Pub Date : 2025-12-17DOI: 10.1016/j.jmoneco.2025.103880
Nils H. Lehr , Pascual Restrepo
Leading AI firms claim to prioritize social welfare. How should firms with a social mandate price and deploy AI? We derive pricing formulas that depart from profit maximization by incorporating incentives to improve welfare and reduce labor disruptions. Using US data, we evaluate several scenarios. A welfarist firm that values both profit and welfare should price closer to marginal cost, as efficiency gains outweigh distributional concerns. A conservative firm focused on labor-market stability should price above the profit-maximizing level in the short run, especially when its AI may displace low-income workers. Overall, socially minded firms face a trade-off between expanding access to AI and the resulting loss in profits and labor market risks.
{"title":"The price of intelligence: How should socially-minded firms price and deploy AI?","authors":"Nils H. Lehr , Pascual Restrepo","doi":"10.1016/j.jmoneco.2025.103880","DOIUrl":"10.1016/j.jmoneco.2025.103880","url":null,"abstract":"<div><div>Leading AI firms claim to prioritize social welfare. <em>How should firms with a social mandate price and deploy AI?</em> We derive pricing formulas that depart from profit maximization by incorporating incentives to improve welfare and reduce labor disruptions. Using US data, we evaluate several scenarios. A <em>welfarist firm</em> that values both profit and welfare should price closer to marginal cost, as efficiency gains outweigh distributional concerns. A <em>conservative firm</em> focused on labor-market stability should price above the profit-maximizing level in the short run, especially when its AI may displace low-income workers. Overall, socially minded firms face a trade-off between expanding access to AI and the resulting loss in profits and labor market risks.</div></div>","PeriodicalId":48407,"journal":{"name":"Journal of Monetary Economics","volume":"157 ","pages":"Article 103880"},"PeriodicalIF":4.1,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-17DOI: 10.1016/j.jmoneco.2025.103882
Olga Goldfayn-Frank , Pascal Kieren , Stefan Trautmann
Economists widely rely on measures of inflation expectations and uncertainty elicited via density forecasts. This approach, which asks respondents to assign probabilities to pre-specified ranges, has proven highly informative, but also faced criticism in recent periods of elevated and volatile inflation. We propose a new method to elicit the full distribution of inflation expectations, which is rooted in decision theory and can be implemented in standard surveys. In two large surveys and a laboratory experiment, we demonstrate that the proposed method leads to well-defined expectations that fulfil both subjective and objective quality criteria. The method is neither perceived as more difficult nor does it take more time to complete compared to the current standard. In contrast to density forecasts, the method is robust to differences in the state of the economy and thus allows comparisons across time and across countries. The method is portable and can be applied to elicit different macroeconomic expectations.
{"title":"A choice-based approach to the measurement of inflation expectations","authors":"Olga Goldfayn-Frank , Pascal Kieren , Stefan Trautmann","doi":"10.1016/j.jmoneco.2025.103882","DOIUrl":"10.1016/j.jmoneco.2025.103882","url":null,"abstract":"<div><div>Economists widely rely on measures of inflation expectations and uncertainty elicited via density forecasts. This approach, which asks respondents to assign probabilities to pre-specified ranges, has proven highly informative, but also faced criticism in recent periods of elevated and volatile inflation. We propose a new method to elicit the full distribution of inflation expectations, which is rooted in decision theory and can be implemented in standard surveys. In two large surveys and a laboratory experiment, we demonstrate that the proposed method leads to well-defined expectations that fulfil both subjective and objective quality criteria. The method is neither perceived as more difficult nor does it take more time to complete compared to the current standard. In contrast to density forecasts, the method is robust to differences in the state of the economy and thus allows comparisons across time and across countries. The method is portable and can be applied to elicit different macroeconomic expectations.</div></div>","PeriodicalId":48407,"journal":{"name":"Journal of Monetary Economics","volume":"157 ","pages":"Article 103882"},"PeriodicalIF":4.1,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-16DOI: 10.1016/j.jmoneco.2025.103881
Ming Zeng , Guihai Zhao
This paper develops a noisy-information equilibrium model to study how subjective expectations shape the joint dynamics of equity and bond yields. In our framework, movements in asset yields are driven by subjective expectations of dividend and GDP growth, rather than time-varying risk premia. A dual-component dividend structure, together with belief distortions, generates key asset-pricing facts: short-term equity yields are more volatile than long-term yields because short-run dividend growth expectations mean-revert to their stable long-run counterpart; the equity yield slope is procyclical due to countercyclical term structure of expected dividend growth; and the bond-stock correlation changes from positive to negative after the late 1990s, reflecting a shift in the correlation between expected GDP and dividend growth. The model also implies predictable dividend strip returns, with predictability declining with maturity due to dividend forecast revisions, and it successfully replicates the observed dynamics of equity yields and some aggregate moments.
