International output convergence: evidence from an autocorrelation function approach

This paper uses an autocorrelation function (ACF) approach to develop a new testing procedure for international output convergence. We define convergence in terms of sample ACFs of detrended output per capita, and construct an inference set‐up based on resampling and subsampling techniques for dependent data. Using per capita GDP for 15 OECD countries observed over a century, we find that the hypothesis of conditional convergence is unsupported; that, the USA apart, the linearized neoclassical growth model fails to replicate the transitional dynamics of OECD economies; and that these economies do not behave like a club. Copyright © 2008 John Wiley & Sons, Ltd.     

Economic transition and growth

Some extensions of neoclassical growth models are discussed that allow for cross‐section heterogeneity among economies and evolution in rates of technological progress over time. The models offer a spectrum of transitional behavior among economies that includes convergence to a common steady‐state path as well as various forms of transitional divergence and convergence. Mechanisms for modeling such transitions, measuring them econometrically, assessing group behavior and selecting subgroups are developed in the paper. Some econometric issues with the commonly used augmented Solow regressions are pointed out, including problems of endogeneity and omitted variable bias which arise under conditions of transitional heterogeneity. Alternative regression methods for analyzing economic transition are given which lead to a new test of the convergence hypothesis and a new procedure for detecting club convergence clusters. Transition curves for individual economies and subgroups of economies are estimated in a series of empirical applications of the methods to regional US data, OECD data and Penn World Table data. Copyright © 2009 John Wiley & Sons, Ltd.     

Testing for convergence: evidence from non-parametric multimodality tests

In this paper, we test the convergence hypothesis in a cross-section of 119 countries by means of bootstrap multimodality tests and nonparametric density estimation techniques. By looking at the density distribution of GDP across countries in 1970, 1980 and 1989, we find low mobility patterns of intra-distribution dynamics and increasing evidence for bimodality. The findings stand in sharp contrast with the convergence prediction. © 1997 John Wiley & Sons, Ltd.     

Three Sides of Smoothing: Categorical Data Smoothing, Nonparametric Regression, and Density Estimation

The past forty years have seen a great deal of research into the construction and properties of nonparametric estimates of smooth functions. This research has focused primarily on two sides of the smoothing problem: nonparametric regression and density estimation. Theoretical results for these two situations are similar, and multivariate density estimation was an early justification for the Nadaraya-Watson kernel regression estimator.
A third, less well-explored, strand of applications of smoothing is to the estimation of probabilities in categorical data. In this paper the position of categorical data smoothing as a bridge between nonparametric regression and density estimation is explored. Nonparametric regression provides a paradigm for the construction of effective categorical smoothing estimates, and use of an appropriate likelihood function yields cell probability estimates with many desirable properties. Such estimates can be used to construct regression estimates when one or more of the categorical variables are viewed as response variables. They also lead naturally to the construction of well-behaved density estimates using local or penalized likelihood estimation, which can then be used in a regression context. Several real data sets are used to illustrate these points.     

Nonparametric analysis of returns to scale in the US hospital industry

This paper presents new estimates of scale economies for US hospitals. We show that the common translog specification of hospital costs is a misspecification, and employ nonparametric, local linear estimation with both continuous and discrete covariates. A bootstrap method is used to provide inferences regarding ray scale economies and expansion path scale economies for a large sample of hospitals covering 1984–1996. We find evidence of changes in the structure of hospital costs over the sample period, as well as evidence of locally optimal hospital sizes. Copyright © 2004 John Wiley & Sons, Ltd.     

Growth,Agglomeration and Convergence: a Space-time Analysis for European Regions

Abstract

This paper sheds empirical light on the relationship between agglomeration and economic growth and its impact on the convergence hypothesis. Using a sample of 208 European regions over 25 years, ‘standard’ growth regressions are estimated using panel data techniques. Both the effect of agglomeration within one's own region and the interregional aspect of agglomeration are looked at. The estimation results show that, on average, denser regions grow slower than other regions, indicating a net negative effect of agglomeration. However, being located close to other growing regions is found to stimulate growth in one's own region. The implied convergence rate is also affected when the inter- and intraregional aspects of agglomeration are taken into account, where the former (latter) results in lower (slightly higher) convergence estimates.     

Time-varying leverage effects

Vast empirical evidence points to the existence of a negative correlation, named ”leverage effect”, between shocks to variance and shocks to returns. We provide a nonparametric theory of leverage estimation in the context of a continuous-time stochastic volatility model with jumps in returns, jumps in variance, or both. Leverage is defined as a flexible function of the state of the firm, as summarized by the spot variance level. We show that its point-wise functional estimates have asymptotic properties (in terms of rates of convergence, limiting biases, and limiting variances) which crucially depend on the likelihood of the individual jumps and co-jumps as well as on the features of the jump size distributions. Empirically, we find economically important time-variation in leverage with more negative values associated with higher variance levels.     

