away from a simple linear regression model and are now using a non linear model, in this case, a semi-log y model. The term ε is a random variable representing all other factors that may have direct influence on wage. The econometric goal is to estimate the parameters, β0 and β1 under specific assumptions about the random variable ε. For example, if ε is uncorrelated with years of education, then the equation can be estimated with ordinary least squares.
If the researcher could randomly assign people to different levels of education, the data set thus generated would allow estimation of the effect of changes in years of education on wages. In reality, those experiments cannot be conducted. Instead, the econometrician observes the years of education of and the wages paid to people who differ along many dimensions. Given this kind of data, the estimated coefficient on Years of Education in the equation above reflects both the effect of education on wages and the effect of other variables on wages, if those other variables were correlated with education. For example, people born in certain places may have higher wages and higher levels of education. Unless the econometrician controls for place of birth in the above equation, the effect of birthplace on wages may be falsely attributed to the effect of education on wages.
Although many econometric methods represent applications of standard statistical models, there are some special features of economic data that distinguish econometrics from other branches of statistics. Economic data are generally observational, rather than being derived from controlled experiments. Because the individual units in an economy interact with each other, the observed data tend to reflect complex economic equilibrium conditions rather than simple behavioral relationships based on preferences or technology. Consequently, the field of econometrics has developed methods for identification and estimation of simultaneous equation models. These methods allow researchers to make causal inferences in the absence of controlled experiments. Early work in econometrics focused on time-series data, but now econometrics also fully covers cross-sectional and panel data.
Purpose
The two main purposes of econometrics are to give empirical content to economic theory and to subject economic theory to potentially falsifying tests.
For example, consider one of the basic relationships in economics: the relationship between the price of a commodity and the quantities of that commodity that people wish to purchase at each price (the demand relationship). According to economic theory, an increase in the price would lead to a decrease in the quantity demanded, holding other relevant variables constant to isolate the relationship of interest. A mathematical equation can be written that describes the relationship between quantity, price, other demand variables like income, and a random term ε to reflect simplification and imprecision of the theoretical model:
Regression analysis could be used to estimate the unknown parameters β0, β1, and β2 in the relationship, using data on price, income, and quantity. The model could then be tested for statistical significance as to whether an increase in price is associated with a decrease in the quantity, as hypothesized: β1 < 0.
There are complications even in this simple example, and it is often easy to mistake statistical significance with economic significance. Statistical significance is neither necessary nor sufficient for economic significance.In order to estimate the theoretical demand relationship, the observations in the data set must be price and quantity pairs that are collected along a demand schedule that is stable. If those assumptions are not satisfied, a more sophisticated model or econometric method may be necessary to derive reliable estimates and tests.
Methods
One of the fundamental statistical methods used by econometricians is regression analysis. For an overview of a linear implementation of this framework, see linear regression. Regression methods are important in econometrics because economists typically cannot use controlled experiments. Econometricians often seek illuminating natural experiments in the absence of evidence from controlled experiments. Observational data may be subject to omitted-variable bias and a list of other problems that must be addressed using causal analysis of simultaneous equation models.
Data sets to which econometric analyses are applied can be classified as time-series data, cross-sectional data, panel data, and multidimensional panel data. Time-series data sets contain observations over time; for example, inflation over the course of several years. Cross-sectional data sets contain observations at a single point in time; for example, many individuals' incomes in a given year. Panel data sets contain both time-series and cross-sectional observations. Multi-dimensional panel data sets contain observations across time, cross-sectionally, and across some third dimension. For example, the Survey of Professional Forecasters contains forecasts for many forecasters (cross-sectional observations), at many points in time (time series observations), and at multiple forecast horizons (a third dimension).
Econometric analysis may also be classified on the basis of the number of relationships modeled. Single equation methods model a single variable (the dependent variable) as a function of one or more explanatory (or independent) variables. In many econometric contexts, such single equation methods may not recover the effect desired, or may produce estimates with poor statistical properties. Simultaneous equation methods have been developed as one means of addressing these problems. Many of these methods use variants of instrumental variable to make estimates.
Example
A simple example of a relationship in econometrics from the field of labor economics is:
Economic theory says that the natural logarithm of a person's wage is a linear function of (among other things) the number of years of education that person has acquired. The parameter β1 measures the increase in the natural log of the wage attributable to one more year of education. It should be noted that by using the natural log we have moved away from a simple linear regression model and are now using a non linear model, in this case, a semi-log y model. The term ε is a random variable representing all other factors that may have direct influence on wage. The econometric goal is to estimate the parameters, β0 and β1 under specific assumptions about the random variable ε. For example, if ε is uncorrelated with years of education, then the equation can be estimated with ordinary least squares.
