Effect of events Flashcards

1
Q

Our interest is in the causal effect of finding a
partner. Under which condition could we identify
this causal effect by comparing different persons?

A

Fundamental problem: we cannot observe change in the same person under the same conditions at two different time points

Between solution:
- statistical twins / need to share all characteristics (unit homogeneity)
- no self-selection (self-select due to omitted/unobserved variable is a problem)

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2
Q

Our interest is in the causal effect of finding a
partner. What is better than between estimation?

A
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3
Q

What is the within-transformation?

A
  • differencing the data within each individual by subtracting their individual-specific mean or a fixed effect from each observation
  • by doing so, the individual-specific time-invariant factors, including the random effects, are differenced out and no longer contribute to the analysis –> random effects assumption becomes irrelevant
  • new focus: the time-varying changes within each individual and the factors that drive those changes.
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4
Q

What is the
a) Random-effects assumption
b) Contemporaneous exogeneity assumption

A

Random-effects assumption: no time-constant unobserved heterogeneity (nothing in the error term that confounds the relationship) –> cancels out in FE estimation as poeple become their own control
Contemporaneous exogeneity assumption: nothing changes over time within person -> no time-varying unobserved heterogeneity

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5
Q

What happens to time-constant factors in within estimations?

A

“A major motivation for using panel data
has been the ability to control for possibly
correlated, time-invariant heterogeneity
without observing it (Arellano 2004).“

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6
Q

Who needs to be in the treatment group?

A

The ones that actually experience the event (i.e. finding a partner) - there needs to be variance, i.e. so who is always with partner will not contribute to change bc there is no variance

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7
Q

Interpret the coefficient of par

A

also: u always want a causal explanation but it is harder to get but only holds if exogeneity assumption applies

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8
Q

Why do we use panel-robust SE in FE estimations?

A

in panel data, assumption about residuals are not as easily satisfied, especially that residuals have a constant variance and that they not auto-correlated → if we ignore this = underestimate standard errors & regard our estimates as more precise then they truly are
but not as important as getting the effect estimates right (biased effect with correct SE does not get u far)

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9
Q

What is the problem with comparing those two?

A

Ignores temporal order

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10
Q

How to model time & temporal order

A

(1) Define the sample: Include only those who can experience the event

(2) Define the event
* Anchor the event in time
* Decide whether to remove or keep reverse and repeated transitions (Keep reverse/repeated transitions if the interest is in finding/having a partner, removeif the interest is in finding/keeping a partner)

(3) Control for unobserved heterogeneity
* Use within-estimation (removes time-constant confounders)
* Use controls for the temporal profile of the outcome (time-varying confounders)
* Use a control sample

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11
Q

Which impact functions are there?

A
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12
Q

How to control for time-varying heterogeneity?

A

Control temporal profile (i.e. age, period, anticipation effects)

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13
Q

Why use a control sample?

A

We could estimate the age profile from the treated
subsample (enough pre- and post-treatment observations)

BUT this is inefficient and can lead to collinearity issues
–> Always keep a control sample

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14
Q

How to model age?

A
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15
Q

How to model other time-varying heterogeneity?

A
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16
Q

To estimate the effect of finding a partner on life satisfaction - should we control for income?

A

Selection on growth, control for it without conditioning it out

17
Q

What are anticipation effects?

A
18
Q

What happens if wie ignore anticipation effects?

A
19
Q

Should we model antipation effects?

A
20
Q

Should you be careful with anticipation effects?

A
21
Q

Finding a partner - anticipation effect included?

A
22
Q

How to model heterogeneous effects for events?

A

1) Seperate models (men vs women)
2) Cross-level interaction effects

23
Q

Why does including gender not work in a FE model?

+ solution

A
24
Q

Interpret

A
25
Q

Hybrid model

+ / -

A
26
Q

Divorce example of hybrid model: what does this tell us?

A
27
Q

What can we do with the hybrid model?

A