Estimating production functions for the US states: the role of public and human capital

2016 ◽  
Vol 52 (2) ◽  
pp. 691-721 ◽  
Author(s):  
Nikos Benos ◽  
Nikolaos Mylonidis ◽  
Stefania Zotou
2020 ◽  
Vol 130 (630) ◽  
pp. 1782-1816 ◽  
Author(s):  
Aslı Leblebicioğlu ◽  
Ariel Weinberger

Abstract We investigate the role of credit markets as a cause for changes in the US labour share. Causal evidence is provided that the labour share declined between 0.8 and 1.2 percentage points following the interstate banking deregulation, explaining more than half of the overall reduction during that period. The lower costs of credit and greater bank competition in each state are mechanisms that led to the decline. To quantify the relationship between credit and factor payments, we calibrate a model with financial frictions and highlight financial development as a potential channel for the reduction in labour share observed globally.


2017 ◽  
Vol 46 (4) ◽  
pp. 477-505 ◽  
Author(s):  
Nicholas Close Subtirelu

AbstractMultilingualism is often framed as human capital that increases individuals’ labor market value. Such assertions overlook the role of ideology in assigning value to languages and their speakers based on factors other than communicative utility. This article explores the value assigned to Spanish-English bilingualism on the United States labor market through a mixed methods analysis of online job advertisements. Findings suggest that Spanish-English bilingualism is frequently preferred or required for employment in the US, but that such employment opportunities are less lucrative. The results suggest a penalty associated with Spanish-English bilingualism in which positions listing such language requirements advertise lower wages than observationally similar positions. Quantitative disparities and qualitative differences in the specification of language requirements across income levels suggest that bilingual labor is assigned value through a racial lens that leads to linguistic work undertaken by and for US Latinxs being assigned less value. (Multilingualism, labor market, Spanish in the United States, economics of language, raciolinguistics, human capital)*


Author(s):  
Jens Südekum

SummaryHuman capital is unequally distributed across cities or regions within a country. The way how the spatial distribution of human capital evolves over time sheds light on the strength of concentration forces for high-skilled workers, such as localised increasing returns to human capital. In this paper I analyse the impact of human capital on local employment growth for Western German regions (1977-2006). Two main empirical facts are established: “Skilled cities” in Western Germany grow faster. At the same time there is convergence of human capital shares across cities, i.e., high-skilled workers do not increasingly concentrate in space. Whereas the first fact (the “smart city hypothesis”) similarly holds in Germany and in the US, there is a striking difference when it comes to the second fact. Some researchers have found an opposite trend of human capital divergence across US metropolitan areas. My findings suggest that human capital exhibits a different spatial trend in different countries. I present a theoretical model which shows that the spatial convergence trend does not imply that concentration forces for high-skilled workers are absent in Western Germany, but only that they are relatively weak compared to countervailing dispersion forces. I further discuss some reasons that may explain the differences between Western Germany and the US. I emphasise the role of the tax system and the impact of pro-dispersive regional policy in Europe.


2021 ◽  
Vol 56 ◽  
pp. 101372
Author(s):  
Xin Sheng ◽  
Hardik A. Marfatia ◽  
Rangan Gupta ◽  
Qiang Ji
Keyword(s):  

2020 ◽  
Author(s):  
Raj Dandekar ◽  
Emma Wang ◽  
George Barbastathis ◽  
Chris Rackauckas

1SUMMARYIn the wake of the rapid surge in the Covid-19 infected cases seen in Southern and West-Central USA in the period of June-July 2020, there is an urgent need to develop robust, data-driven models to quantify the effect which early reopening had on the infected case count increase. In particular, it is imperative to address the question: How many infected cases could have been prevented, had the worst affected states not reopened early? To address this question, we have developed a novel Covid-19 model by augmenting the classical SIR epidemiological model with a neural network module. The model decomposes the contribution of quarantine strength to the infection timeseries, allowing us to quantify the role of quarantine control and the associated reopening policies in the US states which showed a major surge in infections. We show that the upsurge in the infected cases seen in these states is strongly co-related with a drop in the quarantine/lockdown strength diagnosed by our model. Further, our results demonstrate that in the event of a stricter lockdown without early reopening, the number of active infected cases recorded on 14 July could have been reduced by more than 40% in all states considered, with the actual number of infections reduced being more than 100, 000 for the states of Florida and Texas. As we continue our fight against Covid-19, our proposed model can be used as a valuable asset to simulate the effect of several reopening strategies on the infected count evolution; for any region under consideration.


