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Page 1: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

JOHANNES KEPLER

UNIVERSITY LINZ

Page 2: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

JOHANNES KEPLER UNIVERSITY LINZ Altenberger Straße 69 4040 Linz, Austria jku.at

Draft paper version prepared for the Young Economists Conference 2019 1-2 October 2019, ViennaMatthias Aistleitner, [email protected] www.jku.at/icae

(Towards) exploring the genesis of competition in economic thought

Page 3: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

Research background: SPACE project

!3

Page 4: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• Spatial Competition and Economic Policies (SPACE): Discourses,Institutions and Everyday Practices

Research background: SPACE project

!3

Page 5: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• Spatial Competition and Economic Policies (SPACE): Discourses,Institutions and Everyday Practices

‣ Investigating the impact of an increasingly strong reliance oncompetition as a prime mode of social organization

‣ Competition as a core concept for designing social institutions

Research background: SPACE project

!3

Page 6: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• Spatial Competition and Economic Policies (SPACE): Discourses,Institutions and Everyday Practices

‣ Investigating the impact of an increasingly strong reliance oncompetition as a prime mode of social organization

‣ Competition as a core concept for designing social institutions

• Six Working Packages

Research background: SPACE project

!3

Page 7: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

Research background: SPACE project

!4

Page 8: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• SPACE Working Package 1: Building a conceptual foundation.

Research background: SPACE project

!4

Page 9: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• SPACE Working Package 1: Building a conceptual foundation.

‣ How is C defined and conceptualized in economics?

Research background: SPACE project

!4

Page 10: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• SPACE Working Package 1: Building a conceptual foundation.

‣ How is C defined and conceptualized in economics?

Research background: SPACE project

!4

Page 11: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• SPACE Working Package 1: Building a conceptual foundation.

‣ How is C defined and conceptualized in economics?

‣ Which temporal changes has the dominant meaning of Cundergone in the post WWII-period?

Research background: SPACE project

!4

Page 12: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• SPACE Working Package 1: Building a conceptual foundation.

‣ How is C defined and conceptualized in economics?

‣ Which temporal changes has the dominant meaning of Cundergone in the post WWII-period?

Research background: SPACE project

!4

Page 13: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• SPACE Working Package 1: Building a conceptual foundation.

‣ How is C defined and conceptualized in economics?

‣ Which temporal changes has the dominant meaning of Cundergone in the post WWII-period?

‣ How do major economic conceptions of C differ between economicparadigms?

Research background: SPACE project

!4

Page 14: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

Aim of this paper

!5

Page 15: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• A contribution in identifying and deconstructing the conceptualfoundation/evolution of C in economics and its observed changes overtime.

Aim of this paper

!5

Page 16: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• A contribution in identifying and deconstructing the conceptualfoundation/evolution of C in economics and its observed changes overtime.

‣ Analysis of economic research literature via topic modeling

Aim of this paper

!5

Page 17: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• A contribution in identifying and deconstructing the conceptualfoundation/evolution of C in economics and its observed changes overtime.

‣ Analysis of economic research literature via topic modeling‣ Topic models = algorithms that discover the content of large

collections of documents via topics

Aim of this paper

!5

Page 18: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• A contribution in identifying and deconstructing the conceptualfoundation/evolution of C in economics and its observed changes overtime.

‣ Analysis of economic research literature via topic modeling‣ Topic models = algorithms that discover the content of large

collections of documents via topics‣ Latent Dirichlet Allocation (LDA) as a simple and widely used topic

model (Blei et al. 2003)

Aim of this paper

!5

Page 19: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

Aim of this paper

!6

Page 20: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• Topic modeling has emerged as an established method in answeringspecific research questions in economics and economic history.

Aim of this paper

!6

Page 21: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• Topic modeling has emerged as an established method in answeringspecific research questions in economics and economic history.Focus on economic literature

Aim of this paper

!6

Page 22: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• Topic modeling has emerged as an established method in answeringspecific research questions in economics and economic history.Focus on economic literature

‣ located in a specific journal/s (e.g. Lüdering and Winker 2016;Wehrheim 2019, Ambrosino et al. 2018)

Aim of this paper

!6

Page 23: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• Topic modeling has emerged as an established method in answeringspecific research questions in economics and economic history.Focus on economic literature

‣ located in a specific journal/s (e.g. Lüdering and Winker 2016;Wehrheim 2019, Ambrosino et al. 2018)

‣ on a more comprehensive level (e.g. whole sub-fields for a givenperiod (Angrist et al. 2017; Jelveh et al. 2015)

Aim of this paper

!6

Page 24: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• Topic modeling has emerged as an established method in answeringspecific research questions in economics and economic history.Focus on economic literature

‣ located in a specific journal/s (e.g. Lüdering and Winker 2016;Wehrheim 2019, Ambrosino et al. 2018)

‣ on a more comprehensive level (e.g. whole sub-fields for a givenperiod (Angrist et al. 2017; Jelveh et al. 2015)

• In this paper: applying topic modeling to a part of the literature which isex ante constraint by a specific research topic: competition.

Aim of this paper

!6

Page 25: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

Research background: SPACE project

!7

Page 26: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• SPACE Working Package 1: Building a conceptual foundation.

Research background: SPACE project

!7

Page 27: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• SPACE Working Package 1: Building a conceptual foundation.

‣ How is C defined and conceptualized in economics?

Research background: SPACE project

!7

Page 28: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• SPACE Working Package 1: Building a conceptual foundation.

‣ How is C defined and conceptualized in economics?

‣ Which latent topics emerge within the literature that relates tocompetition?

Research background: SPACE project

!7

Page 29: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• SPACE Working Package 1: Building a conceptual foundation.

‣ How is C defined and conceptualized in economics?

‣ Which latent topics emerge within the literature that relates tocompetition?

‣ Which temporal changes has the dominant meaning of Cundergone in the post WWII-period?

Research background: SPACE project

!7

Page 30: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• SPACE Working Package 1: Building a conceptual foundation.

