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on Efficiency and Productivity |
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Issue of 2022‒07‒18
23 papers chosen by |
| By: | Cindy Cunningham; Lucia Foster; Cheryl Grim; John Haltiwanger; Sabrina Wulff Pabilonia; Jay Stewart; Zoltan Wolf |
| Abstract: | Within-industry productivity dispersion is pervasive and exhibits substantial variation across countries, industries, and time. We build on prior research that explores the hypothesis that periods of innovation are initially associated with a surge in business start-ups, followed by increased experimentation that leads to rising dispersion potentially with declining aggregate productivity growth, and then a shakeout process that results in higher productivity growth and declining productivity dispersion. Using novel detailed industry-level data on total factor productivity and labor productivity dispersion from the Dispersion Statistics on Productivity along with novel measures of entry rates from the Business Dynamics Statistics and productivity growth data from the Bureau of Labor Statistics for U.S. manufacturing industries, we find support for this hypothesis, especially for the high-tech industries. |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:bls:wpaper:541&r= |
| By: | Diane Coyle; Kieran Lind; David Nguyen; Manuel Tong Koecklin |
| Abstract: | One possible explanation for the productivity slowdown in advanced economies coinciding with widespread digital adoption is that firms need time to change organisational structures or processes to use the new technologies effectively. Using a unique UK firm-level data set, we explore the links between a large set of digital inputs and investments and productivity. We found that large firms are more digital-intensive than small ones and that digital adopters do have higher productivity than non-adopters, but the nature of the digital variables matters. Those reflecting in-house capabilities are positively related to firm-level total factor productivity (TFP) while those indicating bought-in ones are negatively related. This finding that firms' capabilities matter for the impact of digital adoption on productivity takes advantage of the wide range of digital variables we were able to use, and points to the need for future research on the role of digital technology in driving productivity to take account of organisational capabilities. |
| Keywords: | digital, organisation, productivity |
| JEL: | D22 O33 O40 |
| Date: | 2022–03 |
| URL: | http://d.repec.org/n?u=RePEc:nsr:escoed:escoe-dp-2022-06&r= |
| By: | Cardwell, Ryan; Ghazalian, Pascal L. |
| Abstract: | The Canadian Wheat Board (CWB) was a state-trading enterprise that controlled the sale and distribution of wheat and barley produced in Western Canada from 1935 to 2012. The CWB’s regulatory and bureaucratic structures have been investigated as sources of several market effects, including prices and spatial production patterns. We investigate the effects of the CWB on productivity using farm-level data, and identify how deregulation of the CWB affected total factor productivity (TFP) for CWB-regulated crops. Farm-level production and input data for 13,000 grain farms over 15 years are used to generate a within-farm difference-in-difference (DiD) estimator that identifies how relative TFP changed between CWB and non-CWB crops after deregulation. Cereal farm operators typically grow several (CWB and non-CWB) crops in a single season, allowing us to estimate production functions for multiple crops at the same farm in the same year. Our within-farm DiD empirical strategy identifies the effects of deregulation on changes in relative TFP between crops, while controlling for many of the confounding factors that complicate TFP measurement in other approaches, such as unobserved differences between farms and unobserved changes within farms over time. This research makes a methodological contribution to the productivity literature by developing a within-farm DiD estimator, and contributes to the understanding of how policy interventions affect farm-level productivity. |
| Keywords: | Agricultural and Food Policy, Crop Production/Industries, Production Economics |
| Date: | 2022–04 |
| URL: | http://d.repec.org/n?u=RePEc:ags:aesc22:321159&r= |
| By: | Tensi, Annika Francesca; Ang, Frederic; Fels-Klerx, Ine van der |
