Ch. 2: Economic Tools 1. Micro approach –A. Choices –B. Constraints –C. Maximization (best choice) –D. Comparative statics 2. Utility Function 3. Totals and marginals 4. Supply & demand 5. Empirical methods –A. Regression analysis –B. Natural experiments

Micro Approach Choices: –have a goal (maximize something); –chooser has info and is rational; –choice has limits (budget constraint); –constrained maximization. Variables: –Endogenous (dependent); –Exogenous (independent). Theory: Posits relationship between dependent variable and independent variable(s). Functional form: X* = F(Z). Marginal decision-making. Best choice (solution): x*

Comparative Statics From earlier: X* = F(Z). Theory Predicts:  X*/  Z  0. If Z , this causes  X*. Relate to Law of Demand: –Simple: D = F(P):  P   D –Full version: D = F(price of X, other P, Preferences) Microeconomics: –Economic actors choose endogenous variables to maximize something. –Best choice: satisfies above total condition and a marginal condition. –Theory predicts how best choice changes when exog variables change. –Predictions: comparative static results; use to assess theory.

Functions Functions: convenient way to show what depends on what. –Demand function. –Could write as: D(P). Utility function: –U = U(X,Y). –X & Y are two goods; –Ordinal utility; –utility theory a theory of choice; –rational choice model.

How Make Best Choice Ex.: With 2 goods X & Y. Utility = U = U(X,Y). 1. Maximize total utility. 2. Equate utility on the margin –Marginal decision-making. –On the margin: as  X and  Y (or, as trade off Y for X), utility from that last extra X is equal to utility from that last given up Y; –So:  U/  X =  U/  Y. Or: MU X = MU Y

Supply and Demand Law of Demand –Qd = F(P; other P; Y; Pref) –Negative slope. Law of Supply –Qs = F(P; input prices; techn.) –Positive slope. Equilibrium –Occurs naturally. –Excess supply –Excess demand Change in ceteris paribus factor –Shift demand curve. –Shift supply curve.

Empirical Methods: Regression Methods: –Testing predictions of a specific theory. –Ex: Law of D:  P of X   D for X; qualitative prediction; quantitative prediction. Regression Analysis: statistical technique for estimating relationship between two or more variables. –One dependent variable and one or more independent variables. –Example: prediction from Law of D. –Write as regression equation: –Qd =  +  P +   Qd/  P =  ; (1/  is slope of D curve)  is value of Qd when P = 0; (intercept).  is random error term: See picture.

More on Regression Multiple regression: adds in more independent variables (usually these are ceteris paribus factors). Book calls these more Xs. Want to add in as many relevant Xs as we can (so now have  1,  2, etc.) Qd =  +  1P +  2Y +  3Pref +  –Where Y is income. If leave out an important X, then the estimated values of  and  are “bad.”

More on Regression Types of variables: –Continuous –Dummy: yes/no. –Natural logs: ln(wage) so  on education is a percentage returns to education. –Example: If B 1 = 0.05 in below: Lnw = B 0 + B 1 *Education Then if  Educ by 1 year, returns to education is 5%. Sampling error: to what group do the results relate? R-squared: what percent of variation across individuals in dependent variable is explained by the regression?

Experiments Scientific experiment: testing effectiveness of a new drug. –uses double-blind random assignment. (individuals assigned to get real drug or a placebo and doctor/individual do not know which it is). –Example of real experiment in labor economics: effect of early re- employment bonus on duration of unemployment insurance. Natural experiment: –Useful because economists usually cannot conduct real experiments. –when something like real experiment occurs in real world. –Mariel boatlift of 1980: impact of immigration on local labor markets. This immigration surge not related to local labor market conditions as usually true.