政策评估中的微观计量方法详解演示文稿

1、政策评估中的微观计量方法详 解演示文稿（优选）政策评估中的微观计 量方法评估的意义有利于公共政策制订的科学化(Evidencebased policy making)美国：早在上世纪40年代已把评估纳入公共政策制订 的重要程序和步骤中国：科学发展观，党的十八大提出，建立重大事项社会稳定风险评估机制 有利于稀缺资源的有效配置政策是否有效？政策影响有多大?应该继续实施或停止?评估的分类评估的层面：宏观评估口成本一收益分析、成本一效果分析、成本一效用分析微观评估口主要利用微观计量方法(MicToeconometrics)对某一项目(政 策)的实施效果进行评估评估的时间 事前评估 (prospecti

2、ve evaluation) 事后评估 (retrospective evaluation)微观计量方法已成为项目评估中最常用的研究工 具。,不同学科中的评估问题社会保障学养老保险缴费对居民消费和储蓄率的影响失业保险对失业人群就业行为的影响 劳动经济学教育的回报率：教育程度对收入水平的影响 农民工再就业培训对收入的影响教育经济学班级规模与学生学习成绩课外补习与学生学习成绩评估中的两大难题因果推断问题(causal inference problem)鸡生蛋，还是蛋生鸡？政策干预与所观察到的结果是 否存在因果关系？例如：观察到农民工接受再就业培训后收入水平提高, 是否可以得出结论：再就业培训导致

3、了收入水平的提 咼？在评估文献中，通常把受到政策干预的人群称为处理 组(treatment group),把没有受到政策干预的人 群称为对照组(comparison group)或控制组(control group)o用公式可以表示为：Impact=(Y|T=l)-(Y|T=O)反事实问题(counterfactual problem )评估面临的另一个难题是：我们不可能在同一时间点 上观察到同一个体同时处于两种状态(受到政策干预 和不受到政策干预)，即反事实问题。举例：假设某人患感冒，服用了某制药公司研发 的感冒药，服药后不久死亡。是否可以断定是这 种感冒药导致这个人的死亡？“克隆”有效的对

4、照组是评估的关键。有效的对照组Figure 3.1 The Perfect CloneBen eficiaryClone6 candies4 candiesImpact = 6 4 = 2 can dies研究是否参加医疗保险对医疗费用支出的影响， 没有参加医疗保险的人群是否可以作为参加医疗 保险人群的有效对照组？随机控制试验随机控制试验是识别因果关系、解决反事实问题 的有效方法。医学中的随机双盲试验(Double Blinded Randomized Experiment)卫生经济学中的兰德医疗保险试验(RnndHealth Insurance Experiment)发展经济学中的实地试验(

5、field experiment)不同保险计划对医疗服务需求的影响：兰德试验德试验：迄今为止，研究医疗保险对个人医疗服务需求影响的最大控制性试验。特点：控制性试验：消除个人自选择偏差(Selection Bias)样本量：65岁以下，5,809个人历时时间：1974- 1982试验城市：6 个城市:Dayton OH, Seattle WA, FitchburgMA, Charlston SC, Georgetown County SC, Franklin County MA)总花费：8000万美元(按当时的价格计算)研究设计五类保险计划，随机分配给参加试验的人群 按服务项目付费（免费）计划医

6、疗服务25%共付比计划医疗服务50%共付比计划医疗服务95%共付比计划个人起付线计划对个人医疗服务花费起付线巧。美元，随后的医疗服务花费免费毎以上的保险计划下，个人每年人的最高支付限度为1。美 7L oFace-to- OutpatientFaceExpenses Admis-Visits(1984 $) sionsInpatientDollars(1984 $)Prob. Any Prob. AnyMedical Inpatient(%) (和TotalAdjustedExpenses Total Expenses(1984 $)(1984 $)3PlanFace-to- Outpatien

7、tFaceExpenses Admis-Visits(1984 $) sionsInpatientDollars(1984 $)Prob. Any Prob. AnyMedical Inpatient(%) (和TotalAdjustedExpenses Total Expenses(1984 $)(1984 $)3Plan17 / / I / 09793 QQ7O6 53147044357 7(6 1 5 /6 z(x8 17 ? x)z 994344848 433574140 7 ztx 6 (6 1 5 z(6/ / i/ 5 17 5 5 344627.956.5 o c& Q 7 O

8、7.Q 9 011 /(X z(v Z(N /(X40986.8(32.0)(.81737378.8(43.1)(1.38)45077.2(139)(2-26)31567.7(36.7)(1.76)37372.3(41-5)(154)4,1144.7/ 70 0 6 8 6 8759219757 2000919 0 14 lo丄OO00O丄oI /IV ziv z(v r( z/3 (2 (2 ( 2Free4,55(.168)25 Percent3.33 (.190)50 Percent3.03(-221)95 Percent2.73(.177)Individual3.02Deductibl

9、e(171)0.92 5 L13 1(2 (2.00073900n.s. .000185.311.7.0001 .02C加-Squared (4)b68,8P Value for .0001/-Squared (4). iLviaciiuu num ci ixdiiauiiiiz.ca ilauciiiiium, nntcr lean LLununiic icFigure 4.2 Random Sampling and Randomized Assignment of TreatmentFigure 4.2 Random Sampling and Randomized Assignment o

