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on Efficiency and Productivity |
| By: | Andrea Stella |
| Abstract: | Using data on U.S. manufacturing plants, I estimate a production function model that includes agglomeration intensity as a component of total factor productivity and allows agglomeration benefits to vary across establishments, which can lead to sorting. I find that agglomeration benefits decline with unobserved establishment-level raw productivity. |
| Keywords: | Agglomeration; Sorting; Census of Manufactures |
| JEL: | D22 D24 E24 L11 R11 R32 |
| Date: | 2025–04–23 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:2025-31 |
| By: | Yusuke ADACHI; Hikaru OGAWA; Masafumi TSUBUKU |
| Abstract: | This paper investigates the impact of the exit of regionally dominant establishments on the productivity of the remaining local firms. Using establishment-level data from the Japanese manufacturing sector, we estimate the net effect of these exits through a difference-in-differences analysis, focusing on the top 1% of establishments that exited between 1999 and 2010. Our findings indicate a significant negative effect on regional productivity: exits reduced the productivity of the remaining establishments by about 1% within five years post-exit and by 0.7-0.8% within ten years. However, most of these declines can be attributed to the decreased demand associated with exits. Controlling for the factor of change in demand associated with exits, the exits themselves have little impact on total factor productivity (TFP) in either the short or long term. These results suggest that the legacy of large establishments in improving local TFP largely persists even after their exit. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:eti:dpaper:25039 |
| By: | Linarez, Misael |
| Abstract: | This paper critiques the conventional interpretation of Total Factor Productivity (TFP) as a residual in macroeconomic growth models, particularly in the Solow framework. Drawing on modern econometric techniques—including fixed effects models, LASSO regularization, and causal forests—I propose a redefinition of TFP as a conditional outcome influenced by institutional, structural, and cultural variables. The analysis reveals that institutional quality significantly shapes productivity, but with heterogeneous and nonlinear effects across countries and income groups. By replacing residual-based assumptions with a context-aware framework, this paper offers a path forward for more precise and policy-relevant productivity modeling. |
| Date: | 2025–04–13 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:2vfqc_v1 |
| By: | Kristina McElheran; Mu-Jeung Yang; Zachary Kroff; Erik Brynjolfsson |
| Abstract: | We examine the prevalence and productivity dynamics of artificial intelligence (AI) in American manufacturing. Working with the Census Bureau to collect detailed large-scale data for 2017 and 2021, we focus on AI-related technologies with industrial applications. We find causal evidence of J-curve-shaped returns, where short-term performance losses precede longer-term gains. Consistent with costly adjustment taking place within core production processes, industrial AI use increases work-in-progress inventory, investment in industrial robots, and labor shedding, while harming productivity and profitability in the short run. These losses are unevenly distributed, concentrating among older businesses while being mitigated by growth-oriented business strategies and within-firm spillovers. Dynamics, however, matter: earlier (pre-2017) adopters exhibit stronger growth over time, conditional on survival. Notably, among older establishments, abandonment of structured production-management practices accounts for roughly one-third of these losses, revealing a specific channel through which intangible factors shape AI’s impact. Taken together, these results provide novel evidence on the microfoundations of technology J-curves, identifying mechanisms and illuminating how and why they differ across firm types. These findings extend our understanding of modern General Purpose Technologies, explaining why their economic impact—exemplified here by AI—may initially disappoint, particularly in contexts dominated by older, established firms. |
| Keywords: | Artificial Intelligence, General Purpose Technology, Manufacturing, Organizational Change, Productivity, Management Practices |
| JEL: | D24 O33 M11 L60 |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:cen:wpaper:25-27 |
| By: | V.V. Chari; Vladimir Smirnyagin |
| Abstract: | Researchers estimate sector-specific (at NAICS 3-digit level for manufacturing plants) production functions including capital, labor, materials, and, importantly, a "dirty" factor. To accomplish this task, they bring in facility-level chemical releases data from the EPA's Toxic Releases Inventory and fuzzy-match it with the Census of Manufactures and the Annual Survey of Manufactures based on facilities’ names and addresses. Two sets of production function estimates are constructed and compared. |
