Bank of Spain: Household Data Key to Vulnerability, Policy

The Limitations of Aggregate Economic Data

José Luis Escrivá, Governor of the Bank of Spain, stated in remarks delivered at a conference on 4 September 2026, and published by the Bank for International Settlements (BIS) on 22 September 2026, that central banks extensively use aggregate data on household income, wealth, debt, consumption, and saving to understand the economy.

However, Escrivá noted that these indicators often fail to provide a complete picture. Households vary significantly in their economic and financial situations, and consequently, they experience aggregate shocks in different ways.

The specific effects of inflation, shifts in interest rates, or changes in asset prices depend heavily on the individual income, wealth, and debt positions of households. Furthermore, financial vulnerability typically concentrates within particular groups, which may not be apparent in overall aggregate indicators.

Pioneering Disaggregated Data Collection

To address these analytical gaps, understanding the full consequences of economic shocks requires detailed, disaggregated information on household income, wealth, and debt, Escrivá explained. He recognised the foundational contribution of Olympia Bover, who was instrumental in developing the Spanish Survey of Household Finances (EFF).

When the EFF was established, only a few countries, such as the United States and Italy, possessed disaggregated household wealth data. Bover adapted existing methodologies to the Spanish context, overcoming a key challenge: the high concentration of household wealth.

This was achieved through a collaborative framework involving the Spanish Tax Agency, the National Statistics Institute, and Banco de España, which created a population framework that accurately represented wealthier households.

The EFF later served as a benchmark for the Eurosystem’s Household Finance and Consumption Survey, with Bover supporting other central banks in developing their own surveys.

Debt Distribution Reveals Hidden Vulnerabilities

Escrivá provided the example of household debt to illustrate the EFF's importance for analysis. While Spanish households have collectively reduced their indebtedness substantially since the financial crisis, with median debt falling from approximately 55,000 euros in 2014 to around 30,000 euros in 2024, disaggregated data reveals a more nuanced picture.

The composition of debt has changed, with personal loans gaining prominence relative to mortgage debt. For households in the bottom quartile of the net wealth distribution, personal loans now account for about 23% of their total debt, representing an increase of approximately 17 percentage points since 2014.

This shift highlights specific areas of financial vulnerability that would otherwise remain undetected by aggregate figures, providing crucial insight for policy makers.

Informing Policy and Overcoming Data Sharing Obstacles

This granular data is essential for effective policy formulation, particularly for borrower-based measures, Escrivá emphasised. He noted that introducing interventions such as limits on loan-to-value (LTV) ratios without prior micro-analysis could generate strong and unintended distributional effects across different household segments.

Therefore, making decisions that account for these distributional impacts relies heavily on robust microdata and micro-analysis. Escrivá also highlighted a significant challenge: resistance to data sharing from public administrations.

This resistance often stems from a perception of data ownership, a reluctance to have decisions scrutinised by researchers, or a lack of awareness regarding the substantial positive externalities of making data available to the broader research community. Such obstacles, he argued, must be overcome to fully leverage data as a public good.

Implications for Asian Central Banks

The insights from the Spanish Survey of Household Finances hold relevance for Asian central banks, particularly those in economies facing high household debt or significant wealth disparities.

For instance, institutions like the Bank of Korea, which regularly monitors household debt trends, could refine their macroprudential policy tools, such as loan-to-value or debt-to-income ratios, by integrating more granular data.

This approach would allow for the mitigation of specific pockets of risk, rather than applying broad measures that may have unintended economic consequences across different household segments.

Overcoming resistance to data sharing, as noted by Governor Escrivá, could unlock similar analytical capabilities for Asian policymakers, enabling more targeted and effective financial stability policies in the region.