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\author{Emmanuel Saez}
\date{Berkeley}
\title{230B: Public Economics \\
Labor Supply Responses to Taxes and Transfers} \onlyslides{1-300}
\newenvironment{outline}{\renewcommand{\itemsep}{}}
\begin{document}
\begin{slide}
\maketitle
\end{slide}
\begin{slide}
\begin{center}
{\bf MOTIVATION}
\end{center}
1) Labor supply responses to taxation are of fundamental
importance for income tax policy [efficiency costs and optimal tax
formulas]
2) Labor supply responses along many dimensions:
(a) Intensive: hours of work on the job, intensity of work,
occupational choice [including education]
(b) Extensive: whether to work or not [e.g., retirement and
migration decisions]
3) Reported earnings for tax purposes can also vary due to (a) tax
avoidance [legal tax minimization], (b) tax evasion [illegal
under-reporting]
4) Different responses in short-run and long-run: long-run
response most important for policy but hardest to estimate
\end{slide}
%\begin{slide}
%\begin{center}
%{\bf OUTLINE}
%\end{center}
%
%1) Labor Supply Elasticity Estimation: Methodological Issues
%
%2) Estimates of hours/participation elasticities
%
%3) Responses to low-income transfer programs (EITC)
%
%4) Inter-temporal Models and Macro Estimates
%
%5) Elasticity of\ Taxable Income (separate slides set)
%
%%7) Implications for Preference Parameters
%\end{slide}
%\begin{slide}
%\begin{center}
%{\bf REFERENCES}
%\end{center}
%{\bf 1. Surveys in labor economics:}
%
%a) Pencavel (1986) \textit{Handbook of Labor Economics} vol 1
%
%b) Heckman and Killingsworth (1986) \textit{Handbook of Labor
%Econ} vol 1
%
%c) Blundell and MaCurdy (1999) \textit{Handbook of Labor
%Economics} vol 3
%
%d) Keane JEL'2011 (structural)
%
%{\bf 2. Surveys in public economics:}
%
%a) Hausman (1985) \textit{Handbook of Public Economics} vol 1
%
%b) Moffitt (2003)\textit{\ Handbook of Public Economics} vol 4
%
%c) Saez, Slemrod, and Giertz JEL (2012) (reduced form)
%
%\end{slide}
\begin{slide}
\begin{center}
{\bf STATIC MODEL: SETUP}
\end{center}
Baseline model:\ (a) static, (b) linearized tax system, (c) pure
intensive margin choice, (d) single hours choice, (e) no frictions
Let $c$ denote consumption and $l$ hours worked, utility $u(c,l)$
increases in $c$, and decreases in $l$
Individual earns wage $w$ per hour (net of taxes) and has $y$ in
non-labor income
Key example: pre-tax wage rate $w^p$ and linear tax system with
tax rate $\tau$ and demogrant $G$ $\Rightarrow$ $c=w^p (1-\tau)l +
G$
Individual solves \[ \max_{c,l} u(c,l) \quad \text{subject to} \quad c=wl+y \]
\end{slide}
\begin{slide}
\begin{center}
{\bf LABOR SUPPLY BEHAVIOR}
\end{center}
FOC: $w u_c + u_l =0$ defines uncompensated (Marshallian) labor
supply function $l^u(w,y)$
Uncompensated elasticity of labor supply: $\varepsilon^u = (w/l)
\partial l^u/ \partial w$ [\% change in hours when net wage $w
\uparrow$ by 1\%]
Income effect parameter: $\eta = w \partial l / \partial y \leq
0$: \$ increase in earnings if person receives \$1 extra in
non-labor income
Compensated (Hicksian) labor supply function $l^c(w,u)$ which
minimizes cost $w l - c$ st to constraint $u(c,l) \geq u$.
