Macroeconomic outcomes,collective bargaining and intersectoral productivity differentials: a panel approach

Using a sample of 19 advanced countries from 1990 to 2014 and an Arellano–Bover/Blundell–Bond linear dynamic GMM estimator along with a bootstrap-based bias correction fixed effects estimator for dynamic panels, the paper examines the macroeconomic impact of collective bargaining structures in a context of varying intersectoral heterogeneity in productivity growth among the exposed and sheltered sectors of the economy. Results show a dampening impact of pattern and centralized bargaining structures on unemployment. However, strong domestic demand is a key precondition for such a favourable effect to materialize. Uncoordinated and centralized bargaining structures are the most efficient in terms of labour cost restraint while industry bargaining moderates labour cost growth as intersectoral productivity differentials widen.     

On the estimation and forecasting of international migration: how relevant is heterogeneity across countries?

This paper performs a comparative analysis of estimation as well as of out-of-sample forecasting results of more than 20 estimators common in the panel data literature using the data on migration to Germany from 18 source countries in the period 1967–2001. Our results suggest that the choice of an estimation procedure has a substantial impact on the parameter estimates of the migration function. Out-of-sample forecasting results indicate the following: (1) the standard fixed effects estimators clearly outperforms the pooled OLS estimator, (2) both the fixed effects estimators and the hierarchical Bayes estimator exhibit the superior forecast performance, (3) the fixed effects estimators outperform GMM and other instrumental variables estimators, (4) forecasting performance of heterogenous estimators is mediocre in our data set.     

Intra-Industry Trade: A Static and Dynamic Panel Data Analysis

This study examines the features and determinants of intra-industry trade (IIT), horizontal IIT (HIIT) and vertical IIT (VIIT) between Portugal and the European Union in the period 1996–2002, using a static and a dynamic panel data analysis. The findings indicate that Portuguese VIIT increased significantly during the period in accordance with the values expected for a developed country. The regression results show that there is evidence supporting the explanation of VIIT by Heckscher–Ohlin’s (HO) theory and that Portugal has comparative advantages in low-quality differentiated products. The findings support the theory that, in general, there is no positive statistical association between HIIT and HO variables. The central theme of this paper is to show that it may be preferable to use the GMM approach in empirical studies of IIT rather than pooled OLS, fixed effects or random effects estimators. The results also suggest that the GMM system estimator obtains more reasonable parameter estimates than the first-differenced GMM estimator.

Environmental Kuznets curve: Evidence from the British Household Panel Survey

A well-known hypothesis providing support for a policy that emphasizes economic growth at the expense of environmental protection is the environmental Kuznets curve (EKC) hypothesis. Although this relation has been mainly explored at the macro-economic level, there are few researches examining micro-economic level. This study looks for empirical evidence between air pollution and income using social data from the British Household Panel Survey during the period 1991–2009. The first approach refers to a fixed effects model, the second to dynamic panel data and Arellano–Bond GMM, while the third approach concerns a binary Logit model with fixed effects. The current study's fixed effects results show that there is no evidence of EKC hypothesis for the air pollutants examined. On the contrary, there is a strong evidence of EKC hypothesis for the air pollutants examined based on Arellano–Bond GMM and logit model's results with fixed effects and all the types of household income indicating that EKC hypothesis holds. Furthermore, regarding personal income, using the Arellano–Bond GMM methodology, the EKC hypothesis does not hold. This indicates that air pollution and income might be based on communitarian arrangements instead of on individualistic actions.     

Grouped coefficients to reduce bias in heterogeneous dynamic panel models with small T

We propose the grouped coefficients estimator to reduce bias in dynamic panels with small T that have a multilevel structure to the coefficient and factor loading heterogeneity. If groups are chosen such that the within-group heterogeneity is small, then the grouped coefficients estimator can lead to substantial bias reduction compared to pooled GMM dynamic panel estimators. We also propose using a Wald test that can be used to assess whether pooled estimators suffer from heterogeneity bias. We illustrate the usefulness of grouped coefficients with an application to labour demand in which the coefficients are grouped by sub-sector. Our results suggest that the standard pooled estimates are substantially biased.     