| Abstract: | In this article, we analysed the relationship between farm innovation and farm efficiency. We computed an innovation index based on Dutch Innovation Monitor data and ratings from an expert elicitation. The innovation index is an adaptation and extension of an existing innovation index for Irish dairy farms. We computed technical efficiency scores with a Data Envelopment Analysis (DEA). The DEA scores are computed with Farm Accountancy Data Network (FADN) data. We investigated the relationship with pre-registered ordinary least square (OLS) regression analyses in quadratic form and additional Chi-square tests. Unanimously, we reject the first hypothesis that farm innovation and farm efficiency can be described by an inverse parabolic relationship. Early adopters and innovators are not necessarily less efficient than the early and late majority of innovation adopters. We also reject the second hypothesis that innovation front-runners become more efficient. These are preliminary findings. |
| Keywords: | Farm Management, Research and Development/Tech Change/Emerging Technologies |
| Date: | 2022–04 |
| URL: | http://d.repec.org/n?u=RePEc:ags:aesc22:321163&r= |
| By: | Dirk Czarnitzki; Gastón P Fernández; Christian Rammer |
| Abstract: | Artificial Intelligence (AI) is often regarded as the next general-purpose technology with a rapid, penetrating, and far-reaching use over a broad number of industrial sectors. A main feature of new general-purpose technology is to enable new ways of production that may increase productivity. So far, however, only very few studies investigated likely productivity effects of AI at the firm-level; presumably because of lacking data. We exploit unique survey data on firms’ adoption of AI technology and estimate its productivity effects with a sample of German firms. We employ both a cross-sectional dataset and a panel database. To address the potential endogeneity of AI adoption, we also implement IV estimators. We find positive and significant effects of the use of AI on firm productivity. This finding holds for different measures of AI usage, i.e., an indicator variable of AI adoption, and the intensity with which firms use AI methods in their business processes. |
| Keywords: | Artificial Intelligence, Productivity, CIS data |
| Date: | 2022–02–17 |
| URL: | http://d.repec.org/n?u=RePEc:ete:msiper:690486&r= |
| By: | Fedotenkov, Igor (European Commission); Kvedaras, Virmantas (European Commission); Sanchez-Martinez, Miguel (European Commission) |
| Abstract: | The paper investigates the relationship between employment protection legislation (EPL hereafter) and labour productivity growth in the EU in the context of the Great Recession. We consider the crisis and recovery periods, evaluate the relevance of both levels and changes in EPL for productivity growth, establish the presence of some nonlinearities, and explore the conditioning role played by the skills of the labour force, captured by different levels of education. We find that stricter labour protection reduces labour productivity growth in sectors with a large share of workers with tertiary education, whereas this effect is negligible or positive in sectors where workers with secondary or only primary education are more prevalent, respectively. We establish that overly strict regulation is more harmful, whereas its moderate level can be even beneficial in regular (non-crisis) times. In the long run, we document that an increase in EPL stimulates employers to substitute labour with capital, partially mitigating the overall negative effect on labour productivity growth. We provide several hypotheses that could explain our findings and discuss potential policy implications supported by a back-of-the-envelope calculation. |
| Keywords: | Labour productivity, employment protection legislation, skills, education, Great Recession |
| JEL: | E24 I25 J24 J88 |
| Date: | 2022–05 |
| URL: | http://d.repec.org/n?u=RePEc:jrs:wpaper:202204&r= |
| By: | Antonio Peyrache (School of Economics and Centre for Efficiency and Productivity Analysis (CEPA) at The University of Queensland, Australia); Maria C. A. Silva (CEGE - Cat´olica Porto Business School, Rua Diogo Botelho, 1327, 4169-005 Porto, Portugal.) |
| Abstract: | Kao (2012) proposed a method to decompose DMU efficiency into sub-unit efficiencies for parallel production systems. We provide a numerical example showing that the proposed method can yield negative sub-unit efficiency scores under variable returns to scale, against common sense and standard postulates requiring this score to be non-negative. As a solution, we propose a decomposition based on the directional distance function that does not suffer from this problem and can be also applied to non-convex technologies, therefore providing a more general method to implement such a decomposition. Given the connection between the directional distance function and slack-based efficiency measurement, the method can easily be extended to this case as well. |
| Keywords: | DEA; FDH; Networks; Directional Distance Function; Inefficiency |
