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on Business Economics |
| By: | Bagger, Jesper (University of Edinburgh); Elholm, Malthe (Aarhus University); Maibom, Jonas (Aarhus University); Vejlin, Rune (Aarhus University) |
| Abstract: | Using 1980--2019 Danish matched employer-employee data, we unpack the rise in wage sorting - the correlation between worker and firm wage fixed effects (Abowd et al., 1999) - from 0.06 to 0.18. The rise is driven entirely by reallocation of employment from persistently low-sorting to persistently high-sorting firms, with the average sorting contribution of any given firm remaining stable over time. A decomposition shows that 60 % reflects reallocation among surviving firms and 40 % firm turnover through entry and exit. Regression analysis identifies firm entry and exit and industry reallocation as the dominant firm-side drivers, and rising educational attainment as the key worker-side factor - reflecting concentration of educated workers in high-sorting firms rather than a systematic tendency of educated workers to form high-sorting matches across all employers. Event studies establish direct job-to-job moves as the primary mechanism through which reallocation is implemented at the worker-level. |
| Keywords: | wage inequality, wage sorting, firm dynamics, employment reallocation, job-to-job mobility, matched employer-employee data |
| JEL: | E24 J21 J31 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18502 |
| By: | Wagner, Joachim (Leuphana University Lüneburg) |
| Abstract: | The use of advanced technologies like artificial intelligence, robotics, or smart devices will go hand in hand with, among others, higher productivity, higher product quality, more exports and better chances to survive any crisis. Better firms tend to use advanced technologies. Information on firm level determinants of adoption of these technologies, therefore, is important to inform industrial policies. This paper uses firm level data for manufacturing enterprises from 38 countries collected in 2025 to shed further light on this issue by investigating the link between the use of advanced technologies and firm characteristics. Applying a new machine-learning estimator, Kernel-Regularized Least Squares (KRLS), which does not impose any restrictive assumptions for the functional form of the relation between use of advanced technologies, firm characteristics and any control variables, we find that firms which use advanced technologies tend to be larger and more innovation orientated, while firm age does not matter. |
| Keywords: | advanced technologies, firm characteristics, Flash Eurobarometer 559, kernel-regularized least squares (KRLS) |
| JEL: | D22 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18499 |
| By: | Wei Cai; Andrea Prat; Jiehang Yu |
| Abstract: | Prior research has pointed to differences in organizational capital as a reason for the persistent performance discrepancies among otherwise similar firms. In this paper, we develop and validate a new measure of organizational capital. Based on over a million crowd-sourced employee reviews scraped from Glassdoor, we construct the measure of organizational capital at the firm-year level using the word embedding model and ChatGPT-generated synthetic reviews. Our measure varies over time in accordance with macro trends, and differs both across and within firms, reflecting firm heterogeneity and major internal changes. We validate our measure by testing empirical predictions of the properties of organizational capital discussed in prior literature. Our findings suggest that this measure captures a slowly evolving intangible asset that is significantly associated with firm performance and top management’s influence, aligning with the conceptualization of organizational capital by Dessein and Prat (2022). We further showcase applications of our measure in accounting, economics, finance, and management literature. Taken together, the paper provides implications for various stakeholders who are interested in assessing and managing firms’ organizational capital. |
| JEL: | D22 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35039 |
| By: | El-Haddad, Amirah (German Institute of Development and Sustainability (IDOS)); Krafft, Caroline (University of Minnesota); Selwaness, Irene (Faculty of Economics and Political Science, Cairo University); Assaad, Ragui (University of Minnesota) |