{"title":"Expectation-driven term structure of equity and bond yields","authors":"Ming Zeng , Guihai Zhao","doi":"10.1016/j.jmoneco.2025.103881","DOIUrl":"10.1016/j.jmoneco.2025.103881","url":null,"abstract":"<div><div>This paper develops a noisy-information equilibrium model to study how subjective expectations shape the joint dynamics of equity and bond yields. In our framework, movements in asset yields are driven by subjective expectations of dividend and GDP growth, rather than time-varying risk premia. A dual-component dividend structure, together with belief distortions, generates key asset-pricing facts: short-term equity yields are more volatile than long-term yields because short-run dividend growth expectations mean-revert to their stable long-run counterpart; the equity yield slope is procyclical due to countercyclical term structure of expected dividend growth; and the bond-stock correlation changes from positive to negative after the late 1990s, reflecting a shift in the correlation between expected GDP and dividend growth. The model also implies predictable dividend strip returns, with predictability declining with maturity due to dividend forecast revisions, and it successfully replicates the observed dynamics of equity yields and some aggregate moments.</div></div>","PeriodicalId":48407,"journal":{"name":"Journal of Monetary Economics","volume":"157 ","pages":"Article 103881"},"PeriodicalIF":4.1,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-12DOI: 10.1016/j.jmoneco.2025.103877
Boyan Jovanovic , Peter L. Rousseau
We model several ways in which AI may improve decisions, raise the productivity of firms, and raise human capital growth. Each focuses on activities that involve problem solving, with solutions being guided by signals. If AI raises the accuracy of the signals, humans will then make better decisions — individually and in groups.
{"title":"AI and task efficiency","authors":"Boyan Jovanovic , Peter L. Rousseau","doi":"10.1016/j.jmoneco.2025.103877","DOIUrl":"10.1016/j.jmoneco.2025.103877","url":null,"abstract":"<div><div>We model several ways in which AI may improve decisions, raise the productivity of firms, and raise human capital growth. Each focuses on activities that involve problem solving, with solutions being guided by signals. If AI raises the accuracy of the signals, humans will then make better decisions — individually and in groups.</div></div>","PeriodicalId":48407,"journal":{"name":"Journal of Monetary Economics","volume":"157 ","pages":"Article 103877"},"PeriodicalIF":4.1,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-11DOI: 10.1016/j.jmoneco.2025.103878
Roxana Mihet , Kumar Rishabh , Orlando Gomes
The technology revolution is transforming firm and industry dynamics, yet the roots of firm dominance in the modern economy remain unclear. Is industry dynamism driven by compute capabilities (AI), access to data, or the interaction between them? We develop a dynamic model in which firms gain knowledge from raw data using AI, but face “informational entropy”: without sufficient AI, more raw data leads to information overload and has negative returns. The model has two key predictions: (1) improvements in AI (compute) disproportionately benefit data-rich firms; and (2) access to processed data substitutes for compute, increasing industry dynamism and reducing market concentration. We confirm these predictions using novel data from 2000–2023 and two exogenous shocks: the 2006 launch of Amazon Web Services (AWS) and the 2017 introduction of transformer-based architectures. Our findings suggest that regulating data usability, not just AI models, is essential to preserving competition in the modern economy.
