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A government which robs Peter to pay Paul can always depend on the support of Paul. George B. Shaw	The Snarr Institute All instructional videos that are available on The Snarr Institute are organized below by course: Principles of Macroeconomics, Business Quantitative Methods (statistics), and Labor Economics. An example is shown below. In this video, I analyze the economics of Hurricane Katrina (at minute 0:00), math teacher shortages (15:20), minimum wage hikes (22:30), immigration (26:50), and Lebron James's pay (31:00). My son can be heard in the background leaping off the couch.
	Principles of Macroeconomics
				minute
top a	Lecture 1 (1 of 3)—Demand & Supply basics
		Deriving Big Mac demand		1:40
		Deriving oil supply		12:10
		Law of demand and supply		23:54
	Lecture 1 (2 of 3)—Applying the law of demand & supply
		Economics of Hurricane Katrina		0:00
		Economics of the math teacher shortage		15:20
		Economics of minimum wage hikes		22:30
		Economics of immigration		26:50
		Economics of Lebron James pay		31:00
	Lecture 1 (3 of 3)—Applications of the PPF
		Economics of health care reform		1:20
		Economics of budget deficits		14:00
		A linear model of free trade		26:10
top a	Lecture 2 (1 of 3)—Inflation
		Consumer Price Index (CPI)		0:56
		Price level or cost of the market basket		3:35
		Inflation rate		11:10
		Cost of living adjustment (COLA)		16:34
		Real price		26:00
		Real wage		28:25
		Real interest rate		38:10
	Lecture 2 (2 of 3)—Economic growth
		Nominal GDP		0:23
		Real GDP		12:18
		Economic growth rate		15:24
		Stand of living		20:08
		Business cycle		23:15
		Determination of GDP and price level		24:20
		AD fluctuations and changes in price level and GDP		24:40
	Lecture 2 (3 of 3)—Unemployment
		Unemployment		0:00
		Natural rate of unemployment		3:34
		High unemployment in a recessionary gap		9:43
		Low unemployment in an inflationary gap		12:22
		Phillips Curve		19:36
		Augmented Phillips Curve and natural rate of unemployment		21:32
top a	Lecture 3—Consumer, Import, & Aggregate Expenditure
		Snarrian Keynesian consumption		7:24
		Snarrian consumption		16:18
		iMport function		20:22
		Snarrian aggregate expenditure		23:21
		Keynesian equilibrium		29:20
		Government spending multiplier		34:10
		Tax cut multiplier		38:10
top a	Lecture 4 (1 of 2)—Aggregate Demand & Potential GDP
		Deriving aggregate demand from aggregate expenditure		0:00
		Government spending stimulus		9:23
		Tax cut stimulus		11:40
		Full employment output and the economy's production function		15:35
	Lecture 4 (2 of 2)—Aggregate Supply & AD-AS-Yfe Equilibrium
		Snarrian aggregate supply		2:15
		Inflationary gap		14:04
		Recessionary gap		21:58
		Induced inflationary gap		29:00
top a	Lecture 5 (1 of 4)—Fiscal Policy
		Budget surplus/deficit		0:00
		Financing budget deficits with government bonds		1:28
		Mock Treasury auction		4:27
		Keynesian expansionary fiscal policy		8:39
	Lecture 5 (2 of 4)—Fiscal Policy
		Keynesian expansionary fiscal policy (continued)		0:00
		Keynesian restrictive fiscal policy		1:18
		Keynesian intervention and the crowding out effect		4:05
		New classical view of fiscal policy		4:53
		New classical view of fiscal policy and the loanable funds market		7:37
		Timing of Keynesian fiscal policy		9:24
	Lecture 5 (3 of 4)—Fiscal Policy
		Timing of Keynesian fiscal policy (continued)		0:00
		Applying the Augmented Phillips Curve		6:18
		Automatic stabilizers		9:30
		A modern synthesis of fiscal policy		11:49
		Supply-side fiscal policy		12:20
	Lecture 5 (4 of 4)—Fiscal Policy
		Laffer curve		0:00
		Supply-side tax cuts		3:55
		Percent of federal income tax paid by each income group		6:33
		History of US fiscal policy		10:20
		Economic growth vs. the growth rate of tax revenue		12:38
		Fiscal policy impotent?		13:37
top a	Lecture 6 (1 of 6)—Monetary Policy
		Definition and history of money (0:00),		0:00
		from the GOLD standard to the GOD standard (3:45),		3:45
		money demand and supply model (4:52)		4:52
	Lecture 6 (2 of 6)—Monetary Policy
		Definition of money		0:00
		Hyperinflation		2:17
			Hyperinflation video 1
			Hyperinflation video 2