Political origins of financial structure

There is growing policy interest in the role of financial structure in promoting development. However, very little is known about how different financial structures emerge and evolve. In this paper we empirically assess the political origins of financial structure. Using difference–in difference estimation and annual data, we study the effects of democratization on financial structure in a sample of 96 countries covering the period 1970–2005. Democratization here corresponds to the event of becoming a democracy. We find that democratization leads to a more market-based financial system. Democratic change could also be incremental rather than a one off. To identify the effect of incremental democratic change on financial structure we estimate a separate model and find that democracy matters. We also find that countries with substantial democratic capital are more likely to have a market-based financial structure. Our main results are robust to a variety of controls, Arellano–Bond GMM estimation, alternative measures of democracy and financial structure, and across different samples.     

Estimation of a dynamic model of weight

The ongoing debate about the economic causes of obesity has focused on the changing relative prices of diet and exercise. This paper uses a model that explicitly includes time and spatially varying community-level urbanicity and price measures as instruments to obtain estimates of the effects of diet, physical activity, drinking, and smoking on weight. The instruments control for bias due to unobservables that may be common determinants of weight and these explanatory variables. We apply a dynamic panel system GMM estimation model to longitudinal (1991–2006) data from China to model weight and find that among adult men in China, about 5.4% of weight gain was due to declines in physical activity and 2.8–3.1% was due to dietary changes over this period. Combined, changes in physical activity and diet only explain around 8% of the short run gain in weight and about 11% of the long-run gain in weight among Chinese men.     

Gravity models in integrated panels

This paper shows that standard gravity models of foreign trade include non-stationary variables (bilateral trade and GDP of trading partners). Furthermore, gravity models are characterized by inherited cross-sectional correlation between the panel units (country pairs). Therefore, the results of the standard panel unit root tests are biased and outperformed by the simple cross-sectionally augmented panel unit root test according to Pesaran (J Appl Econom 22:265–312, 2007). Nevertheless, the fixed effects estimator is similar to the dynamic OLS or fully modified OLS, which take into account the non-stationarity of analyzed macroeconomic variables as well as possible endogeneity between output and trade.     

Testing the principle of ‘growth of the fitter’: The relationship between profits and firm growth

This paper is an empirical investigation of the evolutionary principle of ‘growth of the fitter’. Previous studies suggest that growth does not discriminate between firms according to their fitness, when this latter is proxied by productivity. We use the profit rate (operating surplus/value added) as a proxy for fitness and explore its influence on subsequent growth rates by tracking 8405 French manufacturing firms over the period 1996–2004. We overcome problems of unobserved firm-specific effects, persistence and endogeneity by using the ‘system GMM’ estimator developed by Blundell and Bond [Blundell, R., Bond, S., 1998. Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics 87, 115–143]. Whilst non-parametric plots do not reveal any obvious relationship between profit rates and subsequent growth, regression analysis identifies a small positive influence. Considering the reciprocal influence of growth on profit rates, positive and significant results suggest that ‘Penrose effects’ are not a dominant feature of firm dynamics.     

Capital adjustment cost and inconsistency in income‐based dynamic panel models with fixed effects

After the seminal work of Nickell (1981), a vast literature demonstrates the inconsistency of ‘conditional convergence’ estimator in income‐based dynamic panel models with fixed effects when the time horizon (T) is short but the sample of countries (N) is large. Less attention is given to the economic root of inconsistency of the fixed effects estimator when T is also large. Using a variant of the Ramsey growth model with long‐run adjustment cost of capital, we demonstrate that the fixed effects estimator of such models could be inconsistent when T is large. This inconsistency arises because of the long‐run adjustment cost of capital which gives rise to a negative moving average coefficient in the error term. Income convergence will be thus overestimated. We theoretically characterize the order of this inconsistency. Our Monte Carlo simulation demonstrates that the size of the bias is substantial and it is greater in economies with higher capital adjustment costs. We show that the use of instrumental variables that take into account the presence of the negative moving average term in the error will overcome this bias.     

