The Role of Institutions in Cross-Section Income and Panel Data Growth Models: A Deeper Investigation on the Weakness and Proliferation of Instruments

This paper investigates the role of institutions in determining per capita income levels and growth. It contributes to the empirical literature by using different variables as proxies for institutions and by developing a deeper analysis of the issues arising from the use of weak and too many instruments in per capita income and growth regressions. The cross-section estimation suggests that institutions seem to matter, regardless if they are the only explanatory variable or are combined with geographical and integration variables, although most models suffer from the issue of weak instruments. The results from the growth models provides some interesting results: there is mixed evidence on the role of institutions and such evidence is more likely to be associated with law and order and investment profile; government spending is an important policy variable; collapsing the number of instruments results in fewer significant coefficients for institutions.

A Nonlinear Panel Unit Root Test under Cross Section Dependence

We propose a nonlinear heterogeneous panel unit root test for testing the null hypothesis of unit-roots processes against the alternative that allows a proportion of units to be generated by globally stationary ESTAR processes and a remaining non-zero proportion to be generated by unit root processes. The proposed test is simple to implement and accommodates cross sectional dependence. We show that the distribution of the test statistic is free of nuisance parameters as (N, T) −! 1. Monte Carlo simulation shows that our test holds correct size and under the hypothesis that data are generated by globally stationary ESTAR processes has a better power than the recent test proposed in Pesaran [2007]. Various applications are provided.

Structural Interactions in Spatial Panels

Until recently, much effort has been devoted to the estimation of panel data regression models without adequate attention being paid to the drivers of diffusion and interaction across cross section and spatial units. We discuss some new methodologies in this emerging area and demonstrate their use in measurement and inferences on cross section and spatial interactions. Specifically, we highlight the important distinction between spatial dependence driven by unobserved common factors and those based on a spatial weights matrix. We argue that, purely factor driven models of spatial dependence may be somewhat inadequate because of their connection with the exchangeability assumption. Limitations and potential enhancements of the existing methods are discussed, and several directions for new research are highlighted.

Colonialism, European Descendants and Democracy

This paper advances that the share of European descendants in the population is a major determinant of democracy in former colonial countries. We test this hypothesis using cross-section and panel regressions with 60 developing and developed countries that were once colonies. We find that the share of European descendants can explain more than half of the difference in measures of democracy between the least and the most democratic countries in our sample. We control for other potential determinants of democracy and test for endogeneity bias using instrumental variables.

Understanding Interactions in Social Networks and Committees

While much of the literature on cross section dependence has focused mainly on estimation of the regression coefficients in the underlying model, estimation and inferences on the magnitude and strength of spill-overs and interactions has been largely ignored. At the same time, such inferences are important in many applications, not least because they have structural interpretations and provide useful interpretation and structural explanation for the strength of any interactions. In this paper we propose GMM methods designed to uncover underlying (hidden) interactions in social networks and committees. Special attention is paid to the interval censored regression model. Our methods are applied to a study of committee decision making within the Bank of England’s monetary policy committee.

Growth and Survival Determinants of Chinese Private Firms: Fieldwork evidence and econometric estimates

This paper reports on one of the first empirical attempts to investigate small firm growth and survival, and their determinants, in the Peoples’ Republic of China. The work is based on field work evidence gathered from a sample of 83 Chinese private firms (mainly SMEs) collected initially by face-to-face interviews, and subsequently by follow-up telephone interviews a year later. We extend the models of Gibrat (1931) and Jovanovic (1982), which traditionally focus on size and age alone (e.g. Brock and Evans, 1986), to a ‘comprehensive’ growth model with two types of additional explanatory variables: firm-specific (e.g. business planning); and environmental (e.g. choice of location). We estimate two econometric models: a ‘basic’ age-size-growth model; and a ‘comprehensive’ growth model, using Heckman’s two-step regression procedure. Estimation is by log-linear regression on cross-section data, with corrections for sample selection bias and heteroskedasticity. Our results refute a pure Gibrat model (but support a more general variant) and support the learning model, as regards the consequences of size and age for growth; and our extension to a comprehensive model highlights the importance of location choice and customer orientation for the growth of Chinese private firms. In the latter model, growth is explained by variables like planning, R&D orientation, market competition, elasticity of demand etc. as well as by control variables. Our work on small firm growth achieves two things. First, it upholds the validity of ‘basic’ size-age-growth models, and successfully applies them to the Chinese economy. Second, it extends the compass of such models to a ‘comprehensive’ growth model incorporating firm-specific and environmental variables.

