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on Business Economics |
| By: | Iacopo Varotto (BANCO DE ESPAÑA) |
| Abstract: | I study how firms’ defensive investments affect aggregate total factor productivity in a general-equilibrium model where incumbents invest both to raise productivity and to deter entry or imitation; entry occurs either by new firms into existing markets or by leading firms in entirely new product lines. Calibrating the model to US firm size, productivity, and market share distributions, I find that cracking down on defensive investments increases TFP by 1.9 percent, about three-quarters of which reflects higher technical efficiency, driven mainly by improved firm-level productivity. This gain is substantially offset by reduced product variety; absent this loss, the TFP effect would be more than four times as large. Profit taxes targeted at high-productivity leaders – those most prone to block imitation – can stimulate frontier innovation while limiting variety losses. Firm-level US evidence supports these mechanisms. |
| Keywords: | defensive investment, total factor productivity, firm dynamics, competition policy |
| JEL: | E22 D23 D43 L11 L13 L60 O33 O43 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:bde:wpaper:2601 |
| By: | Gert Bijnens (National Bank of Belgium, Research Department); Sam Desiere (Ghent University, Belgium); Rigas Oikonomou (IRES/LIDAM, UCLouvain, Belgium); Tiziano Toniolo (IRES/LIDAM, Uclouvain, Belgium); Bruno Van der Linden (IRES/LIDAM, UCLouvain, Belgium and IZA, CESifo, Germany) |
| Abstract: | Belgium’s 2016 payroll tax exemption for first-time employers triggered a sharp increase in firms hiring their first worker but little growth among larger firms. To account for this pattern, we develop and estimate a directed search model—with discrete hiring, firm heterogeneity, and endogenous entry— using Belgian microdata. The exemption reduces the high marginal cost of the first hire, enabling many previously non-hiring entrepreneurs to become employers, but most lack the productivity needed to expand beyond one worker. The model matches the post-reform size distribution and identifies the conditions under which size-dependent hiring subsidies can foster sustained firm growth. |
| Keywords: | payroll taxes; size-dependent policies; hiring frictions; wage subsidies; competitive search theory. |
| JEL: | H25 J08 J23 J38 L25 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:nbb:reswpp:202601-487 |
| By: | Yueran Ma; Mengdi Zhang; Kaspar Zimmermann |
| Abstract: | We collect new data to document the long-run evolution of the firm size distribution in ten market-based economies in Asia, Europe, North America, and Oceania, where we can obtain comprehensive coverage of the population of firms. Around the world, we observe prevalent increases in the concentration of sales, net income, and equity capital over the past century. These trends hold in the aggregate and at the industry level. Meanwhile, employment concentration has been stable over the long run in most cases. The evidence shows that the rising dominance of large firms is a pervasive phenomenon, not limited to the recent decades or the United States, and that large firms often achieve greater scale without proportionally more workers. |
| JEL: | E01 L1 N1 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34711 |
| By: | Iñaki Aldasoro; Leonardo Gambacorta; Rozalia Pal; Debora Revoltella; Christoph Weiss; Marcin Wolski |
| Abstract: | This paper provides new evidence on how the adoption of artificial intelligence (AI) affects productivity and employment in Europe. Using matched EIBIS-ORBIS data on more than 12, 000 non-financial firms in the European Union (EU) and United States (US), we instrument the adoption of AI by EU firms by assigning the adoption rates of US peers to isolate exogenous technological exposure. Our results show that AI adoption increases the level of labor productivity by 4%. Productivity gains are due to capital deepening, as we find no adverse effects on firm-level employment. This suggests that AI increases worker output rather than replacing labor in the short run, though longer-term effects remain uncertain. However, productivity benefits of AI adoption are unevenly distributed and concentrate in medium and large firms. Moreover, AI-adopting firms are more innovative and their workers earn higher wages. Our analysis also highlights the critical role of complementary investments in software and data or workforce training to fully unlock the productivity gains of AI adoption. |
| Keywords: | artificial intelligence, firm productivity, Europe, digital transformation |
| JEL: | D22 J24 L25 O33 O47 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:bis:biswps:1325 |
| By: | Arrighetti, Alessandro; Foresti, Giovanni; Fumagalli, Serena; Giusti, Sara; Lasagni, Andrea |