If the researcher could randomly assign people to different levels of education, the data set thus generated would allow estimation of the effect of changes in years of education on wages. In reality, those experiments cannot be conducted. Instead, the econometrician observes the years of education of and the wages paid to people who differ along many dimensions. Given this kind of data, the estimated coefficient on Years of Education in the equation above reflects both the effect of education on wages and the effect of other variables on wages, if those other variables were correlated with education. For example, people born in certain places may have higher wages and higher levels of education. Unless the econometrician controls for place of birth in the above equation, the effect of birthplace on wages may be falsely attributed to the effect of education on wages.
Market Distortions
Market distortions by governments can come in many varieties, such as taxes and competition law. However, we will concern ourselves with the two most basic forms of market distortions, which are as follows:
•Price Restrictions
•Quantity Restrictions
Market Distortions - Price Restrictions
There are two types of price restrictions we see in government policy:
•A price ceiling - A maximum price that can be charged by a company for a product or service.
•A price floor - A minimum price that can be charged by a company for a product or service.
In order to make the discussion interesting, we will only consider case where the restriction has some 'bite'. A restriction making it illegal to charge more than 1 million dollars for a cup of coffee is not a particularly interesting scenario to analyze.
Price Ceilings - Maximum Prices
Two of the most common types of price ceilings are as follows:
•Rent Control
•Oil in the 1970s
Consider the following Demand and Supply schedules for oil:
Demand
90 cents - 125M gallons
80 cents - 127M gallons
70 cents - 129M gallons
60 cents - 131M gallons
50 cents - 133M gallons
Supply
90 cents - 150M gallons
80 cents - 142M gallons
70 cents - 129M gallons
60 cents - 121M gallons
50 cents - 108M gallons
Without any price controls put into place, thr equilibrium price would be 70 cents and 129M gallons would be sold.
Now suppose the government places a maximum price of 60 cents on oil.
At this price, we have a quantity demanded of 131M gallons but a quantity supplied of only 121M gallons - a shortage of 10M gallons. In the 1970s this manifested itself with gas stations 'running out' of gasoline and long lines to buy gas from the stations with remaining stations.
Minimum Price Perspective
These are most commonly encountered as minimum wages for low skilled labor.
To keep the analysis simple, we will use a similar example to the one above. What if, instead the government had set a minimum price of 80 cents a gallon. In that case the quantity supplied of gas is 142M gallons and the quantity demanded is 127M - a surplus of 15M gallons. There would be more gas available on the market than consumers would want to buy. In the minimum wage case, it would mean there are more individuals looking for minimum wage jobs than there are jobs available.
•Price Restrictions
•Quantity Restrictions
Market Distortions - Price Restrictions
There are two types of price restrictions we see in government policy:
•A price ceiling - A maximum price that can be charged by a company for a product or service.
•A price floor - A minimum price that can be charged by a company for a product or service.
In order to make the discussion interesting, we will only consider case where the restriction has some 'bite'. A restriction making it illegal to charge more than 1 million dollars for a cup of coffee is not a particularly interesting scenario to analyze.
Price Ceilings - Maximum Prices
Two of the most common types of price ceilings are as follows:
•Rent Control
•Oil in the 1970s
Consider the following Demand and Supply schedules for oil:
Demand
90 cents - 125M gallons
80 cents - 127M gallons
70 cents - 129M gallons
60 cents - 131M gallons
50 cents - 133M gallons
Supply
90 cents - 150M gallons
80 cents - 142M gallons
70 cents - 129M gallons
60 cents - 121M gallons
50 cents - 108M gallons
Without any price controls put into place, thr equilibrium price would be 70 cents and 129M gallons would be sold.
Now suppose the government places a maximum price of 60 cents on oil.
At this price, we have a quantity demanded of 131M gallons but a quantity supplied of only 121M gallons - a shortage of 10M gallons. In the 1970s this manifested itself with gas stations 'running out' of gasoline and long lines to buy gas from the stations with remaining stations.
Minimum Price Perspective
These are most commonly encountered as minimum wages for low skilled labor.
To keep the analysis simple, we will use a similar example to the one above. What if, instead the government had set a minimum price of 80 cents a gallon. In that case the quantity supplied of gas is 142M gallons and the quantity demanded is 127M - a surplus of 15M gallons. There would be more gas available on the market than consumers would want to buy. In the minimum wage case, it would mean there are more individuals looking for minimum wage jobs than there are jobs available.
Supply - The Economics of Supply
What Supply Is:
Economists have a very precise definition of supply. Economists describe supply as the relationship between the quantity of a good or service consumers will offer for sale and the price charged for that good. More precisely and formally the Economics Glossary defines supply as "the total quantity of a good or service that is available for purchase at a given price."
