Hausman-Taylor Model Research Articles

Did Relational Capital Matter During the Financial Crisis?

This paper aims to contribute to the body of empirical studies that address the importance of investments in companies’ relationships and the way in which they influence value creation in the global economic crisis. We employ linear panel analysis using the Hausman-Taylor model to analyse panel data for companies from the five largest European countries in the period 2004-2011. Different types of exogenous and endogenous links which a company could have in different stages of the crisis are investigated. The findings suggest that there is a statistically significant and positive link between relational capital and a firm’s value. Moreover, we identify several differences in the significance of the inputs during different crisis periods. The study provides both theoretical and practical insights into investments in intangibles for framing strategy decisions with a particular focus on the role of relational capital. This could provide scholars and practitioners with a working basis for understanding connections and the implications of strategizing in the context of a company’s networks.

Transportation Costs and US Manufacturing FDI

AbstractIn empirical models of foreign direct investment (FDI), distance is most often used to proxy for transportation costs and other pure‐trade costs. Given that distance is time invariant but transportation costs are not, this approach is less than satisfactory when actual transportation costs rise and fall over time.The contribution of this work is to explicitly control for transportation costs and thereby better understand their impact on FDI. We explore the impact of shipping costs on total US FDI stocks abroad, manufacturing stocks and service stocks using measures of sea‐shipping and air‐shipping costs in a Hausman–Taylor model that controls for endogeneity and allows for time‐invariant variables such as distance. We find that transportation costs have a positive and statistically significant relationship with US total and manufacturing FDI, suggesting a substitute relationship between FDI and trade flows consistent with horizontal MNE activity. As one would expect, these costs are insignificant for service stocks.

Did Relational Capital Matter During the Financial Crisis?

This paper aims to contribute to the body of empirical studies that address the importance of investments in companies’ relationships and the way in which they influence value creation in the global economic crisis. We employ linear panel analysis using the Hausman–Taylor model to analyse panel data for companies from the five largest European countries in the period 2004–2011. Different types of exogenous and endogenous links which a company could have in different stages of the crisis are investigated. The findings suggest that there is a statistically significant and positive link between relational capital and a firm’s value. Moreover, we identify several differences in the significance of the inputs during different crisis periods. The study provides both theoretical and practical insights into investments in intangibles for framing strategy decisions with a particular focus on the role of relational capital. This could provide scholars and practitioners with a working basis for understanding connections and the implications of strategizing in the context of a company’s networks.

Is all space created equal? Uncovering the relationship between competing land uses in subdivisions

Evaluating the importance of different forms of open space to households requires an evaluation of the service flows provided by each type of open space. For many non-market goods, these flows occur over multiple spatial scales and require analysis that simultaneously accounts for capitalization at each scale. To meet this often overlooked need, we apply a newly developed extension to the Hausman-Taylor model that treats multiple housing transactions occurring in a spatial location as a panel. This methodology allows us to account for omitted variables within a subdivision while instrumenting for variables identified through differences between subdivisions. We measure capitalization of open space at three distinct spatial extents: adjacency, walkability, and subdivision-wide. We find that the interactions between subdivision open space and private open space in the form of lot size change from complementarity at small scales to substitutability at large scales. These results confirm much of the intuition developed by ecologists and public planners on the likely service flows associated with open space and show how our approach to accounting for multiple spatial scales of capitalization in the evaluation of non-market goods could be beneficial to other areas of applied research.

Does price matter? Overseas students in UK higher education

This paper explores the determinants of the choice of UK universities by overseas undergraduate applicants. We use data on overseas applicants in Business Studies and Engineering from 2002 to 2007, to 97 UK universities. Estimating using a Hausman–Taylor model to control for the possible correlation between our explanatory variables and unobservable university-level effects, we find that the fees charged may influence the application decision of some students, but that any relationship between levels of fees and applications is non-linear. The quality of education provided is positively and significantly related to the number of applications. Proximity to London and the existing popularity of a university among home applicants, are also significant predictors of university applications.

Endogeneity in Panel Data Models with Time-Varying and Time-Fixed Regressors: To IV or Not IV?

We analyse the problem of parameter inconsistency in panel data econometrics due to the correlation of exogenous variables with the error term. A common solution in this setting is to use Instrumental-Variable (IV) estimation in the spirit of Hausman-Taylor (1981). However, some potential shortcomings of the latter approach recently gave rise to the use of non-IV two-step estimators. Given their growing number of empirical applications, we aim to systematically compare the performance of IV and non-IV approaches in the presence of time-fixed variables and right hand side endogeneity using Monte Carlo simulations, where we explicitly control for the problem of IV selection in the Hausman-Taylor case. The simulation results show that the HausmanTaylor model with perfect-knowledge about the underlying data structure (instrument orthogonality) has on average the smallest bias. However, compared to the empirically relevant specification with imperfect-knowledge and instruments chosen by statistical criteria, the non-IV rival performs equally well or even better especially in terms of estimating variable coefficients for timefixed regressors. Moreover, the non-IV method tends to have a smaller root mean square error (rmse) than both Hausman-Taylor models with perfect and imperfect knowledge about the underlying correlation between r.h.s variables and residual term. This indicates that it is generally more efficient. The results are roughly robust for various combinations in the time and cross-section dimension of the data.

