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Growth and Inequality Dynamics: Spillover, Innovation, and Revenue Diversion /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Growth and Inequality Dynamics: Spillover, Innovation, and Revenue Diversion /Mildred Hager.
作者:	Hager, Mildred,
面頁冊數:	1 electronic resource (121 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-08, Section: A.
附註:	Advisors: Benhabib, Jess; Jovanovic, Boyan Committee members: Moser, Petra; Bisin, Alberto; Clementi, Gianluca.
Contained By:	Dissertations Abstracts International85-08A.
標題:	Political science.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30813610
ISBN:	9798381730968

Growth and Inequality Dynamics: Spillover, Innovation, and Revenue Diversion /
Hager, Mildred,

Growth and Inequality Dynamics: Spillover, Innovation, and Revenue Diversion /Mildred Hager. - 1 electronic resource (121 pages)

Source: Dissertations Abstracts International, Volume: 85-08, Section: A.

How are inequality and growth related, and what are the underlying mechanisms?In many traditional economic growth models, the expectation of higher incomes at the top motivates entrepreneurs to innovate, thus generating higher growth. Throughout the last 40 years though, in the US, income inequality and firm con- centration has increased while economic growth has declined (see chapter 1 for references). Might there be other forces at play? And if so, is it possible to understand and quantify them within a macroeconomic model?We aim to explore two such forces, as well as their interrelation with innovation.First, spillovers or imitation between firms increases growth as knowledge spreads. At the same time, firms are able to catch up with one another, thus compressing the income distribution and lowering inequality. In the first chapter, 'Increasing concentration and inequality, lower growth: have spillovers declined?', we investigate a random growth model with spillovers to match the observed in- creases in inequality and concentration and decrease in growth throughout the last 40 years. We show that we can fit these data equally well as a complex random growth model without spillover, but using only three parameters. In our case, the observed increase in inequality and decline in growth is related to a decline in spillovers between firms, such as less imitation or less knowledge transfer by worker switching to name only a few. Rather than criticising the traditional model, we aim to provide a frame to think about spillovers as a possible alternative cause for growth and inequality. Giving further empirical evidence for declining spillovers across firms and investigating the causality between concentration and spillovers is a cumbersome task though, as spillovers are notoriously difficult to observe and measure, and even more so at the macroeconomic level. For example, data for imitation or deterrence of competition e.g. through patent accumulation or non- compete contracts would be shielded by firms and thus cannot be used. We mainly provide an in-depth literature review of different types of spillovers and show some very preliminary results based on patent citation data.The second mechanism we investigate, in joint work with Jess Benhabib, is revenue diversion. Indeed, if a share of income inequality is related to revenue diversion at the top, this may be detrimental to growth and welfare. If efforts are deployed to benefit from extra revenue not related to productive output, talent may be misallocated and growth lowered. Here, too, we do not aim to show that all inequality originates from revenue diversion, but rather that it is a possible mechanism on top of the rest, that may be thought about within the frame of a simple model of occupational choice. We show that it is possible to match inequality data equally well in the presence of revenue diversion.Finally, a tangential question to the previous ones is which kind of growth models are able to produce realistic outcomes as to the distribution of firms? More precisely, is it possible to write down a random growth model with a finite support of the firm distribution, so that empirical data can be matched? In the previous two sections, we mostly used the asymptotics of the income or firm size distribution to match the tail of the theoretical one. In this section, joint work with Jess Benhabib and E ́ric Brunet, we go a step further and investigate discrete versions of our main model to be able to match with the data. Intuitively, a model with innovation alone will lead to a distribution of firms ever spreading out, so that other mechanisms are needed on top. In most models, death and entry of firms as well as some macroeconomic spillovers, are used to obtain a realistic distribution. Here, we show that imitation (similar to spillovers) is another mechanism, and analyse the quantitative predictions of such a discrete random growth model with interactions in detail. We aim to push this work further by going to the data in upcoming research projects.Beyond these contributions to the ideas about inequality and growth, we also aimed to go beyond macroeconomic models without heterogeneity and especially without interactions among agents, to think about the relevance and quantitative implications if such interactions are indeed present. We have provided a framework to think about these alternative mechanisms for growth and inequality that might be equally present, with important implications for welfare, that have not been explored before.