Compensated elasticity of labor supply: $\varepsilon^c = (w/l)
\partial l^c/ \partial w>0$
Slutsky equation: $\partial l/ \partial w = \partial l^c/ \partial
w + l \partial l /\partial y$ $\Rightarrow$ $\varepsilon^u =
\varepsilon^c + \eta $
\end{slide}
\begin{slide}
\includepdf[pages={108-112}]{laborsupply_attach.pdf}
\end{slide}
%\begin{slide}
%\begin{center}
%{\bf IMPORTANT SPECIAL CASE: NO INCOME EFFECTS}
%\end{center}
%Quasi-linear utility function $u(c,l)=c - h(l)$
%
%$\max_l wl+y -h(l)$ $\Rightarrow$ $h'(l)=w$
%
%$\Rightarrow$ Marshallian $l^u(w,y)=l(w)$ labor supply independent
%of $y$
%
%$\Rightarrow$ Hicksian $l^c(w,u)=l(w)$ labor supply independent
%of $y$ [parallel indifference curves]
%
%$\Rightarrow$ Identical uncompensated and compensated labor supply
%
%$\Rightarrow$ $\eta=0$ and $\epsilon^c = \epsilon^u >0$
%
%Iso-elastic utility function: $u(c,l)=c-a\frac{l^{1+1/\varepsilon }}{%
%^{1+1/\varepsilon }}$ $\Rightarrow$ $w =C \cdot l^{\varepsilon}$
%\end{slide}
\begin{slide}
\begin{center}
{\bf BASIC CROSS SECTION ESTIMATION}
\end{center}
Data on hours or work, wage rates, non-labor income started
becoming available in the 1960s when first micro surveys and
computers appeared:
Simple OLS regression:
$$l_i = \alpha + \beta w_i + \gamma y_i + X_i \delta +
\epsilon_i$$
$w_i$ is the net-of-tax wage rate
$y_i$ measures non-labor income [including spousal earnings for
couples]
$X_i$ are demographic controls [age, experience, education, etc.]
$\beta$ measures uncompensated wage effects, and $\gamma$ income
effects [can be converted to $\varepsilon^u$, $\eta$]
\end{slide}
\begin{slide}
\begin{center}
{\bf BASIC CROSS SECTION RESULTS}
\end{center}
{\bf 1. Male workers} [primary earners when married] (Pencavel,
1986 survey):
a) Small effects $\varepsilon^u=0$, $\eta=-0.1$, $\varepsilon^c=0.1$
with some variation across estimates (sometimes $\varepsilon^c<0$).
{\bf 2. Female workers} [secondary earners when married]
(Killingsworth and Heckman, 1986):
Much larger elasticities on average, with larger variations across
studies. Elasticities go from zero to over one. Average around
0.5. Significant income effects as well
Female labor supply elasticities have declined overtime as women
become more attached to labor market (Blau-Kahn JOLE'07)
\end{slide}
%\begin{slide}%
%\begin{center}
%{\bf PROBLEMS WITH OLS ESTIMATION OF LABOR SUPPLY EQUATION}
%\end{center}
%
%1) Econometric issues
%
%a) Unobserved heterogeneity [tax instruments]
%
%b) Measurement error in wages and division bias [tax instruments]
%
%c) Selection into labor force [selection models]
%
%d) Endogenous tax rates [non-linear budget set methods]
%
%2) Extensive vs. intensive margin responses [participation models]
%
%3) Non-hours responses [taxable income]
%
%\end{slide}
\begin{slide}
\begin{center}
{\bf KEY ISSUE: $w$ correlated with tastes for work}
\end{center}
\[l_i = \alpha + \beta w_i + \gamma y_i + \epsilon_i\]
Identification is based on cross-sectional variation in $w_i$:
comparing hours of work of highly skilled individuals (high $w_i$)
to hours of work of low skilled individuals (low $w_i$)
If highly skilled workers have more taste for work (independent of
the wage effect), then $\epsilon_i$ is positively correlated with
$w_i$ leading to an upward bias in OLS
Plausible scenario: hard workers acquire better education and
hence have higher wages
Controlling for $X_i$ can help but can never be sure that we have
controlled for all the factors correlated with $w_i$ and tastes
for work: {\bf Omitted variable bias}
$\Rightarrow$ Tax changes
provide more compelling identification
\end{slide}
%\begin{slide}
%\begin{center}
%{\bf ISSUE 2: Measurement error in hours}
%\end{center}
%In general $w$ computed as earnings / hours $\Rightarrow$ Can
%create division bias
%
%Let $l^{\ast }$ denote true hours, $l$ observed hours
%
%Compute $w=z/l$ where $z$ is observed earnings%
%\begin{eqnarray*}
%&\Rightarrow &\log l=\log l^{\ast }+\mu \quad \mathrm{measurement} \quad \mathrm{error} \\
%&\Rightarrow &\log w=\log z-\log l=\log z-\log l^{\ast }-\mu =\log
%w^{\ast }-\mu
%\end{eqnarray*}%
%Spurious negative correlation between $\log l$ and $\log w$ [e.g,
