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Artificial Intelligence and Firm-Level Productivity
... More recently, other types of investment in knowledge capital have been identified, including skills of employees, organisational capabilities, as well as branding and product design (Webster and Jensen 2006). The process of digitalisation reinforced the importance of knowledge investment as software routines, data bases and new digital technologies such as artificial intelligence have become a major base for productivity advance (see Brynjolfsson, Rock, and Syverson 2019;Corrado, Haskel, and Jona-Lasinio 2021;Rammer, Czarnitzki, and Fernández 2022;Czarnitzki, Fernández, and Rammer 2022;Yang 2022;Damioli, Van Roy, and Vertesy 2021). ...
... Third, latest evidence on the ongoing process of digitalisation and its investment in new digital technologies such as artificial intelligence (AI) reinforced the importance of investment in intangible capital (see Brynjolfsson, Rock, and Syverson 2019). Although the cross-country-sectoral level study by Corrado, Haskel, and Jona-Lasinio (2021) finds no empirical evidence for the J-curve assumption by Brynjolfsson, Rock, and Syverson (2019), the firm-level studies by Yang (2022), Damioli, Van Roy, and Vertesy (2021) and Czarnitzki, Fernández, and Rammer (2022) find an important contribution of AI-related intangible capital to productivity. ...
This paper analyses the impact of intangibles on firm-level productivity. Unlike previous studies we capture all dimensions of intangibles for both goods-producing and service industries. Based on data from the German part of the Community Innovation Survey (CIS) for the period 2006 to 2018, our results show that intangible capital investment is equal in size to investment in tangible capital since the early 2000s. We find a highly significant and positive relationship between intangible capital and output, with elasticities in line with previous findings for other large EU economies. This positive impact of intangibles on the firm-level productivity is driven by non-R&D intangibles, notably software & databases, training and advertising & marketing. While this finding holds for both goods and service sectors, we find that non-R&D intangibles impact firm-level productivity more strongly in the services. Investment in R&D affects productivity only in the high-tech manufacturing sector.
... Additionally, AI applications for worker comfort include detecting workplace stressors, providing individualized recommendations for ergonomic enhancements, and altering environmental settings based on employee presence [34,35,36]. AI helps with payroll processing, real-time worker productivity feedback, administrative task automation, and quick response mechanisms [37,38,41,44]. Additionally, by automating data collecting and processing, locating key figures within an organization, and improving cooperation, AI can support organizational network analysis (ONA) [54,55,58]. ...
The convergence of Robotics innovation, including AI and the Internet of Things (IoT), has offered a huge opportunity for artificial intelligence (AI) within the workplace. It is projected that Industry 5.0 will improve accuracy, productivity, and adaptability. A number of changes must be made in order to implement Industry 5.0, most notably in the Human Resource (HR) function. Industry 5.0's emphasis on HR gives businesses a competitive edge. HR must be more astute and flexible in order to meet expectations and difficulties. Our study explores how AI may improve and digitise HR in Industry 5.0. Five AI applications and HR readiness were examined in a study with 271 HR professionals from the industrial, administrative, and information technology (IT) sectors. The results, which were obtained using the Statistical Package for Social Sciences (SPSS) and Analysis of Moment Structures (AMOS), emphasised how crucial it is to research hierarchical organisations in order to achieve sustainable growth. The five AI applications in HR all supported the adaptability and capabilities of human assets, highlighting safety and well-being enhancements as essential components of AI application in HR. KOREA REVIEW OF INTERNATIONAL STUDIES
... Utilising these opportunities provides a firm with the potential to generate new trajectories that enable rapid growth processes and thus generate productivity growth (e.g. Czarnitzki et al., 2022;Damioli et al., 2021). The concept of IMI was proposed by Griliches (1957). ...
Artificial intelligence (AI) is seen as a key technology for future economic growth. It is labelled as a general-purpose technology, as well as an invention of a method for inventing. Thus, AI is perceived to generate technological opportunities and through these, innovations, and productivity growth. The leapfrogging hypothesis suggests that latecomer firms can use these opportunities to catch up. The aim of this paper is to provide insight into this catch-up process of latecomer firms through integrating AI into their knowledge portfolio and thereby creating new technological trajectories. The moderating effect of firm size is also analysed. Combining firm-level data with patent data, a regression at the firm level is conducted. Evidence is found that smaller firms experience productivity growth from AI when operating at the productivity frontier, indicating the opposite of the leapfrogging hypothesis. However, there is evidence for the positive impact of AI on firm innovation, which is higher for latecomer firms that are larger in size. In general, we find a diverging pattern of the influence of AI on productivity and innovation growth, indicating the need for a finer grained analysis that takes indirect effects - that also could explain the observed productivity paradox - into account.
