<p>Your first quote seems to be given as an explanation of how they constructed Figure 1 (however I cannot be sure since you did not state pagenumber for the quote). Anyway, Figure 1 compares a treatment group with a control group on outcome variable. The descriptive content of these figures are not rigorously related to the main estimation equation</p>
<p><span class="math-container">$$(1) \ \ Y_{it} = \alpha + \beta (Leniency Law)_{k(i),t} + \delta X_{it} + \theta_t + \gamma_i +\epsilon_{it},$$</span></p>
<p>where <span class="math-container">$k$</span> is country, <span class="math-container">$i$</span> is firm and <span class="math-container">$t$</span> time.</p>
<p>The estimator <span class="math-container">$\hat \beta_{OLS}$</span> do not presuppose existence of control and treatment group there is no group indicator. All that is presupposes is <span class="math-container">$(Leniency Law)_{k(i),t}$</span> implying that for any time <span class="math-container">$t$</span> you know which country <span class="math-container">$k(i)$</span> firm <span class="math-container">$i$</span> is in and whether leniency laws were adopted.</p>
<p>When econometricians still talk about treatment and control groups when they apply the estimator <span class="math-container">$\hat \beta_{OLS}$</span> it is because the believe they can interpret <span class="math-container">$\hat \beta_{OLS}$</span> somewhat like it is possible to interpret <span class="math-container">$\hat \beta_{OLS}^{2\times 2}$</span> where <span class="math-container">$\hat \beta_{OLS}^{2\times 2}$</span> is defined by estimation eqaution (1) in the case of 2 time periods and 2 countries - one adopting leniency laws one not.</p>
<p>This is all summarized clearly in <a href="https://www.nber.org/papers/w25018" rel="noreferrer">Goodman and Bacon (2018)</a> and in this <a href="https://replicationnetwork.com/2019/10/18/reed-eir-more-on-heterogeneity-in-two-way-fixed-effects-models/" rel="noreferrer">blog post</a>.</p>
<p>Obviously, econometricians are interested in causal estimates. One main tool for theorizing about the causal estimates are <a href="http://The%20Rubin%20Causal%20Model%20(RCM)%20is%20a%20formal%20mathematical%20framework%20for%20causal%20inference,%20first%20given%20that%20name%20by%20Holland%20(1986)" rel="noreferrer">the Rubin Causal Model</a>. There exists a literature trying to apply this framework to the interpretation of <span class="math-container">$\hat \beta_{OLS}$</span> in setting going beyond the <span class="math-container">$2 \times 2$</span> setting.</p>
<p>The main insight of this literature is that <span class="math-container">$\hat \beta_{OLS}$</span> can be seen as a weighted average of treatment effects <a href="https://www.aeaweb.org/articles?id=10.1257/aer.20181169" rel="noreferrer">Chaisemartin &amp; D'Haultfæuille (2020)</a>. And since these particular treatment effects can be estimated by a proper choice of 2 x 2 design this can be seen as a step in direction of interpreting <span class="math-container">$\hat \beta_{OLS}$</span> in these terms. However, these interpretations are not performed trivially and are always based on</p>
<p>(1) Specifics of the datastructure (such as staggered design for Goodman and Bacon)</p>
<p>(2) Assumptions imposed on the used RCM model</p>
<p>and to my understanding they do not in general imply that for example any single country can be placed in treatment or control group as you seem to be trying to do.</p>
<p>The simplest of problems is how to expands to a framework with multiple timeperiods but where the two observed groups can still be split up into treated and non-treated in a simple fashion because all treated start being treated at the same time <a href="https://diff.healthpolicydatascience.org/#multiple" rel="noreferrer">see for example here for a good intro to treatment effects with multiple periods</a>.</p>
<p>So to put it in short: So, what should we do in this case? In particular, which sample (control or treatment) that Brazil in 1998 belongs to? If you wanna estimate equation (1) you should do nothing, this is not a problem for that estimator. It may be a problem for a particular application of an RCM model but that remains to be shown.</p>

