class: center, middle, inverse, title-slide # Econ 330: Urban Economics ## Lecture 13 ### John Morehouse ### 20 February, 2020 --- class: inverse, center, middle # Lecture 13: Minimum Wage, Monopsony & Empirics --- name: schedule # Schedule ## Today -- 1) .hi.purple[Monopsony] 2) .hi.purple[Discussion] 3) .hi.purple[Empirics] -- -- ## Upcoming - .hi.slate[HW3 due Feb 25th (one week)] - .hi.slate[Reading] (Chapter 9) -- --- # Competitive Model We built up labor .hi[supply] and .hi[demand]. Where do these come from? -- - Demand: Firms - Supply: Workers -- What did we assume about the market structure? -- - .hi[Perfect Competition] -- --- count: false # Competitive Model We built up labor .hi[supply] and .hi[demand]. Where do these come from? - Demand: Firms - Supply: Workers What did we assume about the market structure? - .hi[Perfect Competition] - Firms pay workers their MV of labor (max WTP) -- Probably not super reasonable -- --- # Monopsony Let's consider a different labor market structure: -- <center> <font size="15"> Monopsony </font> </center> -- - We say a firm is a .hi[monopsonist] if they are .hi.purple[the only employer] of labor in the area (city) - We say a firm has .hi[monopsony power] if they have the ability to influence the market wage -- - Not to be confused with __monopoly__ (in which there is only one .hi.purple[seller] of a good) - __Monopsony__ has to do with one .hi.purple[buyer] of a good -- --- # Examples of Monopsonys Can you think of any? -- - Universities (go GTFF!) - Coal Towns - Amazon / Walmart Towns? -- --- # Monopsony So what do you think the main consequence(s) of .hi[monopsony] are? <center> <font size="15"> Monopsonists have the ability to pay a wage below the marginal value </font> </center> The consequences? -- - .pink[Higher profit] for the firms - Deadweight loss (inefficient outcome) -- We will formalize this in a few slides, but first lets go over some evidence of local monopsonys --- # Monopsony: Formalizing the Result In the competive model, the firm pays the worker `\(w = MRP_l\)`. -- - Is this what the monopsonist would do? - Where is this? -- --- # Recall: The competitive model Remember: in the competitve model, the firm seeks to maximize profits (but does not influence prices). - The competitive firm hires labor until the marginal profit w.r.t to labor is zero `\begin{align*} \pi &= TR - TC\\ \pi &= TR - wL - rK \end{align*}` Profit maxing cond: `\(\frac{\Delta \pi}{\Delta L} = 0 \implies MRP_L -w = 0 \implies w = MRP_L\)` --- # Monopsony: Formalizing the Result With a monopsonist, the amount of labor they hire influences the wage. That is, now -- `\begin{align*} \pi &= TR - w(L)L - rK \end{align*}` -- where `\(w(L)\)` is an .pink[increasing function] of the .pink[amount of labor hired] -- - The firm should hire labor until marginal cost is equalized to marginal benefit (.hi.purple[same as before]) - or: _.pink[marginal profit] wrt labor_ is equal to zero `\begin{align*} \frac{\Delta \pi}{\Delta L} =0 \end{align*}` -- --- # Monopsony: Formalizing the Result Notation: `\(TC(L) =w(L)*L\)` is the TC from labor in monopsony (__note__: `\(w(L)\)` wage is a function of labor) `\begin{align*} \pi &= TR - TC(L) - rK \end{align*}` -- Profit Maximization: `\begin{align*} \frac{\Delta \pi}{\Delta L} &=0 \end{align*}` -- --- count: false # Monopsony: Formalizing the Result Notation: `\(TC(L) =w(L)*L\)` is the TC from labor in monopsony (__note__: `\(w(L)\)` wage is a function of labor) `\begin{align*} \pi &= TR - TC(L) - rK \end{align*}` Profit Maximization: `\begin{align*} \frac{\Delta \pi}{\Delta L} &=0\\ MRP_L - \frac{\Delta TC(L)}{\Delta L} &= 0\\ \end{align*}` --- count: false # Monopsony: Formalizing the Result Notation: `\(TC(L) =w(L)*L\)` is the TC from labor in monopsony (__note__: `\(w(L)\)` wage is a function of labor) `\begin{align*} \pi &= TR - TC(L) - rK \end{align*}` Profit Maximization: `\begin{align*} \frac{\Delta \pi}{\Delta L} &=0\\ MRP_L - \frac{\Delta TC(L)}{\Delta L} &= 0\\ MRP_L &= \frac{\Delta TC(L)}{\Delta L}\\ MRP_L & = MC_L \end{align*}` --- count: false # Monopsony: Formalizing the Result So the monoposonist hires until: `\begin{align*} MRP_L & = MC_L \end{align*}` Compared to the competitve outcome: `\begin{align*} MRP_L & = W \end{align*}` .hi[Important:] Note that in the competitive model, marginal cost of labor was constant (and equal to wage). --- count: false # Monopsony: Formalizing the Result So the monoposonist hires