Health care expenditure and economic growth: Quantile panel-type analysis

This study uses international total health care expenditure data of 31 countries from 1986 to 2007 for exploring the causality between an increase in health care expenditure and economic growth. The empirical procedure is divided into two parts. The first is the panel regression analysis and the second is the quantile regression analysis. The estimation of the panel regression reveals that, expenditure growth will stimulate economic growth; however, economic growth will reduce expenditure growth. With regard to the estimation of quantile regression, when economic growth is quantile, in countries with low level of growth, the influence of expenditure growth on economic growth is different. In countries with medium and high levels of economic growth, the influence of expenditure growth on economic growth is positive; when health care expenditure growth is quantile, the influence of economic growth on expenditure growth is more different.     

How useful is yet another data-driven bandwidth in long-run variance estimation?: A simulation study on cointegrating regressions

This paper investigates how bandwidth choice rules in long-run variance estimation affect finite-sample performance of efficient estimators for cointegrating regression models. Monte Carlo results indicate that Hirukawa's (2010) bandwidth choice rule contributes bias reduction in the estimators.     

Productivity versus elasticity: a normalized constant elasticity of substitution production function applied to historical Soviet data

This study tests the extensive growth hypothesis, which would attribute the Soviet economic slowdown to low elasticity of substitution and over-investment rather than deteriorating productivity growth. To circumvent the low availability of data, widely applicable new methods for estimating productivity and elasticity of substitution were developed based on the normalized constant elasticity of substitution (CES) production function. One is an extended version of the Solow residual calculation, and the other is a direct estimation of the time-varying parameter normalized CES production function. The application of the methods to the Soviet data showed a decreasing trend in productivity growth and a low elasticity of substitution of approximately 0.25. The results neither reject nor support the extensive growth hypothesis because of uncertainty in the interpretation of the low elasticity of substitution. The results suggest that it is reasonable to assert that both deteriorating productivity growth and low elasticity of substitution caused the Soviet economic slowdown. Further empirical studies on productivity and elasticity of substitution in the Soviet and other economies are necessary to fully understand relations between productivity growth, elasticity of substitution and economic development.     

FISCAL DECENTRALIZATION AND ECONOMIC GROWTH OF VIETNAMESE PROVINCES: THE ROLE OF LOCAL PUBLIC GOVERNANCE

This study offers an insight into the public governance role in the relationship between fiscal decentralization and provincial economic growth in Vietnam. Fiscal decentralization measures are assorted. Applying a sequential (two‐stage) estimation for the panel data of 62 provinces of Vietnam over the 2006–2015 period, we find that first, fiscal decentralization is positively related to the economic growth of Vietnamese provinces. Second, the effects of public governance on economic growth vary across provinces depending on various levels of local public governance. Interestingly, the effect of fiscal decentralization is strengthened when this variable is added along with better quality of public governance. In a region of high public governance quality, fiscal decentralization exerts a positive effect on its economic growth. Our findings imply that the design of fiscal decentralization needs to be associated with local governments’ ability of public governance to improve the local economic growth.     

人力资本、空间溢出与经济增长——基于空间面板数据模型的经验分析

基于中国29个省区1990—2006年的面板数据,本文采用最新发展的空间计量分析方法考察人力资本与经济增长的关系。研究发现:(1)我国人力资本和经济增长表现出鲜明的空间相关性,多数省区位于高-高和低-低类型区;(2)人力资本作为知识和技术进步的载体,对区域经济增长具有较强的空间溢出效应。此外,加入空间因素回归结果表明,我国各省区的经济增长依然呈现鲜明的条件收敛特征。     

房价水平、差异化产品区位分布与城市体系

地级及以上城市体系的扁平化趋势可能是我国经济增长粗放、产能过剩的体现。本文将Thünen模型中房价水平对差异化产品区位分布的影响关系植入到新经济地理学Krugman-Helpman模型,详细阐述城市房价水平过快上涨(特别是大型城市)对差异化产品区位选择产生扩散倾向的内在机制,并利用1998—2007年中国地级及以上城市的相关数据进行检验。通过计量模型的参数估计,我们发现,Krugman-Helpman模型中导致城市体系扁平化趋势的"非黑洞"条件在中国是成立的。这一结论意味着,通过遏制房价水平非正常过快上涨等方法来扭转城市体系的扁平化趋势可能对调整经济结构与增长方式等具有启示意义。     

Technical change and total factor productivity growth: The case of Chinese provinces

In the literature technical change (TC) is mostly assumed to be exogenous and specified as a function of the time trend or time dummies. However, some exogenous external factors other than time can also affect TC. In this paper we model TC via time trend (external non-economic) as well as other exogenous (external economic) factors (technology shifters). For this we define technology indices based on the external economic factors and the time trend. The specification of production function is then amended to accommodate these technology indices which are not necessarily separable from the traditional inputs. That is, these technology indices allow for non-neutral shift in the production function. In doing so we are able to decompose TC (a component of TFP change) into two parts. One part is driven by time and the other part is related to producer-specific external economic factors. The latter can further be decomposed into each external economic factors. The empirical model uses panel data on Chinese provinces. We identify a number of key technology shifters and their effect on technical change and TFP growth of provinces are examined.     