| JEL: | C1 C3 |
| Date: | 2022–06 |
| URL: | http://d.repec.org/n?u=RePEc:qld:uqcepa:179&r= |
| By: | Jaanika Merikyll; Alari Paulus |
| Abstract: | Economic recessions can boost the productivity-enhancing reallocation of jobs, yet the Covid-19 crisis has provided limited and mixed evidence of that. The paper studies the link between productivity and reallocation and investigates the role of job retention schemes in it, using a rich administrative dataset for Estonia that covers the whole population of firms from 2004 to 2020. We find persistent evidence for the reallocation of jobs towards more productive sectors and firms. However, the within-sector reallocation was surprisingly unresponsive to productivity in the Covid-19 crisis, in sharp contrast to the experience in the previous major crisis, the Great Recession. We show that a generous job retention scheme supressed the acceleration of within-industry reallocation towards more productive firms, which had negative consequences for aggregate productivity during Covid-19. These estimates appear sufficiently large to imply that there are negative overall welfare effects that offset the positive employment effect. |
| Keywords: | job reallocation, productivity, Covid-19, cleansing effect, firm exit and entry, job retention scheme |
| JEL: | J62 D24 J68 D61 |
| Date: | 2022–06–29 |
| URL: | http://d.repec.org/n?u=RePEc:eea:boewps:wp2022-5&r= |
| By: | Liu, Mengxiao; Wang, Luhang; Yi, Yimin |
| Abstract: | This paper classifies innovation as quality-improving or cost-reducing and estimates a dynamic model incorporating firm export, quality innovation, and cost innovation decisions. Estimation results show that export, quality innovation, and cost innovation increase next-period firm productivity by 1.39%, 1.23%, and 1.27%, respectively. Additionally, quality innovation raises next-period export demand by 47%. Counterfactual analyses suggest that (1) foreign market growth has a larger impact on firm export and innovation decisions than domestic market growth, but neither market significantly affects firm productivity; (2) subsidizing continuing quality innovators generates the highest financial return, and subsidizing continuing cost innovators brings the most productivity gain. |
| Keywords: | export; quality innovation; cost innovation; firm productivity; dynamic estimation; neural network; machine learning; trade liberalization; innovation policy |
| JEL: | C45 F14 L1 L10 L25 |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:113270&r= |
| By: | Pierre-Philippe Combes (ECON - Département d'économie (Sciences Po) - Sciences Po - Sciences Po - CNRS - Centre National de la Recherche Scientifique, CEPR - Center for Economic Policy Research - CEPR); Gilles Duranton (University of Pennsylvania [Philadelphia]); Laurent Gobillon (PSE - Paris School of Economics - ENPC - École des Ponts ParisTech - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris sciences et lettres - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique - EHESS - École des hautes études en sciences sociales - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, PJSE - Paris Jourdan Sciences Economiques - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris sciences et lettres - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement) |
| Abstract: | We propose a new nonparametric approach to estimate the production function for housing. Our estimation treats output as a latent variable and relies on a first-order condition for profit maximization combined with a zero-profit condition. More desirable locations command higher land prices and, in turn, more capital to build houses. For parcels of a given size, we compute housing production by summing across the marginal products of capital. For newly built single-family homes in France, the production function for housing is close to constant returns and is well, though not perfectly, approximated by a Cobb-Douglas function with a capital elasticity of 0.65. |
| Keywords: | Housing,Production function |
| Date: | 2021–10–01 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpspec:halshs-03342578&r= |
| By: | Goyal, Tarini; Roy, Debjit |
| Abstract: | This paper analyses the role of the Internet of Things in increasing worker safety and productivity as well as improving performance appraisal methods in the factory setup. Analysis of productivity levels has been carried out for workers in a steel plant on the basis of data collected from IoT tags. The study depicts how IoT can allow workers to perform tasks smoothly in their respective areas of expertise, along with a robust system of communication. By preventing accidents and boosting productivity, a win-win situation is created for workers and their families, as well as for factory owners and their clients. |
