Cross Sectional and Panel Data II

1. Cross Sectional and Panel Data II Paul Gompers Harvard Business School February 26, 2009

2. Today • Look at a variety of papers that examine panel data. • Start with methodological papers. • Differences in differences is common approach to estimated an effect in panel data when there is an “exogenous” intervention and a contemporaneous non-intervention group. • Look at some of the statistical issues that may plague these tests.

3. Today • A variety of natural experiments that can be viewed as “exogenous” events. • Election cycles • Lawsuits • Oil price shocks • State law passage • Key point from these papers. • All of them examine a particular issue in a panel data set. • Find an intervention that allows the researcher to look at pre and post behavior of the firms to answer an economic question.

4. How much should we trust differences in differences estimates? Bertrand, Dufo, and Mullainathan QJE 2004

5. Motivation • Lots of papers try to control for events by comparing changes in one group that has been affected by an event to a contemporaneous group has not been affected. • Essentially, the non-affected group becomes the control for the affected group. • Look at differences in the differences between these groups. • Most papers focus solely on whether the intervention is endogenous • Standard errors may be inconsistent.

6. Problems • DD estimates are usually OLS run in repeated cross sections. • Potential for significant serial correlation in residuals of OLS regression. • DD estimates usually use long time series. • Dependent variables in DD are typically highly positively serially correlated. • Treatment variables typically changes very little within a state over time.

7. Paper’s Approach • Create a series of placebo laws and look at employment data on female wages. • Laws are not real. They randomly assign years and states for a test. • Would only expect significant relationship between intervention (i.e., the placebo law) and female real wages 5% of the time at typical significance levels. • Also create Monte Carlo simulation. • Compare a variety of fixes to deal with the serial correlation in residuals.

8. Survey of the literature • Looked at 92 papers published in six journals from 1990 to 2000 that utilized differences in differences estimation. • Table I. • Review of papers that use DD.

9. Placebo Tests • Utilize CPS and a sample of women’s wages • Table II • Look at 1979 to 1999 • 540,000 reports and (50*21-1050) state year observations • Do 200 independent observations of placebo laws. • Also, because there are only 50 actual states in the real sample, perform a Monte Carlo simulation where the “states” are generated from the state-level empirical distribution from the CPS with replacement. • Look at Type I and Type II error (inducing a true 2% effect)

10. Placebo Tests • Look at effect of sample size and time series length of panel. • Small T reduces problem of the Type I error. • Table III

11. Corrections • Look at a variety of parametric corrections • To adjust for serial correlation, typical to set an AR(1) structure to residuals. • Table IV. • Typical corrections don’t do very well.

12. Block Bootstrapping • Correction for dealing with autocorrelation structure. • Keep all observations from the same state together. • Construct a bootstrap sample by drawing with replacement 50 matrices using the entire time series of observations for each state (Ys,Vs) • Run the regression of Y on V. • Draw large number of bootstrap samples (200). • Table V. • Works well when N is large.

13. Simple Method • Aggregate time series into two periods. • Works reasonable well. • If all laws passed same time, can do simple aggregation into average before and after intervention. • If interventions are at different times: • Run regressions without intervention variables. • Take residuals for intervention states before and after intervention then average them before and after. • Table VI. • With small N, works okay. • As N increases, works better. • Downside is the power of Type II tests decreases.

14. Empirical Variance-Covariance Matrix • Can estimate the Var-covar matrix by assuming that it is block diagonal with each state having the same autocorrelation process. • Use the states to estimate the Var-covar matrix. • Table VII. • Works well with large N. • Does poorly with small.

15. Conclusion • Need to worry about serial correlation within the DD sample. • A number of techniques to deal with problem. • Based on the structure of your panel, a different approach may be appropriate.