... Mercier-Laurent and Kayakutlu [50] continue the series of workshops since 2012 dealing with Artificial Intelligence for knowledge management, energy, and sustainability where a plenty of practical solutions are presented, specifically in order to reach seventeen SDG. As mentioned by Czarnitzki and Carioli [51] highly innovative economies already face the scarcity of skills which is the greatest threat to the innovative projects. The solution to prevent this threat lays in the managerial perspective of creating the cooperative strategies with external partners to bridge the skills gap of the future employees. ...
The developed concept for Artificial Intelligence (AI) introduction to the management system is concerned with a range of ethical, social, environmental and legal issues. Management system as a form of organising chaos and complexity becomes the only platform to design business and to make it sustainable irrespective of location and personalities engaged. At the time of the world economy demand for social actors, activism in the necessary transition of management oriented to reach the Sustainable Development Goals (SDG) is a crucial factor to form the new management digitized system. Environmental, Social and Governance (ESG) Investing managed assets as a part of all corporate assets. ESG initiative was a proposal of the UN to promote principles for a sustainable economy. Companies with better ESG performance can increase shareholders’ value by managing risks related to emerging ESG issues, namely bring the corporation to have energy transition experience. Different approaches of millennials to managing enterprises show their higher interest than that of predecessors to introduce ESG standards and tasks to the day-to-day management. Millennials are more interested in social values than in the investment return. Even the future of investments is dependent on the basic idea that investors are not short-termist but tend to be loyal to a project about which they have more relevant information. So, they may support new AI-based management in the case it becomes an efficient platform to design a human-oriented enterprise. This study aims at showing what the relationship of the management structure and process should be in order to manage the AI progress. Understanding of management content and work with the AI representation is of strategic importance.
In this chapter, the future of intellectual capital (IC) is considered from several key points of view. As we are witnessing the inflation of the use of the term “capital,” its origin should first be clarified. Furthermore, bearing in mind the emergence and rapid spread of AI in business, it is necessary to define its position within IC. After having a precise picture of what “capital” can be and whether AI is intellectual capital, the whole area should be placed within economic theory. Mainstream economic theory, even after more than 50 years, has not covered IC either at the macro or micro level. However, IC fits perfectly into complexity economics. Finally, in the last part of this chapter, a measurement system suitable for the knowledge-based economy and which connects the micro- and macroeconomic levels is presented.
Artificial Intelligence (AI) is creating big opportunities in the workplace, especially with the combination of AI and the Internet of Things (IoT), which is leading to advancements in robotics. This is known as industry 4.0, and it promises to bring precision, efficiency, and flexibility to businesses. However, to implement Industry 4.0, companies need to make a lot of changes, including changes to their Human Resources (HR) functions. In the new era, HR becomes even more important and can give a company a competitive edge. HR needs to be more adaptable and proactive in order to meet the challenges and demands of Industry 4.0. This study looks at how AI can improve HR practices in the context of Industry 4.0. It focuses on five key areas where AI can be applied in HR: Recruitment and Talent Acquisition, Employee Onboarding and Training, Performance Management, Employee Engagement and Retention, and HR Analytics and Reporting. The study also examined three aspects of HR readiness. Technological Readiness, Organizational Readiness, and Individual Readiness. The results show that AI has the potential to significantly enhance HR capabilities and improve the overall effectiveness of HR functions in Industry 4.0.
This study investigates the impacts of artificial intelligence (AI) and knowledge management (KM) on the competitive advantage (CA) of enterprises. In particular, it delves into the mediating role of knowledge management in the relationship between artificial intelligence and competitive advantage. Structural Equation Modelling (SEM) is employed to assess the relationships among the variables, with data drawn from a sample of 402 respondents in China. The survey findings reveal that the adoption of artificial intelligence significantly improves both knowledge management and the competitive advantage of enterprises; knowledge management not only directly impacts competitive advantage but also serves as a mediating variable in the relationship between artificial intelligence and competitive advantage. This mediating effect of knowledge management demonstrates how the acquisition, sharing and application of knowledge, propelled by artificial intelligence, can enhance competitive advantage over the long term. The findings of this study validate and extend the existing body of knowledge regarding the influences of AI and KM on enhancing organisational competitive advantage. It provides valuable insights for the strategic integration of artificial intelligence within a knowledge-driven framework, particularly within the context of Chinese enterprises. By addressing gaps in the related literature, it contributes to both the theoretical advancement and practical application of enterprise management.