Does urbanization increase pollutant emission and energy intensity? evidence from some Asian developing economies

This paper aims to investigate the effects of urbanization on pollutant emissions and energy intensity in selected Asian developing countries after controlling for the effects of disaggregated (renewable and non-renewable) energy consumption, trade liberalization, and economic growth. We use both linear and nonlinear panel data econometric techniques and employ recently introduced mean group estimation methods, allowing for heterogeneity and cross-sectional dependence. However, to check the robustness of our panel results, we also apply the autoregressive distributed lag (ARDL)-bound testing approach to country-level data. In addition, the relationship between affluence and CO₂ emissions is examined in the context of the Environmental Kuznets Curve (EKC) hypothesis. The estimation results identify the population, affluence, and non-renewable energy consumption as major factors in pollutant emissions in Asian countries. However, the results of the EKC hypothesis show that when countries achieve a certain level of economic growth, their emissions tend to decline. Whereas nonlinear results show that renewable energy, urbanization, and trade liberalization reduce emissions, linear estimations do not confirm these outcomes. Thus, substitution of non-renewable for renewable energy consumption, cautious and planned urbanization programs and more liberal trading regimes may be viable options for sustainable growth of these developing Asian economies.     

Artificial neural network regression models in a panel setting: Predicting economic growth

Economic time series often feature non-linear structures such as non-linear time trends, non-linear autoregressive effects, and non-linear interaction effects. In this paper, it is shown that artificial neural network regression models are suitable tools for the analysis of economic panel data because they allow for a compromise between the ability to model these features and the model size. As model specification is a concern in artificial neural network models, previous approaches are discussed critically. It is shown that the growth rates of the gross domestic product of 24 industrialized economies in the period 1992–2016 follow a non-linear time trend which cannot be explained by autoregressive features or polynomial time variables. The unrestricted functional form of the time trend in the artificial neural network model is also the main reason for the superior statistical performance compared to conventional panel models. This is confirmed by out-of-sample predictions for 2017.     

Estimating average economic growth in time series data with persistency

This paper studies estimation of average economic growth in time series models with persistency. In particular, a joint estimation of the trend coefficient and the autoregressive parameter is considered. An analysis on the proposed estimator is provided. Our analysis is also extended to the case with general disturbance distributions. A nonlinear M estimator and a class of partially adaptive M estimators which adapt themselves with respect to a measure of the tailthickness are considered. The joint estimator and its partially adapted version are compared with several conventional estimators. Monte Carlo experiments indicate that the proposed estimators have good finite sample performance. We use the proposed estimation procedure to estimate the growth rates for real GNP and consumer price index in 40 countries.     

Are prior restrictions on factor shares appropriate in growth accounting estimations?

Several studies make different prior assumptions on the magnitude of factor shares and scale of production when accounting for economic growth. The initial Solow estimations for instance assumed a capital share of 0.3 and constant returns to scale. Most authors have subsequently used the same restrictions just because they were used in previous studies even when production in the countries under study may not necessarily be taking place under constant returns to scale and capital share may be a value not any close to 0.3. This is likely to distort growth accounting estimation results. This study investigates whether these prior restrictions on factor shares and scale of production as commonly used in the literature are appropriate and whether the Solow assumptions are likely biased even in developed countries. Using Kenyan data and structural vector autoregressions, the main findings are; first, in all cases of the unrestricted estimations, the share of physical capital is less than 0.16. An estimation which imposes 0.3 as the share of physical capital in this case would therefore not be in line with the data generating process leading to biased results. Secondly, in all cases, the explanatory power of the model decreases when the restrictions are imposed implying that the restrictions are not appropriate in growth accounting exercises. The paper further disputes the “styled fact” in growth studies that the Solow assumptions may be relevant for developed countries but not developing countries and concludes that the Solow assumptions may be biased even in developed countries.     

The long-run causal relationship between exports and economic growth in the euro area

This article employs panel methods that mitigate heterogeneity and cross-sectional dependence to determine the direction and sign of long run causality between exports and economic growth in the euro area economies from 2000 to 2014. The empirical evidence indicates that there is a unidirectional long-run Granger causality relationship from exports to economic growth in the euro area countries.     

QUEST III: An estimated open-economy DSGE model of the euro area with fiscal and monetary policy

This paper develops a DSGE model for an open economy and estimates it on euro area data using Bayesian estimation techniques. The model features nominal and real frictions, as well as financial frictions in the form of liquidity-constrained households. The model incorporates active monetary and fiscal policy rules (for government consumption, investment, transfers and wage taxes) and can be used to analyse the effectiveness of stabilisation policies. To capture the unit root character of macroeconomic time series we allow for a stochastic trend in TFP, but instead of filtering data prior to estimation, we estimate the model in growth rates and stationary nominal ratios.     