| Abstract: | This paper investigates the determinants and dynamics of labour demand and specifically informal labour in Egypt’s manufacturing sector, using nationally representative firm-level data. We analyse the determinants of total employment, the share of informal labour, and its average annual change over the firm life cycle. Three key findings emerge. First, employment is positively associated with capital, exporting, innovation, industrial zones, worker training, and managerial education, and negatively associated with sole proprietorships, wages, and total factor productivity. Second, informal employment is more common among private sector firms, sole proprietorships, and firms using more part-time workers, and less prevalent among firms adopting technology or led by more educated managers. Third, although most formal firms exhibit no change in the share of informal workers, formal firms that did not initially employ informal labour tend to increase their informal share, while firms that formalised continue to rely heavily on informal employment. Together, these findings underscore the persistence of informality and limited transitions toward full formalisation within Egypt’s formal manufacturing sector. |
| Keywords: | Manufacturing, labour demand, informality, Egypt |
| JEL: | J23 L6 L11 O17 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18500 |
| By: | Hanming Fang; Xian Gu; Hanyin Yan; Wu Zhu |
| Abstract: | We develop a high-precision classifier to measure artificial intelligence (AI) patents by fine-tuning PatentSBERTa on manually labeled data from the USPTO’s AI Patent Dataset. Our classifier substantially improves the existing USPTO approach, achieving 97.0% precision, 91.3% recall, and a 94.0% F1 score, and it generalizes well to Chinese patents based on citation and lexical validation. Applying it to granted U.S. patents (1976–2023) and Chinese patents (2010–2023), we document rapid growth in AI patenting in both countries and broad convergence in AI patenting intensity and subfield composition, even as China surpasses the United States in recent annual patent counts. The organization of AI innovation nevertheless differs sharply: U.S. AI patenting is concentrated among large private incumbents and established hubs, whereas Chinese AI patenting is more geographically diffuse and institutionally diverse, with larger roles for universities and state-owned enterprises. For listed firms, AI patents command a robust market-value premium in both countries. Cross-border citations show continued technological interdependence rather than decoupling, with Chinese AI inventors relying more heavily on U.S. frontier knowledge than vice versa. |
| JEL: | C55 G14 O31 O33 O34 O57 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35022 |
| By: | Joshua Ostry (Geneva Graduate Institute and CEPR) |
| Abstract: | This paper constructs a high-frequency, news-based measure of rare earth supply shocks to examine how disruptions in these critical inputs affect global firm valuations. Using news articles between 2021 and 2025, I identify exogenous rare earth supply events, distinguishing between Chinese trade-restriction and global production shocks. Using a sample of 5800 public firms, I show that negative rare earth supply shocks, which are expected to raise input prices, cause significant and persistent declines in the equity prices of rare earth-exposed firms, especially those in the battery, semiconductor, and motor vehicle industries. Both trade and production shocks depress valuations, though trade restrictions shocks are particularly impactful. These findings highlight a financial channel through which the weaponization of critical-material supply chains transmits across global markets. |
| Keywords: | Rare earth elements; critical minerals; supply chains; supply shocks; large language models; geoeconomics; financial markets |
| JEL: | G14 F14 F51 Q02 |
| Date: | 2026–04–09 |
| URL: | https://d.repec.org/n?u=RePEc:gii:giihei:heidwp10-2026 |
| By: | Vanessa Alviarez; Cheng Chen; Nitya Pandalai-Nayar; Liliana Varela; Kei-Mu Yi; Hongyong Zhang |
| Abstract: | We study how multinational corporations (MNCs) shape firm-level and aggregate structural transformation. Using confidential microdata from Japan and exploiting a quasi-exogenous reform that expanded foreign investment opportunities in China, we assess empirically how this reform affected employment at firms in both the host country (China) and the home country (Japan). In liberalized industries, Japanese manufacturing affiliates in China expanded employment, while parent firms in Japan shifted out of manufacturing and into higher-value service activities, including R&D. To assess the broader relevance of this mechanism, we use microdata from several advanced and middle-income economies, and show that MNCs account for the majority of the middle-income countries' reallocation to manufacturing. |
| Keywords: | multinational firms; manufacturing employment; services employment; foreign direct investment liberalization |
| JEL: | F23 F60 |
| Date: | 2026–03–30 |
| URL: | https://d.repec.org/n?u=RePEc:fip:feddwp:102970 |