Artificial intelligence (AI) is transforming entire industries at an unprecedented pace. Yet, established technology adoption theories offer limited tools for characterizing business AI integration and analyzing its effects. These primarily focus on the factors facilitating or hindering adoption, rather than on adoption patterns and impacts. This paper introduces a novel conceptual framework to address this key gap and applies it to the case of the ESG rating industry. ESG raters play a pivotal role in sustainable finance, providing metrics that guide investment decisions globally. However, little is known about the extent and nature of their AI usage and its implications. Through a mixed‐methods approach combining the analysis of job postings, patent filings, research publications, and corporate websites, we examine AI adoption among major ESG raters. Our investigation explores the specific AI technologies employed, their functional applications, the innovations developed, the intensity of AI integration, and the potential impacts of raters' AI adoption. Our results reveal widespread and growing AI adoption across the industry. Our findings show that raters extensively leverage Natural Language Processing to streamline data collection, processing, and analysis. Furthermore, they have pioneered Machine Learning innovations that significantly expand their sustainability assessment capabilities in various domains. These findings mark a considerable departure from prior academic and gray literature that characterized major ESG raters as having minimal AI use, prompting critical questions regarding the implications of this technological transformation for ESG ratings' reliability, transparency, and potential biases.
Corporate stakeholders are intrigued by the potential collaboration between AI and environmental reporting to maintain competitiveness in the digital and sustainable economy. This exploration is crucial given the persistent pressures driving companies to engage in greenwashing practices for legitimacy. Aiming to shed light on the function of AI in reducing the prevalence of greenwashing by Chinese businesses, the findings, derived from panel data estimation of Chinese A‐share firms, suggest that the implementation of AI has a positive impact on the fight against greenwashing. The investigation presents compelling evidence that greenwashing mechanism control can be accelerated by AI technologies. Organizations that invest strategically in artificial intelligence exhibit a diminished propensity to obfuscate environmental performance by means of AI‐enabled automation and enhanced data‐driven decision‐making. Intriguingly, the study demonstrates that the substantial disparate greenwashing impact of AI depends on ownership structure. In comparison to non‐SOEs, state‐owned enterprises (SOEs) demonstrate diminished AI control over greenwashing. Significantly, the research utilizes a variety of validation methods, such as instrumental variable approach, propensity score matching, and two‐stage least squares, to ensure the validity of the primary findings.
In response to growing environmental challenges, there is an urgent need to understand how corporations can leverage new technologies to boost sustainability and eco‐innovation. This study addresses this need by investigating Artificial Intelligence adoption (AIA) influence on green innovation (greenovation) performance among Chinese firms as China's expanding digital economy and severe ecological pressures make it unique study context. Specifically, panel data on 8722 firm‐year observations from Chinese listed firms from 2008 to 2017 is analyzed to test the relationship. The main findings show that higher AIA is associated with increased greenovation, measured through green patents. This positive effect is more pronounced among privately‐owned enterprises versus state‐owned enterprises. Additionally, financial analysts are found to strengthen the AI‐greennovation link through information dissemination and scrutiny. Importantly, the study findings are robust and validated through a battery of tests, including change regression, instrumental variable methods, propensity score match (PSM), and sysGMM. Overall, this study provides novel empirical evidence that AI holds promise as an enabler of corporate eco‐innovation. The findings have crucial implications for research and practice regarding leveraging digital technologies for sustainability, especially in emerging economies like China that is undergoing rapid technological change.
The main aim of every organization is to improve performance and productivity, but this is only possible with efficient management techniques and technology that produces results. Thus, the main aim of this study is to analyze the effect of managerial skills and technology adaptation on firm performance and productivity through the mediation channel of process innovation and product innovation using the PLS-SEM technique. The data for this study is collected from 150 managers of manufacturing industrial firms located in Lahore, the second largest city in Pakistan. The present used the purposive sampling technique to achieve the research objectives. The study’s empirical findings reveal that managerial skills and technology adaptation are positively and significantly related to process and product innovation. Indirect and total effects show that managers’ skills, technology adaptation, process innovation, and product innovation are positively and significantly related to the productivity and performance of firms. The present research is focused on the implementation of new technology and innovation.