‣ How is C defined and conceptualized in economics?

‣ Which latent topics emerge within the literature that relates tocompetition?

‣ Which temporal changes has the dominant meaning of Cundergone in the post WWII-period?

‣ Focusing on the dominant (mainstream) research literature ineach period: Are there differences in latent topics over time?

Research background: SPACE project

!7

Page 31: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• SPACE Working Package 1: Building a conceptual foundation.

‣ How is C defined and conceptualized in economics?

‣ Which latent topics emerge within the literature that relates tocompetition?

‣ Which temporal changes has the dominant meaning of Cundergone in the post WWII-period?

‣ Focusing on the dominant (mainstream) research literature ineach period: Are there differences in latent topics over time?

‣ How do major economic conceptions of C differ between economicparadigms?

Research background: SPACE project

!7

Page 32: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• SPACE Working Package 1: Building a conceptual foundation.

‣ How is C defined and conceptualized in economics?

‣ Which latent topics emerge within the literature that relates tocompetition?

‣ Which temporal changes has the dominant meaning of Cundergone in the post WWII-period?

‣ Focusing on the dominant (mainstream) research literature ineach period: Are there differences in latent topics over time?

‣ How do major economic conceptions of C differ between economicparadigms?

‣ Are there latent topics which can be assigned to economicparadigms?

Research background: SPACE project

!7

Page 33: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

The LDA model and its main assumptions

!8Source: Steyvers and Griffiths 2007

Page 34: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• LDA is best described by its “imaginary random process by which themodel assumes the documents arose” (Blei 2012, 78)

The LDA model and its main assumptions

!8Source: Steyvers and Griffiths 2007

Page 35: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• LDA is best described by its “imaginary random process by which themodel assumes the documents arose” (Blei 2012, 78)

• In the model, topics are give first and then the documents aregenerated.

The LDA model and its main assumptions

!8Source: Steyvers and Griffiths 2007

Page 36: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• LDA is best described by its “imaginary random process by which themodel assumes the documents arose” (Blei 2012, 78)

• In the model, topics are give first and then the documents aregenerated.

• Documents = bag of words

The LDA model and its main assumptions

!8Source: Steyvers and Griffiths 2007

Page 37: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• LDA is best described by its “imaginary random process by which themodel assumes the documents arose” (Blei 2012, 78)

• In the model, topics are give first and then the documents aregenerated.

• Documents = bag of words

• For every document in the corpus

The LDA model and its main assumptions

!8Source: Steyvers and Griffiths 2007

Page 38: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• LDA is best described by its “imaginary random process by which themodel assumes the documents arose” (Blei 2012, 78)

• In the model, topics are give first and then the documents aregenerated.

• Documents = bag of words

• For every document in the corpus1. Pick a topic

The LDA model and its main assumptions

!8Source: Steyvers and Griffiths 2007

Page 39: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• LDA is best described by its “imaginary random process by which themodel assumes the documents arose” (Blei 2012, 78)

• In the model, topics are give first and then the documents aregenerated.

• Documents = bag of words

• For every document in the corpus1. Pick a topic2. Pick a word

The LDA model and its main assumptions

!8Source: Steyvers and Griffiths 2007

Page 40: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• LDA is best described by its “imaginary random process by which themodel assumes the documents arose” (Blei 2012, 78)

• In the model, topics are give first and then the documents aregenerated.

• Documents = bag of words

• For every document in the corpus1. Pick a topic2. Pick a word3. Place it in the bag

until the document is complete

The LDA model and its main assumptions

!8Source: Steyvers and Griffiths 2007

Page 41: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

The LDA model and its parameters

!9Source: Steyvers and Griffiths 2007

Page 42: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

The LDA model and its parameters

!9# of words per document

word

Topic assignment for word

Source: Steyvers and Griffiths 2007

Page 43: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

The LDA model and its parameters

!9

topic-document distribtution (which topics for which document?)

# of documents

# of words per document

word

Topic assignment for word

Source: Steyvers and Griffiths 2007

Page 44: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

The LDA model and its parameters

!9

topic-document distribtution (which topics for which document?)

# of documents

word-topic distribution (which words for which topic?)

# of topics# of words per document

word

Topic assignment for word

Source: Steyvers and Griffiths 2007

Page 45: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

The LDA model and its parameters

!9

topic-document distribtution (which topics for which document?)

# of documents

word-topic distribution (which words for which topic?)

# of topics# of words per document

word

Topic assignment for word

Hyperparameters (priors)

Source: Steyvers and Griffiths 2007

Page 46: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

The LDA model and its parameters

!9

topic-document distribtution (which topics for which document?)

# of documents

word-topic distribution (which words for which topic?)

# of topics# of words per document

word

Topic assignment for word

Hyperparameters (priors)

Text preprocessing

Source: Steyvers and Griffiths 2007

Page 47: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

The puzzle of statistical inference

!10

Page 48: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• The posterior (conditional) distribution is

The puzzle of statistical inference

!10

Page 49: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• The posterior (conditional) distribution is

The puzzle of statistical inference

!10

Page 50: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• The posterior (conditional) distribution is

The puzzle of statistical inference

!10

Joint distribution of all hidden and observed variables

Page 51: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• The posterior (conditional) distribution is

The puzzle of statistical inference

!10

Joint distribution of all hidden and observed variables

Marginal probability of the observations

Page 52: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• The posterior (conditional) distribution is

“[The marginal probability] is the probability of seeing the observed corpus under any topic model.” (Blei 2012, 81)

The puzzle of statistical inference

!10

Joint distribution of all hidden and observed variables

Marginal probability of the observations

Page 53: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• The posterior (conditional) distribution is

“[The marginal probability] is the probability of seeing the observed corpus under any topic model.” (Blei 2012, 81)➡ Summing up all possible ways of assigning each observed word

(usually in the order of millions!) to one of the topics

The puzzle of statistical inference

!10

Joint distribution of all hidden and observed variables

Marginal probability of the observations

Page 54: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• Collapsed Gibbs Sampling as inference algorithm

