双向固定效应和双重差分演示课件
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DID双重差分回归 PPT课件
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What is nice about the model
• Suppose interventions are not random but systematic
– Occur in states with higher or lower average Y – Occur in time periods with different Y’s
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Y
Yc1 Yt1
Yc2
Yt2
Estimated treatment
True treatment effect
control
treatment
True Treatment Effect
t1
t2
time
12
Basic Econometric Model
• Data varies by
– state (i) – time (t) – Outcome is Yit
• Year effects
– Capture differences over time that are common to all groups
19
Questions to ask?
• What parameter is identified by the quasiexperiment? Is this an economically meaningful parameter?
Difference
After Change Yt2
Yc2
Difference
ΔYt = Yt2-Yt1 ΔYc =Yc2-Yc1 ΔΔY ΔYt – ΔYc
8
Key Assumption
• Control group identifies the time path of outcomes that would have happened in the absence of the treatment
17
What is nice about the model
• Suppose interventions are not random but systematic
– Occur in states with higher or lower average Y – Occur in time periods with different Y’s
11
Y
Yc1 Yt1
Yc2
Yt2
Estimated treatment
True treatment effect
control
treatment
True Treatment Effect
t1
t2
time
12
Basic Econometric Model
• Data varies by
– state (i) – time (t) – Outcome is Yit
• Year effects
– Capture differences over time that are common to all groups
19
Questions to ask?
• What parameter is identified by the quasiexperiment? Is this an economically meaningful parameter?
Difference
After Change Yt2
Yc2
Difference
ΔYt = Yt2-Yt1 ΔYc =Yc2-Yc1 ΔΔY ΔYt – ΔYc
8
Key Assumption
• Control group identifies the time path of outcomes that would have happened in the absence of the treatment
双重差分模型幻灯片
intervention • Can examine time-series changes but,
unsure how much of the change is due to secular changes
2
Y
Yt1 Ya Yb Yt2
True effect = Yt2-Yt1 Estimated effect = Yb-Ya
6
Difference in Difference
Before Change
Group 1 Yt1 (Treat)
Group 2 Yc1 (Control)
Difference
After Change Yt2
Yc2
Difference
ΔYt = Yt2-Yt1 ΔYc =Yc2-Yc1 ΔΔY ΔYt – ΔYc
• Key concept: can control for the fact that the intervention is more likely in some types of states
5
Three different presentations
• Tabular • Graphical • Regression equation
• Application of two-way fixed effects model
1
Problem set up
• Cross-sectional and time serintervention • Have pre-post data for group receiving
t1
ti
t2
time
3
• Intervention occurs at time period t1 • True effect of law
unsure how much of the change is due to secular changes
2
Y
Yt1 Ya Yb Yt2
True effect = Yt2-Yt1 Estimated effect = Yb-Ya
6
Difference in Difference
Before Change
Group 1 Yt1 (Treat)
Group 2 Yc1 (Control)
Difference
After Change Yt2
Yc2
Difference
ΔYt = Yt2-Yt1 ΔYc =Yc2-Yc1 ΔΔY ΔYt – ΔYc
• Key concept: can control for the fact that the intervention is more likely in some types of states
5
Three different presentations
• Tabular • Graphical • Regression equation
• Application of two-way fixed effects model
1
Problem set up
• Cross-sectional and time serintervention • Have pre-post data for group receiving
t1
ti
t2
time
3
• Intervention occurs at time period t1 • True effect of law
DID双重差分回归PPT课件
• Only two periods • Intervention will occur in a group of
observations (e.g. states, firms, etc.)
.
13
• Three key variables
– Tit =1 if obs i belongs in the state that will eventually be treated
unsure how much of the change is due to secular changes
.
3
Y
Yt1 Ya Yb Yt2
True effect = Yb-Ya Estimated effect =Yt2-Yt1
t1
ti
t2
time
.