until: `\begin{align*} MRP_L & = MC_L \end{align*}` Compared to the competitve outcome: `\begin{align*} MRP_L & = W \end{align*}` .hi[Important:] Note that in the competitive model, marginal cost of labor was constant (and equal to wage). - Now: marginal cost is increasing because monopsonist is _only_ buyer of labor --- # An Example: .center[**Monopsonist Wage Schedule**] <table class="table table-striped" style="margin-left: auto; margin-right: auto;"> <caption></caption> <thead> <tr> <th style="text-align:center;"> .pink[Wage] </th> <th style="text-align:center;"> .purple[Labor] </th> <th style="text-align:center;"> TC </th> <th style="text-align:center;"> .hi[MC] </th> </tr> </thead> <tbody> <tr> <td style="text-align:center;line-height: 110%;"> 1 </td> <td style="text-align:center;line-height: 110%;"> 1 </td> <td style="text-align:center;line-height: 110%;"> </td> <td style="text-align:center;line-height: 110%;"> </td> </tr> <tr> <td style="text-align:center;line-height: 110%;"> 2 </td> <td style="text-align:center;line-height: 110%;"> 2 </td> <td style="text-align:center;line-height: 110%;"> </td> <td style="text-align:center;line-height: 110%;"> </td> </tr> <tr> <td style="text-align:center;"> 3 </td> <td style="text-align:center;"> 3 </td> <td style="text-align:center;"> </td> <td style="text-align:center;"> </td> </tr> <tr> <td style="text-align:center;"> 4 </td> <td style="text-align:center;"> 4 </td> <td style="text-align:center;"> </td> <td style="text-align:center;"> </td> </tr> <tr> <td style="text-align:center;"> 5 </td> <td style="text-align:center;"> 5 </td> <td style="text-align:center;"> </td> <td style="text-align:center;"> </td> </tr> </tbody> </table> Let's fill in the table. What do you notice? --- count: false # An Example: .center[**Monopsonist Wage Schedule**] <table class="table table-striped" style="margin-left: auto; margin-right: auto;"> <caption></caption> <thead> <tr> <th style="text-align:center;"> .pink[Wage] </th> <th style="text-align:center;"> .purple[Labor] </th> <th style="text-align:center;"> TC </th> <th style="text-align:center;"> .hi[MC] </th> </tr> </thead> <tbody> <tr> <td style="text-align:center;line-height: 110%;"> 1 </td> <td style="text-align:center;line-height: 110%;"> 1 </td> <td style="text-align:center;line-height: 110%;"> 1 </td> <td style="text-align:center;line-height: 110%;"> </td> </tr> <tr> <td style="text-align:center;line-height: 110%;"> 2 </td> <td style="text-align:center;line-height: 110%;"> 2 </td> <td style="text-align:center;line-height: 110%;"> 4 </td> <td style="text-align:center;line-height: 110%;"> </td> </tr> <tr> <td style="text-align:center;"> 3 </td> <td style="text-align:center;"> 3 </td> <td style="text-align:center;"> 9 </td> <td style="text-align:center;"> </td> </tr> <tr> <td style="text-align:center;"> 4 </td> <td style="text-align:center;"> 4 </td> <td style="text-align:center;"> 16 </td> <td style="text-align:center;"> </td> </tr> <tr> <td style="text-align:center;"> 5 </td> <td style="text-align:center;"> 5 </td> <td style="text-align:center;"> 25 </td> <td style="text-align:center;"> </td> </tr> </tbody> </table> Let's fill in the table. What do you notice? --- count: false # An Example: .center[**Monopsonist Wage Schedule**] <table class="table table-striped" style="margin-left: auto; margin-right: auto;"> <caption></caption> <thead> <tr> <th style="text-align:center;"> .pink[Wage] </th> <th style="text-align:center;"> .purple[Labor] </th> <th style="text-align:center;"> TC </th> <th style="text-align:center;"> .hi[MC] </th> </tr> </thead> <tbody> <tr> <td style="text-align:center;line-height: 110%;"> 1 </td> <td style="text-align:center;line-height: 110%;"> 1 </td> <td style="text-align:center;line-height: 110%;"> 1 </td> <td style="text-align:center;line-height: 110%;"> 1 </td> </tr> <tr> <td style="text-align:center;line-height: 110%;"> 2 </td> <td style="text-align:center;line-height: 110%;"> 2 </td> <td style="text-align:center;line-height: 110%;"> 4 </td> <td style="text-align:center;line-height: 110%;"> 3 </td> </tr> <tr> <td style="text-align:center;"> 3 </td> <td style="text-align:center;"> 3 </td> <td style="text-align:center;"> 9 </td> <td style="text-align:center;"> 5 </td> </tr> <tr> <td style="text-align:center;"> 4 </td> <td style="text-align:center;"> 4 </td> <td style="text-align:center;"> 16 </td> <td style="text-align:center;"> 7 </td> </tr> <tr> <td style="text-align:center;"> 5 </td> <td style="text-align:center;"> 5 </td> <td style="text-align:center;"> 25 </td> <td style="text-align:center;"> 9 </td> </tr> </tbody> </table> Let's fill in the table. What do you notice? -- At .pink[every level of labor], the .purple[marginal cost of labor] exceeds the .pink[wage] -- --- # Graph of Monopsony --- # Monopsony and Minimum Wage - So we saw the .pink[monoposonist] outcome leads to .hi[lower employment and wages] than the .pink[competitive outcome]. -- - In perfect competition, what happens to unemployment with a minimum wage? -- --- count: fale # Monopsony and Minimum Wage - So we saw the .pink[monoposonist] outcome leads to .hi[lower employment and wages] than the .pink[competitive outcome]. - In perfect competition, what happens to unemployment with a minimum wage? - It .pink[increases]. Labor supply outstrips labor demand -- - Is it the same with a monopsony? No! -- --- # Minimum Wage Graph --- class: inverse, middle # Checklist .col-left[ 1) .hi[Monopsony] ✅ - Monopsony Outcome vs Competitive Outcome - Min wage in monopsony 2) .hi.purple[Discussion] ] .col-right[ 3) .hi.purple[Empirics] ] --- # Discussion - .purple[Structure of labor market] leads to .pink[two different effects] for the same policy -- - In reality, most labor markets are somewhere in between perfect competition and monopsony -- - The more competitive a market is, more likely that minimum wage will increase unemployment --- # A Brief History Lesson .col-left[ - __1894__: New Zealand enacts the [Industrial Concilation and Arbitration Act](https://en.wikipedia.org/wiki/Industrial_Conciliation_and_Arbitration_Act_1894) - Worlds first Min wage! ] .col-right[ ] --- count: false # A Brief History Lesson .col-left[ - .ex[__1894__: New Zealand enacts the [Industrial Concilation and Arbitration Act](https://en.wikipedia.org/wiki/Industrial_Conciliation_and_Arbitration_Act_1894)] - .ex[Worlds first Min wage!] - __1912__: Massachusetts enacts the .pink[first minimum wage in the US]. Other states follow ] .col-right[ ] --- count: false # A Brief History Lesson .col-left[ - .ex[1894: New Zealand enacts the [Industrial Concilation and Arbitration Act](https://en.wikipedia.org/wiki/Industrial_Conciliation_and_Arbitration_Act_1894)] - .ex[Worlds first Min wage!] - .ex[__1912__: Massachusetts enacts the .pink[first minimum wage in the US]. Other states follow] - __1938__: [Fair Labor Standards Act](https://www.dol.gov/agencies/whd/flsa) (25 cents per hour .hi[federal] minimum wage) ] .col-right[ ] --- count: false # A Brief History Lesson .col-left[ - .ex[1894: New Zealand enacts the [Industrial Concilation and Arbitration Act](https://en.wikipedia.org/wiki/Industrial_Conciliation_and_Arbitration_Act_1894)] - .ex[Worlds first Min wage!] - .ex[__1912__: Massachusetts enacts the .pink[first minimum wage in the US]. Other states follow] - .ex[__1938__: [Fair Labor Standards Act](https://www.dol.gov/agencies/whd/flsa) (25 cents per hour .hi[federal] minimum wage)] ] .col-right[ - __1968__: Federal Minimum Wage reaches .hi[peak purchasing power] at $1.60 per hour ($11.53 in 2019 dollars) 🤯 ] --- count: false # A Brief History Lesson .col-left[ - .ex[1894: New Zealand enacts the [Industrial Concilation and Arbitration Act](https://en.wikipedia.org/wiki/Industrial_Conciliation_and_Arbitration_Act_1894)] - .ex[Worlds first Min wage!] - .ex[__1912__: Massachusetts enacts the .pink[first minimum wage in the US]. Other states follow] - .ex[__1938__: [Fair Labor Standards Act](https://www.dol.gov/agencies/whd/flsa) (25 cents per hour .hi[federal] minimum wage)] ] .col-right[ - .ex[__1968__: Federal Minimum Wage reaches .hi[peak purchasing power] at $1.60 per hour ($11.53 in 2019 dollars) 🤯] - __2009__: Min wage is $ 7.25 an hour - __2019__: 29 states have a higher minimum wage than federal ] --- # Thoughts So what do you think? Is minimum wage good? Is it bad? .hi.purple[Discuss] -- .hi[Note]: The question _is minimum wage good?