			Hyperinflation video 3
		hyperinflation in Germany		6:20
		hyperinflation in Yugoslavia		8:50
		Banks		10:44
	Lecture 6 (3 of 6)—Monetary Policy
		Money creation via increased bank lending		0:00
		Money destruction via decreased bank lending		3:19
		The Snarrian model of demand in the federal funds market		5:00
		A numerical example of Snarrian demand in the federal funds market		11:57
	Lecture 6 (4 of 6)—Monetary Policy
		Supply in the federal funds market		0:00
		A numerical example of supply		3:45
		Federal funds market equilibrium in normal mode		4:49
		An example of federal funds market equilibrium in normal mode		6:40
		Raising the discount rate in normal mode		8:50
		Raising the required reserve ratio & emergency mode		10:16
		Raising the required reserve ratio and its effect on money supply		13:17
		Raising the required reserve ratio and its effect on the AD-AS model		13:30
	Lecture 6 (5 of 6)—Monetary Policy
		Open Market Purchase (OMP)		0:00
		Economics of an OMP in normal mode		1:59
		The effect of an OMP on money supply		3:15
		The effect of an OMP on the AD-AS model		3:38
		Applying monetary policy using the Augmented Phillips Curve		5:00
		Open Market Sale, summarized		6:32
		The financial crisis, interest on reserves, and crisis mode		7:43
		Unwinding the Fed's balance sheet		9:56
	Lecture 6 (6 of 6)—Keynes vs. Hayek
		Politicians side with Keynes not freedom's friend, Hayek		0:00
		A tree analogy of Keynes vs. Hayek		5:22
		The forest management analogy of Keynes vs. Hayek		6:35
		Economics of the Keynes vs. Hayek debate		7:26
		The flat Keynesian Bush-Obama recovery		8:58
		The Hayek trajectory that would not be		9:38
			Fear the Boom and Bust
			Fight of the Century: Keynes vs. Hayek Round Two
	Business Quantitative Methods
				minute
top a	Chapter 1—Introduction to Business Statistics			0:00
	Chapter 2—Tabular and Graphical data summary
		Categorical data		0:00
		Frequency distribution		1:00
		Relative frequency table		2:50
		Percent frequency		4:28
		Bar char		4:39
		Pie chart		6:01
		Quantitative data		7:48
		Frequency example		8:20
		Histogram		15:00
		Cumulative frequency distribution		18:39
		Ogive		23:07
		Crosstabulation (or pivot) tables		25:42
		Row and column percentages of a crosstabulation table		30:12
		Simpson's Paradox		36:40
		scatterplots		48:13
top a	Chapter 3 (1 of 3)—Central tendency
		Introduction to summarizing quantitative data		0:00
		Population mean		3:00
		Sample mean		6:27
		Median		10:09
		Mode		14:32
		Percentiles		17:05
		Quartiles		21:36
	Chapter 3 (2 of 3)—Variation
		Variation in data		0:00
		Range		2:18
		Interquartile range		2:42
		Population variance		3:46
		Biased sample variance		6:12
		Sample variance		7:43
		Population vs sample standard deviation		7:58
		Coefficient of variation		9:06
		Sample standard deviation example		10:46
	Chapter 3 (3 of 3)—Distributional Shape and Correlation
		introduction of distributional shape and correlation		0:00
		z-score		0:21
		Skewness and histograms		7:35
		Chebyshev's Theorem		15:47
		Empirical Rule		17:21
		Outliers		18:16
		Using z-scores to verify Chebyshev's Theorem or the Empirical Rule		19:28
		Correlation and covariance		22:18
		Population covariance vs sample covariance		23:09
		Scatterplot		22:41
		Correlation		28:54
top a	Chapter 4 (1 of 2)—Introduction to probability
		Definition of probability		0:00
		Experiment and sample space		2:07
		Product rule		5:05
		Tree diagram		6:54
		Counting rule 1-order matters, replacement		10:13
		Counting rule 2-order does not matter, no replacement		16:51
		Counting rule 3-order matters, no replacement		20:59
		Assigning probabilities		23:52
		Classical probabilities		24:33
		Relative frequency probabilities		27:29
		Subjective probabilities		30:30
		Events		36:14
	Chapter 4 (2 of 2)—More on probability
		Probability rules		0:00
		Complement rule		1:19
		Intersection of non-mutually exclusive events		2:55
		Intersection of mutually exclusive events		4:41
		Union of non-mutually exclusive events		6:47
		Union of mutually exclusive events		9:13
		Conditional probability		11:27
		Intersection of dependent events		15:05
		Independent events		17:17