4
• Intervention occurs at time period t1 • True effect of law
– Ait =1 in the periods when treatment occurs – TitAit -- interaction term, treatment states after
the intervention
• Yit = β0 + β1Tit + β2Ait + β3TitAit + εit
Difference in Difference Models
.
1
What is DID
• How can we estimate the effects of higher education reform in China?
• Yang and Chen (2009)
observations (e.g. states, firms, etc.)
.
13
• Three key variables
– Tit =1 if obs i belongs in the state that will eventually be treated
unsure how much of the change is due to secular changes
.
3
Y
Yt1 Ya Yb Yt2
True effect = Yb-Ya Estimated effect =Yt2-Yt1
t1
ti
t2
time
.
4
• Intervention occurs at time period t1 • True effect of law
– Ait =1 in the periods when treatment occurs – TitAit -- interaction term, treatment states after
the intervention
• Yit = β0 + β1Tit + β2Ait + β3TitAit + εit
Difference in Difference Models
.
1
What is DID
• How can we estimate the effects of higher education reform in China?
• Yang and Chen (2009)
DID双重差分回归课件
• In this example, Y falls by Yc2-Yc1 even without the intervention • Note that underlying ‘levels’ of outcomes are not important
(return to this in the regression equation)
• Cross section and time fixed effects
• Use time series of untreated group to establish what would have occurred in the absence of the intervention
• Key concept: can control for the fact that the intervention is more likely in some types of states
• If the intervention occurs in an area with a different trend, will under/over state the treatment effect
• In this example, suppose intervention occurs in area with faster falling Y
Difference in Difference Models
DID双重差分回归
1
What is DID
• How can we estimate the effects of higher education reform in China?
• Yang and Chen (2009)
(return to this in the regression equation)
• Cross section and time fixed effects
• Use time series of untreated group to establish what would have occurred in the absence of the intervention
• Key concept: can control for the fact that the intervention is more likely in some types of states
• If the intervention occurs in an area with a different trend, will under/over state the treatment effect
• In this example, suppose intervention occurs in area with faster falling Y
Difference in Difference Models
DID双重差分回归
1
What is DID
• How can we estimate the effects of higher education reform in China?
• Yang and Chen (2009)
双重差分模型幻灯片differenceindifferencesmodelsppt课件
8
Y
Yc1 Yt1 Yc2 Yt2
t1
Treatment effect= (Yt2-Yt1) – (Yc2-Yc1)
control treatment t2
time
9
Key Assumption
• Control group identifies the time path of outcomes that would have happened in the absence of the treatment
– Min( pY,C) – P=percent replacement – Y = earnings – C = cap
– e.g., 65% of earnings up to $400/month
22
• Concern:
– Moral hazard. Benefits will discourage return to work
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27
Model
• Yit = duration of spell on WC • Ait = period after benefits hike • Hit = high earnings group (Income>E3)
• Yit = β0 + β1Hit + β2Ait + β3AitHit + β4Xit’ + εit • Diff-in-diff estimate is β3
• Year effects
– Capture differences over time that are common to all groups
20
Meyer et al.
Y
Yc1 Yt1 Yc2 Yt2
t1
Treatment effect= (Yt2-Yt1) – (Yc2-Yc1)
control treatment t2
time
9
Key Assumption
• Control group identifies the time path of outcomes that would have happened in the absence of the treatment
– Min( pY,C) – P=percent replacement – Y = earnings – C = cap
– e.g., 65% of earnings up to $400/month
22
• Concern:
– Moral hazard. Benefits will discourage return to work
25
26
27
Model
• Yit = duration of spell on WC • Ait = period after benefits hike • Hit = high earnings group (Income>E3)
• Yit = β0 + β1Hit + β2Ait + β3AitHit + β4Xit’ + εit • Diff-in-diff estimate is β3
• Year effects
– Capture differences over time that are common to all groups
20
Meyer et al.
