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Sara Lamboglia
Monetary Policy
- Division
Capital Markets/Financial Structure
- Current Position
-
Economist
- Fields of interest
-
Microeconomics,Macroeconomics and Monetary Economics
- Education
- 2014-2018
PhD Mathematics, University of Warwick, UK
- Professional experience
- 2020-
Economist-Statistician, Financial Education Directorate, Bank of Italy
- 2018-2020
Post-Doctoral Fellow, Goethe-University, Frankfurt am Main, Germany
- 28 July 2026
- OCCASIONAL PAPER SERIES - No. 395Details
- Abstract
- This paper explores the adoption of artificial intelligence (AI) technologies among euro area firms, using harmonised firm-level data from two dedicated modules of the Survey on the Access to Finance of Enterprises (SAFE) conducted in June and December 2025. Based on responses from around 6,000 firms across 12 euro area countries, the study examines AI adoption rates, drivers, barriers and economic implications. The findings suggest that AI diffusion among euro area firms is progressing rapidly but unevenly, with significant variation across countries and firm characteristics. Approximately 70% of firms report some level of AI use, but only 7% classify their adoption as significant. Adoption is highest in the Netherlands, Finland and Austria, and lowest in Italy and Ireland. Larger and younger firms, particularly in technology-intensive sectors, are leading adopters. Firms identify expected improvements in business processes as the main driver of adoption, while key barriers include skill shortages, data privacy concerns and system incompatibilities. Current AI use and investment are primarily financed through internal funds, complemented by grants and subsidised bank loans. AI adoption is positively associated with firm productivity, turnover growth, fixed investment and own selling price expectations, particularly among intensive users. Survey data show no evidence yet of aggregate labour shedding; instead, AI adoption is positively associated with employment growth. However, firms’ inflation expectations appear largely unaffected by current AI use.
- JEL Code
- C93 : Mathematical and Quantitative Methods→Design of Experiments→Field Experiments
D22 : Microeconomics→Production and Organizations→Firm Behavior: Empirical Analysis
E31 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Price Level, Inflation, Deflation
L25 : Industrial Organization→Firm Objectives, Organization, and Behavior→Firm Performance: Size, Diversification, and Scope
O33 : Economic Development, Technological Change, and Growth→Technological Change, Research and Development, Intellectual Property Rights→Technological Change: Choices and Consequences, Diffusion Processes
- 26 May 2026
- THE ECB BLOGDetails
- JEL Code
- E50 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→General
G10 : Financial Economics→General Financial Markets→General
E31 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Price Level, Inflation, Deflation
- 31 March 2026
- ECONOMIC BULLETIN - BOXEconomic Bulletin Issue 2, 2026Details
- Abstract
- This box presents new information about the adoption of, and investment in, artificial intelligence (AI) technologies by euro area firms, based on the Survey on the Access to Finance of Enterprises (SAFE). The findings reveal that large firms, listed or venture capital-backed companies and young firms are adopting AI more frequently. Firms using AI are more likely to expect an increase in turnover and investment in fixed assets compared with firms not using AI. Similarly, they plan to allocate larger shares of their investment to AI compared with non-users, indicating a reinforcing cycle of adoption and innovation. Ownership structure influences investment patterns, with listed or venture capital-backed companies leading early-stage adoption and privately owned firms dominating at more advanced stages.
- JEL Code
- C83 : Mathematical and Quantitative Methods→Data Collection and Data Estimation Methodology, Computer Programs→Survey Methods, Sampling Methods
D22 : Microeconomics→Production and Organizations→Firm Behavior: Empirical Analysis
L25 : Industrial Organization→Firm Objectives, Organization, and Behavior→Firm Performance: Size, Diversification, and Scope
O33 : Economic Development, Technological Change, and Growth→Technological Change, Research and Development, Intellectual Property Rights→Technological Change: Choices and Consequences, Diffusion Processes
- 12 November 2025
- WORKING PAPER SERIES - No. 3150Details
- Abstract
- We study how survey-based measures of funding needs and availability influence the transmission of euro area monetary policy to investment. We first provide evidence that funding needs are primarily driven by fundamentals, while perceived funding availability captures financial conditions. Using these two measures, we assess how the effectiveness of monetary policy varies with fundamentals and financial conditions. Our results indicate that monetary policy is most effective when firms’ fundamentals are strong. In contrast, firms with favorable financial conditions exhibit a more muted investment response to monetary policy. By combining these two survey-based measures, we construct an indicator of financial constraints and show that financially constrained firms are more sensitive to monetary policy. These findings offer new light on the transmission of monetary policy to corporate investment, emphasizing not only the role of financial conditions, but also the importance of fundamentals, which are beyond the direct influence of central banks
- JEL Code
- C83 : Mathematical and Quantitative Methods→Data Collection and Data Estimation Methodology, Computer Programs→Survey Methods, Sampling Methods
E22 : Macroeconomics and Monetary Economics→Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy→Capital, Investment, Capacity
E52 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Monetary Policy - Network
- Challenges for Monetary Policy Transmission in a Changing World Network (ChaMP)
- 16 June 2025
- ECONOMIC BULLETIN - BOXEconomic Bulletin Issue 4, 2025Details
- Abstract
- This box explores the factors shaping the euro area inflation expectations of firms in the survey on the access to finance of enterprises (SAFE). It finds that the short-term inflation expectations of firms are more volatile and closely tied to current inflation trends compared with their medium-term and long-term expectations. The determinants of these expectations considered in the analysis include the individual characteristics of firms, the sectors these operate in, their country of operation, their anticipated business decisions and euro area inflation. Among these factors, individual characteristics emerge as the primary driver of cross-section variation in inflation expectations of firms, followed by country-specific factors. At the same time, the uncertainty surrounding firms’ five-year inflation expectations is mainly influenced by the country in which they operate.
- JEL Code
- C83 : Mathematical and Quantitative Methods→Data Collection and Data Estimation Methodology, Computer Programs→Survey Methods, Sampling Methods
E20 : Macroeconomics and Monetary Economics→Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy→General
E31 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Price Level, Inflation, Deflation
E52 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Monetary Policy
- 29 October 2024
- ECONOMIC BULLETIN - ARTICLEEconomic Bulletin Issue 7, 2024Details
- Abstract
- The Survey on the Access to Finance of Enterprises (SAFE) provides information on the financing needs of euro area firms, their economic performance, and the availability of external funding. The article illustrates the role that the SAFE has played over the past 15 years. First, it discusses the contribution of the survey to assessing the transmission of monetary policy decisions to firms’ access to finance and their financing conditions. Second, the article shows how SAFE-based data provide timely evidence of the impact of economic crises on firms’ performance. Third, the article documents the ability of SAFE-based indicators to track important shifts in the economic business cycle. Finally, the article discusses new survey modules that facilitate the analysis of the pricing and wage-setting behaviour of firms, along with their inflation expectations.
- JEL Code
- C83 : Mathematical and Quantitative Methods→Data Collection and Data Estimation Methodology, Computer Programs→Survey Methods, Sampling Methods
D22 : Microeconomics→Production and Organizations→Firm Behavior: Empirical Analysis
E58 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Central Banks and Their Policies
G32 : Financial Economics→Corporate Finance and Governance→Financing Policy, Financial Risk and Risk Management, Capital and Ownership Structure, Value of Firms, Goodwill
- 2026
- Entrepreneurship Research Journal
- 2025
- Italian Economic Journal
- 2024
- Empirical Economics