The puzzle of statistical inference

!11

Page 55: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• Collapsed Gibbs Sampling as inference algorithm

The puzzle of statistical inference

!11

Page 56: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

The puzzle of statistical inference

!12

Page 57: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• Collapsed Gibbs sampling (CGS) as inference algorithm (Griffiths andSteyvers 2004)

The puzzle of statistical inference

!12

Page 58: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• Collapsed Gibbs sampling (CGS) as inference algorithm (Griffiths andSteyvers 2004)

• Markov Chain Monte Carlo (MCMC) based method

The puzzle of statistical inference

!12

Page 59: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• Collapsed Gibbs sampling (CGS) as inference algorithm (Griffiths andSteyvers 2004)

• Markov Chain Monte Carlo (MCMC) based method

• Inferring the conditional distribution via estimating z (assignments ofwords to topics)

The puzzle of statistical inference

!12

Page 60: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• Collapsed Gibbs sampling (CGS) as inference algorithm (Griffiths andSteyvers 2004)

• Markov Chain Monte Carlo (MCMC) based method

• Inferring the conditional distribution via estimating z (assignments ofwords to topics)

The puzzle of statistical inference

!12

Page 61: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• Collapsed Gibbs sampling (CGS) as inference algorithm (Griffiths andSteyvers 2004)

• Markov Chain Monte Carlo (MCMC) based method

• Inferring the conditional distribution via estimating z (assignments ofwords to topics)

The puzzle of statistical inference

!12

Page 62: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• Collapsed Gibbs sampling (CGS) as inference algorithm (Griffiths andSteyvers 2004)

• Markov Chain Monte Carlo (MCMC) based method

• Inferring the conditional distribution via estimating z (assignments ofwords to topics)

The puzzle of statistical inference

!12

Page 63: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• Collapsed Gibbs sampling (CGS) as inference algorithm (Griffiths andSteyvers 2004)

• Markov Chain Monte Carlo (MCMC) based method

• Inferring the conditional distribution via estimating z (assignments ofwords to topics)

The puzzle of statistical inference

!12

Page 64: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• Collapsed Gibbs sampling (CGS) as inference algorithm (Griffiths andSteyvers 2004)

• Markov Chain Monte Carlo (MCMC) based method

• Inferring the conditional distribution via estimating z (assignments ofwords to topics)

• Calculating 𝜙 (word-topic distribution)

The puzzle of statistical inference

!12

Page 65: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• Collapsed Gibbs sampling (CGS) as inference algorithm (Griffiths andSteyvers 2004)

• Markov Chain Monte Carlo (MCMC) based method

• Inferring the conditional distribution via estimating z (assignments ofwords to topics)

• Calculating 𝜙 (word-topic distribution)

• Calculating 𝜃 (topic-document distribution)

The puzzle of statistical inference

!12

Page 66: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

Data

!13

Page 67: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• Database: JSTOR Data for Research

Data

!13

Page 68: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• Database: JSTOR Data for Research

• Search term “competition“

Data

!13

Page 69: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• Database: JSTOR Data for Research

• Search term “competition“

• 124.749 items published in 260 journals between 1851-2017

Data

!13

Page 70: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• Database: JSTOR Data for Research

• Search term “competition“

• 124.749 items published in 260 journals between 1851-2017

• Full text ngram counts + bibliometric meta-data

Data

!13

Page 71: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

• Database: JSTOR Data for Research

• Search term “competition“

• 124.749 items published in 260 journals between 1851-2017

• Full text ngram counts + bibliometric meta-data

• “Narrow” sample: items containing “competition” > 3

‣ 27.488 articles

‣ 227 journals (thereof 34 heterodox journals)

‣ Text preprocessing + “pseudo” abstracts as final input for the model

‣ 98.572 unique words (5.496.608 total words in the corpus)

Data

!13

Page 72: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

(preliminary) results I: descriptives

!14

1850 1860 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 20100

2000

4000

6000

8000

10000

no.ofpapers

total resultscompetition >1competition >3

Page 73: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

(preliminary) results I: descriptives

!14

1850 1860 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 20100

2000

4000

6000

8000

10000

no.ofpapers

total resultscompetition >1competition >3

Page 74: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

(preliminary) results I: descriptives

!15

1850 1860 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

0

500

1000

1500

2000

2500

no.ofpapers

competition >1competition >3

Page 75: JOHANNES KEPLER UNIVERSITY LINZ...•SPACE Working Package 1: Building a conceptual foundation. ‣ How is C defined and conceptualized in economics? ‣ Which latent topics emerge

(preliminary) results I: descriptives

!15

1850 1860 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

0

500

1000

1500

2000

2500

no.ofpapers

competition >1competition >3

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(preliminary) results I: descriptives

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(preliminary) results I: descriptives

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(preliminary) results I: descriptives

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4 out of the ‘Big 5’

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(preliminary) results I: descriptives

!16

4 out of the ‘Big 5’

HET journals

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• CGS with overall “narrow” sample

‣ Number of topics ! =30

‣ Dirichlet hyperparameters ⍺ = 50/! , β = 0.01

‣ Number of iterations: 500

TT

(preliminary) results II: a bird’s eye’s view on competition in economic research

!17

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(preliminary) results II: some examples

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Topic1

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David P., Angel, James, Engstrom (1995):Manufacturing Systems and Technological Change: The U.S. Personal Comp

Economic Geography

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(preliminary) results II: some examples

!18

Topic 1 Weight (ϕ)technology 0.0247industry 0.0172innovation 0.0164firms 0.0158product 0.0153technological 0.0151research 0.0147production 0.013process 0.0128firm 0.0119investment 0.0113development 0.0108industries 0.0104products 0.0104industrial 0.0103

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0.35document-topicdistribution(θ)

David P., Angel, James, Engstrom (1995):Manufacturing Systems and Technological Change: The U.S. Personal Comp

Economic Geography

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• Topic1: technological innovation and industrial development