| By: | Jamel Saadaoui; Vanessa Strauss-Kahn; Jerome Creel |
| Abstract: | This paper investigates how geopolitical relationships shape Chinese exports, asking whether exporters systematically favor politically aligned countries - and whether that preference holds during periods of geopolitical turbulence. We leverage a unique high-frequency panel of over 17 million monthly firm-product-destination transactions from Chinese Customs (2000-2006), matched with the Political Relationship Index (𠑃𠑅ð ¼) developed by Tsinghua University, which captures monthly bilateral diplomatic relations from a Chinese perspective. Unlike most studies on geopolitics and trade, we move beyond the typical Western-centric lens of geopolitical risk and focus on export-side behavior. Our empirical strategy is robust: it combines rich fixed effects (firm-product, destination, time), sectorial tariff controls, and interactions with indicators of extreme positive and negative diplomatic events. Our results consistently show that stronger political alignment increases Chinese firms’ exports in both value and quantity. We also find evidence of non-linearity and asymmetric responses: exporters react more strongly to diplomatic improvements than to deteriorations. Using extreme geopolitical events, we show that positive events amplify the export response to political alignment, while negative events tend to dampen it. The patterns are strongest for foreign-invested firms and for differentiated products, suggesting that geopolitical alignment plays a critical role in global value chain dynamics. These findings contribute to understanding how firms incorporate political signals into trade decisions. In a world of growing political fragmentation, "friendtrading" is not just a policy discourse - it is reflected in the strategic behavior of exporters, even in the absence of formal sanctions. |
| Keywords: | international trade, firms' export, geopolitics, countries alignment |
| JEL: | F14 F13 F51 F23 D74 L25 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:een:camaaa:2026-22 |
| By: | Luke Heeney; Christopher R. Knittel; Jasdeep Mandia |
| Abstract: | In many modern industries, firms compete in differentiated-product markets while relying on complex global value chains for intermediate inputs. In such settings, trade policies such as tariffs on vehicles and parts operate not only through consumer substitution and firm pricing, but also through firms’ cost structures and sourcing decisions. We develop a structural model of the U.S. automobile market that integrates random-coefficients demand, multiproduct firm pricing, and a flexible supply-side framework in which shocks to the cost of imported parts transmit imperfectly into manufacturers’ marginal costs. The model is disciplined by novel model-level data on imported-parts exposure and exploits exchange-rate variation to identify cost pass-through. Our counterfactual analysis quantifies the effects of alternative tariff policies on prices, profits, and welfare. First, tariffs on imported vehicles alone reallocate demand toward domestically assembled products and increase U.S. producer surplus, generating a gain of approximately $1 billion for U.S.-headquartered firms, while reducing consumer surplus by about $14 billion. Second, extending tariffs to imported intermediate inputs fundamentally alters these effects: consumer surplus losses roughly double, and producer surplus for U.S.-headquartered firms declines by about $2.6 billion. These aggregate effects mask substantial heterogeneity: firms with greater exposure to imported parts experience losses, whereas those relying more on domestic inputs are better able to increase profits. Overall, the results show that tariff incidence depends critically on firms’ exposure to global value chains and cannot be inferred from final assembly locations alone. |
| JEL: | F13 L13 L62 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35023 |
| By: | Borjas, George (Harvard University) |
| Abstract: | The H-1B program lets firms hire high-skill foreign workers for a six-year term. The annual number of visas allocated to for-profit firms is capped at 85, 000 and there is excess demand for those visas. The analysis merges administrative data, including the I-129 petitions that report the wage offer made to specific H-1B beneficiaries, with the American Community Surveys. On average, H-1B workers earn 15 percent less than comparable natives, suggesting that firms may be willing to pay a one-time fee to obtain the visas. The data are examined using a labor demand model to simulate how a fee alters the hiring decision. For moderate levels of excess demand, the revenue maximizing fee ranges from $97, 000 to $154, 000 after allowing for unobserved productivity gains or costs associated with an H-1B hire, and for wage growth and job turnover in the H-1B workforce. The fee also changes the skill composition of that workforce, making it more skilled. |
