| LDR | | 00000nam u2200205 4500 |
| 001 | | 000000435862 |
| 005 | | 20200228110210 |
| 008 | | 200131s2019 ||||||||||||||||| ||eng d |
| 020 | |
▼a 9781392477168 |
| 035 | |
▼a (MiAaPQ)AAI27712085 |
| 035 | |
▼a (MiAaPQ)OhioLINKosu1557146910531878 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 378 |
| 100 | 1 |
▼a Xu, Shicong. |
| 245 | 10 |
▼a Innovation in the US and China. |
| 260 | |
▼a [S.l.]:
▼b The Ohio State University.,
▼c 2019. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2019. |
| 300 | |
▼a 115 p. |
| 500 | |
▼a Source: Dissertations Abstracts International, Volume: 81-06, Section: B. |
| 500 | |
▼a Advisor: Sam, Abdoul. |
| 502 | 1 |
▼a Thesis (Ph.D.)--The Ohio State University, 2019. |
| 506 | |
▼a This item must not be sold to any third party vendors. |
| 520 | |
▼a All three chapters of my dissertation explore topics related to innovation. The first chapter empirically examines the impact of patented environmental innovation on pollution prevention (P2) activities. Unlike the traditional command-and control approach, P2 initiatives are voluntary programs that accomplish pollution reduction through several mechanisms including procedural changes, better spill and leak prevention, raw material substitution, equipment and product modifications, increasing purity of inputs and changing product specifications. I conduct my econometric analysis using a panel of US manufacturing industries (3-digit standard industrial classification codes) for the 1991 - 2012 period. My results show that environmental innovation, measured by the count of environmental patents, is inversely related to the number of P2 activities, implying a substitution effect between investments in environmental innovation and investments on P2 activities at the industry level. To the best of my knowledge, this is the first empirical paper that measures the direct impact of environmental innovation on P2 activities. The second chapter focuses on firm innovation in China. Specifically, I investigate how much a firm's innovation is impacted by the spatial distribution of nearby firms and the number of successful patent applications within a certain radius. I follow a two-step method similar to the one outlined by Wallsten (2001). First, I used ArcGIS software to plot all parent firms' locations using registered address, latitude, and longitude coordinates. Next, I applied a panel data method to estimate how spatial proximity and industry makeup within a special boundary affect knowledge spillover at the firm level. My results show that number of successful patent applications near firm i have a significant and positive impact on firm i's successful patent applications. However, this magnitude of this effect diminishes as distance increases. In addition, proximity of alike firms deters innovation while having a diversity of firms (firms from different industries) promotes innovation. This is the first study, to my knowledge, to test the Marshall-Arrow-Romer (MAR) and Jacobs spillover theories in the context of innovation by private firms in China. The third chapter also studies the topic of innovation in China. Specifically, it investigates how urban density affects firm innovativeness. Carlino et al (2007) and Ciccone and Hall (1996) have found that places with higher urban density have greater labor productivity and tend to innovate more. In other words, the agglomeration of people has a direct and positive effect on technological innovation. The well-known Hukou system in China acts as a great barrier to free migration through a "caste" system. Because the agricultural hukou holders do not have access to the benefits and services the central government provides to the non-agricultural hukou holders, it prevents an influx of people moving from rural areas to densely populated urban areas. This study utilizes a panel data model to investigate how urban density affects industry innovation at the prefecture level. The results show that a 1% increase in urban density leads to an increase in patent intensity of between 0.823% to 1.2%. The elasticity is approximately four times larger than the elasticity estimate found by Carlino et al. (2007), which suggests that the Chinese industries' rate of innovation is much more sensitive to urban density. This sensitivity could be the result of the Hukou System. |
| 590 | |
▼a School code: 0168. |
| 650 | 4 |
▼a Economics. |
| 650 | 4 |
▼a Intellectual property. |
| 650 | 4 |
▼a Environmental management. |
| 650 | 4 |
▼a Asian studies. |
| 650 | 4 |
▼a American studies. |
| 690 | |
▼a 0501 |
| 690 | |
▼a 0513 |
| 690 | |
▼a 0474 |
| 690 | |
▼a 0342 |
| 690 | |
▼a 0323 |
| 710 | 20 |
▼a The Ohio State University.
▼b Agricultural, Environmental and Developmental Economics. |
| 773 | 0 |
▼t Dissertations Abstracts International
▼g 81-06B. |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0168 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2019 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15494733
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 202002
▼f 2020 |
| 990 | |
▼a ***1008102 |
| 991 | |
▼a E-BOOK |