Industrial Organization

1. Industrial Organization Econometric Identification

2. A central issue in IO • Estimate demand (and/or supply) parameters • For example: • 1/b is the price elasticity of demand. A common exercise is to infer market power using b, because: Maybe unobservable.

3. The Problem, more generally • Empirical economists mainly concerned with identification • Understanding what is the causal relationship between variables • This is perhaps the MOST important issue in empirical work • “Correlations are a good place to start but not a good place end.”

4. The Problem • Identification problems come in different flavors: • Structural models: Example is identifying demand slope. Would regressing quantity on price tell us something reliable (theoretically)? • Policy questions: • Does increasing number of policemen decrease crime? • Does the government interest rate affect GDP? • Management questions: • Does the type of management affect productivity?

5. Does this mean demand slopes up? $ Q

7. Correlation v. Causation • Let’s assume variables Yt and Xt are correlated • How do we make sure we have causation? (i.e. are we measuring something meaningful?) • Key assumption in OLS regression: • E(u|x)=0 • Also known as the “orthogonality” condition

8. Ways to Achieve E(u|x) • Experiments – you generate the x • Natural Experiments – nature (or luck) generates the x for you • Instrumental variables – if E(u|x)=0 is violated, you look (and find!) a z that can proxy for x and complies with E(u|z)=0

9. Experiments • Experiments are standard in Science & Medicine • For example: • Set up a treatment and control group for a new drug, making sure these are comparable (or randomly selected) • Ensure the sample sizes are large enough to obtain statistical significance • Ensure there are no confounding effects: i.e. placebo and treatment groups are treated equally in every respect (except for “injection”) • Run the experiment and compare the differential effects.

10. Experiments • Hypothetical example (1): • Choose two similar regions (e.g. Portici and Naples) • Increase police force in Naples, keep the same in Portici • Look at how much crime decreased in Naples, after accounting for common crime trends (provided by control group – Portici)

11. Experiments • Hypothetical example (2): • Interested in the effect of entry on prices (or performance) • Randomly “mandate” that a new firm enters several industries in Italy • Measure the effect of entry (or new concentration) on prices (or performance)

12. Experiments (Lab and Field) • Experiments are rare in economics, they are expensive. Although they are becoming more popular: • Development economics – cheaper to run experiments in the third World • Consumer economics – small stakes experiments that are easy to administer • Individual business applications – firms can finance these

13. Natural Experiments • Fortunate situations that allow identification • Essence is similar to experiments, but not as ideal • There might be confounding effects because the experiment was not designed for our purposes • Example: Beer tax • In January 1991, beer tax doubled from $9 to $18/ barrel, increasing all brewers’ marginal cost by the same amount • Unfortunately, there is no control group, but you can study firm behavior (for example) • How are costs passed-through consumers?

14. Natural Experiments • Example: Want to understand the impact of small firms employment tax credit • Assume credit introduced in 2000 for firms with 250 or less employees • So could look at firms before and after credit • But other things also changing (2000 peak of dotcom boom, etc.) • So need to set up a control group of companies look similar to firms getting the credit except don’t get the credit • Compare firms with 240 employees to those with 260 • Compare differences: • Between pre and post the credit (1999 versus 2001) • Between the treated (240 employees) and untreated firms (260 employees)

15. Instrumental Variables • Assume estimating equation below in Ordinary Least Squares: Y = βX + u • The estimate of βo = (X’X)-1X’Y = (X’X)-1X’ (βX + u) E(βo) = β + E(u’X)/E(X’X) = β only if u and X are independent • But if u and X are correlated then the estimated is biased, and X is called “endogenous” (correlated with the error)

16. Instrumental Variables • Thus, estimation of the following demand equation would be biased: Qi = a + b1 Pi + ui because Pi and ui are correlated (due to simulteneity) E[b1]≠b1 • Because we ignore simultaneity (supply and demand usually move together), this makes the error term correlated with price and we get a biased estimate of the demand slope.

17. Instrumental Variables • Imagine we had a variable – called an instrument Z – that was correlated with price but not with the demand error term (ui). • Basically, what we want is to come up with a variable that gives us the equivalent of an experiment: • In the case of demand, we can use supply shifters: • Cost shock (e.g. technological shock) • Weather?

18. Instrumental Variables • In the case of schooling and earnings (where ability is unobserved), one could use a variable such as “born on Tuesday” (Government told everyone born on Tuesday to spend 1 more year in school)

19. Instrumental Variables • In practice instruments are often hard to find. • IV’s are central to empirical work that we will see in this class. • No good instruments = unreliable inference.

20. Instrumental Variables • In practice instruments are often hard to find. • IV’s are central to empirical work that we will see in this class. • No good instruments = unreliable inference.

21. Experiments • Hypothetical example (1): • Monopolist knows demand will not move for 1 month • Changes prices every day • Records the quantity sold at each price • Unrealistic example but doable (i.e. controlling for trends, seasonalities, etc.)