双向固定效应和双重差分ppt课件
9
Near universal agreement in results
10% increase in price reduces demand by 4% Change in smoking evenly split between
Reductions in number of smokers Reductions in cigs/day among remaining smokers
Rates differ by type of alcohol, alcohol content Nearly all cigarettes taxed the same
4
Current excise tax rates
Cigarettes
Low: SC($0.07), MO ($0.17), VA($0.30) High: RI ($3.46), NY ($2.75); NJ($2.70) Average of $1.32 across states Average in tobacco producing states: $0.40 Average in non-tobacco states, $1.44 Average price per pack is $5.12
permanent differences between groups vt – time fixed effects. Impacts common to
all groups but vary by year εit -- idiosyncratic error
3
Excises taxes on poor health
10
Taxes now an integral part of antismoking campaigns
Near universal agreement in results
10% increase in price reduces demand by 4% Change in smoking evenly split between
Reductions in number of smokers Reductions in cigs/day among remaining smokers
Rates differ by type of alcohol, alcohol content Nearly all cigarettes taxed the same
4
Current excise tax rates
Cigarettes
Low: SC($0.07), MO ($0.17), VA($0.30) High: RI ($3.46), NY ($2.75); NJ($2.70) Average of $1.32 across states Average in tobacco producing states: $0.40 Average in non-tobacco states, $1.44 Average price per pack is $5.12
permanent differences between groups vt – time fixed effects. Impacts common to
all groups but vary by year εit -- idiosyncratic error
3
Excises taxes on poor health
10
Taxes now an integral part of antismoking campaigns
双重差分模型幻灯片 - difference in differences models
Difference
After Change Yt2
Yc2
Difference
ΔYt = Yt2-Yt1 ΔYc =Yc2-Yc1 ΔΔY ΔYt – ΔYc
8
Y
Yc1 Yt1 Yc2 Yt2
t1
Treatment effect= (Yt2-Yt1) – (Yc2-Yc1)
control treatment t2
3
Y
Yt1 Ya Yb Yt2
True effect = Yt2-Yt1 Estimated effect = Yb-Ya
t1
ti
t2
time
4
• Intervention occurs at time period t1 • True effect of law
– Ya – Yb
• Only have data at t1 and t2
• Attempts to mimic(模拟) random assignment with treatment and “comparison” sample
• Application of two-way fixed effects model
2
Problem set up
• Cross-sectional and time series data • One group is ‘treated’ with intervention
– Ait =1 in the periods when treatment occurs – TitAit -- interaction term, treatment states after
（干预） • Have pre-post data for group receiving
After Change Yt2
