1 / 27

The Mismeasure of Poverty, Happiness & Bias, and Regression

The Mismeasure of Poverty, Happiness & Bias, and Regression

ostinmannual
Download Presentation

The Mismeasure of Poverty, Happiness & Bias, and Regression

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. The Mismeasure of Poverty, Happiness & Bias, andRegression

  2. In “The Mismeasure of Poverty,” Nicholas Eberstadt notes that the percentage of Americans living under the poverty rate has been stagnant for 30 years, suggesting that antipoverty efforts have failed, and that life has not improved for the nation's poor.

  3. The problem, Eberstadt continues, is that there are anomalies in the official statistics. Why does the poverty rate tend to go up as unemployment falls? Why hasn't it budged while the amounts of money the government spends on the poor have more than doubled in constant dollars?

  4. Eberstadt: The Mismeasure of Poverty • Eberstadt goes back and looks at how the poverty rate is calculated, and finds two realities that are masked by our current statistics, one heartening, one disheartening. • The first reality is that people living under the poverty line are materially much better off than they were three decades ago. - They live in much bigger homes. - Three-quarters own at least one motor vehicle. - They spend roughly twice as much as they report as income, and not because they are going into debt. (Networths have not declined.) In general, poor people today live at about the same standard of living as middle-class people did in the 1960s.

  5. Eberstadt: The Mismeasure of Poverty On the other hand, they live with greater insecurity. In fact, relatively few people live permanently in poverty (power-law distributions). Butnearly a third of the U.S. population dips into poverty from time to time. Eberstadt paints a picture of greater volatility at the bottom end of the income scale -- a different image from the one portrayed by the immobile statistics, with radically different policy implications (such as?).

  6. Daniel Gilbert: “I’m O.K., You’re Biased” Research shows that decision-makers don’t realize how easily and often their objectivity is compromised. Much of what happens in the brain is not evident to the brain itself. And yet, if decision-makers are more biased than they realize, they are less biased than the rest of us suspect: • bathroom scales • saliva test strips for dangerous enzyme deficiency • evaluating students’ intelligence by examining info. one piece at a time; when subjects liked the student, they kept turning cards searching for one good piece of info; when they disliked the students, they turned over a few cards, shrugged and quit • The majority of people claim to be less biased than the majority of people: 84% of medical resident claimed that their colleagues were influenced by gifts from Rx drug companies, but only 16% thought they were similarly influenced.

  7. Daniel Gilbert: “I’m O.K., You’re Biased” Because the brain cannot see itself fooling itself, the only reliable method for avoiding bias is to avoid the situations that produce it. Doctors should refuse to accept gifts from those who supply drugs to their patients, when justices refuse to hear cases involving those with whom they share familial ties and when chief executives refuse to let their compensation be determined by those beholden to them, then everyone sleeps well. Professors should grade “blind” as often as possible. What about college admissions?

  8. Stock Market Volatility & Regression to the Mean

  9. Stock Market Volatility & Regression to the Mean

  10. Regression Allows you to go beyond correlation and to predict how much, say, a dependent variable Y (e.g., income) changes if independent variable X (e.g., years of education) changes by 1 unit.

  11. Regression Two kinds of regression analysis: - bivariate(income by years of education) - multivariate (or multiple regression) controls for other independent variables (income by education, age, race, gender, etc)

  12. Bivariate Regression Y = a + bx Y = dependent variable (what you basically want to predict/determine) x = independent variable a = Y-intercept (the point at which the regression line intercepts or crosses the Y-axis: anchor) b = slope of the regression line (the regression coefficient)

  13. Bivariate Regression Example: Amex Business Service does routine accounting as one of its basic job offerings. Its rate is $20/per hour plus a $25 floppy disk charge. • The total cost to a customer depends, of course, on the number of hours it takes to complete the job. • So the total cost, Y, of a job that takes x hours is . . . • Y = $25 + 20x • Time & Cost: 5 hours ($125), 7.5 hours ($175), 15 hours ($325), 20 hours ($425), 22.5 hours ($475)

  14. Bivariate Regression

  15. Bivariate Regression Y = a + bx + e (error term) Real-life applications in the natural and social sciences are seldom this straight-forward. Instead, what we usually have are sets of scores for various independent and dependent variables. We display them on a scatterplot and use inferential statistics to determine both: the [1] slope (the regression coefficient) and, then, [2] predictions for other independent variables. For example, consider the education levels and income of 32 employees ...

