Xavier Gabaix:Behavioral Macro Via

Sparse Dynamic Programming

Discussion by Thomas Chaney

Toulouse School of Economics

Banque de France, June, 2016

Thomas Chaney (Toulouse) Sparse Dynamics Programming BdF, June 2016 1 / 11

Some perspective

Lucas JET 1972:

1

confronted large unexplained puzzles (Philips curve)

2

confronted lack of micro-foundations for dynamic macro

3

offered novel concept (rational expectation equilibrium)

4

offered tractable tools (dynamic programming)

Gabaix 2014/16:

1

confronts a series of (smaller?) puzzles

2

confronts unease with macro models (full rationality)

3

offers novel concept (sparse bounded rationality)

4

offers tractable tools (sparse dynamic programming)

Thomas Chaney (Toulouse) Sparse Dynamics Programming BdF, June 2016 2 / 11

Some perspective

Lucas JET 1972:

1

confronted large unexplained puzzles (Philips curve)

2

confronted lack of micro-foundations for dynamic macro

3

offered novel concept (rational expectation equilibrium)

4

offered tractable tools (dynamic programming)

Gabaix 2014/16:

1

confronts a series of (smaller?) puzzles

2

confronts unease with macro models (full rationality)

3

offers novel concept (sparse bounded rationality)

4

offers tractable tools (sparse dynamic programming)

Thomas Chaney (Toulouse) Sparse Dynamics Programming BdF, June 2016 2 / 11

Roadmap

1

Representative agent/heterogeneous agents.

2

Utility accounting.

3

Setting the “default” model.

4

Small comments.

5

A network application.

Thomas Chaney (Toulouse) Sparse Dynamics Programming BdF, June 2016 3 / 11

1- Representative agent?

Unlikely a “representative agent” will be sparse BR.

What about the aggregation of sparse BR agents?

You can make progress:

1

Your model: which agent drops which state variable.

2 ,! extensive margin of attention.

3

Composition effect from this extensive margin.

4

Tractability should allow to deal with heterogeneity?

Thomas Chaney (Toulouse) Sparse Dynamics Programming BdF, June 2016 4 / 11

1- Representative agent?

Unlikely a “representative agent” will be sparse BR.

What about the aggregation of sparse BR agents?

You can make progress:

1

Your model: which agent drops which state variable.

2 ,! extensive margin of attention.

3

Composition effect from this extensive margin.

4

Tractability should allow to deal with heterogeneity?

Thomas Chaney (Toulouse) Sparse Dynamics Programming BdF, June 2016 4 / 11

1- Representative agent? (cont’d)

De-coupling of dimensions?

1

Narrow framing or not? (footnote 51)

2

If I buy a house, do I become aware of the interest rate for other

decisions (e.g. consumption-saving)?

3

Ultimately, an empirical question.

Thomas Chaney (Toulouse) Sparse Dynamics Programming BdF, June 2016 5 / 11

1- Representative agent? (cont’d)

Lucas critique:

1

Lucas: what looks like money illusion (Philips curve).

2

Gabaix: sort of the same.

3

Smooth version of Lucas: big/small shocks, aware/unaware agents.

4

Gabaix is empirically relevant version of Lucas.

Thomas Chaney (Toulouse) Sparse Dynamics Programming BdF, June 2016 6 / 11

2- Utility accounting

Importance of mental cost for welfare/policy recommendations.

Cost, kg (m), does not appear in preferences (only through A).

Does accounting for mental cost affect time [in]consistency?

Thomas Chaney (Toulouse) Sparse Dynamics Programming BdF, June 2016 7 / 11

3- Setting the default model

Very upfront about the arbitrariness of choosing a default model.

But you can say more: how changing the default model affects

behavior!

Example:

1

Tax code tutorials make it the default model (Chetty et al.)

2

Changes in the tax code (with tutorials) change the default model.

3

Gabaix tells us how other shocks interact with the tax code.

Thomas Chaney (Toulouse) Sparse Dynamics Programming BdF, June 2016 8 / 11

3- Setting the default model

Very upfront about the arbitrariness of choosing a default model.

But you can say more: how changing the default model affects

behavior!

Example:

1

Tax code tutorials make it the default model (Chetty et al.)

2

Changes in the tax code (with tutorials) change the default model.

3

Gabaix tells us how other shocks interact with the tax code.

Thomas Chaney (Toulouse) Sparse Dynamics Programming BdF, June 2016 8 / 11

Smaller comment I: cross partials

Agent weighs utility gain against cognitive cost one state variable at a

time.

What about two variables at once? any combination of them?

Question: Could it be the agent misses out on cross-partial terms?

Thomas Chaney (Toulouse) Sparse Dynamics Programming BdF, June 2016 9 / 11

Smaller comment II: outsourcing complexity

Well defined utility cost of contemplating one state variable (kg (mi )).

Well defined benefit as well (could be expressed in income-equivalent).

Question: Could complexity be outsourced?

Example:

1

e.g. g (m) = mawith a = 0 (fixed cost).

2

Economies of scale for a service provider (e.g. if similar clients).

3

Model makes predictions re: when outsourcing is more likely.

4

Refinement: An intermediary can easily cheat a sparse BR agent.

5

Should it be regulated?

Thomas Chaney (Toulouse) Sparse Dynamics Programming BdF, June 2016 10 / 11

Smaller comment II: outsourcing complexity

Well defined utility cost of contemplating one state variable (kg (mi )).

Well defined benefit as well (could be expressed in income-equivalent).

Question: Could complexity be outsourced?

Example:

1

e.g. g (m) = mawith a = 0 (fixed cost).

2

Economies of scale for a service provider (e.g. if similar clients).

3

Model makes predictions re: when outsourcing is more likely.

4

Refinement: An intermediary can easily cheat a sparse BR agent.

5

Should it be regulated?

Thomas Chaney (Toulouse) Sparse Dynamics Programming BdF, June 2016 10 / 11

Application: Oligopoly in an input-output network

Thomas Chaney (Toulouse) Sparse Dynamics Programming BdF, June 2016 11 / 11