Censored Quantile Instrumental Variable

Transcription

1 1 / 53 Censored Quantile Instrumental Variable NBER June 2009

2 2 / 53 Price Motivation Identification Pricing & Instrument Data Motivation Medical care costs increasing Latest efforts to control costs focus on consumer price sensitivity Research Questions How much does medical expenditure respond to the prices that consumers face? Does this response vary across the quantiles of the expenditure distribution?

3 3 / 53 Challenges to Estimation Motivation Identification Pricing & Instrument Data 1 Skewness 95% of expenditures on 25% of individuals Price responsiveness could vary from mean estimates 2 Censoring 40% of individuals consume zero care Traditional methods require parametric assumptions 3 Endogeneity Price is a function of expenditure RAND Health Insurance Experiment ( ) randomized prices

4 My estimation approach: Censored Quantile Instrumental Variable Motivation Identification Pricing & Instrument Data 4 / 53 Developed by Chernozhukov, Fernandez-Val, and (2008) Censored 40% of individuals consume zero care Traditional methods require parametric assumptions handles censoring nonparametrically Quantile 95% of expenditures on 25% of individuals Price responsiveness could vary from mean estimates allows for variable treatment effects, less sensitive to extreme values Instrumental Variable Price is a function of expenditure incorporates endogeneity with an instrumental variable

5 5 / 53 Preview of Findings Motivation Identification Pricing & Instrument Data Strong price responsiveness at highest quantiles Price elasticity of expenditure stable from.65 to.95 quantile at -2.3

6 6 / 53 Plan for Presentation Motivation Identification Pricing & Instrument Data 1 2 Graphical Other Estimators 3

7 7 / 53 Instrumental Variable Identification Strategy Motivation Identification Pricing & Instrument Data Relationship of Interest: E = f(p) E on medical care by year-end (beneficiary + insurer) P Marginal year-end price Problem: Endogeneity: E = f(p(e)) Solution: Instrument Z such that P = g(z)

8 Marginal Pricing for Medical Total Beneficiary Payments Cost Sharing for Individuals Motivation Identification Pricing & Instrument Data Stoploss Deductible price = 1 price = Coinsurance price = 0 8 / 53 Deductible [(Stoploss - Deductible)/Coinsurance] + Deductible Total Beneficiary + Insurer Payments (E)

9 9 / 53 Family Pricing Interactions Motivation Identification Pricing & Instrument Data Individual Deductible: $500 Family Deductible: $1,500

10 10 / 53 Instrumental Variable Motivation Identification Pricing & Instrument Data Z = 1 if family member has injury in given categories Intracranial Superficial Crushing Foreign Body Burn Complications of Trauma Injuries to the Nerves and Spinal Cord

11 11 / 53 Medstat Data Motivation Identification Pricing & Instrument Data Advantages Large firm in the US retail trade industry - over 500, 000 insured employees Large cross-section of people under age 65 Plans vary only in deductible and stoploss Limitations Do not observe insurance options outside the firm Do not observe income Do not observe premium

12 Empirical Family Pricing Interactions Cost Sharing Comparison Individual Plan A $350 Plan B $500 Plan C $750 Plan D $1,000 Deductible Family $1,050 $1,500 $2,250 $3,000 Motivation Identification Pricing & Instrument Data Stoploss (Includes Deductible) Individual Family $2,100 $4,550 $3,000 $6,500 $4,500 $9,750 $6,000 $10,000 $13,000 in 2004 Coinsurance (Beneficiary) In-Network Out-of-Network 20% 40% 20% 40% 20% 40% 20% 40% 12 / 53

13 Data Accuracy Motivation Identification Pricing & Instrument Data 13 / 53 Total Beneficiary Payments Empirical Cost Sharing for Individuals Total Beneficiary and Insurer Payments (E) Sample includes 2004 employees in couples in $500 deductible plan. Graph depicts 97.5% of observations. Observations with total beneficiary payments greater than $3,100 omitted. Observations with total beneficiary and insurer payments greater than $21,000 omitted.

14 14 / 53 Sample Selection Motivation Identification Pricing & Instrument Data Families of four or more All family members continuously enrolled January 1-December final estimation sample: 127,119 individuals in 29,010 families

15 15 / 53 Summary Statistics 2004 Employee Sample Motivation Identification Pricing & Instrument Data Year-end ($) % Sample to to 1, , to 10, , to 100, , and up 0.0 Year-end Price 0 (Met Deductible & Stoploss) (Met Deductible) (Did Not Meet Deductible) 57.3 Family Injury 0 (NO Family Injury) (Family Injury) 13.4

16 16 / 53 Plan for Presentation Graphical Other Estimators 1 2 Graphical Other Estimators 3

17 17 / 53 Graphical Graphical Other Estimators Reduced form Effect of family injury on expenditure First stage Effect of family injury on price

18 18 / 53 CDF s by Family Injury Higher Graphical Other Estimators Lower Year-end Price

19 19 / 53 Plan for Presentation Graphical Other Estimators 1 2 Graphical Other Estimators 3

20 20 / 53 Introducing Graphical Other Estimators Random Coefficients by Quantile Conversion to Elasticity Estimates Nonparametric Handling of Censoring Endogeneity with Control Function Approach

21 21 / 53 Random Coefficients by Quantile Graphical Other Estimators Linear Model for Estimation: ln E = max(ln E, C) = T((ln E i ) ) (ln E) = α(u)p + W β(u) + γ(u)v P = φ(v, W, Z) U U(0, 1) P, W, C, V. where α(u) are the random coefficients of interest.