%workers with high misreported hours also have low imputed wages]
%biasing elasticity estimate downward
%
%Solution:\ tax instruments again
%\end{slide}
%\begin{slide}
%\begin{center}
%{\bf ISSUE 3: Non-participation}
%\end{center}
%Consider model with fixed costs of working, where some individuals
%choose not to work
%
%Wages are unobserved for non-labor force participants
%
%Thus, OLS regression on workers only includes observations with $%
%l_{i}>0$
%
%This can bias OLS\ estimates: low wage earners must have very high
%unobserved propensity to work to find it worthwhile
%
%Requires a selection correction pioneered by Heckman in 1970s
%(e.g. Heckit, Tobit, or ML estimation): problem is that
%identification is based on strong functional form assumptions [See
%Killingsworth and Heckman (1986) for implementation]
%
%Current approach: use tax instruments and look directly at participation
%margin
%\end{slide}
%\begin{slide}%
%\begin{center}
%{\bf Extensive vs. Intensive Margin}
%\end{center}
%
%Related issue:\ want to understand effect of taxes on labor force
%participation decision
%
%With fixed costs of work, individuals may jump from
%non-participation to part time or full time work (non-convex
%budget set)
%
%This can be handled using a discrete choice model:%
%\[
%P=\phi (\alpha +\varepsilon \log (1-\tau )-\eta y)
%\]
%where $P\in \{0,1\}$ is an indicator for whether the individual
%works
%
%Function $\phi $ typically specified as logit, probit, or linear
%prob model
%
%Note:\ here it is critical to have tax variation; regression
%cannot be run with wage variation
%
%\end{slide}
%\begin{slide}
%\begin{center}
%{\bf ISSUE 4: Non-hours responses}
%\end{center}
%Traditional literature focused purely on hours of work and labor
%force participation
%
%Problem: income taxes distort many margins beyond hours of work
%
%a) Non-hours margins may be more important quantitatively
%
%b) Hours very hard to measure (most ppl report 40 hours per week)
%
%Two solutions in modern literature:
%
%a) Focus on total earnings ($z=wl$) [or taxable income] as a
%broader measure of labor supply
%
%b) Focus on subgroups of workers for whom hours are better
%measured, e.g. taxi drivers
%\end{slide}
%\begin{slide}
%\begin{center}
%{\bf ISSUE 5: NON-LINEAR BUDGET SETS}
%\end{center}
%Actual tax system is not linear but piece-wise linear with varying
%marginal tax rate $\tau$ due to (a) means-tested transfer
%programs, (b) progressive individual income tax
%
%%Individual maximization problem:
%%
%%$\max u(w^pl-T(w^pl),l)$ $\Rightarrow$ FOC $u_c w^p(1-T')+u_l=0$
%
%Same theory applies when considering the linearized tax system
%$c=wl+y$ with $w=w^p(1-T')$ and $y$ defined as virtual income
%(intercept of budget with x-axis when setting $l=0$)
%
%Main complications:
%
%(a) $w$ [and $y$] become endogenous to choice
%of $l$
%
%(b) FOC may not hold if individual bunches at a kink
%
%(c) FOC may not characterize the optimum choice
%\end{slide}
%
%\begin{slide}
%\includepdf[pages={1}]{laborsupply_attach.pdf}
%\end{slide}
%
%
%\begin{slide}
%\begin{center}
%{\bf ISSUE 5: NON-LINEAR BUDGET SETS}
%\end{center}
%
%Non-linear budget set creates two econometric problems:
%
%1) Model mis-specification:\ OLS regression no longer recovers
%structural elasticity parameter of interest
%
%2) Econometric bias: $\tau _{i}=T'(w_il_i)$ and $y_i$ depends on income $w_{i}l_{i}$
%and hence on $l_{i}$
%
%Tastes for work are positively correlated with $\tau _{i}$ (due to progressive tax system) $%
%\rightarrow $\ downward bias in OLS\ regression of hours worked on
%net-of-tax rates
%
%\end{slide}
%
%\begin{slide}
%\begin{center}
%{\bf OLD NON-LINEAR BUDGET SET METHOD}
%\end{center}
%Issue addressed by non linear budget set studies pioneered by
%Hausman in late 1970s (Hausman, 1985 PE handbook chapter)
%
%Method uses a structural model of labor supply to derive and estimate
%labor supply function fully consistent with theory
%
%Key point: the method still uses the standard cross-sectional variation
%in pre-tax wages $w^p$ for identification. Taxes are seen as a
%problem to deal with rather than an opportunity for
%identification.