Trade in Services: Does Gravity Hold?

This paper assesses the determinants of trade in services using a gravity model, with particular attention given to the role of barriers to services trade. Initially, the application of the gravity equation to services trade is examined. A variety of econometric estimators are tested and the Hausman-Taylor model is found to be the best estimator. The gravity model fits services trade flows in a similar manner to trade in goods. Wealth of countries and a common language are the most important determinants of services trade, distance is generally found to be insignificant. A variable measuring barriers to services trade is introduced into the gravity equation. Although the variable is only found to be weakly significant, a quantified set of tariff equivalents of those barriers is estimated.

Trade in Services: Does Gravity Hold? A Gravity Model Approach to Estimating Barriers to Services Trade

This paper assesses the determinants of trade in services using a gravity model, with particular attention given to the role of barriers to services trade. Initially, the application of the gravity equation to services trade is examined. A variety of econometric estimators are tested and the Hausman-Taylor model is found to be the best estimator. The gravity model fits services trade flows in a similar manner to trade in goods. Wealth of countries and a common language are the most important determinants of services trade, distance is generally found to be insignificant. A variable measuring barriers to services trade is introduced into the gravity equation. Although the variable is only found to be weakly significant, a quantified set of tariff equivalents of those barriers is estimated.

Alternative Techniques for Estimation of Cross-Section Gravity Models

This paper compares four different estimators with respect to their suitability for cross-section gravity model estimation. In many circumstances, a Hausman–Taylor approach can be recommended. This framework may provide consistent parameter estimates, when OLS or the traditional random-effects model are biased. In contrast to the fixed-effects approach, it allows to estimate parameters of variables such as GDP or GDP per capita, which vary only in a single dimension. The Hausman–Taylor model deserves attention in the estimation of cross-sectional gravity models.

The Estimation of Time-Invariant Variables in Panel Analyses with Unit Fixed Effects

This paper analyzes the estimation of time-invariant variables in panel data models with unit-effects. We compare three procedures that have frequently been employed in comparative politics, namely pooled-OLS, random effects and the Hausman-Taylor model, to a vector decomposition procedure that allows estimating time-invariant variables in an augmented fixed effects approach. The procedure we suggest consists of three stages: the first stage runs a fixed-effects model without time-invariant variables, the second stage decomposes the uniteffects vector into a part explained by the time-invariant variables and an error term, and the third stage re-estimates the first stage by pooled-OLS including the time invariant variables plus the error term of stage 2. We use Monte Carlo simulations to demonstrate that this method works better than its alternatives in estimating typical models in comparative politics. Specifically, the unit fixed effects vector decomposition technique performs better than both pooled OLS and random effects in the estimation of time-invariant variables correlated with the unit effects and better than Hausman-Taylor in estimating the time-invariant variables correlated with the unit effects. Finally, we re-analyze recent work by Huber and Stephens (2001) as well as by Beramendi and Cusack (2004). These analyses seek to cope with the problem of time-invariant variables in panel data.

Panel Models in Sociological Research: Theory into Practice

A selection of panel studies appearing in the American Sociological Review and the American Journal of Sociology between 1990 and 2003 shows that sociologists have been slow to capitalize on the advantages of panel data for controlling unobservables that threaten causal inference in observational studies. This review emphasizes regression methods that capitalize on the strengths of panel data for consistently estimating causal parameters in models for metric outcomes when measured explanatory variables are correlated with unit-specific unobservables. Both static and dynamic models are treated. Among the major subjects are fixed versus random effects methods, Hausman tests, Hausman-Taylor models, and instrumental variables methods, including Arrelano-Bond and Anderson-Hsaio estimation for models with lagged endogenous variables.

Efficient estimation of panel data models with strictly exogenous explanatory variables

With panel data, exogeneity assumptions imply many more moment conditions than standard estimators use. However, many of the moment conditions may be redundant, in the sense that they do not increase efficiency; if so, we may establish the standard estimators’ efficiency. We prove efficiency results for GLS in a model with unrestricted error covariance matrix, and for 3SLS in models where regressors and errors are correlated, such as the Hausman–Taylor model. For models with correlation between regressors and errors, and with unrestricted error covariance structure, we provide a simple estimator based on a GLS generalization of deviations from means.