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This chapter develops robust panel estimation in the form of trimmed mean group estimation for potentially heterogenous panel regression models. It trims outlying individuals of which the sample variances of regressors are either extremely small or large. The limiting distribution of the trimmed estimator can be obtained in a similar way to the standard mean group (MG) estimator, provided the random coefficients are conditionally homoskedastic. The authors consider two trimming methods. The first one is based on the order statistic of the sample variance of each regressor. The second one is based on the Mahalanobis depth of the sample variances of regressors. The authors apply them to the MG estimation of the two-way fixed effects model with potentially heterogeneous slope parameters and to the common correlated effects regression, and the authors derive limiting distribution of each estimator. As an empirical illustration, the authors consider the effect of police on property crime rates using the US state-level panel data.

Productivity growth in Italy has been persistently anemic and lagged that of the euro area over the period 1999–2015, while the indebtedness of its corporate sector increased. Using the ORBIS firm-level database, this chapter studies the long-term impact of persistent corporate-debt accumulation on the productivity growth of Italian firms, and investigates whether total factor productivity (TFP) growth varies with the level of corporate indebtedness. The authors employ a novel estimation technique proposed by Chudik, Mohaddes, Pesaran, & Raissi (2017) to account for dynamics, bi-directional feedback effects, cross-firm heterogeneity, and cross-sectional dependence arising from unobserved common factors (e.g., oil price shocks, labor and product market frictions, and the stance of the global financial cycle). Filtering out the effects of unobserved common factors and controlling for firm-specific characteristics, the authors find significant negative effects of persistent corporate-debt build-up on firms’ TFP growth on average, and weak evidence of a threshold level of corporate debt, beyond which productivity growth drops off significantly. The results have strong policy implications, for example the design of the tax system should discourage persistent corporate-debt accumulation, and effective and timely frameworks to reduce corporate-debt overhangs are essential.

We examine a large sample of some 100 economies worldwide to study the impact of financial sector size expansion on labor market performance. Simple linear dynamic panel data models inspired by the well-developed finance-growth literature suggest that (on average) a larger financial sector is beneficial for the labor market as it reduces unemployment rates. However, estimating country- and period-specific benchmark levels of financial sector size, we document that the relative contribution of finance vanishes with excessive levels of finance, and excessive levels of credit may actually be detrimental to employment. These non-linearities in the finance-unemployment nexus are more pronounced within developed economies. Overall, our study sheds new light on the ongoing controversy about the impact of the financial sector on societal well-being and highlights the importance of monitoring the expansion of the financial sector, in particular when it comes to credit markets.

European Union (EU) central and eastern economies have gone through a process of structural change since 1989, when the post-communist transition started. This process was afterwards reinforced by the three EU enlargement waves that took place in 2004, 2007 and 2013. Though exhibiting low levels of aggregate productivity, this group of countries joined the EU with higher levels of human capital than the southern member states, an advantage that should have accelerated real convergence towards the EU15. However, evidence to date suggests that the convergence process came to a halt in 2007–2008 when massive capital inflows stopped, highlighting the fragilities of the growth strategies implemented so far. In these peripheral countries, structural change has been characterised by an expanding services sector alongside growing income inequality. The two strands of literature on these issues highlight that: (a) an expanding services sector may not be detrimental for growth, quite the opposite, depending on services composition and on the capacity of services sub-sectors to incorporate information and communication technologies (ICTs); and (b) inequality is negatively related to growth through the fiscal policy, socio-political instability, borrowing constraints to investment in education and endogenous fertility channels and positively through the savings channel and incentives. We analyse the nexus between structural change, inequality and growth in this group of countries highlighting income inequality as a potential mechanism that connects the other two variables. We provide a descriptive quantitative analysis of the profiles of structural change and income inequality in our sample and apply dynamic panel methods to investigate the existence of causality among services sector expansion, inequality and aggregate productivity considering a maximum period between 1980 and 2010.

Longitudinal regression analysis is conducted to clarify causal relations and control for unwanted influences from actor heterogeneity and state dependence on theoretically important coefficient estimates. Because strategic management contains theory on how firms differ and how firm actions are influenced by their current strategic position and recent experiences, consistency of theory and methodology often requires use of longitudinal methods. We describe the theoretical motivation for longitudinal methods and outline some common methods. Based on a survey of recent articles in strategic management, we argue that longitudinal methods are now used more frequently than before, but the use is still inconsistent and insufficiently justified by theoretical or empirical considerations. In particular, strategic management researchers should use dynamic models more often, and should test for the presence of actor effects, autocorrelation, and heteroscedasticity before applying corrections.

This paper proposes a new class of estimators for the autoregressive coefficient of a dynamic panel data model with random individual effects and nonstationary initial condition. The new estimators we introduce are weighted averages of the well-known first difference (FD) GMM/IV estimator and the pooled ordinary least squares (POLS) estimator. The proposed procedure seeks to exploit the differing strengths of the FD GMM/IV estimator relative to the pooled OLS estimator. In particular, the latter is inconsistent in the stationary case but is consistent and asymptotically normal with a faster rate of convergence than the former when the underlying panel autoregressive process has a unit root. By averaging the two estimators in an appropriate way, we are able to construct a class of estimators which are consistent and asymptotically standard normal, when suitably standardized, in both the stationary and the unit root case. The results of our simulation study also show that our proposed estimator has favorable finite sample properties when compared to a number of existing estimators.

“It should also be noted that the objective of convergence and equal distribution, including across under-performing areas, can hinder efforts to generate growth. Contrariwise, the objective of competitiveness can exacerbate regional and social inequalities, by targeting efforts on zones of excellence where projects achieve greater returns (dynamic major cities, higher levels of general education, the most advanced projects, infrastructures with the heaviest traffic, and so on). If cohesion policy and the Lisbon Strategy come into conflict, it must be borne in mind that the former, for the moment, is founded on a rather more solid legal foundation than the latter” European Commission (2005, p. 9)Adaptation of Cohesion Policy to the Enlarged Europe and the Lisbon and Gothenburg Objectives.