10、f TreatmentPopulation of eligible unitsRandom selection prese rves characteristicsEvaluation sampleExternalValidityRandom selection preserves characteristicsRandom selection preserves characteristicsTreatment group:assigned to treatmentComparison group:not assigned to treatmentInter nalValidityFigur

11、e 4.3 Steps In Randomized Assignment to TreatmentFigure 4.3 Steps In Randomized Assignment to TreatmentStep 1: Eligible unitsStep 2:Evaluation sampleStep 3:Ran domize assignme m to treatment、ComparisonXTreatmentExternal validityInternal validityIneligibleEligible随机试验存在的问题项目成本过高、实施程序复杂 样本损耗：人们在实验过程中可

12、能会离开 结论的局限性：结论可能只适用于某一地区的某一人群Hawtlnone和 John Henry效应因为知道会被观察，处理组和控制组会有不同的表现。因此这些 结果不能被用来概括没有被观察的情况。一般均衡效应(General Equilibrium Effect)由于一般均衡效应，结论推广至全体人群是比较麻烦的：小规模 的实验不能得出一般均衡的结果，而其对于实施与每个人息息相 关的政策是相当重要的。基于观察数据的评估方法由于随机试验成本高昂且不易实施，大量现实生活中的评估是基于观察数据(Observationaldata)来进行的。观察数据获取虽然成本相对较低，但常常存在自 选择偏差(sel

13、f-selection bias),导致模型估计不准确、有效。换句说，由于自选择问题的存在， 没有受到政策干预组不再是受到政策干预组的有 效对照组。在回归模型中，自选择问题导致我们所关注的变量不再是外生变量，从而产生内生性问题。考虑一个简单的一元线性回归模型” =00 + 01 乞 + 6实际研究中我们往往无法得到总体的回归方程， 只能通过样本对总体参数进行估计。当利用样本 统计量bO和bl代替总体回归议程中的“和儿时， 就得到了估计的回归方程。其形式为：X = 2 +乞乞一元回归方程的估计参数b可以表示为:7 工（一元）（ - 刃 COV（X）COV（X,）var（x）var（x）b= /?

14、o +E （兀 7）导致变量内生性的三种情况1 遗漏变量(Omitted Variable Bias;OVB)2 共时性(Simultaneity)3 测量误差(Measurement Error )工具变量法(IV)匕=0o + 0Xi + UiQlV regression breaks X into two parts: a part that might be correlated with u, and a part that is not. By isolating the part that is not correlated with u. it is possible to e

15、stimateuThis is done using an instrumental variable, Zb which is uncorrelated withQThe instnunental variable detects movements in X? that areI)uncorrelated with % and uses these to estimate有效工具的两个条件匕=00十0必十均For an instrumental variable (an instrument Z to be valid, it must satisfy two conditions:1.

16、Instrument relevance : corr(ZZ5X)工 02. Instrument exogeneity : corr(Zj川)=0Suppose for now that you have such a Z, (we 41 discuss how to find instnimental variables later).How can you use Z/ to estimate 0i?两阶段最小二乘法(TSLS)Explanation #1: Two Stage Least Squares (TSLS)As it sounds, TSLS has two stages 一

17、 two regressions:(1) First isolates the part of X that is micorrelated with w: regress X on Z using OLS&=础 + 兀Z + vzBBecause Zz is uncorrelated with uh 咼 + ”Z is uncorrelated with Ui. We doiTt know 观 or but we have estimated them, so 八 the predicted values ofwhere=分 +.i = 1?.(2) Replace Xj by X： in

18、the regression of interest: regress Y on X using OLS:匕=0o + 0i 无+ ui(2)八Because X： is uncorrelated with Ui (if n is large), the firstleast squares assumption holds (if n is large) . Thus 0i can be estimated by OLS using regression (2) OThis argument relies on large samples (so 7)and 羽 are well estim

19、ated using regression (1)BThis the resulting estimator is called the Two Stage Least 卩 Squares (TSLS) estimator B严.Suppose you have a valid instrument, Z卜Stage 1:Regress X? on Zz? obtain the predicted values X，Stage 2:/X/XRegress Yj on X,; the coefficient on Xi is the TSLS estimator, B严.B严 is a cons

20、istent estimator of 0-例1:黄油的供给和需求IV regression was originally developed to estimate demand elasticities for agricultural goods, for example butter:b(Q讐)+ 0iln(严)f= price elasticity of butter = percent change in quantity for a 1% change in price (recall log-log specification discussion) I -1 Data: ob

21、servations on price and quantity of butter for different yearsThe OLS regression of ln(Qtter) on ln(严“ ) suffers from simultaneous causality bias (why?)Simultaneous causality bias in the OLS regression of ln(Q-utte,) on ln(严)arises because price and quantity are determined by the interaction of dema

22、nd and supplyPrice Period 2This interaction of demand and supply produces.PriceQuantity(b) Equilibrium price and quantity for 11 time periodsegression using these data produce theBut. what would you get if only supply shifted?(c) Ec|iiilibi-iuni price and cjiianriry when only the supply curve shifts

23、S estimates the demand curve by isolating shifts in quantity that arise from shifts in supply, a variable that shifts supply but not demand.TSLS in the supply-demand example:11】(0譽)=00 + 0山】(严如)十均Let Z = rainfall in dairypToducing regions.Is Z a valid instrument?(1) Exogenous? corr(ram/?M/) = 0?Plausibly: whether it ra