16. Fixing Market Failures Or Fixing Elections? Agricultural Credit in India Shawn Cole

17. Motivation • India nationalized banks • “Reduce poverty, encourage growth” • Private banks controlled by industrial houses • Farmers victim to rural money-lender • Industrial credit requires co-ordination • At a potential cost? • Inefficiency (lazy bureaucrats) • Capture of banks by politicians

18. ...don't worry, I spoke to banks for loans, told agriculturalists for seeds, discussed with firms for fertilisers, ordered weather dept to ensure a good monsoon!

19. This Paper • Provide micro foundations for observed cross-country relationships between bank ownership and performance • Test theories of capture • Credit follows election year cycles • Agricultural credit is up to 20% higher in election years than non-election years • Rule out other causes for cycles • Captured credit is used strategically targeted • Cycles are twice as large in “swing districts” than in non-swing districts • No evidence politicians reward their supporters • Measure costs of distortions: • Default rates are higher in election years • The marginal political loan has no measurable effect on agricultural output • Even the average loan does not affect agricultural investment

20. Setting • Banking in India • In 1969, and 1980, government nationalized largest banks • Branch expansion law vastly increases scale and scope of banking • Currently more than 60,000 bank branches • Most banks government-owned (ca. 90% assets) • Government intervention increases importance of agricultural lending • Nationalized banks lend more to agriculture • All banks required to lend minimum percentage to agriculture • Agricultural credit is important • Government banks lend substantial amounts to agriculture • 17% of value of portfolio • 40% of loans, or over 20m loans • Private banks lend (less) to agriculture • Agriculture is important • 60% of labor force works in agriculture • 24% of GDP

21. Politicization of Lending • Politicians promise agricultural credit prior to elections • Informal interaction between bankers and politicians • “State Level Bankers Committees” • Comprised of political appointees and bankers • Quarterly meetings to monitor lending • Staff turns over when government changes

22. Data • Panel of 412 districts in 19 states, annual data 1992-1999 • Credit Data • “Basic Statistical Returns,” Reserve Bank of India • Census of loans • Identifies bank, loan amount, location, industry, interest rate, repayment status • Elections Data • Election Commission of India • Constituency-level results, aggregated to district-level • 32 elections in 19 states • Output Data • Planning Commission of India

23. Political Cycles: Empirical Strategy • “Naïve” OLS: Is credit higher in election years? • But, elections may be called early by optimizing politicians. Use constitutional schedule to create an instrument for election year • Stronger test - impose structure of entire election cycle

24. Political Cycles • OLS: • District fixed effect: ad • Region-year fixed effect: grt • Dummy for election year: Est: • (Rain in district as additional control): Raindt • Comparing districts in Gujarat, when Gujarat is having an election, to districts in Maharashtra, when Maharashtra is not having an election Ydt = ad + grt + d Raindt + bEst + edt

25. OLS: Effect of Elections on Total Credit • Table 2 • Examine credit by type of bank. • Use a variety of estimation techniques. Sumstat

26. Political Cycles • Election cycles in a state are required every five years • Elections may be called early (10 of 32 in sample are called early) • Instrument: a dummy for whether it is a scheduled election (Khemani, 2004) • Does not take credit for elections called during “booms”

27. Political Cycles: Lessons • In election years, level of agricultural credit from public sector banks is 6% higher than in non-election years. • No differences for non-agricultural credit (precise) • No differences for private banks (imprecise) • Table 3 – Examines by year and loan type and bank type.

29. Political Cycles-Conclusion • Public sector banks increase agricultural credit by approximately 5-8 percentage points during elections • Private banks too small to “undo” cycle • Amount dwarfs legal campaign spending limits • Average constituency has roughly ~$1.4m in agricultural credit from public sector banks • 5 percent of $1.4m is $70,000 • Implied Subsidy: • Lower interest rate: 3%=$2,100 • Outside option (moneylender) or nonpayment: 51-100%, $70,000 • Total spending on state elections by candidate is limited to $1,000-$14,000

30. Targeted Allocation • Competing theories of targeted allocation of resources: • Politically close areas, to win elections • Areas which support majority party (patronage) • Need measure of the local strength of state governing party (SGP) in previous election • Define SGP as party with more than 50% of seats, or member of ruling coalition • Define Margin of Victory in a constituency (Mcdst) as • Share of votes of SGP minus share of votes of next strongest challenger • 100% if SGP candidate runs unopposed • –(Share of Winner) if SGP does not field a candidate

31. Measure of Political Support • Aggregation • Test for Patronage • Credit targeted to areas in which party enjoys more support • Mdst= District average of Mcdst • Test for Swing Voter • Credit targeted to areas in which previous election was close

32. Test for Constant Swing Targeting • Table 7 • Look at amount of credit controlling for years to election and how close the election was.

