Management Matters

New indications of managerial innovations are created and then used to show that changes in organizational technologies are an important source of economic growth. Specifically, the analysis demonstrates that, first, in response to a positive managerial technology shock, output, productivity and hours significantly increase in the short run, second, these types of innovations are as important as non-managerial ones in explaining movements in these variables at business cycle frequencies, and, third, product and process innovations promote the development of new managerial techniques.Business Cycles; Productivity; Management techniques; Technical Change

Exploring the Behavior of Economic Agents: the role of relative preferences

Standard economic theory assumes individuals choose actions that optimize their expected utility. In this paper we investigate how the existence of players with non-standard preferences may influence economic agents' behavior in some of the most frequently studied non-cooperative games. We find that allowing for the existence of agents with relative preferences can help explain observed economic actions which, at times, appear counter-intuitive.

The Effects of Wealth, and Unemployment Benefits on Search Behavior and Labor Market Transitions

During the past decade, many researchers have examined the theoretical predictions of labor search models with endogenous job search intensity. For a risk adverse individual, search intensity depends on variables such as individual wealth and the level of unemployment benefits. Since wealth and unemployment benefits affect search intensity, they also affect the duration of unemployment spells. Although there are a small number of papers that empirically estimate the relationship between search intensity and unemployment benefits, none focus on the effects of savings on search intensity. This omission is primarily due to the lack of suitable datasets. To determine the effects of wealth and unemployment benefits on search intensity and unemployment duration, we estimate a simultaneous equation model of search intensity, reservation wages, labor market transitions and wealth using a sample from the 1984 Survey of Income and Program Participation. We examine whether wealth and unemployment insurance have different effects on the intensive search margin (the number of contacts) and the extensive search margin (the number of search methods). Our results yield insights into the effectiveness of different methods of search, the effect of the unemployment insurance benefits, and the magnitude of the discouraged worker effect in the U.SLabor Market Transitions, Search Behavior

Measuring Our Ignorance, One Book at a Time: New Indicators of Technological Change, 1909-1949

We present new indicators of U.S. technological change for the period 1909-49 based on information in the Library of Congress’ catalogue. We use these indicators to estimate the connections between technological change and economic activity, and to investigate the relationship between fluctuations in innovative activity and the Great Depression. Although we do find links between technological change, output and productivity, our results suggest that the slowdown in technological progress in the early 1930s did not contribute significantly to the Great Depression. On the other hand, the remarkable acceleration in innovations after 1934 did play a role in the recovery.Technical Change, Productivity, the Great Depression

Volumes of Evidence - Examining Technical Change Last Century Through a New Lens

Although technical change is central in much of modern economics, traditional measures of it are, for a number of reasons, flawed. We discuss in this paper new indicators based on data drawn from the MARC records of the Library of Congress on the number of new technology titles in various fields published in the United States over the course of the last century. These indicators, we argue, overcome many of the shortcomings associated with patents, research and development expenditures, innovation counts, and productivity figures. We find, among other things, the following: the pattern and nature of technical change described by our indicators is, on the whole, consistent with that of other measures; they represent innovation not diffusion; a strong causal relationship between our indicators and changes in TFP and output per capita; innovations in some sub-groups have had a greater impact on output and productivity than others and, moreover, the key players have changed over time. Our indicators can be used to shed light on number of important issues including the empirical relationship between technology shocks and employment, the role of technology in cross-country productivity differences, and the part played by technological change in growing skills premia in the U.S. during the last few decades.Business Cycles, Technical change, productivity, measurement

Read All About it!! What happens following a technology shock?

Existing indicators of technical change are plagued by shortcomings. I present here new measures based on books published in the field of technology that resolve many of these problems and use them to identify the impact of technology shocks on economic activity. They are positively linked to changes in R&D and scientific knowledge and capture the new technologies' commercialization dates. Changes in information technology are found to be important sources of economic fluctuations in the post-WWII period and total factor productivity, investment and, to a lesser extent, labor are all shown to increase following a positive technology shock.business cycles, technical change, information technologies

Gender inference: can chatGPT outperform common commercial tools?

An increasing number of studies use gender information to understand phenomena such as gender bias, inequity in access and participation, or the impact of the Covid pandemic response. Unfortunately, most datasets do not include self-reported gender information, making it necessary for researchers to infer gender from other information, such as names or names and country information. An important limitation of these tools is that they fail to appropriately capture the fact that gender exists on a non-binary scale, however, it remains important to evaluate and compare how well these tools perform in a variety of contexts. In this paper, we compare the performance of a generative Artificial Intelligence (AI) tool ChatGPT with three commercially available list-based and machine learning-based gender inference tools (Namsor, Gender-API, and genderize.io) on a unique dataset. Specifically, we use a large Olympic athlete dataset and report how variations in the input (e.g., first name and first and last name, with and without country information) impact the accuracy of their predictions. We report results for the full set, as well as for the subsets: medal versus non-medal winners, athletes from the largest English-speaking countries, and athletes from East Asia. On these sets, we find that Namsor is the best traditional commercially available tool. However, ChatGPT performs at least as well as Namsor and often outperforms it, especially for the female sample when country and/or last name information is available. All tools perform better on medalists versus non-medalists and on names from English-speaking countries. Although not designed for this purpose, ChatGPT may be a cost-effective tool for gender prediction. In the future, it might even be possible for ChatGPT or other large scale language models to better identify self-reported gender rather than report gender on a binary scale.Comment: 14 pages, 8 table

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

The Media is the Measure: Technical change and employment, 1909-49

Abstract Difficulties in sorting out the empirical relationship between technical change and employment is attributable, at least in part, to the shortcomings associated with traditional measures of the former. In this paper, we use new indicators of technical change that we believe resolve many issues associated with other methods of identifying technology shocks, and use them to explore the impact of technical change on employment from 1909-49. The payoff to this effort is substantial for at least three reasons. First, it sheds light on the role of technology shocks in cyclical fluctuations during this period, second, it informs business cycle model selection (New Keynesian vs. Real Business Cycle), and, third, it contributes to our understanding of the part played by the New Deal Policies in the recovery from the Great Depression