(preliminary) results II: some examples

!18

Topic 1 Weight (ϕ)technology 0.0247industry 0.0172innovation 0.0164firms 0.0158product 0.0153technological 0.0151research 0.0147production 0.013process 0.0128firm 0.0119investment 0.0113development 0.0108industries 0.0104products 0.0104industrial 0.0103

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0.35document-topicdistribution(θ)

David P., Angel, James, Engstrom (1995):Manufacturing Systems and Technological Change: The U.S. Personal Comp

Economic Geography

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• Topic1: technological innovation and industrial development

(preliminary) results II: some examples

!18

Topic 1 Weight (ϕ)technology 0.0247industry 0.0172innovation 0.0164firms 0.0158product 0.0153technological 0.0151research 0.0147production 0.013process 0.0128firm 0.0119investment 0.0113development 0.0108industries 0.0104products 0.0104industrial 0.0103

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0.35document-topicdistribution(θ)

David P., Angel, James, Engstrom (1995):Manufacturing Systems and Technological Change: The U.S. Personal Comp

Economic Geography

Topic 16 Weight (ϕ)countries 0.0178international 0.0161country 0.0142foreign 0.0139trade 0.0128world 0.0122domestic 0.012policy 0.0081export 0.008national 0.0079european 0.0077development 0.0077growth 0.0073economy 0.0073sector 0.0072

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• Topic1: technological innovation and industrial development

(preliminary) results II: some examples

!18

Topic 1 Weight (ϕ)technology 0.0247industry 0.0172innovation 0.0164firms 0.0158product 0.0153technological 0.0151research 0.0147production 0.013process 0.0128firm 0.0119investment 0.0113development 0.0108industries 0.0104products 0.0104industrial 0.0103

Topic1

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0.35document-topicdistribution(θ)

David P., Angel, James, Engstrom (1995):Manufacturing Systems and Technological Change: The U.S. Personal Comp

Economic Geography

Topic 16 Weight (ϕ)countries 0.0178international 0.0161country 0.0142foreign 0.0139trade 0.0128world 0.0122domestic 0.012policy 0.0081export 0.008national 0.0079european 0.0077development 0.0077growth 0.0073economy 0.0073sector 0.0072

Topic 21 Weight (ϕ)management 0.0102based 0.0101research 0.0097different 0.0093role 0.009university 0.0085information 0.0082important 0.008process 0.0075performance 0.0074structure 0.0072specific 0.0072analysis 0.0071approach 0.007business 0.007

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!19

Topic 1 Weight (ϕ)technology 0.0247industry 0.0172innovation 0.0164firms 0.0158product 0.0153technological 0.0151research 0.0147production 0.013process 0.0128firm 0.0119investment 0.0113development 0.0108industries 0.0104products 0.0104industrial 0.0103

Topic1

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0.25document-topicdistribution(θ)

David J., Teece (1989):Inter-Organizational Requirements of the Innovation Process

Managerial and Decision Economics

• Topic1: technological innovation and industrial development

(preliminary) results II: some examples

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Topic 1 Weight (ϕ)technology 0.0247industry 0.0172innovation 0.0164firms 0.0158product 0.0153technological 0.0151research 0.0147production 0.013process 0.0128firm 0.0119investment 0.0113development 0.0108industries 0.0104products 0.0104industrial 0.0103

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David J., Teece (1989):Inter-Organizational Requirements of the Innovation Process

Managerial and Decision Economics

• Topic1: technological innovation and industrial development

(preliminary) results II: some examples

Topic 5 Weight (ϕ)law 0.0133act 0.0119public 0.0099control 0.0082government 0.0078states 0.0075commission 0.0074policy 0.0072federal 0.0067com 0.0067legal 0.0063cases 0.0061power 0.006case 0.0059business 0.0059

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Topic 1 Weight (ϕ)technology 0.0247industry 0.0172innovation 0.0164firms 0.0158product 0.0153technological 0.0151research 0.0147production 0.013process 0.0128firm 0.0119investment 0.0113development 0.0108industries 0.0104products 0.0104industrial 0.0103

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David J., Teece (1989):Inter-Organizational Requirements of the Innovation Process

Managerial and Decision EconomicsTopic 12 Weight (ϕ)price 0.0213prices 0.0198product 0.0191sales 0.0172market 0.0172consumer 0.0171costs 0.0161consumers 0.0157cost 0.0142competitive 0.0135demand 0.0135firms 0.0134markets 0.0131firm 0.013products 0.013

• Topic1: technological innovation and industrial development

(preliminary) results II: some examples

Topic 5 Weight (ϕ)law 0.0133act 0.0119public 0.0099control 0.0082government 0.0078states 0.0075commission 0.0074policy 0.0072federal 0.0067com 0.0067legal 0.0063cases 0.0061power 0.006case 0.0059business 0.0059

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Topic 1 Weight (ϕ)technology 0.0247industry 0.0172innovation 0.0164firms 0.0158product 0.0153technological 0.0151research 0.0147production 0.013process 0.0128firm 0.0119investment 0.0113development 0.0108industries 0.0104products 0.0104industrial 0.0103

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David J., Teece (1989):Inter-Organizational Requirements of the Innovation Process

Managerial and Decision EconomicsTopic 12 Weight (ϕ)price 0.0213prices 0.0198product 0.0191sales 0.0172market 0.0172consumer 0.0171costs 0.0161consumers 0.0157cost 0.0142competitive 0.0135demand 0.0135firms 0.0134markets 0.0131firm 0.013products 0.013

• Topic1: technological innovation and industrial development

(preliminary) results II: some examples

Topic 5 Weight (ϕ)law 0.0133act 0.0119public 0.0099control 0.0082government 0.0078states 0.0075commission 0.0074policy 0.0072federal 0.0067com 0.0067legal 0.0063cases 0.0061power 0.006case 0.0059business 0.0059

Topic 18 Weight (ϕ)theory 0.0204economics 0.0182analysis 0.0126press 0.0118university 0.0118journal 0.0112economists 0.0096economy 0.0094york 0.0089cambridge 0.0088new 0.0084american 0.0074general 0.0074view 0.0074approach 0.0073

