Does Financial Intermediation Development Increase Per Capita Income in Rural China?

This paper investigates the impacts of financial intermediary （or banking） development on village-level per capita income using a Chinese dataset for selected years between 1993 and 2006. The empirical results from a random effect regression model indicate that mean per capita income in rural villages follows an inverted U-shaped path as financial intermediation develops. However, using a pooled quantile regression approach, we find that median per capita income in rural villages follows a positive linear path, rather than an inverted U-shaped path, as financial intermediation develops. The positive linear effect of financial intermediary development is observed at the lower and higher ends of the conditional per capita income distribution. This suggests that development of financial intermediation in China might not have statistically significant differential effects in low-income or high-income rural villages.

Financial sector development and economic growth:evidence from Cameroon

For decades,African economies have embarked on financial sector reforms.However,the empirical implications of these reforms have been divergent.This paper investigates the impact of financial development on Economic growth using time series data in Cameroon.This investigation was carried out using three common indicators of financial development(broad money,deposit/GDP and domestic credit to private sector).Using the Auto Regressive Distributive Lag(ARDL)technique of estimation,it was discovered that there exist a short-run positive relationship between monetary mass(M2),government expenditure and economic growth,a short run negative relationship between bank deposits,private investment and economic growth equally exists.However in the long run,all indicators of financial development show a positive and significant impact on economic growth.This paper thus confirms the existence of a positive and long-term impact of all the indicators of financial development on economic growth through bound test.It is therefore proposed that the financial reforms in Cameroon should be pushed forward in order to boost the development of the financial sector thus an increase in its role on economic growth.

What explains the technical efficiency of banks in Tunisia?Evidence from a two‑stage data envelopment analysis

In this study we examine the potential determinants of technical efficiency for the Tunisian commercial banking sector over the period of 1995–2017.First,we estimate banking technical efficiency with a radial and non-radial bootstrap data envelopment analysis.For the radial technique,we use an input-oriented approach and for non-radial we use the Range Adjusted Measure(RAM).Second,we use a double bootstrapping regression technique to estimate the influence of a set of eventual determinants on technical efficiency.Finally,based on all possible regressions,we gauge the overall effect of each determinant.Our results reveal that the input-oriented and RAM approach gave somewhat similar results.We found that the return on equity,the expense to income ratio,the loan to deposit ratio,and the growth rate are insignificant to Tunisian banking technical efficiency.In particular,banking technical efficiency increases with capitalization and inflation,whereas,it decreases with size,number of bank branches,management to staff ratio,and loan to asset ratio.In addition,we identified evidence supporting the moderate success of the last decade of reforms and a noticeable one for the post-revolution reforms in helping improve banking technical efficiency.The post-revolution reforms,largely revolving around reinforcing the rules of good governance and banking supervision,coupled with the restructuring of public banks,were found to be insufficient to raise overall banking technical efficiency despite improvement in the technical efficiency of private banks.

Default or profit scoring credit systems?Evidence from European and US peer-to-peer lending markets

For the emerging peer-to-peer(P2P)lending markets to survive,they need to employ credit-risk management practices such that an investor base is profitable in the long run.Traditionally,credit-risk management relies on credit scoring that predicts loans’probability of default.In this paper,we use a profit scoring approach that is based on modeling the annualized adjusted internal rate of returns of loans.To validate our profit scoring models with traditional credit scoring models,we use data from a European P2P lending market,Bondora,and also a random sample of loans from the Lending Club P2P lending market.We compare the out-of-sample accuracy and profitability of the credit and profit scoring models within several classes of statistical and machine learning models including the following:logistic and linear regression,lasso,ridge,elastic net,random forest,and neural networks.We found that our approach outperforms standard credit scoring models for Lending Club and Bondora loans.More specifically,as opposed to credit scoring models,returns across all loans are 24.0%(Bondora)and 15.5%(Lending Club)higher,whereas accuracy is 6.7%(Bondora)and 3.1%(Lending Club)higher for the proposed profit scoring models.Moreover,our results are not driven by manual selection as profit scoring models suggest investing in more loans.Finally,even if we consider data sampling bias,we found that the set of superior models consists almost exclusively of profit scoring models.Thus,our results contribute to the literature by suggesting a paradigm shift in modeling credit-risk in the P2P market to prefer profit as opposed to credit-risk scoring models.