_ is __not__ a good question. Good is normative. Better: - Does minimum wage impact all low wage workers .pink[equally?] -- - Does minimum wage .pink[cause] increases in .purple[unemployment?] -- - Does minimum wage lead to firms reducing other, non-mandated benefits? -- Above questions: quantifiable, with answers that can be answered empirically --- class: inverse, middle # Checklist .col-left[ 1) .hi[Monopsony] ✅ - Monopsony Outcome vs Competitive Outcome - Min wage in monopsony 2) .hi[Discussion] ✅ ] .col-right[ 3) .hi.purple[Empirics] ] --- # Introduction We are going to talk about .pink[causality]. Some of these notes are based on written by [Ed Rubin](http://edrub.in/) & [Kyle Raze](https://kyleraze.com/) --- # Introduction Hisotrically, social sciences had limited data to study policy questions. __Result__: Social sciences were .pink[theoretical] fields - Economists used .purple[mathematical models] - Sociologists developed .pink[qualitative theories] - Both used their theories to make policy reccomendations --- count: false # Introduction Hisotrically, social sciences had limited data to study policy questions. __Result__: Social sciences were .pink[theoretical] fields - Economists used .purple[mathematical models] - Sociologists developed .pink[qualitative theories] - Both used their theories to make policy reccomendations __Problem__: Theories can be (and often are) wrong - 5 economists often have 5 answers to .pink[the same question] - Leads to a politicization of questions that, in principle, have scientific answers (ie: does minimum wage cause increased in unemployment?) --- # Nowadays .hi.purple[Today]: social sciences are increasingly .pink[empirical] thanks to the growing availability of data - Ability to test and improve theories using real data - Data driven answers `\(\implies\)` less politicization --- # Discussion The economists toolkit: - .hi[Empirics]: tells us what actually happened - .hi.purple[Theory]: helps us understand why things happened the way they did -- __Be careful in distinguishing:__ - empirical facts such as _.pink[average unemployment was lower after minimum wage was placed]_ -- - empirical or theoretical claims (supported by facts) such as: _.purple[average unemployment was lower] .hi.purple[because] .purple[of minimum wage]_ or - _average unemployment was lower after minimum wage was placed .pink[because] the market was a monosopsony_ -- -- --- # Path to Causality Suppose we want to answer the question: _Does minimum wage lead to increases in unemployment_? What is the _ideal_ comparision? (experiment) --- count: false # Path to Causality Suppose we want to answer the question: _Does minimum wage lead to increases in unemployment_? What is the _ideal_ comparision? (experiment) ## The Ideal Experiment 1) .pink[Implement minimum wage] (in say NJ -- this will give us the causal effect in NJ only). --- count: false # Path to Causality Suppose we want to answer the question: _Does minimum wage lead to increases in unemployment_? What is the _ideal_ comparision? (experiment) ## The Ideal Experiment 1) .pink[Implement minimum wage] (in say NJ -- this will give us the causal effect in NJ only). 2) Compute unemployment in NJ .purple[post minimum wage], call it `\(u_{\text{min wage}, NJ}\)` --- count: false # Path to Causality Suppose we want to answer the question: _Does minimum wage lead to increases in unemployment_? What is the _ideal_ comparision? (experiment) ## The Ideal Experiment 1) .pink[Implement minimum wage] (in say NJ -- this will give us the causal effect in NJ only). 2) Compute unemployment in NJ .purple[post minimum wage], call it `\(u_{\text{min wage}, NJ}\)` 3) Have a .hi[parallel universe] in which you did .pink[not implement min wage] in NJ. Compute `\(u_{\text{no min wage}, NJ}\)` --- count: false # Path to Causality Suppose we want to answer the question: _Does minimum wage lead to increases in unemployment_? What is the _ideal_ comparision? (experiment) ## The Ideal Experiment 1) .pink[Implement minimum wage] (in say NJ -- this will give us the causal effect in NJ only). 2) Compute unemployment in NJ .purple[post minimum wage], call it `\(u_{\text{min wage}, NJ}\)` 3) Have a .hi[parallel universe] in which you did .pink[not implement min wage] in NJ. Compute `\(u_{\text{no min wage}, NJ}\)` 4) .hi[Treatment effect] of the min wage for NJ, given by: `$$\tau_{\text{min wage}, NJ} = u_{\text{min wage}, NJ} - u_{\text{no min wage}, NJ}$$` --- # Issues - Unfortunately, we do not have a parallel universe at our disposal -- - This is called the .hi[fundamental problem of causal inference]. Put in a different way: - We can never see the same individual (unit) when they are both treated and untreated -- - So we can never _.pink[garuntee]_ all else is equal, but we will try our best. -- -- __Q__: How can we answer this question, without a parallel universe? Let's dive in. --- # Setup __Q1__: _Does minimum wage lead to increases in unemployment_? - We