Yc2
Difference
ΔYt = Yt2-Yt1 ΔYc =Yc2-Yc1 ΔΔY ΔYt – ΔYc
8
Y
Yc1 Yt1 Yc2 Yt2
t1
Treatment effect= (Yt2-Yt1) – (Yc2-Yc1)
control treatment t2
3
Y
Yt1 Ya Yb Yt2
True effect = Yt2-Yt1 Estimated effect = Yb-Ya
t1
ti
t2
time
4
• Intervention occurs at time period t1 • True effect of law
– Ya – Yb
• Only have data at t1 and t2
• Attempts to mimic(模拟) random assignment with treatment and “comparison” sample
• Application of two-way fixed effects model
2
Problem set up
• Cross-sectional and time series data • One group is ‘treated’ with intervention
– Ait =1 in the periods when treatment occurs – TitAit -- interaction term, treatment states after
（干预） • Have pre-post data for group receiving
《因果推断实用计量方法》大学教学课件 第9章 双重差分法
• 处置组和控制组在2014后的业绩均值差异为：
= 1 − = 0
= 1 + = 1 − = 0
横截面单重差分估计偏差
横截面单重差分的偏差
• 如果这个模型回归得到的 的系数መ1 能够得到1 无偏估计的条件是
−ሾE = 0， = 1
− E = 0， = 0 ሿ
= 0 + 1 + 2 + 3 − 0 + 1
− 0 + 2 − 0
= ሾ2 +3 ሿ − ሾ2 ሿ
= 3
双重差分法回归模型系数
•
方法2：从纵向差异理解：处置组和控制
E = 1， = 0 = 0 + 1
•
处置组在处置事件发生后 的均值(图9.2中B点)为：
E = 1， = 1 = 0 + 1 + 2 + 3
双重差分法回归模型系数
= 0 + 1 + 2 +3 ∗ +
双重差分法回归模型系数
•
方法1：从横向差异理解：处置组在处置前后
的均值差异 B − A − 控制组在处置前后 的均
值差异 D − C 等于：
(B − A) − (D − C）
= ሾE = 1， = 1
− E = 1， = 0 ሿ
E = 1 − = 0 = 0，
即处置组和控制组在事件发生的2014年后，除了受税法实施与否影响的差异外，不存在其他
差别。
• 显然这个条件是很难成立的。在本例中，如果通过税法的省都是经济较发达的省，即使没有
税法的影响，处置组和控制组的企业在2014后的平均业绩水平也不相同。
= 1 − = 0
= 1 + = 1 − = 0
横截面单重差分估计偏差
横截面单重差分的偏差
• 如果这个模型回归得到的 的系数መ1 能够得到1 无偏估计的条件是
−ሾE = 0， = 1
− E = 0， = 0 ሿ
= 0 + 1 + 2 + 3 − 0 + 1
− 0 + 2 − 0
= ሾ2 +3 ሿ − ሾ2 ሿ
= 3
双重差分法回归模型系数
•
方法2：从纵向差异理解：处置组和控制
E = 1， = 0 = 0 + 1
•
处置组在处置事件发生后 的均值(图9.2中B点)为：
E = 1， = 1 = 0 + 1 + 2 + 3
双重差分法回归模型系数
= 0 + 1 + 2 +3 ∗ +
双重差分法回归模型系数
•
方法1：从横向差异理解：处置组在处置前后
的均值差异 B − A − 控制组在处置前后 的均
值差异 D − C 等于：
(B − A) − (D − C）
= ሾE = 1， = 1
− E = 1， = 0 ሿ
E = 1 − = 0 = 0，
即处置组和控制组在事件发生的2014年后，除了受税法实施与否影响的差异外，不存在其他
差别。
• 显然这个条件是很难成立的。在本例中，如果通过税法的省都是经济较发达的省，即使没有
税法的影响，处置组和控制组的企业在2014后的平均业绩水平也不相同。
双重差分固定效应
双重差分固定效应
双重差分固定效应（double difference fixed effects）是一种用于估计政策干预效果的方法。
它的基本思想是通过比较干预组和对照组的变化，在控制了时间和组别固定效应的基础上，来消除可能存在的其他影响因素，从而更准确地估计政策的效果。
具体来说，在双重差分固定效应模型中，研究者会选择两个组别（通常是干预组和对照组），在不同的时间点上测量它们的观测变量（比如收入、就业率等），然后通过扣除时间和组别固定效应来计算干预组和对照组之间的差异。
这个差异就是政策干预的效果。
与其他估计政策效应的方法相比，双重差分固定效应具有一些优势。
首先，它能够减少时间和组别的固定效应对结果的影响，从而更加准确地估计政策的效果。
其次，它不需要对时间和组别的固定效应进行假设，因此更加灵活。
最后，它可以用来解决因果推断中的选择性偏差问题，因为它能够消除随时间变化的任何影响因素。
