1、政策評估中的微觀計量方法詳 解演示文稿（優(yōu)選）政策評估中的微觀計 量方法評估的意義有利于公共政策制訂的科學化(Evidencebased policy making)美國：早在上世紀40年代已把評估納入公共政策制訂 的重要程序和步驟中國：科學發(fā)展觀，黨的十八大提出，建立重大事項社會穩(wěn)定風險評估機制 有利于稀缺資源的有效配置政策是否有效？政策影響有多大?應(yīng)該繼續(xù)實施或停止?評估的分類評估的層面：宏觀評估口成本一收益分析、成本一效果分析、成本一效用分析微觀評估口主要利用微觀計量方法(MicToeconometrics)對某一項目(政 策)的實施效果進行評估評估的時間 事前評估 (prospecti

2、ve evaluation) 事后評估 (retrospective evaluation)微觀計量方法已成為項目評估中最常用的研究工 具。,不同學科中的評估問題社會保障學養(yǎng)老保險繳費對居民消費和儲蓄率的影響失業(yè)保險對失業(yè)人群就業(yè)行為的影響 勞動經(jīng)濟學教育的回報率：教育程度對收入水平的影響 農(nóng)民工再就業(yè)培訓對收入的影響教育經(jīng)濟學班級規(guī)模與學生學習成績課外補習與學生學習成績評估中的兩大難題因果推斷問題(causal inference problem)雞生蛋，還是蛋生雞？政策干預(yù)與所觀察到的結(jié)果是 否存在因果關(guān)系？例如：觀察到農(nóng)民工接受再就業(yè)培訓后收入水平提高, 是否可以得出結(jié)論：再就業(yè)培訓導致

3、了收入水平的提 咼？在評估文獻中，通常把受到政策干預(yù)的人群稱為處理 組(treatment group),把沒有受到政策干預(yù)的人 群稱為對照組(comparison group)或控制組(control group)o用公式可以表示為：Impact=(Y|T=l)-(Y|T=O)反事實問題(counterfactual problem )評估面臨的另一個難題是：我們不可能在同一時間點 上觀察到同一個體同時處于兩種狀態(tài)(受到政策干預(yù) 和不受到政策干預(yù))，即反事實問題。舉例：假設(shè)某人患感冒，服用了某制藥公司研發(fā) 的感冒藥，服藥后不久死亡。是否可以斷定是這 種感冒藥導致這個人的死亡？“克隆”有效的對

4、照組是評估的關(guān)鍵。有效的對照組Figure 3.1 The Perfect CloneBen eficiaryClone6 candies4 candiesImpact = 6 4 = 2 can dies研究是否參加醫(yī)療保險對醫(yī)療費用支出的影響， 沒有參加醫(yī)療保險的人群是否可以作為參加醫(yī)療 保險人群的有效對照組？隨機控制試驗隨機控制試驗是識別因果關(guān)系、解決反事實問題 的有效方法。醫(yī)學中的隨機雙盲試驗(Double Blinded Randomized Experiment)衛(wèi)生經(jīng)濟學中的蘭德醫(yī)療保險試驗(RnndHealth Insurance Experiment)發(fā)展經(jīng)濟學中的實地試驗(

5、field experiment)不同保險計劃對醫(yī)療服務(wù)需求的影響：蘭德試驗德試驗：迄今為止，研究醫(yī)療保險對個人醫(yī)療服務(wù)需求影響的最大控制性試驗。特點：控制性試驗：消除個人自選擇偏差(Selection Bias)樣本量：65歲以下，5,809個人歷時時間：1974- 1982試驗城市：6 個城市:Dayton OH, Seattle WA, FitchburgMA, Charlston SC, Georgetown County SC, Franklin County MA)總花費：8000萬美元(按當時的價格計算)研究設(shè)計五類保險計劃，隨機分配給參加試驗的人群 按服務(wù)項目付費（免費）計劃醫(yī)

6、療服務(wù)25%共付比計劃醫(yī)療服務(wù)50%共付比計劃醫(yī)療服務(wù)95%共付比計劃個人起付線計劃對個人醫(yī)療服務(wù)花費起付線巧。美元，隨后的醫(yī)療服務(wù)花費免費毎以上的保險計劃下，個人每年人的最高支付限度為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隨機試驗存在的問題項目成本過高、實施程序復雜 樣本損耗：人們在實驗過程中可

12、能會離開 結(jié)論的局限性：結(jié)論可能只適用于某一地區(qū)的某一人群Hawtlnone和 John Henry效應(yīng)因為知道會被觀察，處理組和控制組會有不同的表現(xiàn)。因此這些 結(jié)果不能被用來概括沒有被觀察的情況。一般均衡效應(yīng)(General Equilibrium Effect)由于一般均衡效應(yīng)，結(jié)論推廣至全體人群是比較麻煩的：小規(guī)模 的實驗不能得出一般均衡的結(jié)果，而其對于實施與每個人息息相 關(guān)的政策是相當重要的。基于觀察數(shù)據(jù)的評估方法由于隨機試驗成本高昂且不易實施，大量現(xiàn)實生活中的評估是基于觀察數(shù)據(jù)(Observationaldata)來進行的。觀察數(shù)據(jù)獲取雖然成本相對較低，但常常存在自 選擇偏差(sel

13、f-selection bias),導致模型估計不準確、有效。換句說，由于自選擇問題的存在， 沒有受到政策干預(yù)組不再是受到政策干預(yù)組的有 效對照組。在回歸模型中，自選擇問題導致我們所關(guān)注的變量不再是外生變量，從而產(chǎn)生內(nèi)生性問題。考慮一個簡單的一元線性回歸模型” =00 + 01 乞 + 6實際研究中我們往往無法得到總體的回歸方程， 只能通過樣本對總體參數(shù)進行估計。當利用樣本 統(tǒng)計量bO和bl代替總體回歸議程中的“和兒時， 就得到了估計的回歸方程。其形式為：X = 2 +乞乞一元回歸方程的估計參數(shù)b可以表示為:7 工（一元）（ - 刃 COV（X）COV（X,）var（x）var（x）b= /?

14、o +E （兀 7）導致變量內(nèi)生性的三種情況1 遺漏變量(Omitted Variable Bias;OVB)2 共時性(Simultaneity)3 測量誤差(Measurement Error )工具變量法(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有效工具的兩個條件匕=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?兩階段最小二乘法(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&=礎(chǔ) + 兀Z + vzBBecause Zz is uncorrelated with uh 咼 + ”Z is uncorrelated with Ui. We doiTt know 觀 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 無+ 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嚴.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嚴.B嚴 is a cons

20、istent estimator of 0-例1:黃油的供給和需求IV regression was originally developed to estimate demand elasticities for agricultural goods, for example butter:b(Q讐)+ 0iln(嚴)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(嚴“ ) suffers from simultaneous causality bias (why?)Simultaneous causality bias in the OLS regression of ln(Q-utte,) on ln(嚴)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】(嚴如)十均Let Z = rainfall in dairypToducing regions.Is Z a valid instrument?(1) Exogenous? corr(ram/?M/) = 0?Plausibly: whether it ra