  16. Bivariate Regression

  17. Bivariate Regression & its Measure of Association • Remember that Lambda (for nominal variables) and Kendall’s tau-b/c (for ordinal variables) are PRE measures, which stands for Proportional Reduction in Error. • PRE: When predicting a dependent variable, how much error can you reduce by knowing the independent variable? • Regression has a similar statistic: R-Square or (R2) or “goodness of fit” always between 0 and 1 (0=no reduction in error, 1=error eliminated)

  18. 2.5 2.0 1.5 Visits to Cardiologists per enrollee 1.0 0.5 R2 = 0.49 0.0 0.0 2.5 5.0 7.5 10.0 12.5 15.0 Number of Cardiologists per 100,000 residents Association between cardiologists and visits per person to cardiologists among Medicare enrollees: 306 HRRs Source: John Wennberg (2005)

  19. 400 Discharges for all Medical Conditions R2 = 0.54 350 300 250 200 Discharge Rate 150 100 Discharges for Hip Fracture R2 = 0.06 50 0 1.0 2.0 3.0 4.0 5.0 # of Hospital Beds/per 1,000 Residents Association between # of hospital beds per 1,000 residents and discharges per 1,000 Medicare enrollees in 306 HRRs Source: John Wennberg (2005)

  20. Bivariate Regression & Measure of Association

  21. Bivariate Regression & Measure of Association X = 6 years of education & $8,000 incomeRegression line estimated approx. $9,000 in income for 6 years of education.Yet the mean score ($13,866) would have left you with $5,866 in estimated error.With the regression line (coefficient), you reduced the amount of error by $4,866; you are left with $1,000 in estimated error. In other words, by knowing the independent variable, you have $4,866 of explained variation and $1,000 of unexplained variation.explained ($4,866)2explained ($4,866)2 + unexplained ($1,000)2R-Square = aggregating (summing) this for all of the data points in the scatterplot

  22. Bivariate Regression & Measure of Association .751 x .751 = 0.56r2 = R2

  23. Multivariate or Multiple Regression Y = a + b1x1 + b2x2 + b3x3 + e Allows you to determine the marginal effect of an independent variable on one dependent variable by simultaneously controlling for several other independent variables. The regression coefficient b1 estimates the average change in Y (dependent variable) for each unit change in independent variable x1, controlling for the other independent variables: x2 and x3, etc. Same concepts as bivariate regression, but in a three-dimensional + scatterplot (which can’t be graphically illustrated) The difference is that you obtain partialslopes and partial regression coefficients, which means the “marginal” effect that each independent variable has on the dependent variable.

  24. Methylphenidate and Amphetamine Distribution (DEA data)(average = 4,150 grams/100,000 individuals) grams/per 100,000 Individuals 0 to 1,600 Low (4.6%) 1,600 to 3,150 Below Average (25.5%) 3,150 to 5,150 Average (43.5%) 5,150 to 6,750 Above Average (19.6%) 6,750 to 8,350 High (4.9%) 8,350 to 11,000 Extremely High (1.8%)

  25. Analysis of the Demand for Psychostimulants:Multivariate Regression Data Sources: • Dependent variable I: DEA data provides the distribution of methylphenidate and amphetamine in grams down to the 5 digit zip code-level (1998-2001) • Dependent variable II: Source® Territory Manager by NDC HEALTH provides Rx Sales and Rx Quantity sold by product down to zip/county or HSA level from 1996 onwards; ** Average price to be derived from Total Sales/Total Quantity ** • Independent variables: Area Resource Files (ARF), InterStudy, Census Estimates, County Business Patterns, Dept. of Education’s Common Core of Data Market Definitions: • 3,030 U.S. counties

  26. Selected Regression Coefficients for Market Model * indicates that the coefficient is significant at the 10% level, ** at the 5% level, *** at the 1% level

  27. Selected Regression Coefficients for Market Model * indicates that the coefficient is significant at the 10% level, ** at the 5% level, *** at the 1% level R2 (Ritalin) = 0.715 R2 (Adderall) = 0.672

More Related