Do macroprudential policies influence FinTech credit growth: Quarterly Panel Dataset (2005q1–2018q4)

Data Description
This dataset comprises an unbalanced panel of quarterly data for 25 advanced and emerging economies, spanning the period 2005q1 to 2018q4. It is constructed to empirically to examine the impact of macroprudential policy (MaPP) actions on FinTech credit growth, using a wide range of macroeconomic and financial control variables control variables. We hypothesize that MaPP positively influences FinTech credit growth. Employing fixed effects (FE) and feasible generalized least squares (FGLS) models, the analysis reveals that an aggregate macroprudential policy action positively influences FinTech credit growth. The analysis further uncovers that the tightening action of macroprudential policies largely drives this effect, suggesting regulatory arbitrage.

Purpose of the Dataset
The dataset was compiled to support the author's PhD research at Bournemouth University, focusing on how macroprudential policies shape the evolution of FinTech credit. It serves multiple purposes:
1. Peer reviewers – for verifying the accuracy and robustness of the study.
2. Researchers – to replicate, validate, or extend the analysis.
3. Journal editors – to ensure data transparency and compliance with data-sharing policies.
4. Policymakers and regulators – to extract insights for informed decisions on FinTech oversight and macroprudential governance.

Data Creation
• FinTech Credit Data represented by FinTech credit volumes (in USD) comprising balance sheet lending, P2P/marketplace lending and invoice trading. It was sourced from various sources using Web scraping of loan-level origination data from FinTech lending platforms (website) loan books. The data was supplemented with data from: national P2P associations, platform annual reports, private databases (e.g., S&P Global Market Intelligence, CIEC and Brismo). FinTech credit data were cross-checked using multiple sources (publicly available datasets, industry reports, and private databases).

• Macroprudential Policy Data was sourced from the Integrated Macroprudential Policy (iMaPP) Database by Alam et al. (2019). The dataset covers 17 macroprudential tools implemented by central banks and regulators. Policy action indicators were coded as +1 (tightening), -1 (loosening), 0 (no action).

• Control variables comprise Real GDP growth rate (RGDPG), monetary policy rate (MPR), real effective exchange rate (REER), Domestic credit to private sector (DCPS), financial openness index (FINOP), Regulation quality index (REGQ), crisis dummy (Crisis). Data for control variables were sourced from IMF Financial Soundness Indicators (FSI), World Bank (GFDD, WBI), OECD, Bank of International Settlement (BIS), and BankScope.

Contributors
Onneetse L. Sikalao-Lekobane (PhD)

Citation
Sikalao-Lekobane, O. L. (2025). Do macroprudential policies influence FinTech credit growth: Quarterly Panel Dataset (2005q1–2018Q4) [Data set]. Mendeley Data.