| Keywords: | high-skill immigration, H-1B program, visa fees |
| JEL: | J08 J18 J69 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18487 |
| By: | Manuel Arellano; Orazio Attanasio; Margherita Borella; Mariacristina De Nardi; Gonzalo Paz-Pardo |
| Abstract: | We develop a new approach to estimating earnings, job, and employment dynamics using subjective expectations data from the NY Fed Survey of Consumer Expectations. These data provide beliefs about future earnings offers and acceptance probabilities, offering direct information on counterfactual outcomes and enabling identification under weaker assumptions. Our framework avoids biases from selection and unobserved heterogeneity that affect models using realized outcomes. First-step fixed-effects regressions identify risk, persistence, and transition effects; second-step GMM recovers the covariance structure of unobserved heterogeneities such as ability, mobility, and match quality. We find lower risk and persistence of the individual productivity component than in prior work, but greater heterogeneity in ability and match quality. Simulations show that reduced-form estimates overstate persistence and volatility on individual-level productivity due to job transitions and sorting. After accounting for heterogeneity, volatility declines and becomes flat across the earnings distribution. These results underscore the value of expectations data. |
| JEL: | C23 C8 D15 J01 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35027 |
| By: | Rentaro Utamaru |
| Abstract: | Production function estimates underpin the measurement of firm-level markups, allocative efficiency, and the productivity effects of policy interventions. Since Olley and Pakes (1996), every major proxy variable estimator has identified the production function through a first-order Markov assumption on unobserved productivity; I show that misspecification of this assumption generates persistent upward bias in the materials elasticity that propagates into overestimated markups and inflated treatment effects. I replace the Markov restriction with conditional independence across three intermediate input demands, a static condition grounded in input market segmentation, and establish nonparametric identification from a single cross-section. I develop a GMM estimator and establish consistency and asymptotic normality. Monte Carlo simulations confirm that the proposed estimator is unbiased across Markov and non-Markov environments, while the standard estimator exhibits persistent bias of up to 63 percent of the true materials elasticity. In 502 Japanese manufacturing industries, the proposed method yields systematically lower markups than the standard method across the entire distribution (median 0.93 vs. 1.03), reducing the share of industries with markups above unity from 54 to 37 percent. In a difference-in-differences analysis of the 2011 Tohoku earthquake, the standard method overstates the productivity loss by 0.40 percentage points, roughly $3.6 billion (400 billion yen) per year. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.04458 |
| By: | Autor, David (MIT); Chin, Caroline (MIT); Salomons, Anna (Tilburg University and Utrecht University); Seegmiller, Bryan (Northwestern University) |
| Abstract: | We study the role of expertise in new work—novel occupational roles that emerge as technological and economic conditions evolve—using newly available 1940 and 1950 Census Complete Count files and confidential American Community Survey data from 2011–2023. We show that new work is systematically distinct from simply more work in existing occupations in four respects. First, it attracts workers with distinct characteristics: new work is disproportionately performed by younger and more educated workers, even within detailed occupation-industry cells. Second, new work commands wage premiums that persist beyond workers’ initial entry into new work, consistent with returns to scarce, specialized expertise rather than temporary market disequilibrium. Third, these premiums decline across vintages as expertise diffuses, with ‘newer’ new work commanding larger premiums. Fourth, the emergence of new work can be traced to regional demand shocks, suggesting that expertise formation responds to economic opportunities. These findings suggest that new work is a countervailing force to automation-driven job displacement not merely by creating additional employment, butby generating new domains of human expertise that command market premiums. |
| Keywords: | new work, technological change, occupations, tasks |
| JEL: | E24 J11 J23 J24 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18504 |