The evolution of business surveys is being redefined as central banks globally explore modern approaches to collect economic data. Traditional survey methods, which have historically provided valuable insights into the economic landscape, now face growing challenges, including declining participation rates and the emergence of faster, more competitive data sources. A recent discussion led by authorities from Banca d’Italia highlights the urgent need to adapt these surveys to harness advancements in digital technologies and artificial intelligence (AI).
Since the 1960s, business surveys have proven fundamental for gathering insights on economic conditions and firm-level behaviours. Banca d’Italia, known for its extensive expertise in this field, initiated several key survey programmes over the decades, such as the Survey on Household Income and Wealth and the Survey of Industrial and Service Firms (INVIND). These surveys serve to inform policymakers about economic trends, inflation expectations, and uncertainties faced by businesses, enabling them to better align monetary and fiscal policies with the needs of the economy.
Despite their longstanding utility, the increasing challenges in conducting traditional surveys cannot be overlooked. Notably, participation rates have plummeted, with the item non-response rate for investment plans in the INVIND survey rising to 15%, effectively tripling since its inception. Moreover, central banks grapple with the burdensome nature of survey reporting and rising concerns regarding data privacy, which complicate participation. Speaking to the CEPR, representatives from Banca d’Italia stressed the importance of revisiting how data is collected and processed to improve response rates and data quality.
The potential for evolving towards ‘business surveys 2.0’ is significant, particularly with the rise of AI and machine learning. Digitalisation of information systems and the increasing availability of both administrative and big data resources unlock new avenues for more efficient and effective survey methodologies. The integration of these technologies could enhance the design of surveys and streamline the data collection process. For instance, researchers propose incorporating large language models (LLMs) into the preparation phase of surveys to assist in formulating questions and managing data entries, ultimately improving the accuracy of survey responses.
Furthermore, natural language processing techniques could allow for rapid extraction of signals from open-ended survey responses or interviews, leading to innovative ways to interpret qualitative data. Such methodologies have become increasingly relevant in understanding complex economic phenomena, as evidenced by recent studies extracting economic indicators from text used during earnings calls.
AI-assisted interviewing methods are also emerging, utilising advanced technologies to improve the interview process. These tools could create more interactive and adaptive interviewing experiences, potentially boosting response rates by minimising human error and bias. In parallel, machine learning models may offer improved methods for handling missing data, capturing intricate relationships within datasets and facilitating more comprehensive analyses.
As Banca d’Italia’s research suggests, the potential to revolutionise survey methodologies is not without its challenges, particularly regarding data confidentiality. Nonetheless, the merits of alleviating the burden on respondents and enhancing the richness of data captured are driving interest in these modern approaches.
The potential transformation of traditional business surveys into more agile and responsive systems exemplifies ongoing advancements in research and technology within the economics field. As central banks increasingly adopt these methods, the implications could significantly reshape the way economic data is collected, analysed, and reported, ultimately reinforcing the role of surveys in informing critical economic policy decisions. As they navigate these advancements, policymakers will need to remain mindful of the balance between innovation and the fundamental principles of data integrity and confidentiality.
Source: Noah Wire Services