		Intersection of independent events		17:44
		Bayes' Theorem and drug testing		20:25
top a	Chapter 5 (1 of 3)—Discrete Probability Distributions
		Discrete vs continuous random variables		0:00
		Numerical example of discrete PDF		6:09
		Expected value of discrete variable		8:53
		Standard deviation of discrete variable		10:46
		Uniform discrete PDF		13:18
		Binomial PDF		19:48
	Chapter 5 (2 of 3)—Discrete Probability Distributions
		Numerical example of Binomial PDF		0:00
		Using Binomial PDF to compute Pick Three Lottery		11:58
		Expected winnings of Pick Three Lottery		13:37
		Poisson PDF		14:40
	Chapter 5 (3 of 3)—Discrete Probability Distributions
		Hypergeometric vs Binomial		0:00
		Hypergeometric PDF		0:58
		Numerical example of Hypergeometric PDF		3:25
		Hypergeometric and PowerBall Lottery		10:55
		Expected winnings of PowerBall Lottery		14:21
top a	Chapter 6—Continuous Probability Distributions
		Continuous vs. Discrete Probability Distributions		0:00
		Uniform continuous distribution		2:53
		Expected value and variance of uniform continuous distribution		4:49
		Normal distribution		8:18
		Changing the mean of a normal distribution		10:00
		Changing the standard deviation of a normal distribution		10:32
		Standard normal distribution		11:07
		Verifying the Empirical Rule		12:42
		Using the standard normal table to find probabilities		13:41
		Using the standard normal table to find z values		27:43
		Standardizing non-standard normal distributions		31:48
		Numerical example of non-standard normal distribution		32:39
		Exponential probability distribution		39:31
top a	Chapter 7—Sampling Distribution
		Difference between a distribution and sampling distribution		0:00
		Sampling error		1:18
		Example: census versus a sample		2:25
		Population mean and proportion		3:24
		Population standard deviation		4:51
		Simple sampling		7:33
		Sample mean and proportion		11:31
		Sample standard deviation		12:33
		Comparing population parameters and sample statistics		15:12
		Sampling distribution of the mean		17:03
		Standard error of the mean		17:54
		Normality of the sampling distribution of the mean		19:10
		Numerical example of sampling distribution		19:50
		Numerical example of the Law of Large Numbers		26:01
		Sampling distribution of the proportion		28:27
		Intuition of the standard error of the proportion		29:12
		Normality of the sampling distribution of the proportion		34:27
		Numerical example of sampling distribution of the proportion		34:54
top a	Chapter 8—Confidence Intervals
		Intuition of confidence interval		0:00
		Interval Estimate (IE) of mu with sigma known		2:58
		Normality conditions of IE with sigma known		11:55
		Numerical example of IE with sigma known		12:36
		The sigma unknown case		16:01
		t distribution		16:45
		t and standard normal are identical for large samples		17:52
		Finding t values in the t table		19:22
		IE of mu with sigma unknown		21:31
		Numerical example of IE with sigma unknown		30:03
		Conditions needed to use t-distribution		32:19
		Necessary sample size for IE of mu		33:29
		IE of the proportion		36:46
		Normality condition		37:02
		Example of IE of proportion		38:34
		Necessary sample size for IE of proportion		40:39
top a	Chapter 9 (1 of 5)—Hypothesis testing involving one mean
		Definition of null and alternative hypotheses		2:34
		Example of developing null and alternative hypotheses		4:40
		Type I and II errors		6:54
		One-tail test with known variance		8:56
		Determining the p value with known variance		11:28
	Chapter 9 (2 of 5)—Hypothesis testing involving one mean
		Determining the p value (continued)		0:00
		Determining z critical values with known variance		4:23
	Chapter 9 (3 of 5)—Hypothesis testing involving one mean
		One tail hypothesis test involving one mean with known variance		0:00
		Two tail hypothesis test involving one mean with known variance		4:12
	Chapter 9 (4 of 5)—Hypothesis testing involving one mean
		Hypothesis testing with unknown variance		0:00
		A numerical example of hypothesis testing with unknown variance		2:58
		A numerical example of a two tail test with unknown variance		6:50
	Chapter 9 (5 of 5)—Hypothesis testing involving one mean