%
%New literature identifying labor supply elasticities using tax
%changes has a totally different perspective: taxes are seen as an
%{\bf opportunity} to identify labor supply
%\end{slide}
%
%
%\begin{slide}
%\begin{center}
%{\bf ISSUE 4: NON-LINEAR BUDGET SETS}
%\end{center}
%Actual tax system is not linear but piece-wise linear with varying
%marginal tax rate $\tau$ due to (a) means-tested transfer
%programs, (b) progressive individual income tax, (c) ceiling in
%payroll tax
%
%Individual maximization problem:
%
%$\max u(w^pl-T(w^pl),l)$ $\Rightarrow$ FOC $u_c w^p(1-T')+u_l=0$
%
%Same theory applies when considering the linearized tax system
%$c=wl+y$ with $w=w^p(1-T')$ and $y$ defined as virtual income
%(intercept of budget with x-axis when setting $l=0$)
%
%Main complications: (a) $w$ [and $y$] become endogenous to choice
%of $l$, (b) FOC may not hold if individual bunches at a kink, (c)
%FOC may not characterize the optimum choice
%\end{slide}
%
%
%\begin{slide}
%\includepdf[pages={1}]{laborsupply_attach.pdf}
%\end{slide}
%
%
%\begin{slide}
%\begin{center}
%{\bf ISSUE 4: NON-LINEAR BUDGET SETS}
%\end{center}
%
%Non-linear budget set creates two problems:
%
%1) Model mis-specification:\ OLS regression no longer recovers
%structural elasticity parameter $\varepsilon $ of interest
%
%Two reasons:\ (a)\ underestimate response because people pile up
%at kink and (b) mis-estimate income effects
%
%2) Econometric bias: $\tau _{i}$ depends on income $w_{i}l_{i}$
%and hence on $l_{i}$
%
%Tastes for work are positively correlated with $\tau _{i}$ $%
%\rightarrow $\ downward bias in OLS\ regression of hours worked on
%net-of-tax rates
%
%Solution to problem \#2:\ only use reform-based variation in tax
%rates. But problem \#1 requires fundamentally different estimation method
%
%\end{slide}
%
%\begin{slide}
%\begin{center}
%{\bf NON-LINEAR BUDGET SET METHOD}
%\end{center}
%Issue addressed by non linear budget set studies pioneered by
%Hausman in late 1970s (Hausman, 1985 PE handbook chapter)
%
%Method uses a structural model of labor supply
%
%Key point: the method uses the standard cross-sectional variation
%in pre-tax wages $w^p$ for identification. Taxes are seen as a
%problem to deal with rather than an opportunity for
%identification.
%
%New literature identifying labor supply elasticities using tax
%changes has a totally different perspective: taxes are seen as an
%{\bf opportunity} to identify labor supply
%\end{slide}
%
%
%\begin{slide}
%\begin{center}
%{\bf NON-LINEAR BUDGET SET METHOD}
%\end{center}
%
%1) Assume an\ uncompensated labor supply equation:%
%\[
%l=\alpha +\beta w(1-\tau )+\gamma y+\epsilon
%\]
%
%2) Error term $\epsilon $ is normally distributed with variance
%$\sigma ^{2}$
%
%3) Observed variables: $w_{i}$, $\tau _{i}$, $y_{i}$, and $l_{i}$
%
%4) Technique: (a) construct likelihood function given observed
%labor supply choices on NLBS, (b) find parameters ($\alpha ,\beta
%,\gamma $) that maximize likelihood
%
%5) Important insight:\ need to use \textquotedblleft virtual
%incomes\textquotedblright\ in lieu of actual unearned income with
%NLBS
%
%\end{slide}
%
%
%\begin{slide}%
%\begin{center}
%{\bf NLBS Likelihood Function (2 brackets)}
%\end{center}
%Individual can locate on first bracket, on second bracket, or at
%the kink $l_{K}$
%
%Likelihood = probability that we see individual $i$ at labor supply $%
%l_{i}$ given a parameter vector
%
%Decompose likelihood into three components
%
%Component 1:\ individual $i$ on first bracket: $0l_{K}$
%
%2) If tax is $\tau ^{2}$ and virtual income $y^{2}$ individual
%wants to work $l&l_{K}>\alpha
%+\beta w_{i}(1-\tau ^{2})+\gamma y^{2}+\epsilon _{i} \\
%l_{K}-(\alpha +\beta w_{i}(1-\tau ^{1})+\gamma y^{1} &<&\epsilon
%_{i}0$: work more today to take advantage of
%temporarily higher wage
%
%%In separable case:
%%\begin{eqnarray*}
%%v'(l_{t}) &=&\lambda w_{t}/[\beta (1+r)]^{t} \\
%%&\Rightarrow &\frac{\partial l}{\partial w_{t}}|_{\lambda }=\frac{\lambda }{%
%%[\beta (1+r)]^{t}v^{\prime \prime }(l_{t})}>0
%%\end{eqnarray*}
%\end{slide}
%\begin{slide}%
%\begin{center}
%{\bf Frisch vs Hicksian Elasticity:\ Illustrative Example}
%\end{center}
% Suppose that you are paid a piece rate
%
% It takes 1 hour of work to make a piece
%
% You usually work from 8am-12pm and 1pm-5pm.
