Research on the Credit Cycle and Business Cycle with a Focus on Ten States from Central,Eastern, and Southeastern Europe

By performing an econometric analysis of the credit cycle and business cycle from an individual as well as a comparative perspective, with a focus on ten relevant economies from the areas of Central, Eastern, and Southeastern Europe, this research offers a fresh view regarding the importance of banks in promoting long-term economic growth through their lending capacity. The purpose is to better understand the behavior (the short- and medium-term dynamics) of the credit cycle and business cycle and the effects of the interactions between them. The results of this study offer valuable insights for both academics and policymakers and provide a warning to regulators not to overregulate or put too much pressure on banking activity.     

Inflation targeting in an import dependent indebted economy

This paper develops a one-sector Kaleckian model of an import dependent indebted small open economy; where the mark-up rate is sensitive to both changes in the interest rate and the exchange rate and foreigners provide part of the long-term finance. The short-run consequences of an inflation targeting policy in the form of high interest rates and strong domestic currency are explored. Among the possible short-run scenarios, the one most relevant for developing countries involves a decline in the profit rate, the capacity utilization rate and the rate of accumulation as well as the employment rate and the real wage. Leverage ratio of the firms and the extent of external indebtedness play an important role in bringing about this result. Long-run analysis reveals that this scenario is associated with instability in the long run and that, also in the long run, the extent of foreign indebtedness and the responsiveness of capital inflows to the return on existing portfolios are important in determining the direction of the effects of inflation targeting on the equilibrium debt–capital ratio.     

Bayesian Inference in Spatial Sample Selection Models

In this study, we consider Bayesian methods for the estimation of a sample selection model with spatially correlated disturbance terms. We design a set of Markov chain Monte Carlo algorithms based on the method of data augmentation. The natural parameterization for the covariance structure of our model involves an unidentified parameter that complicates posterior analysis. The unidentified parameter – the variance of the disturbance term in the selection equation – is handled in different ways in these algorithms to achieve identification for other parameters. The Bayesian estimator based on these algorithms can account for the selection bias and the full covariance structure implied by the spatial correlation. We illustrate the implementation of these algorithms through a simulation study and an empirical application.     

Does data matter? Statewide longitudinal data systems and student performance in the U.S.

Although many industries have benefited from advances in data-driven technology, education is making small steps in capitalizing on the huge potential of data systems. Since 2005, the U.S. Federal Government has been making large grants to help states build statewide longitudinal data systems (SLDS) with the goal of improving programme and teacher evaluation and engage in data-driven decision-making. We analyse whether the introduction of SLDSs improved student performance, measured using test scores for math and reading tests for 4th and 8th graders, as well as high school graduation rates. We find no effects of SLDSs on student performance up to 10 years from implementation. However, we find suggestive evidence that these systems may have long-run effects, emphasizing the long-run nature of educational data collection and policy analysis.     

Heteroskedasticity-consistent covariance matrix estimators for spatial autoregressive models

In the presence of heteroskedasticity, conventional test statistics based on the ordinary least squares (OLS) estimator lead to incorrect inference results for the linear regression model. Given that heteroskedasticity is common in cross-sectional data, the test statistics based on various forms of heteroskedasticity-consistent covariance matrices (HCCMs) have been developed in the literature. In contrast to the standard linear regression model, heteroskedasticity is a more serious problem for spatial econometric models, generally causing inconsistent extremum estimators of model coefficients. This paper investigates the finite sample properties of the heteroskedasticity-robust generalized method of moments estimator (RGMME) for a spatial econometric model with an unknown form of heteroskedasticity. In particular, it develops various HCCM-type corrections to improve the finite sample properties of the RGMME and the conventional Wald test. The Monte Carlo results indicate that the HCCM-type corrections can produce more accurate results for inference on model parameters and the impact effects estimates in small samples.     