CC BY-SA 4.0
2021-05-31T21:18:00.317Z
Hi @Jesper Hypel, regarding the Beta OLS explanation(sorry I do not know how to write down it in the comment, I generated a post here about this problem, could you please have a look on it if it is convenient to you ? https://stats.stackexchange.com/questions/526619/why-do-not-we-include-post-and-treatment-variables-separarately-in-generalized-d
Phil Nguyen
CC BY-SA 4.0
2021-05-31T21:21:07.407Z
@BeautifulMindset sure ... i look at it now
Jesper Hybel
CC BY-SA 4.0
2021-05-31T22:05:22.153Z
Thank you @Jesper Hybel. Can you have a look at the latter part of my post "Another example, Lithuania...." , or do you think I should separate it to another post then?
Phil Nguyen
CC BY-SA 4.0
2021-05-31T22:11:40.677Z
@BeautifulMindset I would gladly comment on it but I completely fail to understand that part of the question. You are welcome to try and make a seperate post offcourse but I cannot garanty I will understand it.
Jesper Hybel
CC BY-SA 4.0
2021-05-31T22:16:46.537Z
may I ask the last sentence in your answer. so, in the end, which group that Brazil 1998 belongs to : treatment or control, please? Although it does not matter that Brazil 1998 in treatment or control in this regression (because by anyway, it will receive the value 0 for (Leniency Law) variable), but it is needed for plotting the Figure 1 as in Dasgupta paper.
Phil Nguyen
CC BY-SA 4.0
2021-05-31T22:26:31.507Z
With respect to replicability of the descriptive plot I would say that if the description given in the article is indeterminate up to the inclusion or non-inclusion of Brazil in 1998 then it should be possible to do a figure where brazil is included and one where it is not and if the qualitative conclusion they draw based on the figure is not robust to this then there is a problem.Otherwise the vague description leaving some room for indeterminacy with respect to grouping of brazil is just not important.I get that psychologically you may crave an answer but I'm not convinced science needs one.
Jesper Hybel
CC BY-SA 4.0
2021-05-31T22:27:36.253Z
Let us [continue this discussion in chat](https://chat.stackexchange.com/rooms/124922/discussion-between-jesper-hybel-and-beautifulmindset).
Jesper Hybel
CC BY-SA 4.0
2021-05-31T14:05:34.497Z
2021-05-31T14:22:13.773Z
2021-05-31T14:22:13.773Z
Jesper Hybel
<p>In a <a href="https://academic.oup.com/rfs/article/32/7/2587/5079300" rel="nofollow noreferrer">generalized Difference-in-Differences in Dasgupta,2019 paper</a>, he documented that</p>
<blockquote>
<p>control countries did not have a leniency law introduced over 2 years
before to 5 years after the introduction of the leniency law for the
treated countries.</p>
</blockquote>
<p>And it is how he describe sample selection</p>
<blockquote>
<p>the treated group comprises all firms that are headquartered in
countries that have passed a leniency law by year t⁠. The control
group comprises firms in countries that never adopted a leniency law
in our sample period and firms headquartered in countries that adopted
a leniency law at some later point of time</p>
</blockquote>
<p>There is an example, Brazil implemented the law in <strong>2000</strong> and United States implemented the law in <strong>1993</strong>.
U.S implement the laws in 1993 means that Brazil in 1998 is control for US (<em>instead of using <strong>firms in Brazil</strong>, I used <strong>Brazil</strong> here for shortening purposes</em>). However, Brazil also passed the law in 2000, meaning that 1998 also belong to treatment sample.</p>
<p>So, what should we do in this case? In particular, which sample (control or treatment) that Brazil in 1998 belongs to?</p>
<p>Another example, <strong>Lithuania passed the law in 2008, Ukraine passed the law in 2017,so whether the firms in Lithuania from 2014 to 2017 is the control of Ukraine (because Lithuania from 2006-&gt;2013 are treatments). It makes sense if based on the <a href="https://economics.stackexchange.com/a/44052/33142">switch on and off of Thomas from this discussion</a>. However, on the other hand, it makes no sense, because the control for Ukraine cannot compared to the change of Lithuania before and after 2017.</strong> More detail about this example can be seen from the explanation <a href="https://stats.stackexchange.com/questions/526787/how-to-plot-the-graph-for-testing-paralell-trend-for-staggered-did">here</a>.</p>