{"title":"Is it AI or data that drives firm market power?","authors":"Roxana Mihet , Kumar Rishabh , Orlando Gomes","doi":"10.1016/j.jmoneco.2025.103878","DOIUrl":"10.1016/j.jmoneco.2025.103878","url":null,"abstract":"<div><div>The technology revolution is transforming firm and industry dynamics, yet the roots of firm dominance in the modern economy remain unclear. Is industry dynamism driven by compute capabilities (AI), access to data, or the interaction between them? We develop a dynamic model in which firms gain knowledge from raw data using AI, but face <em>“informational entropy”</em>: without sufficient AI, more raw data leads to information overload and has negative returns. The model has two key predictions: (1) improvements in AI (compute) disproportionately benefit data-rich firms; and (2) access to processed data substitutes for compute, increasing industry dynamism and reducing market concentration. We confirm these predictions using novel data from 2000–2023 and two exogenous shocks: the 2006 launch of Amazon Web Services (AWS) and the 2017 introduction of transformer-based architectures. Our findings suggest that regulating data usability, not just AI models, is essential to preserving competition in the modern economy.</div></div>","PeriodicalId":48407,"journal":{"name":"Journal of Monetary Economics","volume":"157 ","pages":"Article 103878"},"PeriodicalIF":4.1,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-10DOI: 10.1016/j.jmoneco.2025.103879
Haishi Li
Using a new database on global multinational production (MP), I document that world multinational enterprise (MNE) sales declined as sharply as trade during the Great Recession (2008–2009). This collapse was driven by MNEs from a few key headquarters countries and associated with steeper GDP declines in MP-intensive countries. MNEs amplified the trade collapse because their overall sales fell while they maintained higher trade intensity than domestic firms. In a calibrated quantitative model with flexible vertical and horizontal MNE structures, international trade, and input–output linkages, I show that productivity shocks, which disproportionately affected trade-intensive MNEs, contributed more to the trade collapse than demand shocks. MNEs’ productivity shocks accounted for over half of the global GDP decline during the Great Recession. MP linkages significantly amplified the transmission of headquarters-country productivity shocks to global GDP, MP, and trade.
{"title":"Multinational production and global shock propagation during the great recession","authors":"Haishi Li","doi":"10.1016/j.jmoneco.2025.103879","DOIUrl":"10.1016/j.jmoneco.2025.103879","url":null,"abstract":"<div><div>Using a new database on global multinational production (MP), I document that world multinational enterprise (MNE) sales declined as sharply as trade during the Great Recession (2008–2009). This collapse was driven by MNEs from a few key headquarters countries and associated with steeper GDP declines in MP-intensive countries. MNEs amplified the trade collapse because their overall sales fell while they maintained higher trade intensity than domestic firms. In a calibrated quantitative model with flexible vertical and horizontal MNE structures, international trade, and input–output linkages, I show that productivity shocks, which disproportionately affected trade-intensive MNEs, contributed more to the trade collapse than demand shocks. MNEs’ productivity shocks accounted for over half of the global GDP decline during the Great Recession. MP linkages significantly amplified the transmission of headquarters-country productivity shocks to global GDP, MP, and trade.</div></div>","PeriodicalId":48407,"journal":{"name":"Journal of Monetary Economics","volume":"157 ","pages":"Article 103879"},"PeriodicalIF":4.1,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-02DOI: 10.1016/j.jmoneco.2025.103876
Enrique Ide, Eduard Talamàs
We analyze how Artificial Intelligence (AI) reshapes global knowledge work in a two-region world where firms organize production hierarchically to use knowledge efficiently: the most knowledgeable individuals specialize in problem-solving, while others perform routine work. Before AI, the Advanced Economy specializes in problem-solving services, whereas the Emerging Economy focuses on routine work. AI converts compute — which is located in the Advanced Economy — into autonomous “AI agents” that perfectly substitute for humans with a given level of knowledge. Basic AI reduces the Advanced Economy’s net exports of problem-solving services, potentially reversing pre-AI trade patterns. In contrast, sophisticated AI expands these exports, reinforcing existing trade patterns. Finally, we show that a global ban on AI autonomy redistributes AI’s gains toward lower-skilled workers, while a regional ban — such as prohibiting autonomy only in the Emerging Economy — offers little benefit to lower-skilled workers and harms the most knowledgeable individuals in that region.