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Topic 1 Weight (ϕ)technology 0.0247industry 0.0172innovation 0.0164firms 0.0158product 0.0153technological 0.0151research 0.0147production 0.013process 0.0128firm 0.0119investment 0.0113development 0.0108industries 0.0104products 0.0104industrial 0.0103

Topic1

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David J., Teece (1989):Inter-Organizational Requirements of the Innovation Process

Managerial and Decision EconomicsTopic 12 Weight (ϕ)price 0.0213prices 0.0198product 0.0191sales 0.0172market 0.0172consumer 0.0171costs 0.0161consumers 0.0157cost 0.0142competitive 0.0135demand 0.0135firms 0.0134markets 0.0131firm 0.013products 0.013

• Topic1: technological innovation and industrial development

(preliminary) results II: some examples

Topic 21 Weight (ϕ)management 0.0102based 0.0101research 0.0097different 0.0093role 0.009university 0.0085information 0.0082important 0.008process 0.0075performance 0.0074structure 0.0072specific 0.0072analysis 0.0071approach 0.007business 0.007

Topic 5 Weight (ϕ)law 0.0133act 0.0119public 0.0099control 0.0082government 0.0078states 0.0075commission 0.0074policy 0.0072federal 0.0067com 0.0067legal 0.0063cases 0.0061power 0.006case 0.0059business 0.0059

Topic 18 Weight (ϕ)theory 0.0204economics 0.0182analysis 0.0126press 0.0118university 0.0118journal 0.0112economists 0.0096economy 0.0094york 0.0089cambridge 0.0088new 0.0084american 0.0074general 0.0074view 0.0074approach 0.0073

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Robert L., Ohsfeldt (2003):If the "Business Model" of Medicine Is Sick, What's the Diagnosis, and

The Independent Review

(preliminary) results II: some examples

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Topic 15 Weight (ϕ)health 0.0199insurance 0.0166care 0.0164services 0.0146service 0.0109cost 0.0108quality 0.0101medical 0.01coverage 0.0096premium 0.0093risk 0.0085airline 0.0077insurers 0.0077airlines 0.0074plans 0.0068

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document-topicdistribution(θ)

Robert L., Ohsfeldt (2003):If the "Business Model" of Medicine Is Sick, What's the Diagnosis, and

The Independent Review

(preliminary) results II: some examples

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Topic 15 Weight (ϕ)health 0.0199insurance 0.0166care 0.0164services 0.0146service 0.0109cost 0.0108quality 0.0101medical 0.01coverage 0.0096premium 0.0093risk 0.0085airline 0.0077insurers 0.0077airlines 0.0074plans 0.0068

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document-topicdistribution(θ)

Robert L., Ohsfeldt (2003):If the "Business Model" of Medicine Is Sick, What's the Diagnosis, and

The Independent Review

(preliminary) results II: some examples• Topic15: health care and insurance

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Topic 15 Weight (ϕ)health 0.0199insurance 0.0166care 0.0164services 0.0146service 0.0109cost 0.0108quality 0.0101medical 0.01coverage 0.0096premium 0.0093risk 0.0085airline 0.0077insurers 0.0077airlines 0.0074plans 0.0068

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document-topicdistribution(θ)

Robert L., Ohsfeldt (2003):If the "Business Model" of Medicine Is Sick, What's the Diagnosis, and

The Independent Review

(preliminary) results II: some examples• Topic15: health care and insurance

Topic 12 Weight (ϕ)price 0.0213prices 0.0198product 0.0191sales 0.0172market 0.0172consumer 0.0171costs 0.0161consumers 0.0157cost 0.0142competitive 0.0135demand 0.0135firms 0.0134markets 0.0131firm 0.013products 0.013

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Topic 15 Weight (ϕ)health 0.0199insurance 0.0166care 0.0164services 0.0146service 0.0109cost 0.0108quality 0.0101medical 0.01coverage 0.0096premium 0.0093risk 0.0085airline 0.0077insurers 0.0077airlines 0.0074plans 0.0068

Topic1

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document-topicdistribution(θ)

Robert L., Ohsfeldt (2003):If the "Business Model" of Medicine Is Sick, What's the Diagnosis, and

The Independent Review

Topic 16 Weight (ϕ)countries 0.0178international 0.0161country 0.0142foreign 0.0139trade 0.0128world 0.0122domestic 0.012policy 0.0081export 0.008national 0.0079european 0.0077development 0.0077growth 0.0073economy 0.0073sector 0.0072

(preliminary) results II: some examples• Topic15: health care and insurance

Topic 12 Weight (ϕ)price 0.0213prices 0.0198product 0.0191sales 0.0172market 0.0172consumer 0.0171costs 0.0161consumers 0.0157cost 0.0142competitive 0.0135demand 0.0135firms 0.0134markets 0.0131firm 0.013products 0.013

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Topic 15 Weight (ϕ)health 0.0199insurance 0.0166care 0.0164services 0.0146service 0.0109cost 0.0108quality 0.0101medical 0.01coverage 0.0096premium 0.0093risk 0.0085airline 0.0077insurers 0.0077airlines 0.0074plans 0.0068

Topic1

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document-topicdistribution(θ)

Robert L., Ohsfeldt (2003):If the "Business Model" of Medicine Is Sick, What's the Diagnosis, and

The Independent Review

Topic 16 Weight (ϕ)countries 0.0178international 0.0161country 0.0142foreign 0.0139trade 0.0128world 0.0122domestic 0.012policy 0.0081export 0.008national 0.0079european 0.0077development 0.0077growth 0.0073economy 0.0073sector 0.0072

(preliminary) results II: some examples• Topic15: health care and insurance

Topic 12 Weight (ϕ)price 0.0213prices 0.0198product 0.0191sales 0.0172market 0.0172consumer 0.0171costs 0.0161consumers 0.0157cost 0.0142competitive 0.0135demand 0.0135firms 0.0134markets 0.0131firm 0.013products 0.013