Transmission of macro-liquidity shocks to liquidity-sorted stock portfolios’ returns: The role of the financial crisis

This study examines the impact of macro-liquidity shocks on the returns of UK stock portfolios sorted on the basis of a series of micro-liquidity measures. The macro-liquidity shocks are extracted on the meeting days of the Bank of England Monetary Policy Committee relative to market expectations embedded in futures contracts on the 3-month LIBOR during the period June 1999- December 2009. We report definitive evidence that these shocks are transmitted to the cross-section of liquidity-sorted portfolios, with most liquid stocks playing a very active role. Our results emphatically document that the shocks-returns relationship has reversed its sign during the recent financial crisis; the standard inverse relationship between interest rate surprises and portfolios’ returns before the crisis has turned into positive during the crisis. This finding confirms the inability of interest rate cuts to boost returns in the shortrun during the crisis, because these were perceived by market participants as a signal of a deteriorating economic outlook.

Spatial Interactions in Hedonic Pricing Models: The Urban Housing Market of Aveiro, Portugal

Spatial heterogeneity, spatial dependence and spatial scale constitute key features of spatial analysis of housing markets. However, the common practice of modelling spatial dependence as being generated by spatial interactions through a known spatial weights matrix is often not satisfactory. While existing estimators of spatial weights matrices are based on repeat sales or panel data, this paper takes this approach to a cross-section setting. Specifically, based on an a priori definition of housing submarkets and the assumption of a multifactor model, we develop maximum likelihood methodology to estimate hedonic models that facilitate understanding of both spatial heterogeneity and spatial interactions. The methodology, based on statistical orthogonal factor analysis, is applied to the urban housing market of Aveiro, Portugal at two different spatial scales.

Estimation of the Spatial Weights Matrix under Structural Constraints

While estimates of models with spatial interaction are very sensitive to the choice of spatial weights, considerable uncertainty surrounds de nition of spatial weights in most studies with cross-section dependence. We show that, in the spatial error model the spatial weights matrix is only partially identi ed, and is fully identifi ed under the structural constraint of symmetry. For the spatial error model, we propose a new methodology for estimation of spatial weights under the assumption of symmetric spatial weights, with extensions to other important spatial models. The methodology is applied to regional housing markets in the UK, providing an estimated spatial weights matrix that generates several new hypotheses about the economic and socio-cultural drivers of spatial di¤usion in housing demand.

Firm size and trade secret intensity: evidence from the Economic Espionage Act

This paper considers trade secrecy as an appropriation mechanism in the context ofb the US Economic Espionage Act (EEA) 1996. We examine the relation between trade secret intensity and firm size, using a cross section of 95 court cases. The paper builds on extant work in three respects. First, we create a unique body of evidence, using EEA prosecutions from 1996 to 2008. Second, we use an econometric approach to measurement, estimation and hypothesis testing. This allows us comprehensively to test the robustness of findings. Third, we focus on objectively measured valuations, instead of the subjective, self-reported values used elsewhere. We find a stable, robust value for the elasticity of trade secret intensity with respect to firm size, which indicates that a 10% reduction in firm size leads to a 7% increase in trade secret intensity. We find that this result is not sensitive to industrial sector, sample trimming, or functional form.

Extending contingency: the impact of strategy, technology, size and business environment on the organisational form of small Chinese firms

This paper is inspired by articles in the last decade or so that have argued for more attention to theory, and to empirical analysis, within the well-known, and long-lasting, contingency framework for explaining the organisational form of the firm. Its contribution is to extend contingency analysis in three ways: (a) by empirically testing it, using explicit econometric modelling (rather than case study evidence) involving estimation by ordered probit analysis; (b) by extending its scope from large firms to SMEs; (c) by extending its applications from Western economic contexts, to an emerging economy context, using field work evidence from China. It calibrates organizational form in a new way, as an ordinal dependent variable, and also utilises new measures of familiar contingency factors from the literature (i.e. Environment, Strategy, Size and Technology) as the independent variables. An ordered probit model of contingency was constructed, and estimated by maximum likelihood, using a cross section of 83 private Chinese firms. The probit was found to be a good fit to the data, and displayed significant coefficients with plausible interpretations for key variables under all the four categories of contingency analysis, namely Environment, Strategy, Size and Technology. Thus we have generalised the contingency model, in terms of specification, interpretation and applications area.