Artificial Intelligence (AI) is likely to be the next general purpose technology. The U.S. and China are important players in the development of AI. Germany has a vibrant AI startup scene and is among the first third of EU countries in applying AI technologies. In order not to lose touch with international developments, Germany should work toward creating research- and innovation-friendly framework conditions. Appropriate measures include improving data availability, building AI expertise and enabling flexible regulation.
The current advances in Artificial Intelligence (AI) are likely to have profound economic implications and bring about new trade‐offs, thereby posing new challenges from a policymaking point of view. What is the impact of these technologies on the labor market and firms? Will algorithms reduce consumers' biases or will they rather originate new ones? How competition will be affected by AI‐powered agents? This study is a first attempt to survey the growing literature on the multi‐faceted economic effects of the recent technological advances in AI that involve machine learning applications. We first review research on the implications of AI on firms, focusing on its impact on labor market, productivity, skill composition and innovation. Then we examine how AI contributes to shaping consumer behavior and market competition. We conclude by discussing how public policies can deal with the radical changes that AI is already producing and is going to generate in the future for firms and consumers.
Research Summary
We create and validate a new measure of an occupation's exposure to AI that we call the AI Occupational Exposure (AIOE). We use the AIOE to construct a measure of AI exposure at the industry level, which we call the AI Industry Exposure (AIIE) and a measure of AI exposure at the county level, which we call the AI Geographic Exposure (AIGE). We also describe several ways in which the AIOE can be used to create firm level measures of AI exposure. We validate the measures and describe how they can be used in different applications by management, organization and strategy scholars.
Managerial Summary
Although artificial intelligence (AI) promises to spur economic growth, there is widespread concern that it could displace workers, alter industry trajectories, and reshape organizations. Despite the interest in this area, we have limited ability to study the effects of AI on occupations, firms, industries, and geographies because of limited availability of data that measures exposure to AI. To address this limitation, we create and validate a new measure of an occupation's exposure to AI that we call the AI Occupational Exposure (AIOE). We use the AIOE to construct a measure of AI exposure at the industry level (AIIE) and county level (AIGE). We describe how our measures can be useful to scholars and policy‐makers interested in identifying the effect of AI on markets.
Recent evidence indicates an upsurge in artificial intelligence and robotics (AI) patenting activities in the latest years, suggesting that solutions based on AI technologies might have started to exert an effect on the economy. We test this hypothesis using a worldwide sample of 5257 companies having filed at least a patent related to the field of AI between 2000 and 2016. Our analysis shows that, once controlling for other patenting activities, AI patent applications generate an extra-positive effect on companies’ labor productivity. The effect concentrates on SMEs and services industries, suggesting that the ability to quickly readjust and introduce AI-based applications in the production process is an important determinant of the impact of AI observed to date.
Technical advancements within the subject of artificial intelligence (AI) leads towards development of human-like machines, able to operate autonomously and mimic our cognitive behavior. The progress and interest among managers, academics and the public has created a hype among many industries, and many firms are investing heavily to capitalize on the technology through business model innovation. However, managers are left with little support from academia when aiming to implement AI in their firm’s operations, which leads to an increased risk of project failure and unwanted results. This paper aims to provide a deeper understanding of AI and how it can be used as a catalyst for business model innovation. Due to the increasing range and variety of the available published material, a literature review has been performed to gather current knowledge within AI business model innovation. The results are presented in a roadmap to guide the implementation of AI to firm’s operations. Our presented findings suggest four steps when implementing AI: (1) understand AI and organizational capabilities needed for digital transformation; (2) understand current BM, potential for BMI, and business ecosystem role; (3) develop and refine capabilities needed to implement AI; and (4) reach organizational acceptance and develop internal competencies.
There are numerous emerging studies addressing big data and its application in different organizational aspects, especially regarding its impact on the business innovation process. This study in particular aims at analyzing the existing relationship between Big Data Analytics Capabilities and Co-innovation. To test the hypothesis model, structural equations by the partial least squares method were used in a sample of 112 Colombian firms. The main findings allow to positively relate Big Data Analytics Capabilities with better and more agile processes of product and service co-creation and with more robust collaboration networks with stakeholders internal and external to the firm.