need a setting to study this. For example, in 1993 NJ rose minimum wage from 4.25 to 5.05. --- count: false # Setup __Q1__: _Does minimum wage lead to increases in unemployment_? - We need a setting to study this. For example, in 1993 NJ rose minimum wage from 4.25 to 5.05. --- count: false # Setup __Q1__: _Does minimum wage lead to increases in unemployment_? - We need a setting to study this. For example, in 1993 NJ rose minimum wage from 4.25 to 5.05. __Q2__: What .pink[comparisons could you make] to under the the effect of NJ min wage on unemployment? --- count: false # Setup __Q1__: _Does minimum wage lead to increases in unemployment_? - We need a setting to study this. For example, in 1993 NJ rose minimum wage from 4.25 to 5.05. __Q2__: What .pink[comparisons could you make] to under the the effect of NJ min wage on unemployment? 1. Compare average unemployment in NJ .pink[before] and .purple[after] the policy --- count: false # Setup __Q1__: _Does minimum wage lead to increases in unemployment_? - We need a setting to study this. For example, in 1993 NJ rose minimum wage from 4.25 to 5.05. __Q2__: What .pink[comparisons could you make] to under the the effect of NJ min wage on unemployment? 1. Compare average unemployment in NJ .pink[before] and .purple[after] the policy 2. Also could compare average unemployment in .pink[NJ] to .purple[other states] (where min wage remained the same) --- count: false # Setup __Q1__: _Does minimum wage lead to increases in unemployment_? - We need a setting to study this. For example, in 1993 NJ rose minimum wage from 4.25 to 5.05. __Q2__: What .pink[comparisons could you make] to under the the effect of NJ min wage on unemployment? 1. Compare average unemployment in NJ .pink[before] and .purple[after] the policy 2. Also could compare average unemployment in .pink[NJ] to .purple[other states] (where min wage remained the same) __Q3__: .hi[Problems?] --- count: false # Setup __Q1__: _Does minimum wage lead to increases in unemployment_? - We need a setting to study this. For example, in 1993 NJ rose minimum wage from 4.25 to 5.05. __Q2__: What .pink[comparisons could you make] to under the the effect of NJ min wage on unemployment? 1. Compare average unemployment in NJ .pink[before] and .purple[after] the policy 2. Also could compare average unemployment in .pink[NJ] to .purple[other states] (where min wage remained the same) __Q3__: .hi[Problems?] 1. Other factors could be influencing unemployment at same time as NJ as min wage implementation --- count: false # Setup __Q1__: _Does minimum wage lead to increases in unemployment_? - We need a setting to study this. For example, in 1993 NJ rose minimum wage from 4.25 to 5.05. __Q2__: What .pink[comparisons could you make] to under the the effect of NJ min wage on unemployment? 1. Compare average unemployment in NJ .pink[before] and .purple[after] the policy 2. Also could compare average unemployment in .pink[NJ] to .purple[other states] (where min wage remained the same) __Q3__: .hi[Problems?] 1. Other factors could be influencing unemployment at same time as NJ as min wage implementation 2. Could be _many_ things that cause average wages to be different in NJ and other states (not related to min wage) --- # Issues ## A high bar Both issues discussed on the last slides were violations of the _.pink[all else equal]_ assumption -- When _.pink[all]_ factors are held constant, statistical comparisons detect .pink[causal relationships]. You have likely heard the saying: > Correlation is not causation -- -- - This saying just points out that often times there are .purple[violations] of the .pink[all else equal assumption] -- --- # Path to Causality So our minimum wage comparisons might and probably violate the .pink[all else equal] assumption. What is a possible solution? ## Random Experiments --- count: false # Path to Causality So our minimum wage comparisons might and probably violate the .pink[all else equal] assumption. What is a possible solution? ## Random Experiments - Ideally, we would randomly assign firms to have min wage - Randomization helps us maintain the _all else equal_ assumption --- count: false # Path to Causality So our minimum wage comparisons might and probably violate the .pink[all else equal] assumption. What is a possible solution? ## Random Experiments - Ideally, we would randomly assign firms to have min wage. - Randomization helps us maintain the _all else equal_ assumption - Here we have __two groups__: 1. .hi.slate[Treatment:] Assigned minimum wage --- count: false # Path to Causality So our minimum wage comparisons might and probably violate the .pink[all else equal] assumption. What is a possible solution? ## Random Experiments - Ideally, we would randomly assign firms to have min wage. - Randomization helps us maintain the _all else equal_ assumption - Here we have __two groups__: 1. .hi.slate[Treatment:] Assigned minimum wage 2. .hi.slate[Control:] Not assigned minimum wage --- count: false # Path to Causality So our minimum wage comparisons might and probably violate the .pink[all else equal] assumption. What is a possible solution? ## Random Experiments - Ideally, we would randomly assign firms to have min wage. - Randomization helps us maintain the _all else equal_ assumption - Here we have __two groups__: 1. .hi.slate[Treatment:] Assigned minimum wage 2. .hi.slate[Control:] Not assigned minimum wage - .hi[Average Treatment Effect]: ATE = Average(treated) - Average(control) - Unobservable/observable differences average out to zero due to random assignment --- # Problem We can't randomly assign firms to minimum wage. If we _invited_ firms to participate (very few would, probably), we would have .hi[selection bias] (non-random assignment of treatment) - Simple comparisons of treatment and control units might violate .pink[all else equal] -- ## What do we do? -- 1. Give up 2. Think of a different comparison that gets us closer to all else equal. --- count: false # Problem We can't randomly assign firms to minimum wage. If we _invited_ firms to participate (very few would, probably), we would have .hi[selection bias] (non-random assignment of treatment) - Simple comparisons of treatment and control units might violate .pink[all else equal] ## What do we do? 1. Give up 2. .hi[Think of a different comparison that gets us closer to all else equal.] --- # Another Comparsion So we tried: 1) Comparing NJ to itself before and after the policy ❌ --- count: false # Another Comparsion So we tried: 1) Comparing NJ to itself before and after the policy ❌ 2) Comparing NJ to another state after the policy ❌ --- count: false # Another Comparsion So we tried: 1) Comparing NJ to itself before and after the policy ❌ 2) Comparing NJ to another state after the policy ❌ Here is __another idea__: --- count: false # Another Comparsion So we tried: 1) Comparing NJ to itself before and after the policy ❌ 2) Comparing NJ to another state after the policy ❌ Here is __another idea__: - What if we .pink[compared] the .purple[difference] between NJ and another state .hi[before] __&__ after the minimum wage? --- count: false # Another Comparsion So we tried: 1) Comparing NJ to itself before and after the policy ❌ 2) Comparing NJ to another state after the policy ❌ Here is __another idea__: - What if we .pink[compared] the .purple[difference] between NJ and another state .hi[before] __&__ after the minimum wage? - If the _.pink[pre-treatment]_ difference is constant, then comparing this to the _.purple[post-treatment]_ should give us the treatment effect of the policy. --- count: false # Another Comparsion So we tried: 1) Comparing NJ to itself before and after the policy ❌ 2) Comparing NJ to another state after the policy ❌ Here is __another idea__: - What if we .pink[compared] the .purple[difference] between NJ and another state .hi[before] __&__ after the minimum wage? - If the _.pink[pre-treatment]_ difference is constant, then comparing this to the _.purple[post-treatment]_ should give us the treatment effect of the policy. - This is called the .hi[Difference in Differences] estimator (or DiD, double diff, etc.) --- # Differences-in-Differences ## Card and Krueger (1994) .center[**Effect of Minimum Wage on Employment**] <table class="table" style="margin-left: auto; margin-right: auto;"> <caption>Outcome: Number Full-Time Workers</caption> <thead> <tr> <th style="text-align:left;"> Group </th> <th style="text-align:center;"> Before </th> <th style="text-align:center;"> After </th> </tr> </thead> <tbody> <tr> <td style="text-align:left;line-height: 110%;"> .pink[Treatment (NJ)] </td> <td style="text-align:center;line-height: 110%;"> 20.44 </td> <td style="text-align:center;line-height: 110%;"> 21.03 </td> </tr> <tr> <td style="text-align:left;line-height: 110%;"> .purple[Control (PA)] </td> <td style="text-align:center;line-height: 110%;"> 23.33 </td> <td style="text-align:center;line-height: 