What Supply Is Not:
Supply is not simply the number of an item a shopkeeper has on the shelf, such as '5 oranges' or '17 pairs of boots', because supply represents the entire relationship between the quantity available for sale and all possible prices charged for that good. The specific quantity desired to sell of a good at a given price is known as the quantity supplied. Typically a time period is also given when describing quantity supplied.
Supply - Examples of Quantity Supplied:
When the price of an orange is 65 cents the quantity supplied is 300 oranges a week.
If the price of copper falls from $1.75/lb to $1.65/lb, the quantity supplied by a mining company will fall from 45 tons a day to 42 tons a day.
Supply Schedules:
A supply schedule is a table which lists the possible prices for a good and service and the associated quantity supplied. The supply schedule for oranges could look (in part) as follows:
75 cents - 470 oranges a week
70 cents - 400 oranges a week
65 cents - 320 oranges a week
60 cents - 200 oranges a week
Supply Curves:
A supply curve is simply a supply schedule presented in graphical form. The standard presentation of a supply curve has price given on the Y-axis and quantity supplied on the X-axis.
The Law of Supply:
The law of supply states that, ceteribus paribus (latin for 'assuming all else is held constant'), the quantity supplied for a good rises as the price rises. In other words, the quantity demanded and price are positively related. Supply curves are drawn as 'upward sloping' due to this positive relationship between price and quantity supplied. Note: There are theoretical instances where the law of supply might not hold, though these are rarely, if ever, seen in the real world.
Price Elasticity of Supply:
The price elasticity of supply represents how sensitive quantity supplied is to changes in price.
Economists have a very precise definition of supply. Economists describe supply as the relationship between the quantity of a good or service consumers will offer for sale and the price charged for that good. More precisely and formally the Economics Glossary defines supply as "the total quantity of a good or service that is available for purchase at a given price."
What Supply Is Not:
Supply is not simply the number of an item a shopkeeper has on the shelf, such as '5 oranges' or '17 pairs of boots', because supply represents the entire relationship between the quantity available for sale and all possible prices charged for that good. The specific quantity desired to sell of a good at a given price is known as the quantity supplied. Typically a time period is also given when describing quantity supplied.
Supply - Examples of Quantity Supplied:
When the price of an orange is 65 cents the quantity supplied is 300 oranges a week.
If the price of copper falls from $1.75/lb to $1.65/lb, the quantity supplied by a mining company will fall from 45 tons a day to 42 tons a day.
Supply Schedules:
A supply schedule is a table which lists the possible prices for a good and service and the associated quantity supplied. The supply schedule for oranges could look (in part) as follows:
75 cents - 470 oranges a week
70 cents - 400 oranges a week
65 cents - 320 oranges a week
60 cents - 200 oranges a week
Supply Curves:
A supply curve is simply a supply schedule presented in graphical form. The standard presentation of a supply curve has price given on the Y-axis and quantity supplied on the X-axis.
The Law of Supply:
The law of supply states that, ceteribus paribus (latin for 'assuming all else is held constant'), the quantity supplied for a good rises as the price rises. In other words, the quantity demanded and price are positively related. Supply curves are drawn as 'upward sloping' due to this positive relationship between price and quantity supplied. Note: There are theoretical instances where the law of supply might not hold, though these are rarely, if ever, seen in the real world.
Price Elasticity of Supply:
The price elasticity of supply represents how sensitive quantity supplied is to changes in price.
Demand - The Economics of Demand
Economists have a very precise definition of demand. For them demand is the relationship between the quantity of a good or service consumers will purchase and the price charged for that good. More precisely and formally the Economics Glossary defines demand as "the want or desire to possess a good or service with the necessary goods, services, or financial instruments necessary to make a legal transaction for those goods or services."
What Demand Is Not:
Demand is not simply a quantity consumers wish to purchase such as '5 oranges' or '17 shares of Microsoft', because demand represents the entire relationship between quantity desired of a good and all possible prices charged for that good. The specific quantity desired for a good at a given price is known as the quantity demanded. Typically a time period is also given when describing quantity demanded.
Demand - Examples of Quantity Demanded:
When the price of an orange is 65 cents the quantity demanded is 300 oranges a week.
If the local Starbucks lowers their price of a tall coffee from $1.75 to $1.65, the quantity demanded will rise from 45 coffees an hour to 48 coffees an hour.
Demand Schedules:
A demand schedule is a table which lists the possible prices for a good and service and the associated quantity demanded. The demand schedule for oranges could look (in part) as follows:
75 cents - 270 oranges a week
70 cents - 300 oranges a week
65 cents - 320 oranges a week
60 cents - 400 oranges a week
Demand Curves:
A demand curve is simply a demand schedule presented in graphical form. The standard presentation of a demand curve has price given on the Y-axis and quantity demanded on the X-axis.