22 22 / 53 Random Coefficients by Quantile Conversion to Elasticity Estimates Graphical Other Estimators General Arc Elasticity η arc = ln( ya y b ) ln( a b ). Arc Elasticity from P = 1 to P =.2 η = (ln E P = 0.2) (ln E P = 1) ln( ) = α(0.2 1) ln( ).50 α.

23 23 / 53 Censoring - Nonparametric Graphical Other Estimators General Quantile Objective (no endogeneity) n θ(τ) minimizes ρ τ (Y i X i θ). i=1 Censored Quantile Objective (no endogeneity) n θ(τ) minimizes ρ τ (Y i T(X i θ)). i=1 where ρ τ (u) = {(1 τ)1(u < 0) + τ1(u > 0)} u. Problem: The censored quantile objective function is not well-behaved because of the transformation function T().

24 24 / 53 Censoring with II Graphical Other Estimators Based on Chernozhukov and Hong (2002) computational algorithm: 1 Parametrically predict which observations are less likely to be censored based on X s 2 Estimate the quantile regression on this sample. Based on prediction, select observations with prediction above censoring point. 3 Estimate the quantile regression on this expanded sample.

25 25 / 53 - Control Function Approach Graphical Other Estimators Control function approach Endogeneity between P and (lne) results in a lack of orthogonality between P and structural disturbance U Estimates based on the structural equation alone would be inconsistent. Following Hausman (1978), if Z U W, X, then E(U V) = δv + η. By construction, E(η V) = 0. Therefore, including the control term ˆV in the structural equation eliminates the lack of orthogonality - it controls for endogeneity.

26 26 / 53 Main Elasticities Price Elasticities 2004 Employee Sample Graphical Other Estimators Quantile Lower Bound Elasticity Upper Bound

27 27 / 53 Main Elasticities Graphical Other Estimators 2003 Year-End Price Coefficients for Various Samples Dependent variable: Ln() Censored Quantile IV 2004 Employee Price Elasticity lower bound upper bound N=29,010

28 28 / 53 Plan for Presentation Graphical Other Estimators 1 2 Graphical Other Estimators 3

29 Comparison to Other Estimators Graphical Other Estimators 29 / 53 Preview of s From Comparison Elasticity large regardless of estimator Tobit IV assumptions are restrictive Endogeneity biases estimate away from zero Mean Estimates for Comparison to Tobit IV, Tobit, Truncated model, 2-part model Mean estimate based on quantile estimates Estimates from the literature Quantile vs. Mean Estimates Estimate different quantities Similar if underlying treatment effect is linear and error distribution is symmetric and homoskedastic Unlikely to be the same in this application given censoring and skewness requires no parametric assumptions for censoring, less sensitive to extreme values

30 Comparison of to Tobit IV and Tobit Graphical Other Estimators 30 / Year-End Price Coefficients for Various Samples Dependent variable: Ln() Censored Quantile IV 2004 Employee Tobit IV Price Elasticity lower bound upper bound N=29,010 Tobit (IV) assumptions Tobit: error normal and homoskedastic Tobit IV: homoskedastic first stage error, join normality of structural and first stage error Hausman test comparison of Tobit IV to rejects null hypothesis that assumptions hold Tobit estimate of 4.1 implies that endogeneity biases estimate away from zero

31 31 / 53 Other Comparisons Estimate: 2.3 Graphical Other Estimators Traditional censored estimators Truncated model with IV first stage: 0.8 Two part model with IV first stage: 1.6 Mean estimate based on estimates (1.65) 2.3 = 0.8 Estimates from the literature Eichner (1997, 1998) Tobit IV: 0.22 to 0.32, 0.8 RAND: 0.22 Based on myopia assumption, discussed later

32 32 / 53 Plan for Presentation 1 2 Graphical Other Estimators 3

33 33 / 53 Couples Test Indirect test of exclusion restriction Longitudinal test Indirect test of exclusion restriction Test of forward inter-year shifting hypothesis

34 34 / 53 Couples Test Indirect test of exclusion restriction No family pricing interaction in families of two No first stage possible Estimate reduced form instead of IV

35 Couples Test 35 / 53 Dependent variable: Ln() Censored Quantile Regression Tobit A. Employees in Couples Family Injury lower bound upper bound Includes zero: no yes no yes yes yes yes no N=29,010 + Mean : $2, B. Employees in Families of Four or More Family Injury lower bound upper bound Includes zero: no no no no no no no no N=29,010 Mean : $1, C. Employees in Families of Four or with Injury to Spouse or No Family Injury Family Injury lower bound upper bound Includes zero: no no no no no no no no N=25,884 Mean : $1,442.12