总之，双重差分固定效应是一种在政策评估中广泛使用的工具，它能够帮助研究者探索政策干预的效果，并为政策制定者提供更加有效的政策建议。
双向固定效应的双重差分模型
双向固定效应的双重差分模型哎呀，今天咱们来聊聊一个挺有意思的经济学模型，叫做双向固定效应的双重差分模型。
这名字听着挺复杂，但其实它背后有个简单的故事。
想象一下你家附近开了一家新餐馆，大家都在谈论那里的美食。
你就好奇，这个餐馆对周围环境的影响有多大，是不是让大家都爱上了美食，变得更快乐了呢？这时候，咱们就可以用双重差分模型来帮忙。
得搞清楚什么是“双重差分”。
简单来说，就是你想知道某件事发生前后的变化。
这就像你和朋友一起去看电影，观影前你们的心情、观影后大家的反应，差别是不是大得多呢？再加上双向固定效应，就像给这个模型装上了一双“火眼金睛”，能更好地捕捉到不同情况下的变化。
这可不是瞎说，真的能帮我们找到更准确的答案。
想象一下，一个小镇上，老百姓的日子过得风生水起，突然来了个新，大家伙儿都在琢磨这个会不会给他们带来好运。
然后，这个模型就像个侦探，把前后的数据进行对比。
你会发现，某些地方的变化比其他地方明显得多。
就像在餐馆前排队的人多了，背后可能是个很棒的优惠活动吸引了大家。
再说说“固定效应”，这是个厉害的东西。
它能帮助我们消除一些混杂因素的影响。
想象一下，有些人天生就是乐观派，无论外界怎样，他们总能笑对人生。
可是你想知道他们的变化到底有多大，这时候固定效应就可以把这些天生的乐观抹去，让你看到更真实的数据。
这就像是在看电影，有时你需要调整镜头，才能看到真正的画面。
我们还可以考虑时间的维度。
就像生活中常说的“时间是检验真理的唯一标准”，而模型恰恰能给你提供一个时间的视角。
你可以观察到，在不同的时间节点上，这个到底带来了什么样的变化。
比如说，一开始人们可能并没有觉得有什么特别，但经过一段时间后，大家发现生活质量提高了，餐馆的生意也越来越好。
哎呀，真是“一朝一夕”就能看出不同。
咱们不能忽视数据的收集和分析。
假如你仅仅依靠个人感觉，那可就像是在黑暗中摸索，很容易走弯路。
双重差分模型就像是给你提供了一盏明灯，让你清楚地看到哪些因素真正在起作用。
双重差分模型和固定效应模型
双重差分模型和固定效应模型
双重差分模型和固定效应模型是两种用于处理面板数据的常见
方法。
双重差分模型可以用来估计政策干预对于特定群体的影响，而固定效应模型则可以用来控制时间不变的个体特征对于结果变量的
影响。
这篇文章将讨论这两种模型的原理、应用和优缺点。
双重差分模型和固定效应模型都是基于线性回归模型的扩展，因此本文还会介绍一些基本的面板数据分析方法和回归模型的假定和检验方法。
最后，文章会通过实例来展示如何使用这些模型来解决实际问题。
- 1 -。
双重差分的双向固定效应命令
双重差分的双向固定效应命令双重差分（Double Difference）方法是一种常用的实证研究方法，尤其适用于面板数据研究。
它的核心思想是通过比较处理组与对照组在处理前后的差异来估计处理效应。
在双重差分方法中，我们关注的是处理组和对照组之间的差异。
为了进行比较，我们需要在处理前后进行观测。
此外，为了控制可能的时间固定效应，我们还需要引入时间虚拟变量或固定效应。
双重差分方法的步骤如下：1.定义处理组和对照组：首先，我们需要选择一个处理组和一个对照组。
处理组是接受某种处理或政策措施的个体或单位，对照组是没有接受处理或政策措施的个体或单位。
这两组应该在处理之前具有类似的特征。
2.收集面板数据：为了进行双重差分分析，我们需要收集处理组和对照组在处理前后的面板数据。
面板数据可以是个体级别的长面板数据，也可以是单位级别的宽面板数据。
3.估计双重差分模型：在双重差分模型中，我们引入时间虚拟变量或固定效应来控制时间固定效应。
具体地，我们引入处理前后的时间虚拟变量或固定效应（通常用二进制变量表示），并与处理组和对照组的虚拟变量进行交互。
这样，我们可以估计处理前后处理组和对照组之间的差异。
4.控制其他可能的影响因素：在双重差分模型中，我们还可以控制其他可能的影响因素，如个体特征、时间趋势、观测误差等。
这可以通过引入其他解释变量或控制变量来实现。
5.检验双重差分模型：在估计完双重差分模型后，我们需要对模型进行检验来评估模型的拟合程度和稳健性。
常用的检验方法包括F 检验、t检验、各种统计指标的显著性检验等。
双重差分方法的优点是可以控制时间固定效应和个体固定效应，有效减少内生性问题，提高研究结果的可靠性。
此外，该方法还可以在面板数据中利用更多的信息，提高实证研究的效率。
然而，双重差分方法也有一些局限性。
首先，该方法要求在处理前后进行观测，因此只适用于处理实施时间明确的情况。
其次，双重差分方法假设处理组和对照组在处理前具有类似的特征，但研究结果的可靠性依赖于此假设的成立程度。
DID双重差分回归ppt课件
– Cross section and time fixed effects
• Use time series of untreated group to establish what would have occurred in the absence of the intervention
• Key concept: can control for the fact that the intervention is more likely in some types of states