		Hypothesis testing with unknown variance		0:00
		Hypothesis tests about a population proportion		4:27
		A numerical example of a hypothesis tests about a population proportion		5:56
top a	Chapter 10 (1 of 5)—Hypothesis testing involving two means
		The sampling distribution of the mean		2:18
		The sampling distribution of the differences in means		4:08
		Confidence interval for the difference in means		4:40
		A numerical example with known variances		6:48
	Chapter 10 (2 of 5)—Hypothesis testing involving two means
		One tail test with known populations variances		0:00
		Deriving the test statistic from the confidence interval		3:08
		A numerical example of a one tail test, known variances		5:12
		Testing when population, unknown variances		10:30
		A numerical example a confidence interval, unknown variances		12:27
	Chapter 10 (3 of 5)—Hypothesis testing involving two means
		Example a confidence interval, unknown variances (continued)		0:00
		Example hypothesis testing, unknown variances		5:07
		Example matched samples, unknown variances		9:33
	Chapter 10 (4 of 5)—Hypothesis testing involving two means
		Example matched samples, unknown variances (continued)		0:00
		Sampling distribution of a difference in proportions		5:52
		Example of interval estimate of a difference in proportions		8:25
	Chapter 10 (5 of 5)—Hypothesis testing involving two means
		Example one tail test involving two proportions		0:00
		Example two tail test involving two proportions		4:53
top a	Chapter 11 (1 of 3)—Hypothesis testing involving one and two variances
		Sampling distribution of chi-square statistic		0:00
		Finding chi-square critical values		6:18
		Deriving the interval estimate of the population variance		10:02
		Example of confidence interval of the population variance		12:31
	Chapter 11 (2 of 3)—Hypothesis testing involving one and two variances
		Example of confidence interval of the population variance (continued)		0:00
		Hypothesis tests about a population variance		4:43
		Example of one tail test of population variance		5:28
		Hypothesis tests about two population variance		8:34
		Deriving F-stat		8:57
	Chapter 11 (3 of 3)—Hypothesis testing involving one and two variances
		Example of test involving two population variances		0:00
top a	Chapter 12 (1 of 4)—Goodness of fit and Independence tests
		Goodness of fit test		0:00
		Independence tests		8:00
	Chapter 12 (2 of 4)—Goodness of fit and Independence tests
		Independence tests - continued		0:00
		Goodness of fit test for Normal Distribution		8:14
	Chapter 12 (3 of 4)—Goodness of fit and Independence tests
		Example: Goodness of fit test for Normal Distribution		0:00
	Chapter 12 (4 of 4)—Goodness of fit and Independence tests
		Example: Goodness of fit test for Poisson Distribution		0:00
	Chapter 13 (1 of 3)—Analysis Of Variance (ANOVA)
		Use ANOVA to test equality of three or more means		0:00
		Completely randomized design		5:02
	Chapter 13 (2 of 3)—Analysis Of Variance (ANOVA)
		Completely randomized block design (continued)		0:00
		Example of completely randomized block design		6:30
	Chapter 13 (3 of 3)—Analysis Of Variance (ANOVA)
		Example of completely randomized block design (continued)		0:00
		Randomized block design		1:14
top a	Chapter 14 (1 of 5)—Simple Regression
		Regression equation vs. estimated regression equation		0:00
		Residual versus error		6:50
		Least squares method		12:21
		Slope coefficient vs. correlation		13:33
		Shortcut to compute slope coefficient		14:15
	Chapter 14 (2 of 5)—Simple Regression
		Simple regression example		0:00
		Sample means		0:52
		Sample variances		1:19
		Covariance		5:40
		Compute coefficient b1		7:03
		Compute the intercept		7:29
		Forming the predicted equation		8:09
		SST, SSR, SSE, R square, and correlation		10:37
	Chapter 14 (3 of 5)—Simple Regression
		Scatterplot y vs. x		0:00
		Graphing the predicted equation		0:38
		Computing R square, SST, SSE, and SSR		2:54
		Interpreting the R square		11:19
		Square root of R square equals the correlation		11:58
	Chapter 14 (4 of 5)—Simple Regression
		Assumptions of the error		0:00
		Verifying the assumptions		3:07
