E02 - Institutions and the MacroeconomyReturn

There is no consensus on the key drivers of financial inclusion due to variation in the socioeconomic features of countries, use of indicators and research methods. The main objective of this study is, therefore, to empirically examine the key drivers of financial inclusion across 26 selected Sub-Saharan African (SSA) economies for the period between 2000 and 2019, using a system generalized method of moments (GMM). A principal component analysis (PCA) is applied to construct a composite index of financial inclusion to address the multi-dimensional nature of the variable. The findings of the study indicate that both the macroeconomic and microeconomic factors influence the level of financial inclusion of the SSA countries. Specifically, the lag effect, economic growth, financial stability, inflation, financial deepening, liquidity, profitability, and bank efficiency are important drivers of financial inclusion in the SSA region. It is therefore important for policy makers and regulators to consider these factors while developing policies and strategies that foster access to financial products and services and ensure financial inclusion in the region.

This paper examines the relationship between financial stability and income inequality in 35 developing countries from 2004 to 2020 using system generalized method of mo- ments (GMM) estimation. Four dimensions of the financial sector, namely financial stability, depth, access and efficiency were included as regressors. The results for the relationship of each of the financial dimensions with income inequality are mixed. In this study, inequality increases with an increase in the stability of the financial sector; on the contrary, the depth of the financial sector reduces inequality. Furthermore, not only does the dimension of the financial sector matter in addressing income inequality issues, but the quality of institutions is important. It is important for policy makers to understand linkages between financial dimensions and inequality so as to come up with appropriate prudential regulatory mechanisms.

Economic growth has been the subject of much focus throughout the history of economic thought as it has profound economic, social and political consequences. The sources of economic cycles are surrounded by intense and controversial scientific dispute. In our article, we want to contribute to the institutional economics debate by analysing selected institutional factors and testing their influence on economic growth. On a 2012-2018 dataset, we prove that soft factors such as property rights, freedom of corruption, level of freedom on different markets and other components of the Index of Economic Freedom and legal framework explain the differences in GDP per capita dynamics across countries. We present new evidence on how institutional factors determine economic growth. Unlike previously conducted studies, we use panel data and a set of general control variables in an attempt to respect causal inference. Moreover, we show that the mainstream economic conviction - more economic freedom leads to higher economic growth - fails in some cases, and regulation does not always hamper economic growth.

The paper estimates institutional efficiency of a sample of EU and OECD countries. We employ an output-oriented Data Envelopment Analysis (DEA-like) approach where six Worldwide Governance Indicators published by the World Bank in 2009 constitute a vector of outputs. Assuming that all countries should aim at the same level of institutional quality, inputs were considered unimportant. The results, as to in which areas and how much individual countries need to improve, were not surprising. Concerning the overall efficiency scores and rankings, the most institutionally efficient countries are situated in northern Europe. The Czech Republic ranked 24th in the overall sample and 3rd among New EU Member States. The biggest necessary improvements are in the area of government effectiveness and control of corruption. The robustness check using Principal Component Analysis revealed significant qualitative correlation with the DEA-like results, with the correlation coefficient of 0.9653. We propose that both sets of results could be used equally well as explanatory variables in growth and other regressions.