| Abstract: | This paper challenges the widespread assumption that migrant-owned firms inevitably suffer from persistent performance disadvantages due to structural liabilities. Using a matched-sample design based on firm-level administrative data for the period 2019–2023, we compare migrant- and native-owned enterprises across multiple performance dimensions, including value added, sales, total assets, and employment growth. While descriptive statistics confirm migrant-owned firms’ lower capital intensity and value added levels, our regression estimates reveal no evidence of a systematic performance disadvantage associated with the Liability of Foreignness (LoF). Moreover, when LoF and other liabilities (Liability of Newness, LoN, and Liability of Smallness, LoS) are jointly considered, interaction effects are either neutral or positive. In particular, young migrant firms (LoF × LoN) and micro-sized migrant firms (LoF × LoS) often outperform native-owned enterprises’ in growth indicators. These results seem to suggest that eventual disadvantages caused by the Liability of Foreignness can be offset by some strategic assets, such as transnational networks, flexibility, and adaptive capabilities, that usually characterized migrant-owned firms. The findings contribute to a more context-sensitive understanding of migrant entrepreneurship, with implications for both theory and policy. |
| Keywords: | Migrant entrepreneurship, Native firms, Liability of Foreignness, Liability of Newness, Liability of Smallness, Growth, Performance, Matched-pair Analysis |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:esprep:335547 |
| By: | Pierre Cahuc (ECON - Département d'économie (Sciences Po) - Sciences Po - Sciences Po - CNRS - Centre National de la Recherche Scientifique, IZA - Forschungsinstitut zur Zukunft der Arbeit - Institute of Labor Economics, CEPR - Center for Economic Policy Research); Marco G Palladino (Banque de France) |
| Abstract: | This paper provides a review of the existing literature on the effects of employment protection legislation (EPL) on job allocation across industries, firms, and workers, and its implications for innovation and economic growth. We analyze empirical studies to assess how EPL influences resource allocation, firm dynamics, and labor market segmentation. The review highlights the heterogeneous effects of EPL on different firms and workers' groups. Additionally, we discuss the channels identified in the structural literature through which EPL-induced job reallocation affects productivity, innovation, and overall growth. While existing evidence demonstrates the significant influence of EPL on all these outcomes, further quantification of these effects remains a research challenge. |
| Keywords: | innovation, productivity, economic growth, job allocation, job protection |
| Date: | 2024–12–10 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05446770 |
| By: | Patrick Kline |
| Abstract: | Economists often rely on estimates of linear fixed effects models developed by other teams of researchers. Assessing the uncertainty in these estimates can be challenging. I propose a form of sample splitting for network data that breaks two-way fixed effects estimates into statistically independent branches, each of which provides an unbiased estimate of the parameters of interest. These branches facilitate uncertainty quantification, moment estimation, and shrinkage. Algorithms are developed for efficiently extracting branches from large datasets. I illustrate these techniques using a benchmark dataset from Veneto, Italy that has been widely used to study firm wage effects. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.08101 |
| By: | Hui Chen; Yuhan Cheng; Yanchu Liu; Ke Tang |
| Abstract: | Structural economic models, while parsimonious and interpretable, often exhibit poor data fit and limited forecasting performance. Machine learning models, by contrast, offer substantial flexibility but are prone to overfitting and weak out-of-distribution generalization. We propose a theory-guided transfer learning framework that integrates structural restrictions from economic theory into machine learning models. The approach pre-trains a neural network on synthetic data generated by a structural model and then fine-tunes it using empirical data, allowing potentially misspecified economic restrictions to inform and regularize learning on empirical data. Applied to option pricing, our model substantially outperforms both structural and purely data-driven benchmarks, with especially large gains in small samples, under unstable market conditions, and when model misspecification is limited. Beyond performance, the framework provides diagnostics for improving structural models and introduces a new model-comparison metric based on data-model complementarity. |
| JEL: | C45 C52 G13 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34713 |