		Computing predicted values and residuals		5:00
		Standard deviation of the residuals		6:26
		Leverage		7:23
		Standardized residual plot		13:19
	Chapter 14 (5 of 5)—Simple Regression
		Testing for significance		0:00
		Distribution of coefficient b1		0:59
		Coefficient significance test		3:07
		Interpreting coefficient b1 - t test		4:05
		Test of model significance - F test		5:05
		ANOVA table		7:32
		Prediction and confidence intervals		8:38
top a	Chapter 15 (1 of 8)—Multiple Regression Analysis
		Definition of multiple regression 0:00		0:00
		Research question and specifying the model 3:35		3:35
		Building testable hypotheses using theory in graphs 6:33		6:33
		Building testable hypotheses using constrained utility optimization		11:45
	Chapter 15 (2 of 8)—Multiple Regression Analysis
		Descriptive statistics		0:00
		Scatterplots of y versus the independent variables		8:42
		Correlations & multicollinearity versus omitted variable bias		10:11
		Computing regression coefficients using sample statistics		13:39
	Chapter 15 (3 of 8)—Multiple Regression Analysis
		Computing regression coefficients using sample statistics		0:00
		Predicted equation, y-hat, residuals, SSE, SSR, & SST		2:03
		Coefficient significance test		5:49
		Omitted variable bias in simple regression		8:57
		Interpreting coefficient		9:42
		egression output table		12:12
		Interpreting R-square		14:12
	Chapter 15 (4 of 8)—Multiple Regression Analysis
		Computing regression coefficients using sample statistics		0:00
		Predicted equation, y-hats, residuals, SSE, SSR, and SST		3:01
		Coefficient significance test		7:55
		Interpreting the coefficient of a logged independent variable		10:51
		Omitted variable bias in simple regression		14:26
	Chapter 15 (5 of 8)—Multiple Regression Analysis
		Omitted variable bias in simple regression (continued)		0:00
		Why multicollinearity presents a problem for Excel		2:40
		Reducing omitted variable bias		3:41
		Forming the predicted equation		7:22
		Error assumptions		8:33
		Residuals are estimated errors and verifying the assumptions		9:00
		Test for zero mean of the error		11:00
	Chapter 15 (6 of 8)—Multiple Regression Analysis
		Testing for heteroscedasticity		0:00
		Testing for normality		6:42
	Chapter 15 (7 of 8)—Multiple Regression Analysis
		Testing for normally (continued)		0:00
		Autocorrelation test		5:10
		Testing for linearity		9:43
		F test of model significance		11:53
	Chapter 15 (8 of 8)—Multiple Regression Analysis
		F test of model significance (continued)		0:00
		Coefficient significance tests		4:04
		Interpreting coefficients of variables and logged variables		9:08
top a	Regression Analysis in Excel (1 of 7)
		Descriptive statistics & scatterplots of the variables		0:00
	Regression Analysis in Excel Part (2 of 7)
		Using Excel's regression package to estimate a linear equation		0:00
		Forming the predicted equation		2:20
		Computing predicted values and residuals by hand		3:01
		Interpreting R square		5:03
		Testing coefficients for significance		6:00
		Testing for model significance		7:12
		Residuals are used to test the error assumptions		7:39
	Regression Analysis in Excel (3 of 7)
		Test errors have a mean equal to zero (0:00		0:00
		Test linearity of model		0:51
	Regression Analysis in Excel (4 of 7)
		Testing errors have constant variance (homoscedasticity)		0:00
		White's test		2:43
	Regression Analysis in Excel Part (5 of 7)
		Test normality of the errors		0:00
	Regression Analysis in Excel (6 of 7)
		Testing autocorrelation in the errors		0:00
	Regression Analysis in Excel (7 of 7)
		Testing for significance in the coefficients and model		0:00
		Interpreting the coefficients		4:20
	Labor Economics
				minute
top a	Labor Economics lecture 1 (1 of 2)—Introduction
		Labor market participants		0:00
		Why is there a shortage of high school mathematics teachers?		1:12
		Minimum wage hikes and unemployment		10:30
		Immigration and its affect on wages		18:44
		Using multiple regression to confirm theory		30:18
		Model specification		32:34
		Using economic theory in graphs to build a testable hypothesis		35:05
		Using economic theory and calculus to build a testable hypothesis		41:49