%
% Suppose your employer tells you that the piece rate will be twice
%as high only during the 12pm-1pm time slot
%
% What do you do?
%
%$\rightarrow $Have lunch earlier at 11am-12pm and work from
%12pm-1pm
%
%\end{slide}
%\begin{slide}
%\begin{center}
%{\bf ITLS and Income Effects}
%\end{center}
%Single inter-temporal budget constraint: $$ \sum c_{t}/(1+r)^{t} \leq A_{0}+\sum
%w_{t}l_{t}/(1+r)^{t}$$ $\Rightarrow$
%Receiving \$ $M$ in year $0$ vs. \$ $(1+r)^t \cdot M$ in year $t$
%has the same impact on labor supply
%
%Temporary transfer has a small effect on labor in {\bf all}
%periods
%
%In reality, temporary transfers seem to have large effects on
%labor supply [e.g., severance payments, Card-Chetty-Weber QJE'08]
%$\Rightarrow$
%
%(1) Many people are credit constrained: static labor supply model
%might be a better depiction of reality
%
%(2) People might not make intertemporal choices as in ITLS model
%[behavioral economics]
%\end{slide}
%\begin{slide}
%\begin{center}
%{\bf Dynamic Life Cycle Model: Three Types of Wage Changes}
%\end{center}
%1) Evolutionary change:\ movements along profile (life-cycle)
%
%2) Parametric change: temporary tax cut
%
%3) Profile shift: changing the wage rate in all periods
%
%a) Equivalent to a permanent parametric change
%
%b) Implicitly the elasticity that static studies estimate with
%unanticipated tax changes
%\end{slide}
%
%\begin{slide}
%\includepdf[pages={70}]{laborsupply_attach.pdf}
%\end{slide}
%\begin{slide}
%\begin{center}
%{\bf Frisch vs. Compensated vs. Uncompensated Elasticities}
%\end{center}
%Frisch elasticity: changing wages in a single period and keeping
%marginal utility of income $\lambda$ constant
%
%Compensated static elasticity: changing wages in all periods but
%keeping utility constant
%
%Uncompensated static elasticity: changing wages in all periods
%with no compensation
%
%Frisch elasticity is of central interest for calibration of macro
%business cycle models
%
%\end{slide}
%\begin{slide}
%\begin{center}
%{\bf Frisch vs. Compensated vs. Uncompensated Elasticities}
%\end{center}
%Intertemporal substitution: Frisch elasticity $\geq$ Compensated
%static elasticity
%
%Income effects: Compensated static elasticity $\geq$ Uncompensated
%static elasticity
%
%Difference in Frisch and Compensated elasticities also loosely
%related to anticipated vs. unanticipated changes
%
%Looney and Singhal (2007) exploit this reasoning to identify
%Frisch elasticity [MTR changes predictably when filers loose a
%child exemption]
%
%Frisch elasticity is of central interest for calibration of macro
%business cycle models
%\end{slide}
%\begin{slide}
%\begin{center}
%{\bf Intertemporal substitution: Tax Holiday in Iceland}
%\end{center}
%
%In 1987, Iceland transitioned from paying taxes on previous year's income to current income
%
%To avoid double taxation during transition, no tax charged over 1987 incomes
%
%Average tax rate of 14.5\% in 1986, 0\% in 1987, 8\% in 1988
%
%Reform announced in late 1986 $\Rightarrow$ unanticipated temporary tax change
%
%Temporary change in incentives $\Rightarrow$ ideal quasi-experiment to intertemporal substitution elasticity
%(work hard in 1987, take a break in 1986 or 1988)
%
%Bianchi et al. AER'01 look at employment effects [hard to know what counterfactual is]
%\end{slide}
%
%\begin{slide}
%\includepdf[pages={83}]{laborsupply_attach.pdf}
%\end{slide}
%\begin{slide}%
%\begin{center}
%{\bf MaCurdy 1983}
%\end{center}
%1) Structural estimate using panel data for men and within-person
%wage variation
%
%2) Find both Frisch and compensated wage elasticity of around 0.15
%
%3) But his wage variation is not exogenous
%
%\end{slide}
%\begin{slide}%
%\begin{center}
%{\bf Pencavel 2002}
%\end{center}
%1) Instruments with trade balance interacted with schooling and
%age
%
%2) Frisch elasticity: 0.2
%
%3) Uncompensated wage elasticity: 0-0.2
%
%Instruments not credibly exogenous but results closer to
%structurally interpretable parameters
%\end{slide}
%
%\begin{slide}%
%\begin{center}
%{\bf Critique of ITLS\ models}
%\end{center}
%$\bullet$ Card critique of value of ITLS\ model
%
%a) Fails to explain most variation in hours over lifecycle
%
%b) Sheds little light on profile-shift elasticities that we care
%about for policy
%
%$\bullet$ Core \textquotedblleft structural vs.