CC BY-SA 4.0
2021-05-31T11:58:06.893Z
Is it staggered design? And what is the main estimation equation defining what you refer to as "generalized Difference-in-Differences"?
Jesper Hybel
CC BY-SA 4.0
2021-05-31T12:11:11.110Z
@JesperHybel yes, it is staggered design
Phil Nguyen
CC BY-SA 4.0
2021-05-31T12:15:25.543Z
@JesperHybel the first equation in this link, thank you https://economics.stackexchange.com/questions/43864/how-to-justify-the-treatment-and-control-groups-for-difference-in-difference-wit/44052#44052
Phil Nguyen
CC BY-SA 4.0
2021-05-31T12:55:37.520Z
Your first quote seems to be given as an explanation of how they constructed figure 1. It is not valid to take this definition of 'control' and 'treatment' group and apply it to the interpretation of the coefficient of interest in the main estimation equation. The interpretation of two way fixed effect estimator in staggered design is dealt with by [Goodman and Bacon](https://www.nber.org/papers/w25018).
Jesper Hybel
CC BY-SA 4.0
2021-05-31T13:14:16.907Z
@JesperHybel consider posting answers as answers
1muflon1
CC BY-SA 4.0
2021-05-31T13:19:53.837Z
@1muflon1 ok then, I'll do it. But only because I like the color of your fur.
Jesper Hybel
CC BY-SA 4.0
2021-06-05T03:47:06.627Z
I cannot comment but I just saw the way you describe the years seems not to be appropriate, which may confuse others. The description can be updated as the discussion [here](https://stats.stackexchange.com/questions/526928/a-doubt-on-different-ways-of-performing-staggered-did).
Phil Nguyen
<econometrics><international-economics><difference-in-difference>
What shoud we do when the expected treatment overlaps control sample in DID?
Phil Nguyen
# What shoud we do when the expected treatment overlaps control sample in DID?

(Asked by: username_0 on 2021-05-31)

In a [generalized Difference-in-Differences in Dasgupta,2019 paper](https://academic.oup.com/rfs/article/32/7/2587/5079300), he documented that

> control countries did not have a leniency law introduced over 2 years
> before to 5 years after the introduction of the leniency law for the
> treated countries.

And it is how he describe sample selection

> the treated group comprises all firms that are headquartered in
> countries that have passed a leniency law by year t⁠. The control
> group comprises firms in countries that never adopted a leniency law
> in our sample period and firms headquartered in countries that adopted
> a leniency law at some later point of time

There is an example, Brazil implemented the law in **2000** and United States implemented the law in **1993**.
U.S implement the laws in 1993 means that Brazil in 1998 is control for US (*instead of using **firms in Brazil**, I used **Brazil** here for shortening purposes*). However, Brazil also passed the law in 2000, meaning that 1998 also belong to treatment sample.

So, what should we do in this case? In particular, which sample (control or treatment) that Brazil in 1998 belongs to?

Another example, **Lithuania passed the law in 2008, Ukraine passed the law in 2017,so whether the firms in Lithuania from 2014 to 2017 is the control of Ukraine (because Lithuania from 2006->2013 are treatments). It makes sense if based on the [switch on and off of Thomas from this discussion](https://economics.stackexchange.com/a/44052/33142). However, on the other hand, it makes no sense, because the control for Ukraine cannot compared to the change of Lithuania before and after 2017.** More detail about this example can be seen from the explanation [here](https://stats.stackexchange.com/questions/526787/how-to-plot-the-graph-for-testing-paralell-trend-for-staggered-did).

**Tags:** econometrics, international-economics, difference-in-difference
> **Comments:**
> **username_1**: Is it staggered design? And what is the main estimation equation defining what you refer to as "generalized Difference-in-Differences"?  
> **username_0**: @JesperHybel yes, it is staggered design  
> **username_0**: @JesperHybel the first equation in this link, thank you https://economics.stackexchange.com/questions/43864/how-to-justify-the-treatment-and-control-groups-for-difference-in-difference-wit/44052#44052  
> **username_1**: Your first quote seems to be given as an explanation of how they constructed figure 1. It is not valid to take this definition of 'control' and 'treatment' group and apply it to the interpretation of the coefficient of interest in the main estimation equation. The interpretation of two way fixed effect estimator in staggered design is dealt with by [Goodman and Bacon](https://www.nber.org/papers/w25018).  
> **username_2**: @JesperHybel consider posting answers as answers  
> **username_1**: @1muflon1 ok then, I'll do it. But only because I like the color of your fur.  
> **username_0**: I cannot comment but I just saw the way you describe the years seems not to be appropriate, which may confuse others. The description can be updated as the discussion [here](https://stats.stackexchange.com/questions/526928/a-doubt-on-different-ways-of-performing-staggered-did).  

---

## 1 answers

### Answer by username_1 on 2021-05-31. Score: 6 | Accepted answer

Your first quote seems to be given as an explanation of how they constructed Figure 1 (however I cannot be sure since you did not state pagenumber for the quote). Anyway, Figure 1 compares a treatment group with a control group on outcome variable. The descriptive content of these figures are not rigorously related to the main estimation equation

$$(1) \ \ Y\_{it} = \alpha + \beta (Leniency Law)\_{k(i),t} + \delta X\_{it} + \theta\_t + \gamma\_i +\epsilon\_{it},$$

where $k$ is country, $i$ is firm and $t$ time.