{"title":"The impact of AI on global knowledge work","authors":"Enrique Ide, Eduard Talamàs","doi":"10.1016/j.jmoneco.2025.103876","DOIUrl":"10.1016/j.jmoneco.2025.103876","url":null,"abstract":"<div><div>We analyze how Artificial Intelligence (AI) reshapes global knowledge work in a two-region world where firms organize production hierarchically to use knowledge efficiently: the most knowledgeable individuals specialize in problem-solving, while others perform routine work. Before AI, the Advanced Economy specializes in problem-solving services, whereas the Emerging Economy focuses on routine work. AI converts compute — which is located in the Advanced Economy — into autonomous “AI agents” that perfectly substitute for humans with a given level of knowledge. Basic AI reduces the Advanced Economy’s net exports of problem-solving services, potentially reversing pre-AI trade patterns. In contrast, sophisticated AI expands these exports, reinforcing existing trade patterns. Finally, we show that a global ban on AI autonomy redistributes AI’s gains toward lower-skilled workers, while a regional ban — such as prohibiting autonomy only in the Emerging Economy — offers little benefit to lower-skilled workers and harms the most knowledgeable individuals in that region.</div></div>","PeriodicalId":48407,"journal":{"name":"Journal of Monetary Economics","volume":"157 ","pages":"Article 103876"},"PeriodicalIF":4.1,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-02DOI: 10.1016/j.jmoneco.2025.103875
Jonathan J. Adams , Min Fang , Zheng Liu , Yajie Wang
We document key stylized facts about the time-series trends and cross-sectional distributions of artificial intelligence (AI)-powered pricing and study its implications for firm performance, both on average and in response to monetary policy shocks. We use the online job postings data from Lightcast to measure the adoption of AI pricing. We infer that a firm is adopting AI pricing if it posts a job that requires AI-related skills and contains the keyword “pricing.” At the aggregate level, the share of AI pricing jobs in all pricing jobs has increased more than tenfold since 2010. The rise of AI pricing jobs has been broad-based, spreading across more industries than other types of AI jobs. At the firm level, larger and more productive firms are more likely to adopt AI pricing. Firms that adopted AI pricing experienced faster growth in sales, employment, assets, and markups, and their stock returns are also more responsive to high-frequency monetary policy surprises than non-adopters. We show that these empirical observations can be rationalized by a simple model where a monopolist firm with incomplete information about its demand function invests in AI pricing to acquire information.
{"title":"The rise of AI pricing: Trends, driving forces, and implications for firm performance","authors":"Jonathan J. Adams , Min Fang , Zheng Liu , Yajie Wang","doi":"10.1016/j.jmoneco.2025.103875","DOIUrl":"10.1016/j.jmoneco.2025.103875","url":null,"abstract":"<div><div>We document key stylized facts about the time-series trends and cross-sectional distributions of artificial intelligence (AI)-powered pricing and study its implications for firm performance, both on average and in response to monetary policy shocks. We use the online job postings data from Lightcast to measure the adoption of AI pricing. We infer that a firm is adopting AI pricing if it posts a job that requires AI-related skills and contains the keyword “pricing.” At the aggregate level, the share of AI pricing jobs in all pricing jobs has increased more than tenfold since 2010. The rise of AI pricing jobs has been broad-based, spreading across more industries than other types of AI jobs. At the firm level, larger and more productive firms are more likely to adopt AI pricing. Firms that adopted AI pricing experienced faster growth in sales, employment, assets, and markups, and their stock returns are also more responsive to high-frequency monetary policy surprises than non-adopters. We show that these empirical observations can be rationalized by a simple model where a monopolist firm with incomplete information about its demand function invests in AI pricing to acquire information.</div></div>","PeriodicalId":48407,"journal":{"name":"Journal of Monetary Economics","volume":"157 ","pages":"Article 103875"},"PeriodicalIF":4.1,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01DOI: 10.1016/j.jmoneco.2025.103874
Chun-Che Chi
This paper studies how the lender structure of external debt affects open economies’ credit conditions via a model with lenders of varying sizes. While atomistic lenders take the collateral price as given, large lenders internalize the pecuniary externality whereby selling foreclosed collateral injects supply and reduces its price. Thus, concentrating external debt in a few large lenders supports a high collateral price during financial downturns, leading borrowers to demand less precautionary savings and overborrow. I document that emerging countries borrow from significantly fewer banks than advanced countries, implying that emerging countries tend to overborrow. This new mechanism complements the existing view of overborrowing due to the pecuniary externality of the borrowers. Under plausible parameterization, the size of the pecuniary externality internalized by lenders is two-thirds of that internalized by borrowers. Finally, allowing lender countries to optimally choose lender structure increases lender concentration, raises debt, and improves borrowers’ welfare.
