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# Applied Econometrics ECON 753

UMass

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This 57 page Class Notes was uploaded by Mr. Kay Bergstrom on Friday October 30, 2015. The Class Notes belongs to ECON 753 at University of Massachusetts taught by Michael Ash in Fall. Since its upload, it has received 18 views. For similar materials see /class/232316/econ-753-university-of-massachusetts in Economcs at University of Massachusetts.

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Date Created: 10/30/15

Applied Econometrics Labor Econometrics Michael Ash Econ 753 Labor Econometrics 7 p145 Outline of Presentation Threeday plan 1 Wage models 2 Labor demand the employment effect of the minimum wage 3 Labor supply the employment effect Today Wage models 1 Why study wage determination 0 Outcome of a labormarket process 0 Distribution of product and surplus 39 Rents both for themselves and as an indicator of market power 2 Building an econometric model Theory and functional form 3 Discrimination 4 Aggregate variables and micro units Labor Econometrics 7 p245 Data sources and de nitions 1 Census CPS NLSY PSID US 2 Luxembourg Income Study OECD GSOEP Germany Wage and wage 0 Annual earnings weekly earnings hourly wage salaries 0 Think about the data in rows observations records and columns variables fields Read and produce tables of regression output I I Labor Econometrics 7 p345 Wagesetting models Competitive market models 0 Marketclearing wage 0 Human Capital HK hedonic wage model 0 Compensating differentials 0 Monopsonistic or exploitation models 0 Institutional models and discrimination models 0 Segmented labor markets 0 Interindustry wage differentials Labor Econometrics 7 p445 On econometric models of wages DoesX orZ raise wages 0 Simultaneityendogeneityselection DoesX realy raise your wages or do people with X tend to have high wage 0 Signalling critique of Human Capital 39 General equilibrium remark on Human Capital 0 Aggregate explanatory variables I I Labor Econometrics 7 p545 Functional form 39 About subscripts and parameters yi y yi B 0 Interpreting the semilog specification 0 The Return to Education 39 Experience or tenure Indicator or dummy variables 0 Indicator interactions 0 Differences in the function over timespacegroup eg skillbiased technical change Labor Econometrics 7 p645 Return to Education HK phrasing Human Capital Regressions are based on a hedonic model of wage determination the wage is a function of the valuable characteristics of the worker What s the return to attending one more year of schooling Career Earnings by Years of School Years attended t O 1 2 0 Y0 O O 1 Y0 Y1 O 2 Y0 Y1 Y2 3 Y0 Y1 Y2 5 Y0 Y1 Y2 T Y0 Y1 Yz Labor Econometrics 7 p745 Return to Education Note that more years of schooling require foregoing years of earnings In a very simple model the return to schooling will equalize the net present value N PV of the alternatives 012 or 3 years of schooling Y0 Y0 Y0 11 000 Y 1r01r11r2 r 0 0 Y1 Y1 1 NPVS1 Y 1r01r11r2 r 1 Equilibrium NPVS 011 Y0 NPVS 1 implies Y1 F1 Y0 I Labor Econometrics 7 p845 I Returns to Education The additional income from one year of school relative to zero years of school represents a return of m The same argument implies that Y2 1F2 Y1 and so on Substituting recursively we find Ys 1 rsYs1 1 rs1r1Yo For the moment assume that rs rs1 r1 r a single percent return to an additional year of education a testable proposition YS 1S Y0 I I Labor Econometrics 7 p945 Measuring and Estimation Recall that by a Taylor series approximation 1 5 e8 and so 1 rs ers e This might also be familiar from compoundinterest