The estimator $\hat \beta\_{OLS}$ do not presuppose existence of control and treatment group there is no group indicator. All that is presupposes is $(Leniency Law)\_{k(i),t}$ implying that for any time $t$ you know which country $k(i)$ firm $i$ is in and whether leniency laws were adopted.

When econometricians still talk about treatment and control groups when they apply the estimator $\hat \beta\_{OLS}$ it is because the believe they can interpret $\hat \beta\_{OLS}$ somewhat like it is possible to interpret $\hat \beta\_{OLS}^{2\times 2}$ where $\hat \beta\_{OLS}^{2\times 2}$ is defined by estimation eqaution (1) in the case of 2 time periods and 2 countries - one adopting leniency laws one not.

This is all summarized clearly in [Goodman and Bacon (2018)](https://www.nber.org/papers/w25018) and in this [blog post](https://replicationnetwork.com/2019/10/18/reed-eir-more-on-heterogeneity-in-two-way-fixed-effects-models/).

Obviously, econometricians are interested in causal estimates. One main tool for theorizing about the causal estimates are [the Rubin Causal Model](http://The%20Rubin%20Causal%20Model%20(RCM)%20is%20a%20formal%20mathematical%20framework%20for%20causal%20inference,%20first%20given%20that%20name%20by%20Holland%20(1986)). There exists a literature trying to apply this framework to the interpretation of $\hat \beta\_{OLS}$ in setting going beyond the $2 \times 2$ setting.

The main insight of this literature is that $\hat \beta\_{OLS}$ can be seen as a weighted average of treatment effects [Chaisemartin & D'Haultfæuille (2020)](https://www.aeaweb.org/articles?id=10.1257/aer.20181169). And since these particular treatment effects can be estimated by a proper choice of 2 x 2 design this can be seen as a step in direction of interpreting $\hat \beta\_{OLS}$ in these terms. However, these interpretations are not performed trivially and are always based on

(1) Specifics of the datastructure (such as staggered design for Goodman and Bacon)

(2) Assumptions imposed on the used RCM model

and to my understanding they do not in general imply that for example any single country can be placed in treatment or control group as you seem to be trying to do.

The simplest of problems is how to expands to a framework with multiple timeperiods but where the two observed groups can still be split up into treated and non-treated in a simple fashion because all treated start being treated at the same time [see for example here for a good intro to treatment effects with multiple periods](https://diff.healthpolicydatascience.org/#multiple).

So to put it in short: So, what should we do in this case? In particular, which sample (control or treatment) that Brazil in 1998 belongs to? If you wanna estimate equation (1) you should do nothing, this is not a problem for that estimator. It may be a problem for a particular application of an RCM model but that remains to be shown.

> **Comments:**
> **username_0**: Hi @Jesper Hypel, regarding the Beta OLS explanation(sorry I do not know how to write down it in the comment, I generated a post here about this problem, could you please have a look on it if it is convenient to you ? https://stats.stackexchange.com/questions/526619/why-do-not-we-include-post-and-treatment-variables-separarately-in-generalized-d  
> **username_1**: @BeautifulMindset sure ... i look at it now  
> **username_0**: Thank you @Jesper Hybel. Can you have a look at the latter part of my post "Another example, Lithuania...." , or do you think I should separate it to another post then?  
> **username_1**: @BeautifulMindset I would gladly comment on it but I completely fail to understand that part of the question. You are welcome to try and make a seperate post offcourse but I cannot garanty I will understand it.  
> **username_0**: may I ask the last sentence in your answer. so, in the end, which group that Brazil 1998 belongs to : treatment or control, please? Although it does not matter that Brazil 1998 in treatment or control in this regression (because by anyway, it will receive the value 0 for (Leniency Law) variable), but it is needed for plotting the Figure 1 as in Dasgupta paper.  
> **username_1**: With respect to replicability of the descriptive plot I would say that if the description given in the article is indeterminate up to the inclusion or non-inclusion of Brazil in 1998 then it should be possible to do a figure where brazil is included and one where it is not and if the qualitative conclusion they draw based on the figure is not robust to this then there is a problem.Otherwise the vague description leaving some room for indeterminacy with respect to grouping of brazil is just not important.I get that psychologically you may crave an answer but I'm not convinced science needs one.  
> **username_1**: Let us [continue this discussion in chat](https://chat.stackexchange.com/rooms/124922/discussion-between-jesper-hybel-and-beautifulmindset).  

---