The role of environmental factors in growth accounting

This paper explores a relatively new methodology, the directional distance function method, to analyse productivity growth. The method allows us to explicitly evaluate the role that undesirable outputs of the economy, such as carbon dioxide and other greenhouse gases, have on the frontier production process which we specify as a piecewise linear and convex boundary function. We decompose productivity growth into efficiency change (catching up) and technology change (innovation). We test the statistical significance of the estimates using recently developed bootstrap methods. We also explore implications for growth of total factor productivity in the OECD and Asian economies. Copyright © 2004 John Wiley & Sons, Ltd.     

WHAT HAVE WE LEARNED FROM THREE DECADES OF RESEARCH ON THE PRODUCTIVITY OF PUBLIC CAPITAL?

The last three decades have witnessed a great deal of research effort devoted to measuring the private output elasticity of public capital. The wide range of available estimates have precluded any consensus so far, however. This paper reconciles the empirical findings of the literature by quantitatively analyzing a sample of 578 estimates collected from 68 studies for the 1983–2008 period. Using meta‐regression analysis, we show how study design characteristics and publication bias can explain a large fraction of the variation across estimates. We find a short‐run output elasticity of public capital supplied at the central government level of 0.083, which increases to 0.122 in the long run. If, in addition, only core infrastructure at a regional/local level of government is considered, these estimates are almost doubled. The average output elasticity of public capital amounts to 0.106. Our results suggest that public capital is undersupplied in OECD economies.     

On Testing Conditional Sigma – Convergence*

In a cross‐section where the initial distribution of observations differs from the steady‐state distribution and initial values matter, convergence is best measured in terms of σ‐convergence over a fixed time period. For this setting, we propose a new simple Wald test for conditional σ‐convergence. According to our Monte Carlo simulations, this test performs well and its power is comparable with the available tests of unconditional convergence. We apply two versions of the test to conditional convergence in the size of European manufacturing firms. The null hypothesis of no convergence is rejected for all country groups, most single economies, and for younger firms of our sample of 49,646 firms.     

Is there convergence amongst shadow economies? International evidence

This paper examines convergence among cross-country shadow economies. Using the Phillips-Sul (2007, 2009) club convergence approach and data for over 150 countries from 1991 to 2017, the results show evidence against absolute convergence for all shadow economies; however, we find evidence of multiple convergence clubs. In particular, we find evidence for seven distinct convergence clubs and six divergent shadow economies. Each club is characterised by an increasingly larger shadow economy with the countries in club 1 having the largest shadow economy and those in club 7 having the smallest shadow economy. Using a two-way fixed effects approach, we also find that the determinants of the shadow economy are somewhat conditional on the convergence club. The existence of multiple equilibria suggests that policy makers in their attempt to combat the shadow economy would benefit by considering the different transitional paths associated with the different convergence clubs.     

Nonparametric fixed effects model for panel data with locally stationary regressors

We develop methods for inference in nonparametric time-varying fixed effects panel data models that allow for locally stationary regressors and for the time series length $T$ and cross-section size $N$ both being large. We first develop a pooled nonparametric profile least squares dummy variable approach to estimate the nonparametric function, and establish the optimal convergence rate and asymptotic normality of the resultant estimator. We then propose a test statistic to check whether the bivariate nonparametric function is time-varying or the time effect is separable, and derive the asymptotic distribution of the proposed test statistic. We present several simulated examples and two real data analyses to illustrate the finite sample performance of the proposed methods.     

Wavelet analysis of change-points in a non-parametric regression with heteroscedastic variance

In this paper we develop wavelet methods for detecting and estimating jumps and cusps in the mean function of a non-parametric regression model. An important characteristic of the model considered here is that it allows for conditional heteroscedastic variance, a feature frequently encountered with economic and financial data. Wavelet analysis of change-points in this model has been considered in a limited way in a recent study by Chen et al. (2008) with a focus on jumps only. One problem with the aforementioned paper is that the test statistic developed there has an extreme value null limit distribution. The results of other studies have shown that the rate of convergence to the extreme value distribution is usually very slow, and critical values derived from this distribution tend to be much larger than the true ones. Here, we develop a new test and show that the test statistic has a convenient null limit N(0,1) distribution. This feature gives the proposed approach an appealing advantage over the existing approach. Another attractive feature of our results is that the asymptotic theory developed here holds for both jumps and cusps. Implementation of the proposed method for multiple jumps and cusps is also examined. The results from a simulation study show that the new test has excellent power and the estimators developed also yield very accurate estimates of the positions of the discontinuities.     