This paper analyzes the relationship between firms' use of big data analytics and their innovative performance in terms of product innovations. Since big data technologies provide new data information practices, they create novel decision-making possibilities, which are widely believed to support firms' innovation process. Applying German firm-level data within a knowledge production function framework we find suggestive evidence that big data analytics is a relevant determinant for the likelihood of a firm becoming a product innovator as well as for the market success of product innovations. These results hold for the manufacturing as well as for the service sector but are contingent on firms' investment in IT-specific skills. Overall, the results support the view that big data analytics have the potential to enable innovation.
In light of rapid advances in the fields of Artificial Intelligence (AI) and robotics, many scientists discuss the potentials of new technologies to substitute for human labour. Fuelling the economic debate, various empirical assessments suggest that up to half of all jobs in western industrialized countries are at risk of automation in the next 10 to 20 years. This paper demonstrates that these scenarios are overestimating the share of automatable jobs by neglecting the substantial heterogeneity of tasks within occupations as well as the adaptability of jobs in the digital transformation. To demonstrate this, we use detailed task data and show that, when taking into accounting the spectrum of tasks within occupations, the automation risk of US jobs drops, ceteris paribus, from 38 % to 9 %.
In this essay, I begin by identifying the reasons that automation has not wiped out a majority of jobs over the decades and centuries. Automation does indeed substitute for labor—as it is typically intended to do. However, automation also complements labor, raises output in ways that leads to higher demand for labor, and interacts with adjustments in labor supply. Journalists and even expert commentators tend to overstate the extent of machine substitution for human labor and ignore the strong complementarities between automation and labor that increase productivity, raise earnings, and augment demand for labor. Changes in technology do alter the types of jobs available and what those jobs pay. In the last few decades, one noticeable change has been a "polarization" of the labor market, in which wage gains went disproportionately to those at the top and at the bottom of the income and skill distribution, not to those in the middle; however, I also argue, this polarization is unlikely to continue very far into future. The final section of this paper reflects on how recent and future advances in artificial intelligence and robotics should shape our thinking about the likely trajectory of occupational change and employment growth. I argue that the interplay between machine and human comparative advantage allows computers to substitute for workers in performing routine, codifiable tasks while amplifying the comparative advantage of workers in supplying problem-solving skills, adaptability, and creativity.
We investigate the hypothesis that the combination of three related innovations—1) information technology (IT), 2) complementary
workplace reorganization, and 3) new products and services—constitute a significant skill-biased technical change affecting
labor demand in the United States. Using detailed firm-level data, we find evidence of complementarities among all three of
these innovations in factor demand and productivity regressions. In addition, firms that adopt these innovations tend to use
more skilled labor. The effects of IT on labor demand are greater when IT is combined with the particular organizational investments
we identify, highlighting the importance of IT-enabled organizational change.
We survey the micro and macro literature on the impact of Information and CommunicationTechnologies (ICTs) on productivity. The "Solow Paradox" of the absence of an impact ofICT on productivity no longer holds, if it ever did. Both growth accounting and econometricevidence suggest an important role for ICTs in accounting for productivity. In fact, theempirical estimates suggest a much larger impact of ICT on productivity than would beexpected from the standard neoclassical model that we focus on. We discuss the variousexplanations for these results, including the popular notion of complementary organizationalcapital. Finally, we offer suggestions for where the literature needs to go.
Whole eras of technical progress and growth appear to be driven by a few 'General Purpose Technologies' (GPT's), such as the steam engine, the electric motor, and semiconductors. GPT's are characterized by pervasiveness, inherent potential for technical improvements, and 'innovational complementarities', giving rise to increasing returns-to-scale. However, a decentralized economy will have difficulty in fully exploiting the growth opportunities of GPT's: arms-length market transactions between the GPT and its users may result in 'too little, too late' innovation. Likewise, difficulties in forecasting the technological developments of the other side can lower the rate of technical advance of all sectors.
The authors assume that firms invest in R&D not only to generate innovations, but also to learn from competitors and extraindustry knowledge sources (e.g., university and government labs). This argument suggests that the ease of learning within an industry will both affect R&D spending, and condition the influence of appropriability and technological opportunity conditions on R&D. For example, they show that, contrary to the traditional result, intraindustry spillovers may encourage equilibrium industry R&D investment. Regression results confirm that the impact of appropriability and technological opportunity conditions on R&D is influenced by the ease and character of learning. Copyright 1989 by Royal Economic Society.