Economic diversification of a single-asset tourism city: evidence from Antalya†

It is often argued that the growth of single-asset tourism places is fragile because it is vulnerable to economic fluctuations and is based on specialization and localization economies that lead to an ever stronger lock-in process of path-dependent urban economic development and, in the end, slow growth. In this paper, it is doubted whether a high urban tourism growth implies an ever stronger specialization and an ever stronger lock-in. This paper shows that the growth of tourism stimulates the growth of related and unrelated industries and generates a diversification of the economy, even though some other sectors are crowded out. Antalya (Turkey) is selected as a case-study area not only because its economy is dominated by tourism (it is a good example of a single-asset tourism city), but also because its economy shows a tendency to economic diversification at the sectoral level. This diversification tendency is shown by means of a shift-share analysis and Herfindahl indexes.     

The impacts of bank-specific and macroeconomic variables on the capital adequacy ratio: evidence from Islamic banks

The study investigates the relationship between the capital adequacy ratio (CAR) and different bank-specific and macroeconomic variables for 28 Islamic banks. We document that there is a statistically significant positive relationship between the CAR and the bank-specific and macroeconomic variables. In particular, bank-specific variables such as ROA, ROE, leverage, credit risk and size show a strong association with the CAR, while on the macroeconomic side, inflation, market capitalization and exchange rate have an impact on the average Islamic bank in our sample study. Furthermore, we run another model (equity to assets ratio) as dependent, with similar control variables, and the results reveal that, except for inflation, all the variables that have a significant effect on the CAR also influence the equity to assets ratio.     

Why (not) cooperate? A cognitive model of farmers’ intention to join producer groups in Romania

After the breakdown of the communist regime in Romania, collective farms were replaced by a large number of small-scale private farms. Although cooperation seems to be a favourable choice for these smallholders, it did not develop as perhaps expected. This article explores the factors that determine the formation of the intention of Romanian vegetable farmers to join marketing cooperatives in the form of so called producer groups. Our theoretical framework refers to Ajzen’s Theory of Planned Behaviour, which we model with a structural equation model. We identified as main intention drivers the expectation for better prices and easier access to capital. Perceived family support is another significant factor. The level of distrust is high. Although our trust variable is not significant as a predictor of the intention to cooperate in the model, we find qualitative indications that trust plays a role when the intention is translated into actual behaviour.     

Real interest rates: nonlinearity and structural breaks

Real interest rate is a crucial variable that determines the consumption, investment and saving behavior of individuals and thereby acts as a key policy tool that the central banks use to control the economy. Although many important theoretical models require the real interest rates to be stationary, the empirical evidence accumulated so far has not been able to provide conclusive evidence on the mean reverting dynamics of this variable. To resolve this puzzle we re-investigate the stochastic nature of the real interest rates by developing unit root tests for nonlinear heterogeneous panels where the alternative hypothesis allows for a smooth transition between deterministic linear trends around which stationary asymmetric adjustment may occur. When the newly developed panel unit root tests are applied to the real interest rates of the 17 OECD countries, we were able to uncover overwhelming empirical support in favor of mean reversion in the short-run and long-run real interest rates. Therefore, these results show that the conclusions drawn from a miss-specified test that ignores the presence of either nonlinearity, structural breaks or cross sectional dependence can give quite misleading results about the stochastic behavior of the real interest rates.     

Corruption,growth, and growth volatility

While the existing studies focus on the corruption–growth relationship, this paper introduces a new focus involving corruption and growth volatility. The Ehrlich–Lui (1999) framework provides the theoretical background of the paper, which produces testable hypotheses regarding the corruption–growth and the corruption–growth volatility relationship. The cross-section dataset that is used in the empirical analysis contains 121 developed and developing countries. In terms of the relationship between the governance-related variables and growth rates, only corruption control and government effectiveness significantly and adversely affect the average growth rate. Regarding the relationship between growth volatility and governance-related variables, the results suggest that higher corruption control, expropriation risk control, government effectiveness, and government consumption decrease growth volatility.