| Keywords: | SSEL, CMF, ASM, LBD |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:cen:tnotes:25-08 |
| By: | Sheila Jiang; Alessandro Rebucci; Gang Zhang |
| Abstract: | We develop and estimate a new model of endogenous growth in bank efficiency and firm productivity in which banks adopt technology embedded in capital goods produced by entrepreneurs, and agents choose whether to become workers or capital-good-producing entrepreneurs. In this framework, bank efficiency influences firm productivity by affecting agents' occupational choices, while firm productivity affects bank efficiency through the relative price of capital goods. We find that increasing technology adoption in the banking system to the level in the top half of the distribution in the data accelerates the economy's long-term growth from 2% to 2.17%. We also find that empirical evidence based on U.S. bank, metropolitan, and state-level data is consistent with the critical mechanisms of our model. |
| JEL: | G21 O3 O4 |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33551 |
| By: | Fabio Farella (University of Bari) |
| Abstract: | This paper examines the evolution of inequality of opportunity (EIOp) and the efficiency of public spending in tertiary education across 31 countries using European Social Survey data for 2010, 2018, and 2023. EIOp is estimated using the conditional inference forests (CIFs) algorithm, with model accuracy assessed using the area under the receiver operating characteristic curve (AUC-ROC). A two-stage Data Envelopment Analysis (DEA) assesses the efficiency of public spending in tertiary education. The findings indicate a general post-COVID-19 decline in EIOp, while cross-country disparities persist. Moreover, for the overall country sample, it would be theoretically possible to reduce the Dissimilarity index of inequality of opportunity, adjusted for the share of tertiary graduates, by 31% in 2010 and 22% in 2018 with current resource levels. A Tobit regression explores exogenous factors associated with efficiency scores. The results are robust to a set of sensitivity analyses and offer a benchmark for policymakers seeking to enhance both fairness and efficiency in higher education. |
| Keywords: | inequality of opportunity; tertiary education; machine learning; two-stage DEA; public spending |
| JEL: | C14 D63 H52 I24 |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:bai:series:series_wp_02-2025 |
| By: | Douglas Gollin; David Lagakos; Xiao Ma; Shraddha Mandi |
| Abstract: | This paper draws on cross-country census data to study how agricultural productivity gaps have evolved over the last four decades. We find little tendency for gaps to decline on average despite global decreases in agricultural employment shares. We analyze the dynamics of agricultural productivity gaps through the lens of an open-economy model of structural change. We calibrate the model using international trade data, which are measured independently from sectoral value added and employment data. Quantitatively, the model predicts that relatively faster physical productivity growth in the non-agricultural sector has, in many countries, offset the movement of labor out of agriculture, leading to persistently lower value added per worker in agriculture. Consistent with the model's predictions, previous exports by sector are strong predictors of agricultural productivity gaps in the current cross-section of countries. |
| JEL: | E01 F11 O11 O41 |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33529 |
| By: | Naomi Hausman; Oren Rigbi; Sarit Weisburd |
| Abstract: | Student use of Artificial Intelligence (AI) in higher education is reshaping learning and redefining the skills of future workers. Using student-course data from a top Israeli university, we examine the impact of generative AI tools on academic performance. Comparisons across more and less AI-compatible courses before and after ChatGPT’s introduction show that AI availability raises grades, especially for lower-performing students, and compresses the grade distribution, eroding the signal value of grades for employers. Evidence suggests gains in AI-specific human capital but possible losses in traditional human capital, highlighting benefits and costs AI may impose on future workforce productivity. |
| Keywords: | generative AI, student achievement, worker productivity, higher education, human capital. |
| JEL: | I23 J24 O33 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11843 |
| By: | Wei Jiang; Junyoung Park; Rachel (Jiqiu) Xiao; Shen Zhang |
| Abstract: | This study investigates how occupational AI exposure impacts employment at the intensive margin, i.e., the length of workdays and the allocation of time between work and leisure. Drawing on individual-level time diary data from 2004–2023, we find that higher AI exposure—whether stemming from the ChatGPT shock or broader AI evolution—is associated with longer work hours and reduced leisure time, primarily due to AI complementing human labor rather than replacing it. This effect is particularly pronounced in contexts where AI significantly enhances marginal productivity and monitoring efficiency. It is further amplified in competitive labor and product markets, where workers have limited bargaining power to retain the benefits of productivity gains, which are often captured by consumers or firms instead. The findings question the expectation that technological advancements alleviate human labor burdens, revealing instead a paradox where such progresses compromise work-life balance. |