Hale Waihona Puke 14Yit = β0 + β1Tit + β2Ait + β3TitAit + εit
Before After Change Change
Difference
Group 1 β0+ β1 (Treat)
Group 2 β0 (Control)
• If the intervention occurs in an area with a different trend, will under/over state the treatment effect
• In this example, suppose intervention occurs in area with faster falling Y
– Ait =1 in the periods when treatment occurs – TitAit -- interaction term, treatment states after
the intervention
• Yit = β0 + β1Tit + β2Ait + β3TitAit + εit
• Use time series of untreated group to establish what would have occurred in the absence of the intervention
• Key concept: can control for the fact that the intervention is more likely in some types of states
Hale Waihona Puke 14Yit = β0 + β1Tit + β2Ait + β3TitAit + εit
Before After Change Change
Difference
Group 1 β0+ β1 (Treat)
Group 2 β0 (Control)
• If the intervention occurs in an area with a different trend, will under/over state the treatment effect
• In this example, suppose intervention occurs in area with faster falling Y
– Ait =1 in the periods when treatment occurs – TitAit -- interaction term, treatment states after
the intervention
• Yit = β0 + β1Tit + β2Ait + β3TitAit + εit
DID双重差分回归PPT课件
• Cross-sectional and time series data • One group is ‘treated’ with
intervention • Have pre-post data for group receiving
intervention • Can examine time-series changes but,
• In this example, Y falls by Yc2-Yc1 even without the intervention
• Note that underlying ‘levels’ of outcomes are not important (return to this in the regression equation)
– Ya – Yb
• Only have data at t1 and t2
– If using time series, estimate Yt1 – Yt2
• Solution?
5
Difference in difference models
• Basic two-way fixed effects model
– Cross section and time fixed effects
• Use time series of untreated group to establish what would have occurred in the absence of the intervention
• Key concept: can control for the fact that the intervention is more likely in some types of states
intervention • Have pre-post data for group receiving
intervention • Can examine time-series changes but,
• In this example, Y falls by Yc2-Yc1 even without the intervention
• Note that underlying ‘levels’ of outcomes are not important (return to this in the regression equation)
– Ya – Yb
• Only have data at t1 and t2
– If using time series, estimate Yt1 – Yt2
• Solution?