%reduced-form\textquotedblright\ divide in applied microeconomics:
%Trade off between credible identification and well defined
%theoretical framework
%\end{slide}
%\begin{slide}%
%\begin{center}
%{\bf Blundell, Duncan, and Meghir 1998}
%\end{center}
%1) Good combination of structural and reduced form methods on
%labor supply
%
%2) Argue against standard DD approach, where treatment/control
%groups are endogenously defined
%
%a) Reduced tax rate may pull households from low income group to
%high income group
%
%b) Need group definitions that are stable over time
%
%3) Use birth cohort (decade) interacted with education (e.g. high
%school or more)
%
%\end{slide}
%
%
%\begin{slide}
%\begin{center}
%{\bf Blundell, Duncan, and Meghir 1998}
%\end{center}
%
%1) Construct group-level labor supply measures for women in
%couples
%
%2) Measure how labor supply co-varies with wages rates net of
%taxes in the UK in 1980s
%
%3) Importantly, tax reforms during this period affected groups
%very differently
%
%4) Use consumption data as a control for permanent income
%
%5) Can therefore obtain a structurally interpretable ($\lambda $
%constant) estimate
%
%\end{slide}
%
%\begin{slide}
%\includepdf[pages={71}]{laborsupply_attach.pdf}
%\end{slide}
%
%\begin{slide}%
%\begin{center}
%{\bf Blundell, Duncan, and Meghir: Results}
%\end{center}
%1) Compensated wage elasticities: 0.15-0.3, depending on number of
%kids
%
%2) No income effects when no kids, moderate income effects when
%kids present
%
%3) Identification assumption is common trends across
%cohort/education groups
%
%4) However, reforms in 80s went in opposite directions at
%different times $\rightarrow$ Secular trends cannot explain
%everything
%
%5) See Blundell and MaCurdy (1999) for additional ITLS estimates
%\end{slide}
%\begin{slide}
%\begin{center}
%{\bf Intertemporal Substitution: High Frequency Studies}
%\end{center}
%1) Recent literature focuses on high frequency substitution
%
%2) Focus on groups with highly flexible and well measured labor
%supply such as:
%
%a) cab drivers [Camerer et al. QJE'97, Farber JPE'05, AER-PP'08,
%Crawford-Meng '09]: debate on whether cab drivers are rational
%or have a daily income target
%
%b) stadium vendors [Oettinger JPE'99]
%
%c) cycling messengers randomized experiment [Fehr-Goette AER'07]
%\end{slide}
%\begin{slide}%
%\begin{center}
%{\bf Camerer et al. QJE'97}
%\end{center}
%Examine how variation across days in wage rate for cab drivers
%(arising from variation in waiting times) correlates with hours
%worked
%
%a) Striking finding: strong negative effect
%
%b) Interpret this as \textquotedblleft target
%earning\textquotedblright\ -- strongly contradicts standard
%intertemporal labor supply model
%
%c) Would suggest very counter intuitive effects for temporary tax
%changes, etc.