{"title":"Lender concentration of external debts and sudden stops","authors":"Chun-Che Chi","doi":"10.1016/j.jmoneco.2025.103874","DOIUrl":"10.1016/j.jmoneco.2025.103874","url":null,"abstract":"<div><div>This paper studies how the lender structure of external debt affects open economies’ credit conditions via a model with lenders of varying sizes. While atomistic lenders take the collateral price as given, large lenders internalize the pecuniary externality whereby selling foreclosed collateral injects supply and reduces its price. Thus, concentrating external debt in a few large lenders supports a high collateral price during financial downturns, leading borrowers to demand less precautionary savings and overborrow. I document that emerging countries borrow from significantly fewer banks than advanced countries, implying that emerging countries tend to overborrow. This new mechanism complements the existing view of overborrowing due to the pecuniary externality of the borrowers. Under plausible parameterization, the size of the pecuniary externality internalized by lenders is two-thirds of that internalized by borrowers. Finally, allowing lender countries to optimally choose lender structure increases lender concentration, raises debt, and improves borrowers’ welfare.</div></div>","PeriodicalId":48407,"journal":{"name":"Journal of Monetary Economics","volume":"157 ","pages":"Article 103874"},"PeriodicalIF":4.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-27DOI: 10.1016/j.jmoneco.2025.103873
Minsu Chang , Hanbaek Lee
This paper investigates the fiscal multiplier of infrastructure investment using an estimated heterogeneous-firm general equilibrium model. We theoretically and quantitatively show that the firm-level non-rivalry in infrastructure usage drives a significant discrepancy in the estimated input elasticities between the firm and state levels. Moreover, it microfounds the increasing returns to scale assumption in a representative-agent framework (Baxter and King, 1993). The quantitative findings indicate a fiscal multiplier of approximately 1.15 over a 2-year horizon, suggesting a significantly greater net economic benefit than the representative-agent model prediction. This is due to the low sensitivity of the firm-level investment to the general equilibrium effect, followed by a significantly dampened crowding out.
本文利用估计的异质企业一般均衡模型研究了基础设施投资的财政乘数。我们从理论上和定量上表明，企业在基础设施使用方面的非竞争导致了企业和州之间估计的投入弹性的显著差异。此外，它还在代表-代理框架中微观发现了规模收益递增假设（Baxter and King, 1993）。定量研究结果表明，在2年的时间跨度内，财政乘数约为1.15，这表明净经济效益明显高于代表性代理模型的预测。这是由于企业层面的投资对一般均衡效应的敏感性较低，其次是明显抑制的挤出效应。
{"title":"Bridging micro and macro production functions: The fiscal multiplier of infrastructure investment","authors":"Minsu Chang , Hanbaek Lee","doi":"10.1016/j.jmoneco.2025.103873","DOIUrl":"10.1016/j.jmoneco.2025.103873","url":null,"abstract":"<div><div>This paper investigates the fiscal multiplier of infrastructure investment using an estimated heterogeneous-firm general equilibrium model. We theoretically and quantitatively show that the firm-level non-rivalry in infrastructure usage drives a significant discrepancy in the estimated input elasticities between the firm and state levels. Moreover, it microfounds the increasing returns to scale assumption in a representative-agent framework (Baxter and King, 1993). The quantitative findings indicate a fiscal multiplier of approximately 1.15 over a 2-year horizon, suggesting a significantly greater net economic benefit than the representative-agent model prediction. This is due to the low sensitivity of the firm-level investment to the general equilibrium effect, followed by a significantly dampened crowding out.</div></div>","PeriodicalId":48407,"journal":{"name":"Journal of Monetary Economics","volume":"157 ","pages":"Article 103873"},"PeriodicalIF":4.1,"publicationDate":"2025-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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