English

ISBN: 9798381730968Subjects--Topical Terms:

174710
Political science.
Subjects--Index Terms:

Growth

Growth and Inequality Dynamics: Spillover, Innovation, and Revenue Diversion /
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520 $a How are inequality and growth related, and what are the underlying mechanisms?In many traditional economic growth models, the expectation of higher incomes at the top motivates entrepreneurs to innovate, thus generating higher growth. Throughout the last 40 years though, in the US, income inequality and firm con- centration has increased while economic growth has declined (see chapter 1 for references). Might there be other forces at play? And if so, is it possible to understand and quantify them within a macroeconomic model?We aim to explore two such forces, as well as their interrelation with innovation.First, spillovers or imitation between firms increases growth as knowledge spreads. At the same time, firms are able to catch up with one another, thus compressing the income distribution and lowering inequality. In the first chapter, 'Increasing concentration and inequality, lower growth: have spillovers declined?', we investigate a random growth model with spillovers to match the observed in- creases in inequality and concentration and decrease in growth throughout the last 40 years. We show that we can fit these data equally well as a complex random growth model without spillover, but using only three parameters. In our case, the observed increase in inequality and decline in growth is related to a decline in spillovers between firms, such as less imitation or less knowledge transfer by worker switching to name only a few. Rather than criticising the traditional model, we aim to provide a frame to think about spillovers as a possible alternative cause for growth and inequality. Giving further empirical evidence for declining spillovers across firms and investigating the causality between concentration and spillovers is a cumbersome task though, as spillovers are notoriously difficult to observe and measure, and even more so at the macroeconomic level. For example, data for imitation or deterrence of competition e.g. through patent accumulation or non- compete contracts would be shielded by firms and thus cannot be used. We mainly provide an in-depth literature review of different types of spillovers and show some very preliminary results based on patent citation data.The second mechanism we investigate, in joint work with Jess Benhabib, is revenue diversion. Indeed, if a share of income inequality is related to revenue diversion at the top, this may be detrimental to growth and welfare. If efforts are deployed to benefit from extra revenue not related to productive output, talent may be misallocated and growth lowered. Here, too, we do not aim to show that all inequality originates from revenue diversion, but rather that it is a possible mechanism on top of the rest, that may be thought about within the frame of a simple model of occupational choice. We show that it is possible to match inequality data equally well in the presence of revenue diversion.Finally, a tangential question to the previous ones is which kind of growth models are able to produce realistic outcomes as to the distribution of firms? More precisely, is it possible to write down a random growth model with a finite support of the firm distribution, so that empirical data can be matched? In the previous two sections, we mostly used the asymptotics of the income or firm size distribution to match the tail of the theoretical one. In this section, joint work with Jess Benhabib and E ́ric Brunet, we go a step further and investigate discrete versions of our main model to be able to match with the data. Intuitively, a model with innovation alone will lead to a distribution of firms ever spreading out, so that other mechanisms are needed on top. In most models, death and entry of firms as well as some macroeconomic spillovers, are used to obtain a realistic distribution. Here, we show that imitation (similar to spillovers) is another mechanism, and analyse the quantitative predictions of such a discrete random growth model with interactions in detail. We aim to push this work further by going to the data in upcoming research projects.Beyond these contributions to the ideas about inequality and growth, we also aimed to go beyond macroeconomic models without heterogeneity and especially without interactions among agents, to think about the relevance and quantitative implications if such interactions are indeed present. We have provided a framework to think about these alternative mechanisms for growth and inequality that might be equally present, with important implications for welfare, that have not been explored before.
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590 $a School code: 0146
650 4 $a Political science. $3 174710
650 4 $a Public policy. $3 280699
653 $a Growth
653 $a Inequality
653 $a Innovation
653 $a Revenue diversion
653 $a Spillover
690 $a 0501
690 $a 0630
690 $a 0454
690 $a 0615
710 2 $a New York University. $b Economics. $3 730386
720 1 $a Benhabib, Jess $e degree supervisor.
720 1 $a Jovanovic, Boyan $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 85-08A.
790 $a 0146
791 $a Ph.D.
792 $a 2024
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