formulas Ys e Yo and taking log of both sides lnYS lnYo rs which can be interpreted as a semilog wage equation that we can estimate lnYS 06 BS Ysand S are measured earnings and schooling and or and B are estimated parameters B is the return to schooling expressed in percent per year and or is the intercept implied log earnings at zero years of education I Labor Econometrics 7 p1045 Critique of Causality In observational studies in the US and elsewhere B is rarely estimated below 005 a 5 percent return to schooling and in LDC s is sometimes estimated as high as 020 a 20 percent return to schooling ls lnYS 0c BS a causal relationship ie if B is positive does schooling cause higher earnings Why or why not Omitted variables that cause B to be an overestimate left and right critiques O Socioeconomic status causes both schooling and earnings 0 Ability causes both schooling and earnings 0 Signalling 39 General equilibrium and social returns 0 Attitudinal modification We will return to this question in more detail later Labor Econometrics 7 p1 145 Experience Next we introduce experience and allow it to have a nonlinear effect on earnings Why Human capital explanations rational to accumulate HK when young rapid accumulation of HK in early years on the job OJT long time to amortize the cost of training forgetting and HK deterioration Neoinstitutional explanations solve PA problem with bond matching and wage gains 39 Institutional explanations customs and norms seniority in unionized and unionavoiding workplaces limited evidence on productivity bus driver safety study Labor Econometrics 7 p 1245 Tenure and experience 39 Would be helpful to resolve some of the competing hypotheses 0 Empirical problem few datasets include measures of tenure then they do the quality is often poor for three reasons recollection bias definition ofjob and employer sampling durations Potential Experience Mincerian Experience for Jacob Mincer Potential Experience Age Years of Schooling 6 Reasonable proxy unless the aim is to make subtle points about tenure versus experience Note that regressions with Potential Experience and Schooling must omit age to prevent perfect collinearity I I Labor Econometrics 7 p 1345 Quadratic speci cation 1n ocSBS ExBEX EXEBEXZ 8 BlnYi aEXl39 This term depends on the level of experience Typical values are BEX 003 and BExz 00004 The negative quadratic term means that the relationship is concave The positive linear term and the size of the two terms means that the return begins positive about 3 percent per year for a new worker at 0 years of experience and then falls You can compute the return at any given level of experience by substituting You can compute the peak of the ageearnings profile by setting the derivative to zero BlnYi E BEX 2EXZ39BEX2 E O I I Labor Econometrics 7 p1445 Experience continued which implies that Expeak BEX 2BEx2 For example with 35x 003 and BExz 00004 the peak of the profile would be at about 38 years of experience I I Labor Econometrics 7 p1545 Dummy variables Allows different intercepts for different categories Simplest example Each observation 139 is in category D 1 or not D O eg nonwhite female or union member categorical nominal variables with a simple level effect lnYz ocDz5XzBX21 The estimated coefficient 5 is the return premium or penalty to being in category D measured in log points read as percent Tip give D a useful name eg Female is a more useful name for an I Indicator than IS Sex Nonwhite IS a more useful name for anLindigatgrsp1645 39 LL I an Interpreting dummy variables To interpret the coefficients on dummy variables Consider a very simple wage equation lnYi 06Di5l 8i Y is hourly