The Law of Demand:
The law of demand states that, ceteribus paribus (latin for 'assuming all else is held constant'), the quantity demanded for a good rises as the price falls. In other words, the quantity demanded and price are inversely related. Demand curves are drawn as 'downard sloping' due to this inverse relationship between price and quantity demanded.
Price Elasticity of Demand:
The price elasticity of demand represents how sensitive quantity demanded is to changes in price. Further information is given in the article Price Elasticity of Demand.
What Demand Is Not:
Demand is not simply a quantity consumers wish to purchase such as '5 oranges' or '17 shares of Microsoft', because demand represents the entire relationship between quantity desired of a good and all possible prices charged for that good. The specific quantity desired for a good at a given price is known as the quantity demanded. Typically a time period is also given when describing quantity demanded.
Demand - Examples of Quantity Demanded:
When the price of an orange is 65 cents the quantity demanded is 300 oranges a week.
If the local Starbucks lowers their price of a tall coffee from $1.75 to $1.65, the quantity demanded will rise from 45 coffees an hour to 48 coffees an hour.
Demand Schedules:
A demand schedule is a table which lists the possible prices for a good and service and the associated quantity demanded. The demand schedule for oranges could look (in part) as follows:
75 cents - 270 oranges a week
70 cents - 300 oranges a week
65 cents - 320 oranges a week
60 cents - 400 oranges a week
Demand Curves:
A demand curve is simply a demand schedule presented in graphical form. The standard presentation of a demand curve has price given on the Y-axis and quantity demanded on the X-axis.
The Law of Demand:
The law of demand states that, ceteribus paribus (latin for 'assuming all else is held constant'), the quantity demanded for a good rises as the price falls. In other words, the quantity demanded and price are inversely related. Demand curves are drawn as 'downard sloping' due to this inverse relationship between price and quantity demanded.
Price Elasticity of Demand:
The price elasticity of demand represents how sensitive quantity demanded is to changes in price. Further information is given in the article Price Elasticity of Demand.
Supply and Demand - The Basics
Supply and Demand analysis is relatively straight-forward once the terminology is understood. The important terms are as follows:
- Price
- Quantity
- Demand and Demand Curve
- Quantity Demanded
- Supply and Supply Curve
- Quantity Supplied
- Equilibrium
- Surplus
- Shortage
Basic supply and demand analysis is done one of two ways - either graphically or numerically. If done graphically, it is important to set up the graph in the 'standard' form.
The Graph
Traditionally economists have placed price (P) on the Y-axis and quantity (Q), as in quantity consumed or quantity purchased/sold on the X-axis. An easy way to remember how to label each axis is to remember 'P then Q', since the price (P) label occurs above and to the left of the quantity (Q) label. Next there are two curves to understand - the demand curve and the supply curve.
The Demand Curve
A demand curve is simply a demand function or demand schedule represented graphically. Note that demand is not simply a number - it is a one-to-one relationship between prices and quantities. The following is an example of a demand schedule:
Demand Schedule
$10 - 200 units
$20 - 145 units
$30 - 110 units
$40 - 100 units
Note that demand is not simply a number such as '145'. The quantity level associated with a particular price (such as 145 units @ $20) is known as a quantity demanded.
The Supply Curve
Supply curves, supply functions and supply schedules are not conceptually different than their demand counterparts. Once again, supply is never represented as a number. When considering the problem from the point of view of the seller the quantity level associated with a particular price is known as quantity supplied.
Equilibrium
Equilibrium occurs when at a specific price P', quantity demanded = quantity supplied. In other words, if there is some price where the amount buyers wish to buy is the same as the amount sellers wish to sell, then equilibrium occurs. Consider the following demand and supply schedules:
Demand Schedule
$10 - 200 units
$20 - 145 units
$30 - 110 units
$40 - 100 units
Supply Schedule
$10 - 100 units
$20 - 145 units
$30 - 180 units
$40 - 200 units
At a price of $20, consumers wish to purchase 145 units and sellers which to provide 145 units. Thus quantity supplied = quantity demanded and we have an equilibrium of ($20, 145 units)
Surplus
A surplus, from the supply and demand perspective, is a situation where, at the current price, quantity supplied exceeds quantity demanded. Consider the demand and supply schedules above. At a price of $30, quantity supplied is 180 units and quantity demanded is 110 units, leading to a surplus of 70 units (180-110=70). Our market, then, is out of equilibrium. The current price is unsustainable and must be lowered in order for the market to reach equilibrium.
Shortage
A shortage is simply the flip-side of a surplys. It is a situation where, at the current price, quantity demanded exceeds quantity supplied. At a price of $10, quantity supplied is 100 units and quantity demanded is 200 units, leading to a shortage of 100 units (200-100=100). Our market, then, is out of equilibrium. The current price is unsustainable and must be raised in order for the market to reach equilibrium.
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