Productivity, convergence and policy: a study of OECD countries and industries

This paper analyses trends in labour productivity and its underlying determinants in a panel of OECD countries from 1979 to 2002. Data Envelopment Analysis (DEA) is used to estimate a Malmquist measure of multifactor productivity (MFP) change. We decompose the growth in labour productivity into (i) net technological change (ii) input biased technical change (IBTC) (iii) efficiency change and (iv) capital accumulation. We analyse the effect of each of these factors in the transition towards the equilibrium growth paths of both labour productivity and per capita GDP for the OECD countries, controlling for the effects of different policies and institutions. The results indicate that on average gaps in productivity or income levels are narrowing down although there is no evidence to suggest that the entire OECD area comprises a single convergence “club”. Using kernel estimation methods we find that that labour productivity and per capita GDP are settling toward a twin peak (bimodal) distribution. Panel unit root tests over an extended (1960–2001) period provide general support for the convergence hypothesis. Analysis of the contributions of productivity growth within industries and sectoral composition changes show that aggregate productivity change is predominantly driven by ‘net’ within sector effects with very little contribution emerging from sectoral shifts (the ‘in-between’ static or dynamic effects resulting from higher or above average productivity industries gaining employment shares or low productivity industries losing shares).     

Nonparametric identification and estimation of current status data in the presence of death

We present a nonparametric study of current status data in the presence of death. Such data arise from biomedical investigations in which patients are examined for the onset of a certain disease, for example, tumor progression, but may die before the examination. A key difference between such studies on human subjects and the survival–sacrifice model in animal carcinogenicity experiments is that, due to ethical and perhaps technical reasons, deceased human subjects are not examined, so that the information on their disease status is lost. We show that, for current status data with death, only the overall and disease‐free survival functions can be identified, whereas the cumulative incidence of the disease is not identifiable. We describe a fast and stable algorithm to estimate the disease‐free survival function by maximizing a pseudo‐likelihood with plug‐in estimates for the overall survival rates. It is then proved that the global rate of convergence for the nonparametric maximum pseudo‐likelihood estimator is equal to O_p(n^?1/3) or the convergence rate of the estimated overall survival function, whichever is slower. Simulation studies show that the nonparametric maximum pseudo‐likelihood estimators are fairly accurate in small‐ to medium‐sized samples. Real data from breast cancer studies are analyzed as an illustration.     

Convergence in international output

This paper proposes and tests new definitions of convergence and common trends for per capita output. We define convergence for a group of countries to mean that each country has identical long-run trends, either stochastic or deterministic, while common trends allow for proportionality of the stochastic elements. These definitions lead naturally to the use of cointegration techniques in testing. Using century-long time series for 15 OECD economies, we reject convergence but find substantial evidence for common trends. Smaller samples of European countries also reject convergence but are driven by a lower number of common stochastic trends.     

Cost efficiency of Slovenian water distribution utilities: an application of stochastic frontier methods

This study estimates cost inefficiency and economies of scale of Slovenian water distribution utilities over the 1997–2003 period by employing several different stochastic frontier methods. The results indicate that significant cost inefficiencies are present in the utilities. An introduction of incentive-based price regulation scheme might help resolve this problem. However, the inefficiency scores obtained from different cost frontier models are not found to be robust. The levels of inefficiency estimates as well as the rankings depend on the econometric specification of the model. The established lack of robustness can be at least partly explained by different ability of the models to separate unobserved heterogeneity from inefficiency. Newly proposed true fixed effects model (Greene, J Econom 126:269–303, 2005; J Prod Anal 23(1):7–32, 2005) appears to perform better than the conventional panel data models with respect to distinguishing between unobserved heterogeneity and inefficiency. On the other hand, different models produce fairly robust results with respect to estimates of economies of output density, customer density and economies of scale. The optimal size of a company is found to closely corresponds to the sample median. Economies of scale are found in small-sized utilities, while large companies exhibit diseconomies of scale.

Density estimation in the uniform deconvolution model

We consider the problem of estimating a probability density function based on data that are corrupted by noise from a uniform distribution. The (nonparametric) maximum likelihood estimator for the corresponding distribution function is well defined. For the density function this is not the case. We study two nonparametric estimators for this density. The first is a type of kernel density estimate based on the empirical distribution function of the observable data. The second is a kernel density estimate based on the MLE of the distribution function of the unobservable (uncorrupted) data.     

Income convergence prospects in Europe: Assessing the role of human capital dynamics

We employ income projection models based on human capital dynamics in order to assess quantitatively the role that educational improvements are expected to play as a driver of future income convergence in Europe. We concentrate on income convergence dynamics between emerging economies in Central and Eastern Europe and Western European countries during the next 50 years. Our results indicate that improvements in human capital contribute significantly to the income convergence potential of European emerging economies. Using realistic scenarios, we quantify the effect that future human capital investment paths are expected to have in terms of speeding up the income convergence process in the region. The income projection exercise shows that the returns to education in terms of income convergence in Europe could be sizeable, although it may take relatively long for the poorer economies of the region to rip the growth benefits.