wage for person 139 ocis the intercept a baseline wage Diis an indicator variable for membership in category D eg DZ 1 for college degree or more and D1 O for less than college degree How can we interpret 8 the coefficient on D Labor Econometrics 7 p 1 745 Interpreting dummy variables For a person without a college degree D O and expected earnings are A YeoceO8eoc1eoc So the baseline wage without a college degree is ed For a person with a college degree D 1 and expected earnings are Yi eOLOel ede What s the percent return Labor Econometrics 7 p 1 845 Interpreting dummy variables YlY 0L 6 or p 16 e e e5 1 Y ea Remark this formula for percent change always works If you want to know the percent return when you are given 8 you can calculate p 66 1 But there is a shortcut when 5 is small Consider a firstorder Taylor approximation of the percent return pas a function of the coefficient 8 As we just saw p8 e8 1 p8 g p0p 0o8 O near80 p 5 65 p0 e0 11 10 p 0 601 A A A I U 1 O O 0 Labor Econometricsipl945 I The Returns to Computer Use John DiNardo and JornSteffen Pischke The Returns to Computer Use Revisited Have Pencils Changed the Wage Structure Too Quarterly Journal of Economics 112 pages 291303 1997 Also a good example of a critical replication 0 Enormous increase in inequality in the United States 0 Between categories 0 Within categories residual inequality 39 Skillbiased technical change 0 Alternatives trade tradeSBTC institutions 0 What are the technical changes in question 0 Alan Krueger offers computers I I Labor Econometrics 7 p2045 The Returns to Computer Use THE RETL39TRNSS T E39 MPEquotTER REVISITED 395 TABLE H 113 Eammam Fm TilTE EMT 43F llin ll USE 133139 PM WHEN VAR mama Lam HUUHLH39 WAGE LETMJDAEII Emma 21 panEHTIIEs ln epen en t U 5 LS U Gannany39 Germany 39J39 r i hh 1931 1935 1993 EST1quot 1935 15535 1531 11392 Cump uter 3131 11135 ELEM ELITE 3515 E11 DEBS m l 1313931 IE39El l UniH HMS THEM u D i 39f i 39L39liquot3 013153 IIJII FE 13111 IZIEI DEI 110032 ELWHII Tamil KIWI Experience i 39 131753 IIM J lJ lam ISEZIJZIIIIII iIm ilb mum i g lj E n i m 1343 39IJ1i153 I1 AID55 EIIII4E39 N 311023 Ellii l il l 4004312 111M HE ELM IA48 042 39EIEHT DEED H335 4 Humbmr ruba 13335 1343 13305 1942 22353 ames The Returns to Computersiand Pencils TABLE 111 THE EFFECT OI DIFFERENT TuuLs 0N PAY y WAGE x NTVARIABLE LOG Hnum STANDARD ERROFS IN YARENTIESES Independent Germany Germany Germany Germany Germany variath 195192 1979 1979 1955 1936 1991 1992 Occupation indicamrs ND 501 501 742 1071 Grades and father39s Na No Yes N0 N0 Occupationquot 39Dmls entered separately Cnmputer 0 1 1 0025 b o 005 o 011 0 011 0 008 o 007 Calculator 0 129 002 0 02 0 El 0 054 0 006 o 000 o 000 0 007 o 005 Telephone 13 06 0 05 0 59 0072 U ODS 00007 D 007 10 007 0007 Penpencil 0127 0055 0052 D 5 0050 0006 0007 0007 0007 D 007 Wnrk while sitting 7 0042 0041 00036 0003 0003 0000 Hand tool 0091 0048 0045 0020 0B20 eg hammer7 00006 00007 0009 m 003 00008 LahaxEcanamemcsrp 2245 Multiple dummy variables Multiple nominal categories eg race industry occupation Create a 10 dummy variable for each category Must omit one of the classes or the dummies will be collinear with the intercept Can alternatively omit the intercept The omitted class is associated with the intercept and all differences the coefficients on the dummies are read relative to the omitted class llez39 06 D22 52 i D3i53 XiBX amp I I Labor Econometrics 7 p2345 Other uses for dummy variables Can also create dummies for a small relative to N number of ordered categories a less parametric approach eg a return for every year of schooling 81 82 816 instead of restricting all years to have a single