%
%\end{slide}%
%%EndExpansion
%
%\begin{slide}
%\includepdf[pages={72,73}]{laborsupply_attach.pdf}
%\end{slide}
%\begin{slide}
%\begin{center}
%{\bf Farber: Division Bias}
%\end{center}
%Argues that Camerer et al. evidence of target earning behavior is
%driven by econometric problems
%
%Camerer et al. regression specification:%
%\[
%h_{it}=\alpha +\beta e_{it}/h_{it}+\varepsilon _{it}
%\]
%
%Camerer et al. recognize this and try to instrument using average
%daily wage $\bar{w}_t$ across all drivers
%
%But there may be a random component to hours at the group level
%(e.g., good weather makes job more pleasant $\Rightarrow$ more
%hours and smaller wages at the group level)
%
%$\Rightarrow$ Spuriously find a negative association between
%average daily wage and average hours
%\end{slide}
%\begin{slide}%
%\begin{center}
%{\bf Farber: Within-Day Volatility}
%\end{center}
%Farber's alternative test for target earnings: hazard model
%$Quit=f(cum\_hours,cum\_inc)$
%
%Result: main determinant of quitting is hours worked (fatigue),
%NOT cumulative income $\Rightarrow$ Rejects target earning, but
%does not yield ITLS\ estimate
%
%Two other studies find positive ITLS:
%
%a) Bicycle messengers (randomized experiment with 25\% wage
%subsidy for 4 weeks): work more days and earn more when wages
%higher but effort per day $\downarrow$ [fatigue effect]
%
%b) Baseball stadium vendors (work more in high attendance games)
%
%But such structural parameters are not of
%direct interest to public finance because they are too high
%frequency
%\end{slide}
%\begin{slide}%
%\begin{center}
%{\bf Use of Frisch elasticity in RBC macro models}
%\end{center}
%
%Real business cycle (RBC) models motivated by the fact
%that output fluctuates more than hours worked
%
%{\bf Short-Run:} Hours worked are strongly pro-cyclical
%[unemployment in recessions and overtime in booms]
%
%RBC models do not have involuntary unemployment
%[questionable assumption]
%
%Shock in technology affects wages
%$\Rightarrow$ Variation in hours due to labor supply $\Rightarrow$ Output
%varies more than hours
%
%$\Rightarrow $ Frisch elasticity must be very large (above 1.5)
%for Real business cycle macro-models to work [Prescott Nobel lecture JPE'06]
%
%Micro labor supply evidence does not support such a high Frisch elasticity
%
%\end{slide}
\begin{slide}
\begin{center}
{\bf Macro Long-Run Evidence}
\end{center}
1) Macroeconomists also estimate elasticities by examining
long-term trends/cross-country comparisons\bigskip
2) Identification more questionable but estimates perhaps more
relevant to long-run policy questions of interest\bigskip
3) Use aggregate hours data and aggregate measures of taxes
(average tax rates)\bigskip
4) Highly influential in calibration of macroeconomic
models\bigskip
\end{slide}
\begin{slide}%
\begin{center}
{\bf Trend-based Estimates and Macro Evidence}
\end{center}
{\bf Long-Run:} US real wage rates multiplied by about 5 from 1900 to
present due to economic growth
Aged 25-54 male hours have fallen 25\% and then stabilized (Ramey and Francis AEJ-macro '09)
$\Rightarrow$ Uncompensated hours of work elasticity is small ($<.1$)
However, taxes are rebated as transfers so can still have labor
supply effects if large compensated elasticity/income effects
Alternative plausible story: utility depends on relative consumption
$\Rightarrow$ Earnings \$10,000 is low today but would have been very good in 1900
(reference point labor supply theory)
\end{slide}
\begin{slide}
\includepdf[pages={85}]{laborsupply_attach.pdf}
\end{slide}
\begin{slide}
\begin{center}
{\bf Long-run cross-country panel: Prescott 2004}
\end{center}
Uses data on hours worked by country in 1970 and 1995 for 7 OECD\
countries [total hours/people age 15-64]
Technique to identify elasticity:\ calibration of GE\ model
Rough intuition: posit a labor supply model, e.g.%
\[
u(c,l)=c-\frac{l^{1+1/\varepsilon }}{1+1/\varepsilon }
\]
Finds that elasticity of $\varepsilon =1.2$ best matches time
series and cross-sectional patterns
Note that this is analogous to a regression without controls for
other variables
Results verified in subsequent calibrations by
Ohanina-Raffo-Rogerson JME'08 and others using more data
\end{slide}
\begin{slide}
\includepdf[pages={74}]{laborsupply_attach.pdf}
\end{slide}
%\begin{slide}
%\begin{center}
%{\bf Davis and Henrekson 2005}
%\end{center}
%Run regressions of hours worked on tax variables with various
%controls
%
%Some panel evidence, but primarily cross-sectional
%
%Separate intensive and extensive margin responses
%
%\end{slide}
%
%\begin{slide}
%\includepdf[pages={75,76}]{laborsupply_attach.pdf}
%\end{slide}
\begin{slide}
\begin{center}
{\bf Reconciling Micro and Macro Estimates}
\end{center}
Recent interest in reconciling micro and macro elasticity
estimates (see Chetty-Guren-Manoli-Weber '13)
Three potential explanations
a) Statistical Bias: culture differs in countries
with higher tax rates [Alesina, Glaeser, Sacerdote 2005, Steinhauer 2018 for
Swiss communities by language]
b) Macro-elasticity captures long-term response which could be larger than
short-term response (frictions, etc. Chetty '12).