Topic 25 Weight (ϕ)public 0.0219government 0.0209tax 0.019policy 0.0164private 0.016income 0.0148benefits 0.0142costs 0.0108taxes 0.0103policies 0.0102social 0.01level 0.0099efficiency 0.0099budget 0.0091economics 0.0088

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Topic 15 Weight (ϕ)health 0.0199insurance 0.0166care 0.0164services 0.0146service 0.0109cost 0.0108quality 0.0101medical 0.01coverage 0.0096premium 0.0093risk 0.0085airline 0.0077insurers 0.0077airlines 0.0074plans 0.0068

Topic 29 Weight (ϕ)data 0.0151table 0.0131results 0.0126significant 0.0098variables 0.0094variable 0.0092average 0.0092using 0.0089empirical 0.0087evidence 0.0085level 0.0084effects 0.008used 0.0079effect 0.0079model 0.0078

Topic1

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document-topicdistribution(θ)

Robert L., Ohsfeldt (2003):If the "Business Model" of Medicine Is Sick, What's the Diagnosis, and

The Independent Review

Topic 16 Weight (ϕ)countries 0.0178international 0.0161country 0.0142foreign 0.0139trade 0.0128world 0.0122domestic 0.012policy 0.0081export 0.008national 0.0079european 0.0077development 0.0077growth 0.0073economy 0.0073sector 0.0072

(preliminary) results II: some examples• Topic15: health care and insurance

Topic 12 Weight (ϕ)price 0.0213prices 0.0198product 0.0191sales 0.0172market 0.0172consumer 0.0171costs 0.0161consumers 0.0157cost 0.0142competitive 0.0135demand 0.0135firms 0.0134markets 0.0131firm 0.013products 0.013

Topic 25 Weight (ϕ)public 0.0219government 0.0209tax 0.019policy 0.0164private 0.016income 0.0148benefits 0.0142costs 0.0108taxes 0.0103policies 0.0102social 0.01level 0.0099efficiency 0.0099budget 0.0091economics 0.0088

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Topic 15 Weight (ϕ)health 0.0199insurance 0.0166care 0.0164services 0.0146service 0.0109cost 0.0108quality 0.0101medical 0.01coverage 0.0096premium 0.0093risk 0.0085airline 0.0077insurers 0.0077airlines 0.0074plans 0.0068

Topic1

Topic2

Topic3

Topic4

Topic5

Topic6

Topic7

Topic8

Topic9

Topic10

Topic11

Topic12

Topic13

Topic14

Topic15

Topic16

Topic17

Topic18

Topic19

Topic20

Topic21

Topic22

Topic23

Topic24

Topic25

Topic26

Topic27

Topic28

Topic29

Topic30

0.00

0.05

0.10

0.15

0.20

0.25document-topicdistribution(θ)

Karen, Eggleston (2000):Risk Selection and Optimal Health Insurance-Provider Payment Systems

The Journal of Risk and Insurance

(preliminary) results II: some examples• Topic15: health care and insurance

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Topic 15 Weight (ϕ)health 0.0199insurance 0.0166care 0.0164services 0.0146service 0.0109cost 0.0108quality 0.0101medical 0.01coverage 0.0096premium 0.0093risk 0.0085airline 0.0077insurers 0.0077airlines 0.0074plans 0.0068

Topic1

Topic2

Topic3

Topic4

Topic5

Topic6

Topic7

Topic8

Topic9

Topic10

Topic11

Topic12

Topic13

Topic14

Topic15

Topic16

Topic17

Topic18

Topic19

Topic20

Topic21

Topic22

Topic23

Topic24

Topic25

Topic26

Topic27

Topic28

Topic29

Topic30

0.00

0.05

0.10

0.15

0.20

0.25document-topicdistribution(θ)

Karen, Eggleston (2000):Risk Selection and Optimal Health Insurance-Provider Payment Systems

The Journal of Risk and Insurance

Topic 3 Weight (ϕ)model 0.0078consider 0.0077equilibrium 0.0077section 0.0068given 0.0068second 0.0064case 0.0064assume 0.0063proposition 0.0062following 0.0061function 0.0057results 0.0057optimal 0.0056result 0.0056game 0.0055

(preliminary) results II: some examples• Topic15: health care and insurance

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Topic 15 Weight (ϕ)health 0.0199insurance 0.0166care 0.0164services 0.0146service 0.0109cost 0.0108quality 0.0101medical 0.01coverage 0.0096premium 0.0093risk 0.0085airline 0.0077insurers 0.0077airlines 0.0074plans 0.0068

Topic1

Topic2

Topic3

Topic4

Topic5

Topic6

Topic7

Topic8

Topic9

Topic10

Topic11

Topic12

Topic13

Topic14

Topic15

Topic16

Topic17

Topic18

Topic19

Topic20

Topic21

Topic22

Topic23

Topic24

Topic25

Topic26

Topic27

Topic28

Topic29

Topic30

0.00

0.05

0.10

0.15

0.20

0.25document-topicdistribution(θ)

Karen, Eggleston (2000):Risk Selection and Optimal Health Insurance-Provider Payment Systems

The Journal of Risk and Insurance

Topic 3 Weight (ϕ)model 0.0078consider 0.0077equilibrium 0.0077section 0.0068given 0.0068second 0.0064case 0.0064assume 0.0063proposition 0.0062following 0.0061function 0.0057results 0.0057optimal 0.0056result 0.0056game 0.0055

(preliminary) results II: some examples• Topic15: health care and insurance

Topic 28 Weight (ϕ)demand 0.0101marginal 0.0099function 0.009output 0.0088price 0.0088equilibrium 0.0081cost 0.0081given 0.0076equal 0.0072constant 0.0071case 0.0071assumption 0.007assumed 0.007production 0.0069analysis 0.0066

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Topic 15 Weight (ϕ)health 0.0199insurance 0.0166care 0.0164services 0.0146service 0.0109cost 0.0108quality 0.0101medical 0.01coverage 0.0096premium 0.0093risk 0.0085airline 0.0077insurers 0.0077airlines 0.0074plans 0.0068