In this paper we explore the relationship between computers and productivity growth at the firm level. We apply standard productivity and growth accounting techniques to data from 600 large US firms over 1987-1994. While we find that computer make a positive and significant contribution to output growth in the short term (using 1 year differences), the implied returns to computers are two to five times greater when differences are taken over seven years instead of one year. Our results challenge the conclusions drawn from aggregate data on computers and productivity, but are consistent with case evidence that the combination of computers and organizational co-investments make a substantial contribution to growth. JEL Categories: O3 Technological Change; D24 Capital and Total Factor Productivity Computers and Productivity Growth Page 1 1.
We study the firm-level implications of robot adoption in France. Of 55,390 firms in our sample, 598 adopted robots between 2010 and 2015, but these firms accounted for 20 percent of manufacturing employment. Adopters experienced significant declines in labor shares, the share of production workers in employment, and increases in value added and productivity. They expand their overall employment as well. However, this expansion comes at the expense of competitors, leading to an overall negative association between adoption and employment. Robot adoption has a large impact on the labor share because adopters are larger and grow faster than their competitors.
Long-run growth in many models is the product of two terms: the effective number of researchers and their research productivity. We present evidence from various industries, products, and firms showing that research effort is rising substantially while research productivity is declining sharply. A good example is Moore’s Law. The number of researchers required today to achieve the famous doubling of computer chip density is more than 18 times larger than the number required in the early 1970s. More generally, everywhere we look we find that ideas, and the exponential growth they imply, are getting harder to find. (JEL D24, E23, O31, O47)
We present a framework for understanding the effects of automation and other types of technological changes on labor demand, and use it to interpret changes in US employment over the recent past. At the center of our framework is the allocation of tasks to capital and labor—the task content of production. Automation, which enables capital to replace labor in tasks it was previously engaged in, shifts the task content of production against labor because of a displacement effect. As a result, automation always reduces the labor share in value added and may reduce labor demand even as it raises productivity. The effects of automation are counterbalanced by the creation of new tasks in which labor has a comparative advantage. The introduction of new tasks changes the task content of production in favor of labor because of a reinstatement effect, and always raises the labor share and labor demand. We show how the role of changes in the task content of production—due to automation and new tasks—can be inferred from industry-level data. Our empirical decomposition suggests that the slower growth of employment over the last three decades is accounted for by an acceleration in the displacement effect, especially in manufacturing, a weaker reinstatement effect, and slower growth of productivity than in previous decades.
Recent progress in artificial intelligence (AI)—a general purpose technology affecting many industries—has been focused on advances in machine learning, which we recast as a quality-adjusted drop in the price of prediction. How will this sharp drop in price impact society? Policy will influence the impact on two key dimensions: diffusion and consequences. First, in addition to subsidies and intellectual property (IP) policy that will influence the diffusion of AI in ways similar to their effect on other technologies, three policy categories—privacy, trade, and liability—may be uniquely salient in their influence on the diffusion patterns of AI. Second, labor and antitrust policies will influence the consequences of AI in terms of employment, inequality, and competition. © 2019 by the National Bureau of Economic Research. All rights reserved.
We analyze for the first time the economic contributions of modern industrial robots, which are flexible, versatile, and autonomous machines. We use novel panel data on robot adoption within industries in 17 countries from 1993-2007, and new instrumental variables that rely on robots’ comparative advantage in specific tasks. Our findings suggest that increased robot use contributed approximately 0.37 percentage points to annual labor productivity growth, while at the same time raising total factor productivity and lowering output prices. Our estimates also suggest that robots did not significantly reduce total employment, although they did reduce low-skilled workers’ employment share
What are the prospects for long-run economic growth?, the present study looks at a more recently launched hypothesis, which I label Singularity. The idea here is that rapid growth in computation and artificial intelligence will cross some boundary or Singularity after which economic growth will accelerate sharply as an ever-accelerating pace of improvements cascade through the economy. The paper develops a growth model that features Singularity and presents several tests of whether we are rapidly approaching Singularity. The key question for Singularity is the substitutability between information and conventional inputs. The tests suggest that the Singularity is not near.