return BS Dummies are equivalent to a withingroup approach The response slope within every group is constrained to be the same but the groups can be at different levels Many dummy variables fixed effects eg for every state for every city for every person Needs more than one observation per fixed effect Cannot nest fixed effects eg neighborhoods and cities I I Labor Econometrics 7 p2445 Dummy interactions Dummydummy interactions Interactions among categorical variables Differences in function over time place or group For example do black women suffer as much discrimination as do black men Are union premiums larger for women or for men Were union premiums larger in 1970 or in 1980 In X Femz BFem i NonWhitejBNW i Fem X Nonwhitei pemww 8 Show regression output For this example BFem lt 0 BNW lt 0 but BFemNW gt Oenough to offset one of the two forms of discrimination ie black women do not receive the cumulative effect of sex and race discrimination Labor Econometrics 7 p2545 Dummydummy interactions Advantages of pooling 1 restrict other coefficients to equality 2 easy to test hypotheses Interact dummy variables for each category of first characteristic with dummy variables for each category of second characteristic Include dummies level or main effect and all dummy interactions To include interactions but not the main effect you must have a good reason or model Multiple interactions are possible Preview treatmentcontrol beforeafter model identify quasiexperimental effects y or Post8pm Treatment grplISTreatment W Post gtlt Treatment gtlt 5Effect 81 SE ectis the effect of treatment on the outcome because it expresses the difference between the treatment and control groups after treatment has Labor Econometrics 7 p2645 knnn vnrsnhlnhl Irvln nn on A39F inInvnrvInhl I ll Imwul Inrinklno Dummy interactions Dummycontinuous interactions different slopes for different folks S39P539i3e Bxp1 Labor Econometrics 7 p2745 Aggregate explanatory variables Moulton Contextual explanations of individual outcomes neighborhood city industry May want to explain an individual outcome with an aggregate characteristic eg a worker s wage may depend on the capitallabor ratio of the industry For worker z39 in industry s 1nYS or Zisy KLSB sis ns with 8239s Tls COV8S ns COV8S ejs COVnS7 m Labor Econometrics 7 p2845 Moulton 1 BOLS is unbiased but 2 seBO39S is underestimated Standard OLS estimation will generate an unbiased estimate of B how capitallabor ratio affects wage but the standard error of B is underestimated Intuition because KLSis aggregate all workers in industry s have the same K L and the same industryspecific error ns There are effectively S not I x S observations Ignoring this understates the estimated standard error of B Good exercise I I Labor Econometrics 7 p2945 I x Ranilwumtnum39ml Ll igim mm Kmeul u HTL p him Moulton variables TAEILF L UMVINEL39M39HS m AGQHELJATE Sum Varnmsmc 1311 IN Rlz nvssncm EXAMPLES Variable DE lli39ljDIl Emu3min Variabllca x E jmalcd rlL39lE ol 3111M mpl cmcm growth I Curr M sisIr relative mpluymcm disiurhantc 11 3 I mdictcd Siam Eisnurbmxcs g Linuu39 mmbina nun r t39 fumcasta If iiiME returnt dislurbnnccs mm Hiawatha mulch ITTEI39II t39il l quotv TiJ riiIIJl eaic 1quot Liv Iah llih mm per 39IMK pl limlmiim 9140 t 1 m ll L Legal abortions 1m mm live iaimhs WEE XIl1 2 1h 2th rm from I mart dim355 per INDIE popula than mam 19quot 1 Drum raj mm micidt pun mum population I39JHJU 1 x111 1 1E 39Jgiuh mm mm pe n39 Lul39 mnglwnns per Iii131139le pap uiminn 19 X m II I aw of divmma nyd nnrlu lnmms pct LEIOD unpubl Iiun 1981017 HIV3 r Fcrmnlagc of persona 5 17 years old cnrulltd in public I ltmtml 39 and secondary schunsl