| JEL: | G3 J2 O3 |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33536 |
| By: | Kristijan Kozheski (Ss. Cyril and Methodius University in Skopje, Faculty of Economics – Skopje); Trajko Slaveski (Ss. Cyril and Methodius University in Skopje, Faculty of Economics – Skopje); Predrag Trpeski (Ss. Cyril and Methodius University in Skopje, Faculty of Economics – Skopje); Borce Trenovski (Ss. Cyril and Methodius University in Skopje, Faculty of Economics – Skopje) |
| Abstract: | This study examines the determinants of labour productivity in the Republic of North Macedonia, with a particular emphasis on key macroeconomic variables such as gross investment, employment, workers' compensation, inflation, gross national income per capita, and human capital. Labour productivity is recognized as a pivotal indicator of labour market efficiency, and worker welfare, and a crucial driver of sustainable economic growth. Despite improvements in employment levels and reductions in unemployment, labour productivity in North Macedonia remains suboptimal, exhibiting stagnation and insufficient growth, especially when contrasted with increasing wages. Through the application of both correlation and regression analyses, this paper explores the strength and causal relationships between labour productivity and macroeconomic variables, highlighting their role in shaping national competitiveness and economic development. The findings align with both theoretical and empirical literature, reinforcing the significance of human capital, gross investment, and overall economic performance in driving productivity improvements. This study contributes to the discourse on structural challenges within North Macedonia's labour market and provides a basis for policy interventions aimed at fostering sustainable productivity growth and enhancing international competitiveness. |
| Keywords: | Labour market, Productivity of labour, Determinants |
| JEL: | J11 J20 J24 |
| Date: | 2024–12–15 |
| URL: | https://d.repec.org/n?u=RePEc:aoh:conpro:2024:i:5:p:174-190 |
| By: | Muhammad Zakir Abdullah ("School of Economics, Finance and Banking, College of Business, Universiti Utara Malaysia, 06010, Sintok, Kedah, Malaysia" Author-2-Name: Shri Dewi Applanaidu Author-2-Workplace-Name: "School of Economics, Finance and Banking, College of Business, Universiti Utara Malaysia, 06010, Sintok, Kedah, Malaysia." Author-3-Name: Kirttana Kalimuthu Author-3-Workplace-Name: "School of Economics, Finance and Banking, College of Business, Universiti Utara Malaysia, 06010, Sintok, Kedah, Malaysia." Author-4-Name: Author-4-Workplace-Name: Author-5-Name: Author-5-Workplace-Name: Author-6-Name: Author-6-Workplace-Name: Author-7-Name: Author-7-Workplace-Name: Author-8-Name: Author-8-Workplace-Name:) |
| Abstract: | " Objective - This study simulates paddy productivity across Malaysia's granary areas over a 10-year period, with a focus on the non-linear effects of climate change, particularly rainfall and temperature variability. This study examines how each granary area evolves and reaches its optimal point as climate variability risks increase over time. Methodology/Technique - Using a Markov Chain Monte Carlo (MCMC) approach, the analysis estimates the impact of these climate factors on paddy yields. The findings reveal that rainfall has a positive effect on productivity in areas with low rainfall, such as IADA BLS, IADA PP, and MADA, while excessive rainfall has a detrimental, non-linear impact across all regions. Temperature variability has mixed effects, enhancing productivity in IADA PP and IADA KETARA but negatively affecting areas such as IADA MADA and IADA SEM. Findings - A key finding from the simulation is that each granary area reaches its optimal productivity at different times. IADA PP is projected to achieve the highest yield (6.47 tonnes/hectare) in the 10th year, whereas IADA KER is expected to reach the lowest maximum productivity (5.45 tonnes/hectare) in the 5th year. Notably, IADA BLS and IADA KER achieve peak productivity within just 5 years, faster than other regions. Novelty - IADA KEM exhibits the largest improvement, with a 58.7% increase in productivity over a 10-year period, despite its vulnerability to climate variability. These findings highlight the diverse impacts of climate change on paddy yields and the need for region-specific adaptive strategies. Type of Paper - Empirical" |
| Keywords: | Climate change, Granary areas, Markov Chain Monte Carlo, Paddy Productivity. |
| JEL: | Q51 Q54 |
| Date: | 2025–03–31 |
| URL: | https://d.repec.org/n?u=RePEc:gtr:gatrjs:afr238 |