2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Raj Dandekar ◽  
Emma Wang ◽  
George Barbastathis ◽  
Chris Rackauckas

In the wake of the rapid surge in the COVID-19-infected cases seen in Southern and West-Central USA in the period of June-July 2020, there is an urgent need to develop robust, data-driven models to quantify the effect which early reopening had on the infected case count increase. In particular, it is imperative to address the question: How many infected cases could have been prevented, had the worst affected states not reopened early? To address this question, we have developed a novel COVID-19 model by augmenting the classical SIR epidemiological model with a neural network module. The model decomposes the contribution of quarantine strength to the infection time series, allowing us to quantify the role of quarantine control and the associated reopening policies in the US states which showed a major surge in infections. We show that the upsurge in the infected cases seen in these states is strongly corelated with a drop in the quarantine/lockdown strength diagnosed by our model. Further, our results demonstrate that in the event of a stricter lockdown without early reopening, the number of active infected cases recorded on 14 July could have been reduced by more than 40% in all states considered, with the actual number of infections reduced being more than 100,000 for the states of Florida and Texas. As we continue our fight against COVID-19, our proposed model can be used as a valuable asset to simulate the effect of several reopening strategies on the infected count evolution, for any region under consideration.


Author(s):  
D. Didenko ◽  
◽  
N. Grineva

Based on historical data, we test our modified production functions, derived from exogenous growth model by Mankiw, Romer, Weil (1992) and theoretical ideas by Romer (1990). Besides physical and human capital, we augment them with proxy indicators for institutional and technological environments, and with a source of endogenous growth, i.e. R&D expenditures. We present our preliminary assessments of the role of these factors in economic growth of the late USSR in inter-country comparison.


Author(s):  
Victor Supyan

The article aims to show role and significance of long-term innovative factors of economic development of the USA on the verge of the third decade of 21st Century. These critical factors of development are indentified as R&D potential, human capital and role of government economic policy. The author reviews in details composition of expenditures on R&D, shows role of different sectors of economy in R&D performance. It is emphasized that high effectiveness of the US R&D is resulted in leading US position in number of Nobel prizes, in scientific publications, in number of patents. The article reviews the role of human capital, as one of the leading long-term factors of development. As noted in the article the current shifts in the labor force composition are characterized by growth of services and high-tech industries, by high level of labor productivity, as a key indicator of economic effectiveness. It is also revealed that participation rate of labor force has declined over recent years. The US labor force is getting more complex in terms of its ethnic and racial composition. The global expenditures allocated for education in the USA exceed significantly similar expenditures in other advanced countries. The same time it is shown that there are serious contradictions in human capital formation in the US – a share of university graduates in overall population is lower here than in many developed countries. There are also significant disproportions in a sphere of education in terms of racial, ethnic and social equality. The article reviews the contribution of a healthcare system into human capital formation. It is shown that despite of huge expenses allocated for healthcare system and its high technological level, from organizational standpoint the US healthcare system is lagging behind many advanced countries. The author observed also the government role in the economy. Despite many principal characteristics of government regulation standing permanently, the change of republican or democratic administration leads to some changes in government regulation. Similar changes took place when President Trump came into office – he neglected many Keynesian receipts for economy. When new President D. Biden was elected he also suggested new economic policy and eliminated a lot of Trump’s economic neoliberal heritage. New policy is proclaimed including how to struggle 2020 economic crisis.  


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