Purchasing Power Parity and aggregation bias for a developing country: The case of Mexico

This paper investigates long-run Purchasing Power Parity (PPP) between the US and Mexico. We use a panel of disaggregated price data between the US and Mexico with a long time series to look at two types of aggregation bias. The first is examined in Imbs et al. — which we refer to as estimator aggregation bias — and the second is put forth by Broda and Weinstein — hereafter, data aggregation bias. The findings indicate substantial estimator aggregation bias and data aggregation bias. Although estimates using aggregate data and imposing homogeneous coefficients provide little evidence of PPP, findings with disaggregated data and heterogeneous coefficient estimators offer strong support. The results also suggest the presence of small-sample bias as examined in Chen and Engel, but with little effect on the qualitative results. Tradable goods and non-tradable goods show little distinction in convergence rates. Estimated half-lives are lower under flexible than fixed exchange rates and indicate rapid convergence during the Mexican peso crisis.     

Policy mix and debt sustainability: evidence from fiscal policy rules

This paper characterizes rules-based fiscal policy setting for G-3 and large EMS countries. We set up a simple fiscal policy rule and then infer on the policymakers’ reaction coefficients by testing with GMM. Our results qualify existing evidence on systematic fiscal policy in two respects. First, fiscal policy usually stabilizes public debt; and there is indeed substantial interaction between fiscal and monetary policies via the policy mix or the debt channel. Second, sustainability is achieved with a “stop–go” cycle of consolidation. Unless debt ratios are high, consolidation does not come at the cost of less cyclical stabilization.     

Financial development and financial openness nexus: the precondition of banking competition

We examine the dynamic relationship between financial development and financial openness using the pooled mean group estimator developed by Pesaran et al. (1999). Our results show that financial openness has a positive effect on financial development in the long run, but may have a negative effect in the short run. Using estimates of country-specific short-run coefficients, we also find that the adverse short-run effects of financial openness are associated with a lower degree of banking competition. The system GMM estimator also supports these findings, suggesting that the financial development and financial openness nexus is contingent on the degree of banking competition. A key policy implication is that a higher degree of banking competition is a precondition for financial openness to promote financial development.     

Comparison of SML and GMM estimators for the random coefficient logit model using aggregate data

A simulated maximum likelihood (SML) estimator for the random coefficient logit model using aggregate data is found to be more efficient than the widely used generalized method of moments estimator (GMM) of Berry et?al. (Econometrica 63:841?C890, 1995). In particular, the SML estimator is better than the GMM estimator in recovery of heterogeneity parameters which are often of central interest in marketing research. With the GMM estimator, the analyst must determine what moment conditions to use for parameter identification, especially the heterogeneity parameters. With the SML estimator, the moment conditions are automatically determined as the gradients of the log-likelihood function, and these are the most efficient ones if the model is correctly specified. Another limitation of the GMM estimator is that the product market shares must be strictly positive while the SML estimator can handle zero market share observations. Properties of the SML and GMM estimators are demonstrated in simulated data and in data from the US photographic film market.     

Small Sample Properties Of Existing And New Estimators Of The Autocorrelation Coefficient And Regression Coefficients In The Context Of Ar(1) Errors

This study systematically and comprehensively investigates the small sample properties of the existing and some new estimators of the autocorrelation coefficient and of the regression coefficients in a linear regression model when errors follow an autoregressive process of order one. The new estimators of autocorrelation coefficient proposed here are based on the jackknife procedure. The jackknife procedure is applied in two alternative ways: first to the regression itself, and second to the residuals of the regression model. Next, the performance of the existing and new estimators of autocorrelation coefficient (thirty-three in total) is investigated in terms of bias and the root mean squared errors. Finally, we have systematically compared all of the estimators of the regression coefficients (again thirty-three) in terms of efficiency and their performance in hypothesis testing. We observe that the performance of the autocorrelation coefficient estimators is dependent upon the degree of autocorrelation and whether the autocorrelation is positive or negative. We do not observe a direct link between the bias and efficiency of an estimator. The performance of the estimators of the regression coefficients also depends upon the degree of autocorrelation. If the efficiency of regression estimator is of concern, then the iterative Prais-Winsten estimator should be used since it is most efficient for the widest range of independent variables and values of the autocorrelation coefficient. If testing of the hypothesis is of concern, then the estimators based on jackknife technique are certainly superior and are highly recommended. However, for negative values of the autocorrelation coefficient, the estimators based on Quenouille procedure and iterative Prais-Winsten estimator are comparable. But, for computational ease iterative Prais-Winsten estimator is recommended.