Topic1

Topic2

Topic3

Topic4

Topic5

Topic6

Topic7

Topic8

Topic9

Topic10

Topic11

Topic12

Topic13

Topic14

Topic15

Topic16

Topic17

Topic18

Topic19

Topic20

Topic21

Topic22

Topic23

Topic24

Topic25

Topic26

Topic27

Topic28

Topic29

Topic30

0.00

0.05

0.10

0.15

0.20

0.25document-topicdistribution(θ)

Karen, Eggleston (2000):Risk Selection and Optimal Health Insurance-Provider Payment Systems

The Journal of Risk and Insurance

Topic 3 Weight (ϕ)model 0.0078consider 0.0077equilibrium 0.0077section 0.0068given 0.0068second 0.0064case 0.0064assume 0.0063proposition 0.0062following 0.0061function 0.0057results 0.0057optimal 0.0056result 0.0056game 0.0055

(preliminary) results II: some examples• Topic15: health care and insurance

Question to the audience: Whats the difference between these two topics?

Some suggestions?

Topic 28 Weight (ϕ)demand 0.0101marginal 0.0099function 0.009output 0.0088price 0.0088equilibrium 0.0081cost 0.0081given 0.0076equal 0.0072constant 0.0071case 0.0071assumption 0.007assumed 0.007production 0.0069analysis 0.0066

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(preliminary) results II: some examples

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Topic 22 Weight (ϕ)anthony 0.0087morris 0.0066occurring 0.0064klaus 0.0053capturing 0.004observers 0.0036tighter 0.0036chowdhury 0.0034tai 0.003phillip 0.0025stayed 0.0025quiet 0.0023vic 0.0023fication 0.0023stacking 0.0021

(preliminary) results II: some examples

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Topic 22 Weight (ϕ)anthony 0.0087morris 0.0066occurring 0.0064klaus 0.0053capturing 0.004observers 0.0036tighter 0.0036chowdhury 0.0034tai 0.003phillip 0.0025stayed 0.0025quiet 0.0023vic 0.0023fication 0.0023stacking 0.0021

(preliminary) results II: some examples• Topic22:???

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Topic 22 Weight (ϕ)anthony 0.0087morris 0.0066occurring 0.0064klaus 0.0053capturing 0.004observers 0.0036tighter 0.0036chowdhury 0.0034tai 0.003phillip 0.0025stayed 0.0025quiet 0.0023vic 0.0023fication 0.0023stacking 0.0021

Topic1

Topic2

Topic3

Topic4

Topic5

Topic6

Topic7

Topic8

Topic9

Topic10

Topic11

Topic12

Topic13

Topic14

Topic15

Topic16

Topic17

Topic18

Topic19

Topic20

Topic21

Topic22

Topic23

Topic24

Topic25

Topic26

Topic27

Topic28

Topic29

Topic30

0.00

0.05

0.10

0.15

0.20

document-topicdistribution(θ)

(1995):Bibliography of Reinhard Selten's PublicationsThe Scandinavian Journal of Economics

(preliminary) results II: some examples• Topic22:???

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Topic 22 Weight (ϕ)anthony 0.0087morris 0.0066occurring 0.0064klaus 0.0053capturing 0.004observers 0.0036tighter 0.0036chowdhury 0.0034tai 0.003phillip 0.0025stayed 0.0025quiet 0.0023vic 0.0023fication 0.0023stacking 0.0021

Topic1

Topic2

Topic3

Topic4

Topic5

Topic6

Topic7

Topic8

Topic9

Topic10

Topic11

Topic12

Topic13

Topic14

Topic15

Topic16

Topic17

Topic18

Topic19

Topic20

Topic21

Topic22

Topic23

Topic24

Topic25

Topic26

Topic27

Topic28

Topic29

Topic30

0.00

0.05

0.10

0.15

0.20

document-topicdistribution(θ)

(1995):Bibliography of Reinhard Selten's PublicationsThe Scandinavian Journal of Economics

(preliminary) results II: some examples• Topic22:???

Topic 18 Weight (ϕ)theory 0.0204economics 0.0182analysis 0.0126press 0.0118university 0.0118journal 0.0112economists 0.0096economy 0.0094york 0.0089cambridge 0.0088new 0.0084american 0.0074general 0.0074view 0.0074approach 0.0073

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• (Preliminary) results of the descriptive statistics show that the discourseon competition

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• (Preliminary) results of the descriptive statistics show that the discourseon competition

‣ Starts with a time lag of about 10-15 years to intensify compared tothe overall development of economic research output

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• (Preliminary) results of the descriptive statistics show that the discourseon competition

‣ Starts with a time lag of about 10-15 years to intensify compared tothe overall development of economic research output

‣ Is highly concentrated in terms of publication outlets: a third of allarticles in the sample are published in 10 journals

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• (Preliminary) results of the descriptive statistics show that the discourseon competition

‣ Starts with a time lag of about 10-15 years to intensify compared tothe overall development of economic research output

‣ Is highly concentrated in terms of publication outlets: a third of allarticles in the sample are published in 10 journals

‣ Is – given the marginalization of heterodox approaches withineconomics – somewhat balanced: a substantial part of the literaturerelated to competition is published in (a few) heterodox journals

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• Results of the first topic modeling exercise are promising

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• Results of the first topic modeling exercise are promising

‣ A bird’s eye view on the overall corpus of research articles delivers atopic structure which links competition to various meaningful topicsthat emerge within the literature.

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• Results of the first topic modeling exercise are promising

‣ A bird’s eye view on the overall corpus of research articles delivers atopic structure which links competition to various meaningful topicsthat emerge within the literature.

‣ Analysis of the topic-document distributions enables

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• Results of the first topic modeling exercise are promising

‣ A bird’s eye view on the overall corpus of research articles delivers atopic structure which links competition to various meaningful topicsthat emerge within the literature.