35. Interpreting Coefficients • Standard deviation of absolute margin of victory is .11 • Moving from a district with a margin of victory of 0 to a margin of victory of .11 reduces the size of the cycle by approximately 5 percentage points

37. Targeted Allocation (Concluded) • Evidence consistent with “swing voter” models; patronage and programmatic redistribution can be ruled out • Government ownership of banks introduces distortions in credit (but cannot yet make welfare statements)

38. Do Elections Affect Loan Repayment? • Challenges • Do not observe panel of loans, only district aggregate • Evidence of cycles in repayment suggest costliness • Use “more than 6 months” late as indicator for default • Many agricultural loans are for harvest/season

39. Lending Cycles and Non-Performing Loans • Table 8 • Examine being late on loans. Table 7

40. Elections Affect Loan Repayment • There are cycle in loan repayment rates • Suggests electoral cycle is costly • Enforcement is more lax in election years • The marginal electoral loan may be more likely to default • Non-performing loans are written off following an election

41. Do Political Loans Affect Output • Data - Agricultural Output at the state level, 1992-1999 • Log real value of agricultural output • Election Schedule serves as instruments for agricultural credit

42. Do Political Loans Affect Output?First Stage & Reduced Form/ IV • Table 9 • Look at output and credit. • Panel A is reduced form and Panel B is IV.

43. Output Conclusion • No measurable effect on output • TWO • FOUR

44. Conclusion • Combining theories of political cycles and tactical transfers helps identify manipulation of public resources • Agricultural lending exhibits substantial lending cycles • Lending is targeted in election years, not in non-election years • Results unlikely to be caused by omitted factors • Private banks do not exhibit these distortions

45. Conclusion • Combining theories of political cycles and tactical transfers helps identify manipulation of public resources • Agricultural lending exhibits substantial lending cycles • Lending is targeted in election years, not in non-election years • Results unlikely to be caused by omitted factors • Private banks do not exhibit these distortions

46. Lessons • Strong evidence for “Political” view of government involvement in bank credit • Evidence against “Development” view • Explains why politicians favor public banks, and agricultural credit in particular • Politicians may care more about re-election than delivering patronage to core supporters

47. What do firms do with cash windfalls? Blanchard, Lopez-de-Silanes, and Shleifer JFE 1994

48. Motivation • How does one provide evidence of investment policy, internal opportunities, and cash flow sensitivity? • What do firms do with cash windfalls when investment opportunities are unchanged? • In perfect capital markets, would give money back to shareholders if holding cash inside firm is tax disadvantaged. • In imperfect capital markets: • If firm was capital constrained, should increase investment in projects it couldn’t do. • If managers pursue their own objectives, then could get very perverse behavior.

49. Data • Process of identifying cash windfall firms is quite selective: • Looked in WSJ index for mentions of “Antitrust”, “Patents”, and “Suits” from 1980 to 1986. • Find 110 firms that won awards • (Easier today with the web and ability to electronically search news.) • Use four criteria to create sample: • Should not affect firm’s marginal Q • Award should be significant • Require 10K and proxies. • Focus only on award winners. • Final sample is 11 firms. • Table 1.

50. Significance of Award • Table 2 • Look at award as fraction of sales and assets. • Table 3 • Look at main line of business • Look at firm Qs, sales, investment, and debt. • Subtract size of award from Q. • These are pretty bad firms with poor investment opportunities.