36 36 / 53 Longitudinal Test Indirect test of exclusion restriction Injury in 2004 cannot affect price in 2003 If no violation, in 2003, individuals with family injuries in 2004 should spend much less than individuals with family injuries in 2003 Investigation of intertemporal shifting Potential forward-shifting of expenditures from 2003 to 2004 in response to injury If forward shifting, in 2004, individuals with family injuries in 2003 should spend much less than individuals with family injuries in 2004

37 37 / 53 Longitudinal Test Censored Quantile Regression N= 3, Tobit A. Dependent variable: Ln( 2003) 2004 Family Injury Only lower bound upper bound B. Dependent variable: Ln( 2004) 2003 Family Injury Only lower bound upper bound Continuously Enrolled Employee Sample Restricted to Employees with Injuries in 2003 or 2004 (But Not Both).

38 38 / 53 Plan for Presentation 1 2 Graphical Other Estimators 3

39 39 / 53 Across Specifications Variation across estimates is small relative to magnitude of main estimates in Sample Employee only vs. Employee & Family 2004 vs in Dependent Variable All vs. Outpatient in Instrument Spouse vs. Child Injuries

40 40 / 53 in Sample: Employees, Spouses, Children Dependent variable: Ln() Censored Quantile IV A Employee Tobit IV Price Elasticity lower bound upper bound N=29,010 B Employee & Spouse Price Elasticity lower bound upper bound N=53,185 C Everyone Price Elasticity lower bound upper bound N=127,119

41 41 / 53 in Sample: 2004 vs vs Dependent variable: Ln() Censored Quantile IV A Employee Tobit IV Price Elasticity lower bound upper bound N=29,010 B Employee Price Elasticity lower bound upper bound N=29,886 C Employee and Spouse Price Elasticity lower bound upper bound N=54,683 D Everyone Price Elasticity lower bound upper bound N=131,815

42 42 / 53 in Dependent Variable: All vs. Outpatient 2004 Employee A. Baseline (Standard Quantiles) Censored Quantile IV Tobit IV Price Elasticity lower bound upper bound N=29,010 A. Baseline Continued (Higher Quantiles) Censored Quantile IV B. Ln(Outpatient ) Censored Quantile IV Tobit IV Price Elasticity lower bound upper bound N=29,010

43 43 / 53 in Instrument: Spouse vs. Child Injuries 2004 Employee Censored Quantile IV A. Baseline Tobit IV Price Elasticity lower bound upper bound N=29,010 B. Injuries to Children Only Price Elasticity lower bound upper bound N=28,385 C. Injuries to Spouse Only Price Elasticity lower bound upper bound N=25,883

44 44 / 53 Plan for Presentation 1 2 Graphical Other Estimators 3

45 45 / 53 Comparison to RAND 1 Calculation of RAND estimates Assumes myopia 2 Evidence of forward-looking behavior in my data 3 Simulation assuming myopia when individuals are forward-looking

46 46 / 53 Calculation of RAND estimates Coinsurance: 0%, 25%, 75%, 95% Stoploss: $1,000 or less in dollars Met by approximately 20% overall Met by 35% in least generous plan Met by 70% with inpatient expenditure My plans: less than 4% meet stoploss

47 47 / 53 Rand Caveat In order to compare our results with those in the literature, however, we must extrapolate to another part of the response surface, namely, the response to coinsurance variation when there is no maximum dollar expenditure. Although any such extrapolation is hazardous (and of little practical relevance given the considerable departure from optimality of such an insurance policy), we have undertaken such an extrapolation rather than forego entirely any comparison with the literature. (Manning et al. (1987), page 267)

48 48 / 53 RAND Elasticity Calculation Keeler and Rolph (1982) η midpoint = (e 1 e 2 )/(e 1 + e 2 ) (p 1 p 2 )/(p 1 + p 2 ) η RAND = (71 55)/( ) (25 95)/( ) 0.22

49 49 / 53 Forward-Looking Behavior Individuals with high expenditure in the previous year should react less to the family injury

50 50 / 53 Forward-Looking Behavior

51 51 / 53 Simulation Exercise 1 Estimate using my data and method 2 Predict expenditures ln E = αp + W β + u ln E = αp + W β 3 Alter predicted expenditures for some forward-looking group who will meet new lower stoploss ln E = α 0 + W β 4 Compare to original estimate

52 52 / 53 Simulation Exercise 1 Using Tobit IV, elasticity is Predict expenditures 3 Place approximately 20% of people into hypothetical plans where year-end marginal price is known to be zero - 6,015 people with no family injuries whose total family spending exceeds $5,500 4 New elasticity is -.34

53 53 / 53 Strong price responsiveness at highest quantiles Price elasticity of expenditure stable from.65 quantile to.95 quantile: -2.3 Future work Welfare analysis Optimal nonlinear structure of health insurance