This paper examines some of the recent literature on the estimation of production functions. We focus on techniques suggested in two recent papers, Olley and Pakes (1996) and Levinsohn and Petrin (2003). While there are some solid and intuitive identification ideas in these papers, we argue that the techniques can suffer from functional dependence problems. We suggest an alternative approach that is based on the ideas in these papers, but does not suffer from the functional dependence problems and produces consistent estimates under alternative data generating processes for which the original procedures do not.
In this paper we consider the specification and estimation of the Cobb-Douglas production function model. After reviewing the "traditional" specifying assumptions for the model which are based on deterministic profit maximization, we develop a model in which profits are stochastic and in which maximization of the mathematical expectation of profits is posited. "Sampling theory" and Bayesian estimation techniques for this model are presented.
We examine how susceptible jobs are to computerisation. To assess this, we begin by implementing a novel methodology to estimate the probability of computerisation for 702 detailed occupations, using a Gaussian process classifier. Based on these estimates, we examine expected impacts of future computerisation on US labour market outcomes, with the primary objective of analysing the number of jobs at risk and the relationship between an occupations probability of computerisation, wages and educational attainment.
Innovation drives economic competitiveness and sustained long-term economic growth. Especially the emergence and intensive utilization of information and communication technologies (ICTs), which spawned the beginning of the digital economy two decades ago, heavily affected the opportunities and efficiency of how firms produce and provide goods and services. We provide an overview of the empirical literature on ICT and productivity and highlight the main results and methodological differences. The majority of studies indicates that the productivity effect of ICT is indeed positive and significant. However, methodological approaches of how to appropriately estimate the ICT effect matter. While aggregate and sectoral growth accounting exercises suggest stronger differences of the ICT effect between US and Europe, firm-level analyses suggest no significant country differences. Moreover, we shed light on the notion of ICT being a General Propose Technology (GPT) enabling further innovations. Most of the GPT evidence on ICT is found for the US, while evidence for European countries is harder to come by. However, more theoretical and empirical research is needed to better understand spillovers and externalities of ICT and how these technologies transform our economies.
This paper analyzes and extends the growing econometric literature on the economic impact of information technology (IT). I begin with a "meta-analysis" to systematically examine the results of twenty empirical studies and show that much of the observed variation in estimates of the output elasticity of IT is predictable and due to differences in model specification and econometric technique. Using a single dataset for U.S. industries, I then find similar variation across alternative specifications and estimation methods. Most results show a productivity effect from IT-use, but the point estimate of the elasticity is fragile and depends on the details of the estimation.
We add to the methods for conditioning out serially correlated unobserved shocks to the production technology. We build on
ideas first developed in Olley and Pakes (1996). They show how to use investment to control for correlation between input
levels and the unobserved firm-specific productivity process. We show that intermediate inputs (those inputs which are typically
subtracted out in a value-added production function) can also solve this simultaneity problem. We discuss some theoretical
benefits of extending the proxy choice set in this direction and our empirical results suggest these benefits can be important.
Technological change and deregulation have caused a major restructuring of the telecommunications equipment industry over the last two decades. Our empirical focus is on estimating the parameters of a production function for the equipment industry, and then using those estimates to analyze the evolution of plant-level productivity. The restructuring involved significant entry and exit and large changes in the sizes of incumbents. Firms' choices on whether to liquidate, and on input quantities should they continue, depended on their productivity. This generates a selection and a simultaneity problem when estimating production functions. Our theoretical focus is on providing an estimation algorithm which takes explicit account of these issues. We find that our algorithm produces markedly different and more plausible estimates of production function coefficients than do traditional estimation procedures. Using our estimates we find increases in the rate of aggregate productivity growth after deregulation. Since we have plant-level data we can introduce indices which delve deeper into how this productivity growth occurred. These indices indicate that productivity increases were primarily a result of a reallocation of capital towards more productive establishments.
This paper develops asymptotic distribution theory for instrumental variables regression when the partial correlations between the instruments and the endogenous variables are weak, here modeled as local to zero. Asymptotic representation are provided for various statistics, including two-stage least squares and limited information maximum likelihood estimators, Wald statistics, and statistics testing overidentification and endogeneity. The asymptotic distributions provide good approximations to sampling distributions with ten-twenty observations per instrument. The theory suggests concrete guidelines for applied work, including using nonstandard methods for construction of confidence regions. These results are used to interpret J. D. Angrist and A. B. Krueger's (1991) estimates of the returns to education.
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