     

On the Inconsistency of Instrumental Variables Estimators for the Coefficients of Certain Dummy Variables

In this paper we consider the asymptotic properties of the Instrumental Variables (IV) estimator of the parameters in a linear regression model with some random regressors, and other regressors that are dummy variables. The latter have the special property that the number of non-zero values is fixed, and does not increase with the sample size. We prove that the IV estimator of the coefficient vector for the dummy variables is inconsistent, while that for the other regressors is weakly consistent under standard assumptions. However, the usual estimator for the asymptotic covariance matrix of the I.V. estimator for all of the coefficients retains its usual consistency. The t-test statistics for the dummy variable coefficients are still asymptotically standard normal, despite the inconsistency of the associated IV coefficient estimator. These results extend the earlier results of Hendry and Santos (Oxf Bull Econ Stat 67:571–595, 2005), which relate to a fixed-regressor model, in which the dummy variables are non-zero for just a single observation, and OLS estimation is used.

     

Testing the lawyer-induced litigation hypothesis in Europe

Utilizing a European panel dataset, we contribute to the scant empirical literature on the lawyer-induced litigation hypothesis. To address endogeneity problems that arise when estimating the effect of the number of lawyers on civil litigation rates, we use two strategies. We first estimate our model by means of the 2SLS procedure. Second, we exploit the instrumental variable approach based on the linear GMM estimator of Arellano and Bond. The estimations result in a positive and significant effect of lawyers that is robust across the different model specifications and estimation methods in which we address endogeneity. In criminal litigation, where lawyers cannot induce demand, we find no such positive relation between lawyers and litigation.     

A generalized method of moments estimator for a spatial model with moving average errors, with application to real estate prices

This paper proposes a new GMM estimator for spatial regression models with moving average errors. Monte Carlo results are given which suggest that the GMM estimates are consistent and robust to non-normality, and the Bootstrap method is suggested as a way of testing the significance of the moving average parameter. The estimator is applied in a model of English real estate prices, in which the concepts of displaced demand and displaced supply are introduced to derive the spatial lag of prices, and the moving average error process represents spatially autocorrelated unmodelled variables.      

The impact of product market competition on GDP per capita growth in the EU countries: does the model of capitalism matter?

This article assesses whether the intensity of product market competition is a factor affecting economic growth (measured by the growth rate of real GDP per capita) and whether this impact depends on the model of capitalism. The study covers the 1997–2015 period and all EU28 countries. Product market competition is measured by two types of variables: product market regulation indicators and the number of enterprises. New elements in the analysis include, among others, nonlinear impact and overlapping observations. The regression equations are estimated on the basis of Blundell and Bond’s GMM system estimator. The results generally indicate that stronger product market regulations (and theoretically lower product market competition) are linked with faster growth of output. However, the impact of product market competition on economic growth depends on the type of capitalism. For post-socialist countries, unlike the Western European model of capitalism, more regulation tends to reduce the rate of economic growth.     

Moment Estimation of the Probit Model with an Endogenous Continuous Regressor

We propose a generalized method of moments (GMM) estimator with optimal instruments for a probit model that includes a continuous endogenous regressor. This GMM estimator incorporates the probit error and the heteroscedasticity of the error term in the first‐stage equation in order to construct the optimal instruments. The estimator estimates the structural equation and the first‐stage equation jointly and, based on this joint moment condition, is efficient within the class of GMM estimators. To estimate the heteroscedasticity of the error term of the first‐stage equation, we use the k‐nearest neighbour (k‐nn) non‐parametric estimation procedure. Our Monte Carlo simulation shows that in the presence of heteroscedasticity and endogeneity, our GMM estimator outperforms the two‐stage conditional maximum likelihood estimator. Our results suggest that in the presence of heteroscedasticity in the first‐stage equation, the proposed GMM estimator with optimal instruments is a useful option for researchers.