5
Difference in difference models
• Basic two-way fixed effects model
– Cross section and time fixed effects
• Use time series of untreated group to establish what would have occurred in the absence of the intervention
• Key concept: can control for the fact that the intervention is more likely in some types of states
最新-双重差分模型幻灯片differenceindifferencesmodelsppt课件-PPT文档资料
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Model
• Yit = duration of spell on WC • Ait = period after benefits hike • Hit = high earnings group (Income>E3)
• Yit = β0 + β1Hit + β2Ait + β3AitHit + β4Xit’ + εit • Diff-in-diff estimate is β3
– Min( pY,C) – P=percent replacement – Y = earnings – C = cap
– e.g., 65% of earnings up to $400/month
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• Concern:
– Moral hazard. Benefits will discourage return to work
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What is nice about the model
• Suppose interventions are not random but systematic
– Occur in states with higher or lower average Y – Occur in time periods with different Y’s
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Yit = β0 + β1Tit + β2Ait + β3TitAit + εit
Before After Change Change
Difference
Group 1 β0+ β1 (Treat)
Group 2 β0 (Control)
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Model
• Yit = duration of spell on WC • Ait = period after benefits hike • Hit = high earnings group (Income>E3)
• Yit = β0 + β1Hit + β2Ait + β3AitHit + β4Xit’ + εit • Diff-in-diff estimate is β3
– Min( pY,C) – P=percent replacement – Y = earnings – C = cap
– e.g., 65% of earnings up to $400/month
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• Concern:
– Moral hazard. Benefits will discourage return to work
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What is nice about the model
• Suppose interventions are not random but systematic
– Occur in states with higher or lower average Y – Occur in time periods with different Y’s
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Yit = β0 + β1Tit + β2Ait + β3TitAit + εit
Before After Change Change
Difference
Group 1 β0+ β1 (Treat)
Group 2 β0 (Control)
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–High: RI ($3.46), NY ($2.75); NJ($2.70)
–Average of $1.32 across states
–Average in tobacco producing states: $0.40
–Average in non-tobacco states, $1.44
–Average price per pack is $5.12
• Beer
–Low (WY, $0.02/gallon)
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Federal taxes
• Cigarettes, $1.01/pack • Wine
– $0.21/750ml bottle for 14% alcohol or less – $0.31/750ml bottle for 14 – 21% alcohol
for permanent differences between groups • vt – time fixed effects. Impacts common to all groups but vary by year • εit -- idiosyncratic error
3
Excises taxes on poor health
Taxes now an integral part of antismoking campaigns
• Key component of ‘Master Settlement’
• Beer, $0.02 a can • Liquor, $13.50 per 100 proof gallon (50% alcohol),
or, $2.14/750 ml bottle of 80 proof liquor • Total taxes on cigarettes are such that in NYC,
• Simple research design
–Prices typically changed due to state/federal tax hikes
–States with changes are ‘treatment’ –States without changes are control
across states/time • Write model as single observation • Yit=α + Xitβ + ui + vt +εit • Xit is (1 x k) vector
2
• Three-part error structure • ui – group fixed-effects. Control
9
• Near universal agreement in results
–10% increase in price reduces demand by 4%
–Change in smoking evenly split between
• Reductions in number of smokers • Reductions in cigs/day among remaining
–Rates differ by type of alcohol, alcohol content
–Nearly all cigarettes taxed the same
4
Current excise tax rates
• Cigarettes
–Low: SC($0.07), MO ($0.17), VA($0.30)
• Medical/psychological view –
certain goods not subject to these
laws
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• Starting in 1970s, several authors began to examine link between cigarette prices and consumption
Two-way fixed-effect models Difference in difference
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Two-way fixed effects
• Balanced panels • i=1,2,3….N groups • t=1,2,3….T observations/group • Easiest to think of data as varying
–Consumption should fall as prices rise
–Generated from a theoretical model of consumer choice
• Thought by economists to be fairly universal in application
smokers
• Results have been replicated
–in other countries/time periods, variety of statistical models, subgroups
–For other addictive goods: alcohol, cocaine, marijuana, heroin, gambling 10
• Alcohol and ciederal, state and local level
• Some states sell liquor rather than tax it (VA, PA, etc.)
• Most of these taxes are excise taxes -- the tax is per unit
you spend more in taxes buying one case of cigarettes than if you buy 33 cases of wine.
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Do taxes reduce consumption?
• Law of demand
–Fundamental result of micro economic theory