Net private capital flows and economic growth–the case of emerging Asian economies

This article makes an exploratory empirical investigation into the relationship between net private capital flows and economic growth using a panel dataset from emerging Asian countries, namely South Korea, Indonesia, Malaysia, Thailand and the Philippines, over the period 1980–2001. Overall, this article indicates that net private capital helps to promote economic growth for the countries in the sample. In addition, this article also supports the view that net private capital flows will better contribute to economic growth under a sound policy and economic environment. This article also seeks to improve the estimation results by controlling for reverse causality as an econometric method that can control, for reverse causality is very important to examining the relationship between net private capital flows and economic growth. To tackle this issue, this article employs the Generalized Method of Moments (GMM) estimation technique, which is an econometric technique that can handle the reverse causality using the lagged explanatory variables as instruments.     

Catching Growth Determinants with the Adaptive Lasso

Abstract. This paper uses the adaptive Lasso estimator to determine variables important for economic growth. The adaptive Lasso estimator is a computationally very efficient procedure that simultaneously performs model selection and parameter estimation. The computational cost of this method is negligibly small compared with standard approaches in the growth regressions literature. We apply this method for a regional dataset for the European Union covering the 255 NUTS2 regions in the 27 member states over the period 1995–2005. The results suggest that initial GDP per capita (with an implied convergence speed of about 1.5% per annum), human capital (proxied by the shares of highly and medium educated in the working age population), structural labor market characteristics (the initial unemployment rate and the initial activity rate of the low educated) as well as being a capital region are important for economic growth.     

Re-examining the threshold effects in the inflation–growth nexus with cross-sectionally dependent non-linear panel: Evidence from six industrialized economies

This paper analyzes the empirical relationship between inflation and output growth using a novel panel data estimation technique, Panel Smooth Transition Regression (PSTR) model, which takes account of the non-linearities in the data. By using a panel data set for 6 industrialized countries that enable us to control for unobserved heterogeneity at both country and time levels, we find that there exists a statistically significant negative relationship between inflation and growth for the inflation rates above the critical threshold level of 2.52%, which is endogenously determined. Furthermore, we also control cross-section dependency by using the CD test modified to non-linear context and remedy cross-section dependency with Seemingly Unrelated Regression Equations through Generalized Least Squares (SURE-GLS) and newly proposed Common Correlated Effects (CCE) estimation techniques. We find that these methods change the critical threshold value slightly. The estimated threshold values from these estimation methods are 3.18% and 2.42%, respectively.     

On the impact of inflation on output growth: Does the level of inflation matter?

This paper investigates the growth effects of inflation on a wide sample of countries, including both industrialized and emerging economies. Relying upon the estimation of smooth transition and dynamic GMM models for panel data, our findings offer strong evidence that inflation non-linearly impacts economic growth. More specifically, there exists a threshold beyond which inflation exerts a negative effect on growth, and below which it is growth enhancing for advanced countries.     

World economic convergence: Does the estimation methodology matter?

An extant empirical literature produces evidence on economic convergence using methods that assume an underlying deterministic trend. Competing approaches that assume a stochastic trend, however, produce only limited evidence of economic convergence. In this paper we address this puzzling feature of the literature by providing a comprehensive analysis of economic convergence using three methodologies that cover all possible underlying assumptions: deterministic, stochastic, and combination trends. We also develop a method for an overall Stochastic Convergence Rate Index, that combines the outcomes of alternative stochastic tests and provides a single measure of the intensity of stochastic convergence. We consider 135 economies over the period 1980–2017. We find that economic convergence occurs at a global level through the formation of convergence clubs, and economic convergence emerges as a deterministic rather than a stochastic process. Tests that ignore deterministic trends tend to understate the evidence for convergence.     

The PPP debate: Multiple breaks and cross-sectional dependence

This study implements panel unit root PPP tests accommodating level and trend breaks and cross-sectional dependence. In the presence of breaks there is evidence of a currency and price index effect. Additionally accounting for cross-sectional dependence overturns support for PPP.     

Temperature,rainfall and economic growth in Africa

Following a recent line of research, this paper investigates the aggregated effects of temperature and rainfall on economic growth in Africa. Our econometric approach is based on a reduced-form model and takes account explicitly of parameter heterogeneity and cross section dependence, relying on ARDL modelling and panel estimators with multifactor structures. We find clear supportive evidence of short- and long-run relations between temperature and per-capita GDP growth, while the role played by rainfall appears to be less important and the evidence on its statistical significance is less clear-cut. Very similar results are reported when the analysis is carried out by focusing solely on Sub-Saharan African countries or considering GDP growth per worker. This evidence is in sharp contrast to the results obtained via standard MG estimation and this confirms that, by not controlling for cross section dependence, traditional panel estimators are likely to provide misleading inference. The empirical results suggest that, far from adapting quickly to weather shocks, African economies appear to be significantly damaged by them. In the absence of corrective measures, the current trends in climate change may impose a progressively heavier burden on African countries.