In the era of the digital economy，the explosive growth and large'scale application of data have made data elements a crucial factor of production and economic resource Just as traditional elements such as land，labor，capital，management，and technology correspond to land economics，labor economics，financial economics，managerial economics，and technical economics，the recognition of data as a vital economic resource and production factor necessitates the establishment of a similar disciplinary framework for data economics&nbsp;<br>Data possesses unique attributes such as virtuality，nonconsumption，noncompetition，timeliness，scarcity，and high liquidity It also exhibits economic characteristics like economies of scale，high complementarity，scenario dependence，value attenuation，ambiguity of rights confirmation，and economic externalities These characteristics present new opportunities and challenges to the mechanisms by which production factors promote productivity，alter production relations，and advance economic globalization&nbsp;<br>As key production factors，data elements extensively participate in social production，promote the development of new digital productivity，and unleash a “multiplier effect” on economic development The impact of data elements on productivity is mainly reflected in four aspects：direct involvement in the production process，improvement of resource allocation efficiency，promotion of industrial structure upgrading，and enhancement of government decisionmaking quality The impact on production relations is evident in labormanagement relations，exchange relations，consumption relations，and distribution relations Data elements not only profoundly affect human production and lifestyle but also play a key role in reorganizing global factor resources，reshaping the global economic structure，and altering the global competitive landscape&nbsp;<br>Therefore，it is essential to prioritize the research of data economics and strengthen theoretical exploration and empirical research This is fundamentally significant for constructing Chinas independent economic knowledge system and provides a solid theoretical and practical foundation for China to secure a favorable position and assert its voice in the global digital economy competition<br><div> <br></div>

&nbsp;企业生产经营中通过提高数据要素利用水平提升投资效率的机制，有赖于利用数据要素激活冗余资源所发挥的中介作用以及企业自身吸收能力的调节作用。笔者基于资源基础理论、代理理论以及吸收能力理论，构建了一个有调节的企业数据要素利用水平影响投资效率机制的研究模型，以国泰安数据库（CSMAR）2011&mdash;2021年沪深A股上市公司相关有效数据，运用多元线性回归分析方法，实证检验了企业数据要素利用水平与其投资效率之间的关联及其变化状况。检验结果证实：企业数据要素利用水平与其投资效率正相关；企业利用数据要素激活冗余资源对企业利用数据要素提高其投资效率具有中介作用；企业自身吸收能力正向调节企业利用数据要素激活冗余资源之间的关系。本研究尝试性地将资源基础理论、代理理论和吸收能力理论拓展到实证检验企业数据要素利用水平与其投资效率之间的内在关系上，所揭示的关于企业数据要素利用水平影响投资效率机制之研究结论，将丰富有关数据要素、投资效率、冗余资源、企业吸收能力等方面的相关文献，有助于为企业利用数据要素提升投资效率提供理论依据。

We examine the impact of product market competition on innovation in markets for technology. An innovator makes an investment in quality-improving innovation that can be licensed to one (targeted licensing) or all (market-wide licensing) product market competitors. Our model points to a U-shaped relationship between competition in licensee product markets and innovation in the market for technology: at low levels of competition, market-wide licensing is optimal, and competition reduces innovation, whereas at high levels of competition, targeted licensing is optimal and competition increases innovation. Our empirical analysis using a large panel of U.S. data provides clear support for these predictions linking competition, innovation, and licensing.

&nbsp;党的二十大报告明确指出要&ldquo;深入实施区域协调发展战略，完善财政转移支付体系，守住不发生系统性风险底线&rdquo;。2008年金融危机后，地方政府债务规模快速扩张，这成为推动地方建设发展的重要力量，然而&ldquo;举债发展&rdquo;能否促进经济长期健康发展与社会福利水平提升存在着较大争议。特别是人口流出地区的债务扩张究竟是发挥后发优势实现跨越发展的另辟蹊径，还是不顾自身比较优势和发展基础的竭泽而渔？笔者基于空间政治经济学的分析框架，从生产、消费、成本三个维度将人口流动、地方政府债务扩张、区域经济发展纳入统一分析框架，探讨人口流动视域下地方政府债务扩张与经济发展的运行逻辑、传导机制。再将新口径的城投债基础数据库与手工整理核对的人口流动数据、城市宏观数据相匹配，建立面板数据模型对人口流动、地方政府债务扩张与区域经济发展的影响关系展开实证检验。研究发现：第一，人口流出地的地方政府债务扩张对人均GDP增长起到显著负向作用，随着人口流出规模的扩大这一抑制作用不断增强。第二，人口流入地的地方政府债务扩张对GDP增长起到显著正向作用，随着人口流入规模的扩大这一推动作用不断增强。第三，由东中部地区调任至西部地区的官员会进一步扩大人口流出地的地方政府债务扩张对人均GDP增长的抑制作用。