110%;"> 21.17 </td> </tr> <tr> <td style="text-align:left;"> Difference </td> <td style="text-align:center;"> </td> <td style="text-align:center;"> </td> </tr> </tbody> </table> Difference-in-differences .mono[=] --- count: false # Differences-in-Differences ## Card and Krueger (1994) .center[**Effect of Minimum Wage on Employment**] <table class="table" style="margin-left: auto; margin-right: auto;"> <caption>Outcome: Number Full-Time Workers</caption> <thead> <tr> <th style="text-align:left;"> Group </th> <th style="text-align:center;"> Before </th> <th style="text-align:center;"> After </th> </tr> </thead> <tbody> <tr> <td style="text-align:left;line-height: 110%;"> .pink[Treatment (NJ)] </td> <td style="text-align:center;line-height: 110%;"> 20.44 </td> <td style="text-align:center;line-height: 110%;"> 21.03 </td> </tr> <tr> <td style="text-align:left;line-height: 110%;"> .purple[Control (PA)] </td> <td style="text-align:center;line-height: 110%;"> 23.33 </td> <td style="text-align:center;line-height: 110%;"> 21.17 </td> </tr> <tr> <td style="text-align:left;"> Difference </td> <td style="text-align:center;"> -2.89 </td> <td style="text-align:center;"> -0.14 </td> </tr> </tbody> </table> Difference-in-differences .mono[=] --- count: false # Differences-in-Differences ## Card and Krueger (1994) .center[**Effect of Minimum Wage on Employment**] <table class="table" style="margin-left: auto; margin-right: auto;"> <caption>Outcome: Number Full-Time Workers</caption> <thead> <tr> <th style="text-align:left;"> Group </th> <th style="text-align:center;"> Before </th> <th style="text-align:center;"> After </th> </tr> </thead> <tbody> <tr> <td style="text-align:left;line-height: 110%;"> .pink[Treatment (NJ)] </td> <td style="text-align:center;line-height: 110%;"> 20.44 </td> <td style="text-align:center;line-height: 110%;"> 21.03 </td> </tr> <tr> <td style="text-align:left;line-height: 110%;"> .purple[Control (PA)] </td> <td style="text-align:center;line-height: 110%;"> 23.33 </td> <td style="text-align:center;line-height: 110%;"> 21.17 </td> </tr> <tr> <td style="text-align:left;"> Difference </td> <td style="text-align:center;"> -2.89 </td> <td style="text-align:center;"> -0.14 </td> </tr> </tbody> </table> Difference-in-differences .mono[=] -.pink[0.14] .mono[-] .purple[-2.89] -- <br> `\(\quad\)` .mono[=] 2.76. ( a .hi[13%] increase!!) -- **Result:** Increasing the minimum wage did not reduce employment! --- # The Evidence and Metrics - [Card & Krueger (1993)](https://www.nber.org/papers/w4509) _Minimum Wages and Employment: A Case Study of the Fast Food Industry in New Jersey and Pennsylvania_ - Min wage in NJ rose from $ 4.25 to $ 5.05 -- - Here: NJ is the .hi.slate[treatment] group and Penn is the .hi.slate[control] group. NJ exposed to policy, NY not -- - Finds that employment in NJ __increased__! --- # Metrics ## Research Design - Compares employment of fast food restaurants in NJ to Pennsylvania (where min wage stayed at $4.25) - .pink[Treatment]: NJ, .purple[Control]: Pennsylvania --- count: false # Metrics ## Research Design - Compares employment of fast food restaurants in NJ to Pennsylvania (where min wage stayed at $4.25) - .pink[Treatment]: NJ, .purple[Control]: Pennsylvania - .hi[DiD estimates] of min wage effect on unemployment, prices, and wages --- count: false # Metrics ## Research Design - Compares employment of fast food restaurants in NJ to Pennsylvania (where min wage stayed at $4.25) - .pink[Treatment]: NJ, .purple[Control]: Pennsylvania - .hi[DiD estimates] of min wage effect on unemployment, prices, and wages ## Main Findings 1. Policy _increased_ employment in NJ fast food establishments by a whopping .hi[13]% --- count: false # Metrics ## Research Design - Compares employment of fast food restaurants in NJ to Pennsylvania (where min wage stayed at $4.25) - .pink[Treatment]: NJ, .purple[Control]: Pennsylvania - .hi[DiD estimates] of min wage effect on unemployment, prices, and wages ## Main Findings 1. Policy _increased_ employment in NJ fast food establishments by a whopping .hi[13]% 2. Business increased prices, .pink[suggesting] that most of the burden of the min wage was handed to others --- # Comments - Pretty clear result from the paper: minimum wage increased employment .hi[in fast food, in NJ]. - They comment about possible monopsony power in this labor market, which would be consistent with our earlier theory -- ## Issues - Nothing about