| Date: | 2022–06–03 |
| URL: | http://d.repec.org/n?u=RePEc:iim:iimawp:14677&r= |
| By: | Josh Martin; Kyle Jones |
| Abstract: | The coronavirus pandemic exposes some fundamental shortcomings in the accepted methods used to estimate productivity, notably the failure to adjust for variations in the utilisation of capital. In a time of national lockdown, the consequent introduction of furloughing (workers away from jobs but still being paid) and a massive shift to homeworking, capital utilisation is expected to fall rapidly. Official measures of productivity, including those produced by the UK Office for National Statistics (ONS), have not historically taken into account variations in capital utilisation over time. In this case, Multi-Factor Productivity (MFP) appears to fall too far, since measured capital input is near constant. There is no internationally agreed method to adjust for capital utilisation; although the literature offers a number of options, none are widely accepted due to conceptual, data availability and data quality issues. We offer an extension to an existing approach of using labour hours worked as a proxy for capital hours worked, overcoming conceptual issues by matching worker types (occupations) to capital types (assets). We use data from the US O*NET database, mapped to UK occupation codes, to inform the matching of UK occupation codes to assets, then measure the hours worked of those occupations relative to a baseline in order to measure deviations in capital utilisation by asset. We also introduce a conceptual framework to apply these adjustments, noting that not all assets will be subject to variation in utilisation to the same degree. We test a number of sensitivities in the methods, including methods to construct the baseline and the degree of variation allowed for each asset. Our central estimate shows a decline in capital utilisation of around 9 per cent in the UK market sector in the height of the pandemic, recovering over half of this by the end of 2020. This subdues, but does not eliminate, the fall in MFP through 2020. |
| Keywords: | capacity utilisation, capital, coronavirus, multi-factor productivity, official statistics |
| JEL: | D24 E22 E24 |
| Date: | 2022–05 |
| URL: | http://d.repec.org/n?u=RePEc:nsr:escoed:escoe-dp-2022-11&r= |
| By: | Nicholas Oulton |
| Abstract: | According to Paul Krugman (1994, chapter 1), "Productivity isn't everything, but in the long run it is almost everything. A country's ability to improve its standard of living over time depends almost entirely on its ability to raise its output per worker." But productivity and the standard of living are different concepts and are measured in different ways, so the question is, what is the linkage between them? Productivity is typically measured by GDP per hour. The standard of living has potentially many aspects such as health, longevity, personal security, and relationships. But here I take a narrower view and stick to the national accounts. So the standard of living is measured by the household disposable income of the median individual. I use the median rather than the mean so that inequality is taken into account. I develop a decomposition of the growth of median household income which relates it to the growth of productivity via eight additional factors, one of which is inequality; four other factors are measures of labour market performance. I apply this decomposition to the UK over the period 1977 to 2019. I find that productivity growth was far and away the most important factor in accounting for the growth of living standards which was substantial up to 2007; rising inequality prior to 2007 retarded the growth of living standards but not by much. Since 2007 productivity growth has collapsed as has also the growth of living standards. The fall in the latter has been mitigated a bit by a fall in inequality. |
| Keywords: | inequality, productivity, standard of living, welfare |
| JEL: | D31 E01 I31 O47 |
| Date: | 2022–03 |
| URL: | http://d.repec.org/n?u=RePEc:nsr:escoed:escoe-dp-2022-07&r= |
| By: | Janice Eberly; Jonathan Haskel; Paul Mizen |