c) Other programs: retirement, education affect labor supply at
beginning and end of working life (Blundell-Bozio-Laroque '13) and child
care affecting mothers (Kleven JEP'14)
\end{slide}
\begin{slide}
\begin{center}
{\bf Blundell-Bozio-Laroque '13}
\end{center}
Strong evidence that variation in aggregate hours of work across
countries happens among the young and the old: (a) schooling-work
margin (b) presence of young children (for women), (c) early
retirement
Serious cross-country time series analysis would require to put
together a better tax wedge by age groups which includes all those
additional govt programs [welfare, retirement, child care]
This has been done quite successfully in the case of retirement by
series of books by Gruber and Wise, {\em Retirement around the
world}
$\Rightarrow$ Need to develop a more comprehensive international /
time series database of tax wedges by age and family types
\end{slide}
\begin{slide}
\includepdf[pages={77,78}]{laborsupply_attach.pdf}
\end{slide}
\begin{slide}
\includepdf[pages={79}]{laborsupply_attach.pdf}
\end{slide}
\begin{slide}
\includepdf[pages={80,82}]{laborsupply_attach.pdf}
\end{slide}
\begin{slide}
\includepdf[pages={81}]{laborsupply_attach.pdf}
\end{slide}
\begin{slide}
\begin{center}
{\bf Long-term effects: Evidence from the Israeli Kibbutz}
\end{center}
Abramitzky '18 book based on series of academic papers
Kibbutz are egalitarian and socialist communities in Israel, thrived for
almost a century within a more capitalist society
1) Social sanctions on shirkers effective in small communities with limited privacy
2) Deal with brain drain exit using communal property as a bond
3) Deal with adverse selection in entry with screening and trial period
4) Perfect sharing in Kibbutz has negative effects on high school students performance
but effect is small in magnitude (concentrated among kids with low education parents)
\end{slide}
\begin{slide}
\begin{center}
{\bf Long-term effects: Evidence from the Israeli Kibbutz}
\end{center}
Abramitzky-Lavy ECMA'14 show that
high school students study harder once their
kibbutz shifts away from equal sharing
Uses a DD strategy: pre-post reform and comparing reform Kibbutz to non-reform Kibbutz.
Finds that
1) Students are 3\% points more likely to graduate
2) Students are 6\% points more likely to achieve a matriculation
certificate that meets university entrance requirements
3) Students get an average of 3.6 more points in their exams
Effect is driven by students whose parents have
low schooling; larger for males; stronger in
kibbutz that reformed to greater degree
\end{slide}
\begin{slide}
\begin{center}
{\bf Culture of Welfare across Generations}
\end{center}
Conservative concern that welfare promotes a culture of dependency: kids growing up
in welfare supported families are more likely to use welfare
Correlation in welfare use across generations is obviously not necessarily causal
Dahl, Kostol, Mogstad QJE'2014 analyze causal effect of parental use of Disability Insurance (DI)
on children use (as adults) of DI in Norway
Identification uses random assignment of judges to denied DI applicants who appeal [some judges are severe, some lenient]
Find evidence of causality: parents on DI increases odds of kids on DI over next 5 years by 6 percentage points
\small
Mechanism seems to be learning about DI availability rather than reduced stigma from using DI [because no effect on other
welfare programs use]
\end{slide}
\begin{slide}
\includepdf[pages={101}]{laborsupply_attach.pdf}
\end{slide}
\begin{slide}
\begin{center}
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\end{center}
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}
\end{slide}
\end{document}