M. Muniagurria Econ 364 Microeconomics Handout (Part 1) I. TECHNOLOGY (1) : Production Function, Marginal Productivity of Inputs, Isoquants
Case of One Input: L (Labor): q = f (L) Let q equal output so the production function relates L to q. (How much out
Production Function, Average and Marginal Products, Returns to Scale, Change of Variables Production Function: links inputs to amont of output. Assume we have 2 inputs: Labor (L) and Capital (K), and we use Y for output . Then we write: Y = F (L , K) , wh
M. Muniagurria MICROECONOMIC HANDOUT (Part 2) V. TWO SECTOR ECONOMY: PRODUCTION SIDE
Case of one input: L (Labor) - Assume 2 produced goods: M & F - Fixed amount of labor: L (Needs to be allocated to the 2 sectors) - Firms maximize profits taking pric
Patterns of Trade in H-O model. (The H-O Theorem)
1.) Construct 3 pp! for -a country.
It will look like this:
'Why the increasing opportunity costshape?
Lets think abodt this;
' A nice motivating picture of the H0 model is the following.
' 3 2
Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data. One variable is considered to
be an explanatory variable (or regressor), and the other is considered to be a depend
Handout on HK calculation
Weil : Table 6.2 and Figures 6.9 and 6.10
According to Table 6.2 labor has seven different skill levels: Raw Labor (zero schooling) and 4 -8-10-12-14-16
years of schooling.
Let wi be the wage paid to a worker with i years of scho
Ricardian Model Example used in lecture :
Consider a model with two countries (Home and Foreign (*) , two goods (Textiles and Soy) and one input
(Labor) . The production technologies are specified by the following unit labor requirements:
Specific Factors Models: Argument to show that an increase in the endowment of the specific factor decreases the real return (per unit) of both specific factors. Assumptions: (1) 3 factors : labor (L, mobile) , land (T, specific to food sector ), capital
Instrumental variables - Human Capital
Recall from previous discussion the linear regression model
y = 0 + 1 x +
with its main assumption: E(|x) = E(u) = 0. This assumption is telling us that for any g