		Summarizing data		45:05
		Correlations and multicollinearity		52:57
		Log variable transformation		56:03
		Ordinary least squares		57:20
	Labor Economics lecture 1 (2 of 2)—Introduction
		Excel's simple regression output		0:00
		Interpreting the R square in simple regression		0:38
		Omitted variable bias		1:53
		Excel's multiple regression output		2:40
		Interpreting the R square in multiple regression		3:03
		Forming the predicted equation		4:10
		Computing residuals		5:40
		Heteroskedasticity		9:56
		Autocorrelation		16:20
		Linearity		21:48
		Normality		24:18
		Test of model significance		31:08
		Test of coefficient significance		34:04
		Coefficient interpretation		42:22
top a	Labor Economics lecture 2—Worker utility and labor supply
		Utility: more consumption and leisure is better		0:00
		Law of Diminishing Marginal Utility		1:41
		Solving for C yields the indifference curve		5:08
		Graphing indifference curve U = 10		6:40
		Graphing indifference curve U = 20		7:45
		Budget line constructed		9:13
		Graphing the budget line		11:30
		Shifting the budget line after non-earned income		14:17
		Rotating the budget line after wages rise		15:18
		Utility maximization		16:41
		Utility max after an increase in non-earned income		19:40
		Utility max with increase in wage and substitution effect dominant		22:04
		Utility maximization with increase in wage and income effect dominant		23:36
		Income effect dominates		24:24
		Substitution effect dominates		27:06
		Reservation wage		28:29
		Backward bending labor supply and labor supply elasticity		33:21
		Application: the benefit reduction rate		39:05
		Application: Earned Income Tax Credit		49:46
top a	Labor Economics lecture 3—Firm production and labor demand
		Long run production function		0:00
		Short run production function		1:27
		Law of Diminishing Marginal Productivity		3:48
		Graphing total and marginal product of labor		5:56
		Short run output, expenses, revenue, and profit equations		7:20
		Short run profit maximization rule using graphs		10:14
		Short run profit maximization rule using calculus		12:00
		Short run labor demand equation		16:11
		Graphing the labor demand equation		17:03
		Shifting short run labor demand		19:15
		Short run profit maximization rule		21:54
		Long run cost minimization		22:40
		Solving long run production for K yields the isoquant		22:56
		Graphing isoquants		24:09
		Isocost		25:53
		Graphing isocost		26:54
		Rising cost shifts isocost		28:03
		Rising price of K rotates isocost		29:01
		Rising wage rotates isocost		31:19
		Long run cost minimization		33:15
		Long run labor demand and the output effect of falling wage		38:41
		Scale and substitution effects		42:28
		Elasticity of substitution and curvature of the isoquant		45:57
		Application: the free market punishes firms that discriminate		47:00
		Application: affirmative action can work		48:06
		Application: affirmative action can put firms out of business		49:29
		Perfect substitutes and perfect complements in production		54:48
top a	Labor Economics lecture 4 (1 of 2)—Labor market equilibrium
		Competitive labor market equilibrium		0:00
		Minimum wage creates unemployment		2:18
		Cobweb web model		5:13
		Long run dynamics and data observation		13:33
		Pareto efficient		17:17
		Producer surplus		19:32
		Worker surplus		20:31
		The competitive market maximizes total surplus		21:10
		Payroll taxes assessed on firms		22:09
		Payroll taxes assessed on employees		28:55
		Payroll taxes assessed on firms and employees		34:22
		Payroll taxes assessed on firms with inelastic labor supply		37:39
		Employment subsidies paid to firms		40:46
	Labor Economics lecture 4 (2 of 2)—Labor market equilibrium
		Immigration equalizes wages in two different regions		0:00
		Evidence of wage equalization across regions		6:26
		Immigrants and natives are perfect substitutes		9:39
		Immigrants and natives are perfect complements		14:07
		Evidence of wage being associated with immigration		15:41
		Immigration is Pareto efficient		18:40
		Perfectly discriminating monopsonist		20:46
		Perfectly nondiscriminating monopsonist		25:35
		Perfectly nondiscriminating monopolist		29:10