| Abstract: | The impact of an economic shock depends both on its severity and the resilience of the economic response. Resilience can include the ability to relocate factors, for example, even when new technologies or skills are not yet at the ready. This resilience per se buffers production and has an economic value, which we estimate. The COVID-19 pandemic caused a widespread decline in recorded GDP. Yet, as catastrophic as the collapse was, it was buffered by an unprecedented and spontaneous deployment of what we call "Potential Capital," the dwelling/residential capital and connective technologies used alongside working from home. Together potential capital and labor working from home provided additional output margins and capacity. We estimate the contribution of this capital, and the remote work that it facilitated, to have roughly halved the decline in GDP in the US reducing the fall in GDP to 9.4 log points in 2020Q2 at the trough of the recession. Similar effects are seen in the 6 OECD countries for which data are available, output fell by 14 log points, but would have fallen by 26 log points had only workplace inputs been available. Accounting for the contribution of "Potential Capital" also revises downwards estimated total productivity gains in the business sector during the pandemic from 8 log points to 5 log points in 2020Q2. We also find an output elasticity of domestic non-dwellings capital to be similar to that of workplace ICT capital, reflecting its role as productive capital. Turning to the future, changes in working from home depend upon relative costs, relative technologies and, crucially, the elasticity of substitution between home and work tasks. We estimate that that elasticity to be more than unity, meaning that the growth of ICT will raise the share of work done remotely. |
| Keywords: | covid-19, productivity growth, working from home |
| JEL: | E01 E22 O47 |
| Date: | 2021–11 |
| URL: | http://d.repec.org/n?u=RePEc:nsr:escoed:escoe-dp-2021-15&r= |
| By: | Estrada, Ricardo; Lombardi, María |
| Abstract: | This paper studies the impact of high dismissal protection on bureaucratic turnover and productivity in the context of public school teachers in Chile. We take advantage of a law that required education administrators to grant a permanent contract to temporary teachers with a minimum seniority and implement a difference-in-differences strategy comparing eligible and ineligible teachers. We find that high dismissal protection reduces turnover by 25 percent in the first two years. The reduction is only statistically significant among teachers at the bottom and top of the distribution of baseline performance. We then examine the impact on teacher productivity and find a significant decline in the learning of students taught by teachers with low baseline performance. These findings are consistent with the hypothesis that high dismissal protection can be a double-edged sword. It can help to retain high-performing employees, but at the cost of making it more difficult to separate and motivate low-performing employees. |
| Keywords: | Desempleo, Docentes, Educación, Sector público, |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:dbl:dblwop:1915&r= |
| By: | Reinhilde Veugelers; Frederic Warzynski |
| Abstract: | This Working Paper is an output from the MICROPROD project, which received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 822390. Research and development is seen as a key contributor to growth because it generates knowledge, leading to new or improved products through product innovation, and makes firms more efficient at producing goods through process innovation. Firm level studies generally find evidence of strong positive... |
| Date: | 2022–06 |
| URL: | http://d.repec.org/n?u=RePEc:bre:wpaper:49021&r= |
| By: | Emily Gray; Darryl Jones |
| Abstract: | This report assesses Viet Nam’s agricultural sector through the lens of the OECD Agro-food Productivity-Sustainability-Resilience (PSR) Policy Framework. Agriculture has played an important role in Viet Nam’s remarkable economic growth over the past thirty years. In the 1990s, government policies contributed to strong agricultural productivity growth, but this has since fallen. OECD Agri-Environmental indicators also reveal weaknesses in the environmental footprint of growth, notably with respect to nutrient balances, as a result of the excessive use of agro-chemicals and poor animal waste management practices. The agricultural sector faces significant resilience challenges from climate change impacts, including sea level rises and more frequent and severe storm events. Although the level of agricultural support provided to farmers is relatively low, policies such as land use regulations are skewed in favour of rice production, thereby maintaining a production structure dominated by small part-time household farms that limit innovation. Viet Nam’s support for general services for agriculture (GSSE) was equivalent to 2.5% of agricultural value added in 2018-20, well below the OECD average. Shifting the focus of support towards research, development, and innovation partnerships with the private sector will contribute to improving the agri-environmental performance of agriculture in Viet Nam. This should ideally be accompanied by a reform of land use regulations. |