s 19 he IIJ 39 31 t nal mud 1mm in Equine kllrrmslen x I39IJ39 Til 15 Tmal water a a in square kilmm crs 33 ID 39 quotIIL r Eireanon of highcs pain in meters 1 RI W39s 139 Per cupilu state Icgisiatwii appropriations for arts H agendas EITHER I I M II Tum num hcr of black clan101 n 39minls July 1 MD quotJ I Daily newspaper cirrulnlium par i tpll s 19371 I E El 393 x 111quot quot l39 gt r39quotquot 7 quot 39 K r 7quot quot quot39quot39 quot Labor Econometrics 7 p3045 v1 dunch mm n anmlnl slab mu mumnu HI39IIELIIJIILTIJ EII IL IHIL TIIEII 39lhE ENE hEI E Moulton results 39l39amr l ES lman39l n fangI15 1le El thfI S39E39ATE VARMELm am am Lajzrj arr landwqu WFilIZL39r rij Ann Sagan lunlmr WM I 2 Cunel ciem Llnmljustcd Adjusted ll ai ciem ledjummi auljmrad Variable animate J39 Etatistiu r ituiigji Eatima39lc P Mariam I REMESHC Emammic k u ahlm 139 I131 1m H554 Dam ELEM inIE x 1157 MI UM UH 15 I UH 131 1132 I t 15 L39i39 MIMI Irml 39um Variables 1 t r 7 11 52 M7 tj Ll 101 ELSE 1 7 043 L 333 1 x L11 LJb ISLEE x1 nth LEIS 114 m LMS 1316 Ill 1 m r 7 L114 Lurl 4123 L a ELW 20 15 1 II Iquot39 315 11839 4 39 LilH M3 1111 I L39 1173 1323 I 411 125 IJ3HI r m W 1 E Il LINE 5 1 39 1 I w MEI I I1quot 1119 sum MES 103 icsipsms p m H 1am in ma Aggregate explanatory variables Moulton Alternative solutions 1 Estimate with standard errors adjusted for clustering 2 Estimate with dummy variables for the aggregates yis Zisy 5s i 813 Then estimate the relationship between the s premium and characteristics of aggregate s 5s st i us Labor Econometrics 7 p3245 LaborMarket Discrimination 39 What are the right questions 0 What s wrong with reverse regression 0 Oaxaca decomposition O Decomposition of intergroup averagewage gap into average characteristics and price per characteristic 39 Compute means for the explanatory variables for the two groups 39 Run separate regressions for the two groups 1nYW 1nYb WBW 473 WW 4pr MW 473 For B5 W mb discrimination in returns difference in characteI I I Labor Econometrics 7 p3345 Notes on Oaxaca decomposition 39 Note and interpret the alternative decomposition 1nYW 1nYb WBW 473 WBW w 7Bw 7Bb mar B5 W mw 1 0 Recall thatXincludes the constant 1 1SZXiX12 I I Labor Econometrics 7 p3445 Labor Demand 39 Minimumwage 0 Immigration 39 Factor demand models Labor Econometrics 7 p3545 TimeSeries Minimum Wage Studies 10 percent increase in the minimum wage causes a 1 3 percent reduction in teenage employment Critiques index Mm 2i mIwizCt coverage and relative wage Sensitive to specification 39 Publication bias tratios do not grow with the square root of sample size 0 E MW Relationship weakened between 1950 s1970 s and 1980 s 1990 s 0 Sex oddity Labor Econometrics 7 p3645 Minimum Wage Natural Experiment Employer Responses to the Minimum Wage Evidence from the FastFood Industry The Employment Effects of the New Jersey Minimum Wage 39 Date of New Jersey minimumwage increase 1 April 1992 0 New Jersey minimum wage before 1 April 1992 425 per hour 39 New Jersey minimum wage after 1 April 1992 505 per hour 39 Federal minimum wage before and after 1 April 1992 425 per hour 39 Pennsylvania minimum wage before and after 1 April 1992 425 per hour I I Labor Econometrics 7 p3745 Research Design Where will the minimum wage have bite Confounding factors 0 National or regional trends 0 Policy endogeneity 0 Control or comparison groups Labor Econometrics 7 p3845 The fast food industry Approximately onehalf of employees are 20 years of age or older 66 percent of employees are female 77 percent of employees are white 65 percent of employees have at least a highschool diploma Turnover is very high onehalf of employees have less than one