‣ Analysis of the topic-document distributions enables

‣ a reconstruction of the genesis of specific topics (and itsdisappearance)

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• Results of the first topic modeling exercise are promising

‣ A bird’s eye view on the overall corpus of research articles delivers atopic structure which links competition to various meaningful topicsthat emerge within the literature.

‣ Analysis of the topic-document distributions enables

‣ a reconstruction of the genesis of specific topics (and itsdisappearance)

‣ an analysis of co-occurrences between specific topics that relate tocompetition

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• However, model parameters still need to be optimized

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• However, model parameters still need to be optimized

‣ Number of Topics (more vs. less)

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• However, model parameters still need to be optimized

‣ Number of Topics (more vs. less)

‣ Different Dirichlet priors (see also Tang et al. 2014, Wallach et al. 2009)

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• However, model parameters still need to be optimized

‣ Number of Topics (more vs. less)

‣ Different Dirichlet priors (see also Tang et al. 2014, Wallach et al. 2009)

‣ ⍺ = small: each document is associated with few topics

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• However, model parameters still need to be optimized

‣ Number of Topics (more vs. less)

‣ Different Dirichlet priors (see also Tang et al. 2014, Wallach et al. 2009)

‣ ⍺ = small: each document is associated with few topics

‣ β = small: topics are word sparse (large β means more word-diffused and similar topics)

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Summary & Outlook

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• However, model parameters still need to be optimized

‣ Number of Topics (more vs. less)

‣ Different Dirichlet priors (see also Tang et al. 2014, Wallach et al. 2009)

‣ ⍺ = small: each document is associated with few topics

‣ β = small: topics are word sparse (large β means more word-diffused and similar topics)

‣ Symmetrical vs. asymmetrical priors

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Summary & Outlook

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• However, model parameters still need to be optimized

‣ Number of Topics (more vs. less)

‣ Different Dirichlet priors (see also Tang et al. 2014, Wallach et al. 2009)

‣ ⍺ = small: each document is associated with few topics

‣ β = small: topics are word sparse (large β means more word-diffused and similar topics)

‣ Symmetrical vs. asymmetrical priors

‣ Text preprocessing (see e.g. Schofield et al. 2017)

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Summary & Outlook

!25

• However, model parameters still need to be optimized

‣ Number of Topics (more vs. less)

‣ Different Dirichlet priors (see also Tang et al. 2014, Wallach et al. 2009)

‣ ⍺ = small: each document is associated with few topics

‣ β = small: topics are word sparse (large β means more word-diffused and similar topics)

‣ Symmetrical vs. asymmetrical priors

‣ Text preprocessing (see e.g. Schofield et al. 2017)

‣ “Pseudo-abstracts” vs. full-text analysis?

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Summary & Outlook

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• However, model parameters still need to be optimized

‣ Number of Topics (more vs. less)

‣ Different Dirichlet priors (see also Tang et al. 2014, Wallach et al. 2009)

‣ ⍺ = small: each document is associated with few topics

‣ β = small: topics are word sparse (large β means more word-diffused and similar topics)

‣ Symmetrical vs. asymmetrical priors

‣ Text preprocessing (see e.g. Schofield et al. 2017)

‣ “Pseudo-abstracts” vs. full-text analysis?

‣ Remove standard stop-words vs. topic-specific stop-words?

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Summary & Outlook

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• However, model parameters still need to be optimized

‣ Number of Topics (more vs. less)

‣ Different Dirichlet priors (see also Tang et al. 2014, Wallach et al. 2009)

‣ ⍺ = small: each document is associated with few topics

‣ β = small: topics are word sparse (large β means more word-diffused and similar topics)

‣ Symmetrical vs. asymmetrical priors

‣ Text preprocessing (see e.g. Schofield et al. 2017)

‣ “Pseudo-abstracts” vs. full-text analysis?

‣ Remove standard stop-words vs. topic-specific stop-words?

‣ Word stemming or not?

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• Possible next steps are

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• Possible next steps are

‣ Analyzing the corresponding bibliometric meta-data

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Summary & Outlook

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• Possible next steps are

‣ Analyzing the corresponding bibliometric meta-data

‣ via publication year: when do these topics emerge/disappear?

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Summary & Outlook

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• Possible next steps are

‣ Analyzing the corresponding bibliometric meta-data

‣ via publication year: when do these topics emerge/disappear?

‣ via publication outlet: where do these topics emerge/disappear?

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Summary & Outlook

!26

• Possible next steps are

‣ Analyzing the corresponding bibliometric meta-data

‣ via publication year: when do these topics emerge/disappear?

‣ via publication outlet: where do these topics emerge/disappear?

‣ via cited references: what are the conceptual foundations of atopic?

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Summary & Outlook

!26

• Possible next steps are

‣ Analyzing the corresponding bibliometric meta-data

‣ via publication year: when do these topics emerge/disappear?

‣ via publication outlet: where do these topics emerge/disappear?

‣ via cited references: what are the conceptual foundations of atopic?

‣ A more nuanced analysis / Extensions

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Summary & Outlook

!26

• Possible next steps are

‣ Analyzing the corresponding bibliometric meta-data

‣ via publication year: when do these topics emerge/disappear?

‣ via publication outlet: where do these topics emerge/disappear?

‣ via cited references: what are the conceptual foundations of atopic?

‣ A more nuanced analysis / Extensions

‣ Extracting topics from period/decade subsamples (e.g. 1960s,1970s,… see also Ambrosino et al. 2018)

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Summary & Outlook

!26

• Possible next steps are

‣ Analyzing the corresponding bibliometric meta-data

‣ via publication year: when do these topics emerge/disappear?

‣ via publication outlet: where do these topics emerge/disappear?

‣ via cited references: what are the conceptual foundations of atopic?

‣ A more nuanced analysis / Extensions

‣ Extracting topics from period/decade subsamples (e.g. 1960s,1970s,… see also Ambrosino et al. 2018)

‣ Replication for other fields (e.g. Sociology, Political Science,Anthropology)

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!27

Many thanks for your attention