| Keywords: | Agricultural policies, Agricultural productivity, Environmental sustainability |
| JEL: | O13 O3 Q1 Q18 Q24 |
| Date: | 2022–06–22 |
| URL: | http://d.repec.org/n?u=RePEc:oec:agraaa:181-en&r= |
| By: | Ana Galvao |
| Abstract: | Different methodologies for computing total annual hours worked are adopted across G7 statistical offices to estimate productivity, and, therefore, we cannot be sure about international comparisons of productivity. This paper describes the design, implementation, and results of an online randomised controlled experiment to assess the impact of communicating this uncertainty in comparing productivity between the UK and the other G7 countries. The online survey results support the proposed communication tools as an effective way of conveying the uncertainty on the estimates of the international comparison of productivity for the UK public. They are effective even for respondents with limited knowledge of what productivity is. But communication tools are likely to be more helpful to members of the public that are familiar with the concept as they are better at making an inference based on the communicated data. |
| Keywords: | g7 countries, productivity measurement, randomised online experiment, uncertainty communication |
| JEL: | C92 E70 O47 |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:nsr:escoed:escoe-dp-2022-02&r= |
| By: | Victor H. Aguiar (University of Western Ontario); Nail Kashaev (University of Western Ontario); Roy Allen (University of Western Ontario) |
| Abstract: | This paper studies nonparametric identification and counterfactual bounds for heterogeneous firms that can be ranked in terms of productivity. Our approach works when quantities and prices are latent, rendering standard approaches inapplicable. Instead, we require observation of profits or other optimizing-values such as costs or revenues, and either prices or price proxies of flexibly chosen variables. We extend classical duality results for price-taking firms to a setup with discrete heterogeneity, endogeneity, and limited variation in possibly latent prices. Finally, we show that convergence results for nonparametric estimators may be directly converted to convergence results for production sets. |
| Keywords: | Counterfactual Bounds, Cost Minimization, Nonseparable Heterogeneity, Partial Identification, Profit Maximization, Production Set, Revenue Maximization, Shape Restrictions |
| JEL: | C5 D24 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:uwo:uwowop:20226&r= |
| By: | Mahdi Ghodsi; Branimir Jovanovic |
| Abstract: | This study investigates the determinants of FDI in Austria, as well as their spillovers to innovating technologies, productivity, and employment, using firm-level data, for the period 2008-2018. The findings point out that a decrease in the costs of trade increases investment in foreign-owned subsidiaries in Austria, and that FDI is pre-dominantly carried out in industries characterised by greater capital-intensity, higher wages, more agglomeration and regional concentration. Furthermore, FDI is higher in regions with a larger GDP and with a larger share of the population with upper secondary and post-secondary nontertiary education. The study also finds that there are positive spillovers of FDI to the domestic economy, which are strongest and most positive for innovative activities in environmental technologies. In other words, FDI helps Austrian firms to become more innovative in major environmental technologies. Such innovative efforts are best supported at the firm-level by supporting the total assets and investment of domestic firms, and at the regional level by increasing the share of the population with higher levels of education and employing more R&D personnel. The active presence of innovative foreign MNEs that enjoy extensive technological capacities, high-skilled labour, experienced management, and large-scale resources are also conducive to innovative activities. |
| Keywords: | FDI, Austria, spillovers, innovation, environmental technologies |
| JEL: | F21 F23 O30 Q55 |
| Date: | 2022–06 |
| URL: | http://d.repec.org/n?u=RePEc:wsr:ecbook:2022:i:viii-001&r= |
| By: | Bhattacharya, D.; Shvets, J.; ; |