Growth and Survival Determinants of Chinese Private Firms: Fieldwork evidence and econometric estimates

This paper reports on one of the first empirical attempts to investigate small firm growth and survival, and their determinants, in the Peoples’ Republic of China. The work is based on field work evidence gathered from a sample of 83 Chinese private firms (mainly SMEs) collected initially by face-to-face interviews, and subsequently by follow-up telephone interviews a year later. We extend the models of Gibrat (1931) and Jovanovic (1982), which traditionally focus on size and age alone (e.g. Brock and Evans, 1986), to a ‘comprehensive’ growth model with two types of additional explanatory variables: firm-specific (e.g. business planning); and environmental (e.g. choice of location). We estimate two econometric models: a ‘basic’ age-size-growth model; and a ‘comprehensive’ growth model, using Heckman’s two-step regression procedure. Estimation is by log-linear regression on cross-section data, with corrections for sample selection bias and heteroskedasticity. Our results refute a pure Gibrat model (but support a more general variant) and support the learning model, as regards the consequences of size and age for growth; and our extension to a comprehensive model highlights the importance of location choice and customer orientation for the growth of Chinese private firms. In the latter model, growth is explained by variables like planning, R&D orientation, market competition, elasticity of demand etc. as well as by control variables. Our work on small firm growth achieves two things. First, it upholds the validity of ‘basic’ size-age-growth models, and successfully applies them to the Chinese economy. Second, it extends the compass of such models to a ‘comprehensive’ growth model incorporating firm-specific and environmental variables.

Stock Market Liquidity and Macro-Liquidity Shocks: Evidence from the 2007-2009 Financial Crisis

We develop an empirical framework that links micro-liquidity, macro-liquidity and stock prices. We provide evidence of a strong link between macro-liquidity shocks and the returns of UK stock portfolios constructed on the basis of micro-liquidity measures between 1999-2012. Specifically, macro-liquidity shocks, which are extracted on the meeting days of the Bank of England Monetary Policy Committee relative to market expectations embedded in 3-month LIBOR futures prices, are transmitted in a differential manner to the cross-section of liquidity-sorted portfolios, with liquid stocks playing the most active role. We also find that there is a significant increase in shares’ trading activity and a rather small increase in their trading cost on MPC meeting days. Finally, our results emphatically document that during the recent financial crisis the shocks-returns relationship has reversed its sign. Interest rate cuts during the crisis were perceived by market participants as a signal of deteriorating economic prospects and reinforced “flight to safety” trading.

Intergenerational Mobility and the Informative Content of Surnames

We propose a new methodology for measuring intergenerational mobility in economic wellbeing. Our method is based on the joint distribution of surnames and economic outcomes. It circumvents the need for intergenerational panel data, a long-standing stumbling block for understanding mobility. A single cross-sectional dataset is su cient. Our main idea is simple. If `inheritance' is important for economic outcomes, then rare surnames should predict economic outcomes in the cross-section. This is because rare surnames are indicative of familial linkages. Of course, if the number of rare surnames is small, this won't work. But rare surnames are abundant in the highly-skewed nature of surname distributions from most Western societies. We develop a model that articulates this idea and shows that the more important is inheritance, the more informative will be surnames. This result is robust to a variety of di erent assumptions about fertility and mating. We apply our method using the 2001 census from Catalonia, a large region of Spain. We use educational attainment as a proxy for overall economic well-being. Our main nding is that mobility has decreased among the di erent generations of the 20th century. A complementary analysis based on sibling correlations con rms our results and provides a robustness check on our method. Our model and our data allow us to examine one possible explanation for the observed decrease in mobility. We nd that the degree of assortative mating has increased over time. Overall, we argue that our method has promise because it can tap the vast mines of census data that are available in a heretofore unexploited manner.