Do macroprudential policies influence FinTech credit growth: Quarterly Panel Dataset (2005q1–2018q4)
Description
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.
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Steps to reproduce
Link to Replication Folder Table 2: Summary statistics for regression variables "Table 2, run script sum tot t l lnfs_s GDP_GR REER DCPS crisis mop kaopen regulation." Table 3: Collinearity diagnostics "Table 4, run script reg lnfs_s tot GDP_GR REER DCPS crisis mop kaopen regulation." "Then, run script vif" SQRT is the square root of VIF. Table 4 "Table 4 (model 1), run script xtreg lnfs_s tot GDP_GR REER DCPS crisis mop kaopen regulation, fe." "Table 4 (model 2), run script xtreg lnfs_s tight GDP_GR REER DCPS crisis mop kaopen regulation, fe." "Table 4 (model 3), run script xtreg lnfs_s loose GDP_GR REER DCPS crisis mop kaopen regulation, fe." "Table 4 (model 4), run script xtreg lnfs_s tight loose GDP_GR REER DCPS crisis mop kaopen regulation, fe." *****To perform Wooldridge test for autocorrelation run script, xtserial lnfs_s tot GDP_GR REER DCPS crisis mop kaopen regulation Repeat it for the rest of the models. *****To perform Pesaran CD test, run script, xtcsd, pesaran abs (after estimating the model) Repeat it for the rest of the models. *****To perform Modified Wald test, run script, xttest3 (after estimating the model) Repeat it for the rest of the models. Table 5 "Table 5 (model 1), run script xtgls lnfs_s tot GDP_GR REER DCPS crisis mop kaopen regulation, p(h) "Table 5 (model 2), run script xtgls lnfs_s tight GDP_GR REER DCPS crisis mop kaopen regulation, p(h) "Table 5 (model 1), run script xtgls lnfs_s loose GDP_GR REER DCPS crisis mop kaopen regulation, p(h) "Table 5 (model 1), run script xtgls lnfs_s tight loose GDP_GR REER DCPS crisis mop kaopen regulation, p(h) Table 6: Robustness checks "Table 6 (model 1), run script xtreg lnfs_s map_n2 GDP_GR REER DCPS crisis mop kaopen regulation, fe." "Table 6 (model 2), run script xtreg lnfs_s map_nt GDP_GR REER DCPS crisis mop kaopen regulation, fe." "Table 6 (model 3), run script xtreg lnfs_s map_nl GDP_GR REER DCPS crisis mop kaopen regulation, fe." "Table 6 (model 4), run script xtreg lnfs_s map_nt map_nl GDP_GR REER DCPS crisis mop kaopen regulation, fe." Table A2: Correlation coefficient matrix "Table A2, run script pwcorr lnfs_s tot GDP_GR REER DCPS crisis mop kaopen regulation, print(.05) star(.01)" Fig.1: Global new FinTech credit volumes from 2005q1-2018q4 period. "Fig.1, is a simple trend of FinTech credit volumes. Use variable Fig.2: Frequency of Macroprudential policy actions over the 2005q1-2018q4 period. "Fig.2, run script tab t TIME (tightening actions) and run script tab l TIME (for loosening actions). Then combine in one graph. We formatted the graph in EXCEL. The loosening values are multiplied by (-1).
Institutions
- Bournemouth University