| Abstract: | Does increasing diversity in university-intake require sacrificing academic performance, and if so, by how much? We develop an empirical framework to explore this trade-off ex-post, using admissions data matched with post-admission academic outcomes. We propose a simple, theoretical model of admissions for a university that values both future academic performance and diversity, and faces capacity-constraints. We show that the implicit weight on equity vis-a-vis expected future performance in the university's objective-function is captured by the ratio of inter-group difference in the admission-rate and that in the post-entry academic performance of marginal entrants. The problem of identifying marginal entrants can be mitigated using performance data for students admitted from waitlists, leading to bounds for the relative weights. These bounds (a) hold irrespective of whether researchers observe all applicant characteristics known to admission officers and (b) require no information about rejected candidates, who are typically not followed up. We apply this idea to admissions data from Cambridge, using scores on blindly-marked post-admission exams as the performance metric. In mathematical subjects, where female enrolment is relatively low, we and robust evidence that improving gender-balance requires significant performance sacrifice, and conclude an implicit weight of at least 10-20% on gender-equity in the university's objective function. There is no evidence of such trade-off in equally competitive non-mathematical subjects and, contrary to popular perception, for applicants' school-type. Our methods and results illustrate a formal way to quantify ex-post efficiency costs of diversity in a context where societal objective encompasses both equity and efficiency. |
| Keywords: | Affirmative action, Equity-efficiency trade-off, University Admission, Ex-post Evaluation, Marginal Admits, Waitlist Admission |
| JEL: | D61 J71 I23 I24 |
| Date: | 2022–06–15 |
| URL: | http://d.repec.org/n?u=RePEc:cam:camdae:2238&r= |
| By: | Kendra Asher; John Glaser; Peter B. Meyer; Jay Stewart; Jerin Varghese |
| Abstract: | BLS’s estimates of quarterly labor productivity, output per hour worked, are revised because of revisions to source data. Early estimates of hours worked and output are subject to substantial revisions for a variety of reasons. The BLS productivity program produces three regularly scheduled estimates of labor productivity growth: the preliminary estimate, the first revised estimate, and the second revised estimate. We consider revisions to the preliminary and first revised estimate relative to the second revised estimate. Our goal is to develop intervals to help data users better assess the size of these revisions. Most of the revisions result from regularly scheduled updates of source data. We analyze these revisions to get a better understanding of their sources and to determine whether there are any systematic patterns that could be exploited to construct intervals. We find no evidence of trends or systematic patterns that we could exploit. Most notably, the largest revisions to current and prior quarter output coincide with the BEA’s annual revision to GDP. We then consider three alternative methodologies for constructing intervals: modified confidence intervals, model-based intervals, and percentile-based intervals. We argue that the percentile based intervals are preferable, because they are less sensitive to outliers and therefore result in narrower intervals for a given level of statistical confidence. |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:bls:wpaper:538&r= |
| By: | Michael D. Giandrea; Robert J. Kornfeld; Peter B. Meyer; Susan G. Powers |
| Abstract: | The Bureau of Economic Analysis (BEA) and the Bureau of Labor Statistics (BLS) use estimates of depreciation rates for structures and equipment to construct estimates of capital stock from data on capital investments. The depreciation rates are based mainly on research by Hulten and Wykoff from the early 1980s, and may be out of date. Recent studies by Statistics Canada (2007 and 2015), using Canadian data on used asset transactions from Canada’s Annual Capital Expenditures and Repair Survey (CAPEX) of establishments, found relatively faster depreciation rates, especially for structures. A study by Bokhari and Geltner (2019) used U.S. data on used asset prices and also found faster depreciation rates for structures. To illustrate the potential effects of implementing these estimates from newer studies, we created a concordance to match Canadian to U.S. asset categories, and then re-estimated BEA capital stock measures and the BLS capital and multifactor productivity measures using depreciation rates based on the CAPEX survey. We find that using these faster depreciation rates results in substantially lower estimates of net capital stocks and higher estimates of depreciation in BEA’s accounts, and has minimal effects on growth rates of multifactor productivity (MFP) in the BLS accounts. |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:bls:wpaper:539&r= |