year of job tenure Work is gender segregated female employees and employees with higher seniority are more likely to work in the front of the store I I Labor Econometrics 7 p3945 Survey Method 39 Telephone survey of almost 500 Burger King KFC Wendy s and Roy Rogers restaurants in eastern Pennsylvania and New Jersey 39 First wave of survey FebruaryMarch 1992 87 percent response rate 39 Second wave of survey NovemberDecember 1992 100 percent of firstwave respondents 39 Fulltime equivalent FTE employment E fulltime employees onehalf of parttime employees I Labor Econometrics 7 p40 45 Analysis 39 Establish that the NJ minimum wage had bite in New Jersey and not in Pennsylvania the treatment group received treatment and the comparison group did not 39 Measure the average change in employment in NJ restaurants 0 Appropriate counterfactual average change in employment in PA restaurants Labor Econometrics 7 p4145 Bite 32 lvldvnn lmm In kal bod lnduslrv 7 m Porccntagc a 115mm an E gw E 766 5 gm 3 Ll n as usua uss a Wag Rang New km Pemuylvanu gure 22 mmbuuan oi swung ane mm A hymnMam 992 a NovambepDeczmbeerZ Labnr Ecnnnmemcs 7 p 4245 Results TAMI1 22 J Worago Employment per Restaurant Before and After Increase in New Jamey Minimum Wage Rt39smu an MS Di erm i AH PA NJ NI PA H L 1 3 HJ 391 FI39E Employment chnre 2103 2333 2044 239 All Availath Obsewa un 049 135 051 144 2 FTE Empluyment AltEr All 2105 21 21136 l4 Available Dbsen nns 0346 091 052 111539 3 Change in Mean FTP 005 2l 0551 26 Empluyment 05m 11 25 054 136 4 Change in Mann FTE Em L39U 228 04 275 ployment Balanced Sample v45 125 143 13421 of R sfaurants39 5 Change in Mean FYE Em l 218 023 251 pluyment Setting I TE at Ra 125 12149 135J Temporarin Closed Restau rants to 21cm Labor Econometrics 7 134345 Other Outcomes Labor Econometrics 7p4445 Labor Supply 39 Participation and hours 0 Structural models income and substitution effects 0 Natural experiment models 0 Criticisms labor demand 0 Structural models 0 Reduced formdifference in difference Labor Econometrics 7 p4545 Applied Econometrics Labor Econometrics Michael Ash Econ 753 Labor Econometrics 7 p112 Outline of Presentation Threeday plan 1 Wage models 2 Labor demand the employment effect of the minimum wage 3 Labor supply 39 Why study labor supply 0 Analysis of tax and transfer policy 0 Labor supply and the Earned Income Tax Credit Labor Econometrics 7 p212 Basic Issues in Labor Supply 39 Participation and hours 0 Structural models 39 Quasiexperimental models 0 Constrained demand addedworkerdiscouragedworker Labor Econometrics 7 p312 Structural models of labor supply 39 Consumptionnonmarkettime model maxUxH subject tox Y erH x7 Wage changes offsetting income and substitution effects Basic Empirical Labor Supply Specification H od7BwZys 39 Criticisms 0 Unobserved heterogeneity systematic overestimate of labor supply elasticity people with high wage may have high unobserved propensity to work 0 Wage unobserved for nonparticipants 0 Male chauvinist second earner models 0 Family utility models unitary bargaining Labor Econometrics 7 p412 Kinked budget constraint models Concave and convex kinks as a result of taxes and transfers or fixed costs ofjobholding 39 Progressive income tax Phaseout region of transfer programs 0 Fixedcost of employment 39 CBC vs LBC estimation Labor Econometrics 7 p512 Empirical Speci cations and Results 39 Female labor supply estimates male labor supply elasticity found to be low 39 Hausman results 39 Mroz survey Berndt Table 112 p 638 0 Change in annual hours for 1 change in wage rate1000 change in income 0 Wide range of estimates including negative of laborsupply elasticity 0 Sensitive to wage