hours worked. Employment might have increased, but its not clear that average number of hours worked increased - .qa[Q]: Do we care about unemployment? Or do we care about maximizing incomes for the largest group of people? Different things. -- --- # Another Problem ## Issues part 2 The .pink[mechansim] for the increase is not tested or clear. Possible stories: 1. Fast food chains generally have more capital than small businesses. Small food places went out of business, and demand shifted to the fast food chains (causing employment to increase) -- 2. Monoposony power in fast food chains 3. Both 1 and 2? Something else? -- .hi.slate[Policy Implications] will depend .hi[heavily] on what the underlying mechanism is. If most of the results are driven by 1, maybe the net-effect on employment is negative. --- # Internal Vs. External Validity ## .pink[Internal Validity] Addresses the question: _.pink[should we believe this study?]_ -- - A study has internal validity if we believe the .hi[causal effect] of a variable on another variable has been .purple[well identified] (ie: we have maintained .pink[all else equal]) -- --- count: false # Internal Vs. External Validity ## .pink[Internal Validity] Addresses the question: _.pink[should we believe this study?]_ - A study has internal validity if we believe the .hi[causal effect] of a variable on another variable has been .purple[well identified] (ie: we have maintained .pink[all else equal]) ## .purple[External Validity] Addresses the question: _.purple[how far can we generalize the results of this study?]_ -- - External validity is often harder to show. Need to argue that your context is similar to other contexts. Even then, you might not be believed. -- __Card & Krueger:__ very hard to argue external validity --- # DiD Plot Ex 1 <img src="lecture_13_files/figure-html/did_12-1.svg" style="display: block; margin: auto;" /> --- count:false # DiD Plot Ex 1 <img src="lecture_13_files/figure-html/did_22-1.svg" style="display: block; margin: auto;" /> --- count:false # DiD Plot Ex 1 <img src="lecture_13_files/figure-html/did_32-1.svg" style="display: block; margin: auto;" /> --- count:false # DiD Plot Ex 1 <img src="lecture_13_files/figure-html/did_342-1.svg" style="display: block; margin: auto;" /> --- count:false # DiD Plot Ex 1 <img src="lecture_13_files/figure-html/did_42-1.svg" style="display: block; margin: auto;" /> --- count: false # DiD Plot Ex 1 <img src="lecture_13_files/figure-html/did_82-1.svg" style="display: block; margin: auto;" /> --- # DiD Plot Ex 2 <img src="lecture_13_files/figure-html/did_1-1.svg" style="display: block; margin: auto;" /> --- count:false # DiD Plot Ex 2 <img src="lecture_13_files/figure-html/did_2-1.svg" style="display: block; margin: auto;" /> --- count:false # DiD Plot Ex 2 <img src="lecture_13_files/figure-html/did_3-1.svg" style="display: block; margin: auto;" /> --- count:false # DiD Plot Ex 2 <img src="lecture_13_files/figure-html/did_34-1.svg" style="display: block; margin: auto;" /> --- count:false # DiD Plot Ex 2 <img src="lecture_13_files/figure-html/did_4-1.svg" style="display: block; margin: auto;" /> --- count: false # DiD Plot Ex 2 <img src="lecture_13_files/figure-html/did_8-1.svg" style="display: block; margin: auto;" /> To be clear, the average treatment effect, `\(\tau\)` is given by: `\begin{align*} \tau = (\underbrace{y_{\text{treat, post}} - y_{\text{control, post}}}_\text{diff (post)}) - (\underbrace{y_{\text{treat, pre}} - y_{\text{control, pre}}}_\text{diff (pre)}) \end{align*}` --- # Not a Silver Bullet - Note: DiD is a clever way of getting treatment effects. - .hi[Treatment effect] is identified by assuming: in the .pink[absence of treatment], the average difference between .pink[treated] and .purple[control units] would have remained constant -- - We need this difference to be constant before hand. Called the .pink[parallel trends] assumption -- --- # Bad Control Ex <img src="lecture_13_files/figure-html/did9-1.svg" style="display: block; margin: auto;" /> --- class: inverse, middle # Checklist .col-left[ 1) .hi[Monopsony] ✅ - Monopsony Outcome vs Competitive Outcome - Min wage in monopsony 2) .hi[Discussion] ✅ ] .col-right[ 3) .hi[Empirics] ✅ - Treatment and control effects - The ideal experiment - Making Comparisons - Diff in Diff - External vs Internal Validity ] --- exclude: true exclude: true ```r p_load(pagedown) pagedown::chrome_print(here::here("013-min_wage","lecture_13.html")) ```