Empirical studies of economic growth are flawed because many of ...

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Empirical studies of economic growth are flawed because many of the truly important underlying determinants, such as culture and institutions, are very hard to measure.
 
  Discuss this statement paying particular attention to simple cross-section data and panel data models. Use equations whenever possible to underscore your argument.
  What will be an ideal response?

Question 2

The accompanying table gives the outcomes and probability distribution of the number of times a student checks her e-mail daily:
 
  Probability of Checking E-Mail
 
  Outcome (number of e-
  mail checks) 0 1 2 3 4 5 6
  Probability distribution 0.05 0.15 0.30 0.25 0.15 0.08 0.02
 
  Sketch the probability distribution. Next, calculate the c.d.f. for the above table. What is the probability of her checking her e-mail between 1 and 3 times a day? Of checking it more than 3 times a day?
  What will be an ideal response?

[mcinincha279]

Answer to Question 1

Answer: Although some cultural and institutional variables, such as corruption, black market activity, central bank independence, trust, etc., are hard to measure, authors have developed such series for the countries of the world. Still, either these variables are measure with error or not all cultural and institutional aspects are bound to be captured. Hence you would expect omitted variable bias to be present in cross-sectional studies. However, if you could argue that these effects are constant across time or at least slowly changing, then introducing country fixed effects in panel studies goes some way to alleviate the omitted variable problem. Similarly by using time fixed effects, common world business cycle effects can be largely eliminated. For an empirical study of economic growth using U.S. states, time fixed effects would eliminate common effects of monetary policy and inflation.

Answer to Question 2

Answer:
Outcome (number of e-
mail checks) 0 1 2 3 4 5 6
Cumulative probability distribution 0.05 0.20 0.50 0.75 0.90 0.98 1.00

Pr(1  Y  3) 0.70 ; Pr(Y > 0.25).

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TYSM

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