endogeneity Labor Econometrics 7 p612 Mroz Survey Table 112 MRCIZ S Survey of Estimates of Married Woman39s LGIZNZIF Supply Fc equnses Siudy Estimation Mmhmi Wage Ifl i ec income Effec Ruskin 1111273 ln39erumcmaI variablea 3F m Cugan 93011 Tuba EM 323 7ng 79307 Instru mcn ml vai39iahlcs 31quot I 39T Eugan I M h 39l39unhit a 41253 Egan l I MJUN Fixml mints lSl39h Pagan j I JE 1 Fixed emu 3amp1 124 Urccnhalgh 931 Inslmmcuwl variubtcs 2 56 iiusmzm WENJ Convax hudg 5113 328 1130 Hangman WEN NOHCGHVEK budget 5413 335 1 5M l ausmun I WEN Fina mils 305 1 130 Heckmzm lWEvsn Tubil I462 TIquot4 IIuL39kmu n WT a Swamiin Tobil A499 Sl Hackman WEN Gcnurajjzcd quotInipil NIH HRS Lugum 1 Eli 19m jl hhil 13 1 151 Layart 1 HI 9813 lmtrumentui variables 32 I Lculhnld I973 I945quot cslimalcs I4 3i Ixutl1qu HITS I39Jfa estimates 45 TH Leuthnld WEI 9H estimates 33 58 Nakamum and Gcnumliwd Tubi39l lb l Nakmrmm WE a Schultz NEH Tubil 123 all Schultz IEJEU Inwummual vun uhlcs Er 19 NINE Thn n Ttx L ur t r luutc J at 1 ml39e39x wag ul SJ 5U er s lnlun a SE1L hintsml AF ul SILI J Izumaml huun 31 37W huthn ld nnnhh r i ll llh39 of NIL nml bhnr and nnnlnmr r llxl u Ln rum er I39 11k and lil 33315 l l sm llntly Th run ll39ll Ifn il l5 wrSII39KNj m I39Tu39 n Labor Econometn39cs 7 p712 Empirical Speci cations and Results 39 Mroz study Panel Study of Income Dynamics 1975 the small income and wage effects found in this study provide a much more accurate picture of the behavioral responses of working women to variations in nonlabor income and wage than those found in most previous studies 0 Labor supply elasticity similar to that of men 0 Limited role of sample selectivity bias 0 Unobserved taste for work is a significant omitted variable affecting current and past particpation and hours 0 the influence of taxes on the estimates of the labor supply parameters appears to be at most a second order effect Labor Econometrics 7 p812 Eissa and Liebman Earned Income Tax Credit Enacted 1975 expanded in 1986 1990 and 1993 0 34 B 19 M recipients largest means tested cash transfer program 0 Current 2003 parameters unmarried with two or more children 0 040 credit per earned 100 up to 10500 earned income credit of 4204 0 Flat credit of 4204 for earned income between 10500 and 13500 0 Credit reduced by 021 per earned 100 until credit is zero at earned income of 34000 0 Different parameters for onechild married families and much smaller for workers without children 39 E and L 1987 Expansion TRA86 39 Definition of treatment and comparison groups Labor Econometrics 7 p912 Eissa and Liebman 39 Quality of the comparison groups 39 Quantity effects participation and hours What about wage effects I I Labor Econometrics 7 p 1012 Eissa and Liebman mm 11 mo Fem PAm39wIIm HUN mm or Ummn Watm DiHmnmm I mTEASE pawrims Di emnm dx erences m 2 3 m A 7mmquot gmum Wm children 0129 m mm 0753 wmm o 024 mums mm 0mm gmup Wuhuut childmn 0952 m cm 0952 mum o om m m 0024 m Ims 432371 a mum gmp ADS Lhan high ml with chzldxen um mm o m 00m n mg 5014 m onlml gmnp 1 35 than high when wiLhnuL childxzn o m mom 0 751mm a azs 1o om n04 a 0191 I anlml gmup 2 seynud high icluml Wm Children 0 91 0mm 0920 mom was 10 mm was In 0151 5712 atmznt gmup 1gb dmnl mm chlldnn u 764 mm 0 787 10067 o m 0003 7021 vmol gmnp L gh mm vulhnut swam 09x mm um 40me 7mm 0004 09251 mm 6577 mm gram 2 mm high schnul with ch dren u 911 1110115 0920 a cast was 0007 a 014 7 4m 7125 Closing Remarks on Labor Supply No model of labor demand except via wage and nonclearing markets 0 Limited literature on Wage Squeeze 0 Gender family literature Labor Econometrics 7 p 1212

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