Entrepreneurial Orientation, Intangible Assets and Firm Growth: the impact of ‘Spirit and Material’ on the growth of Chinese private firms

This paper has three contributions. First, it shows how field work within small firms in PR Chinese has provided new evidence which enables us to measure and calibrate Entrepreneurial Orientation (EO), as ‘spirit’, and Intangible Assets (IA), as ‘material’, for use in models of small firm growth. Second, it uses inter-item correlation analysis and both exploratory and confirmatory factor analysis to provide new measures of EO and IA, in index and in vector form, for use in econometric models of firm growth. Third, it estimates two new econometric models of small firm employment growth in PR China, under the null hypothesis of Gibrat’s Law, using our two new index-based and vector-based measures of EO and IA. Estimation is by OLS with adjustment for heteroscedasticity, and for sample selectivity. Broadly, it finds that EO attributes have had little significant impact on small firm growth, and indeed innovativeness and pro-activity paradoxically may even dampen growth. However, IA attributes have had a positive and significant impact on growth, with networking, and technological knowledge being of prime importance, and intellectual property and human capital being of lesser but still significant importance. In the light of these results, Gibrat’s Law is generalized, and Jovanovic’s learning theory is extended, to emphasise the importance of IA to growth. These findings cast new empirical light on the oft-quoted national slogan in PR China of “spirit and material”. So far as small firms are concerned, this paper suggests that their contribution to PR China’s remarkable economic growth is not so much attributable to the ‘spirit’ of enterprise (as suggested by propaganda) as, more prosaically, to the pursuit of the ‘material’.

Stochastic Search Variable Selection in Vector Error Correction Models with an Application to a Model of the UK Macroeconomy

This paper develops methods for Stochastic Search Variable Selection (currently popular with regression and Vector Autoregressive models) for Vector Error Correction models where there are many possible restrictions on the cointegration space. We show how this allows the researcher to begin with a single unrestricted model and either do model selection or model averaging in an automatic and computationally efficient manner. We apply our methods to a large UK macroeconomic model.

Handling the endogeneity of income to health using a field experiment in Taiwan

This paper uses an exogenous increase in income for a specific sub-group in Taiwan to explore the extent to which higher income leads to higher levels of health and wellbeing. In 1995, the Taiwanese government implemented the Senior Farmer Welfare Benefit Interim Regulation (SFWBIR) which was a pure cash injection, approximately US$110 (£70) per month in 1996, to senior farmers. A Difference-in-differences (DiD) approach is used on survey data from the Taiwanese Health and Living Status of Elderly in 1989 and 1996 to evaluate the short term effect of the SFWBIR on self-assessed health, depression, and life satisfaction. Senior manufacturing workers are employed as a comparison group for the senior farmers in the natural experiment because their demographic backgrounds are similar. This paper provides evidence that the increase in income from the SFWBIR significantly improved the mental health of senior farmers by reducing the scale of depression (CES-D) by 1.718, however, it had no significant short term impact on self-assessed health or life satisfaction.

Changing Eating Habits - A Field Experiment in Primary Schools

We conduct a field experiment in 31 primary schools in England to test whether incentives to eat fruit and vegetables help children develop healthier habits. The intervention consists of rewarding children with stickers and little gifts for a period of four weeks for choosing a portion of fruit and vegetables at lunch. We compare the effects of two incentive schemes (competition and piece rate) on choices and consumption over the course of the intervention as well as once the incentives are removed and six months later. We find that the intervention had positive effects, but the effects vary substantially according to age and gender. However, we find little evidence of sustained long term effects, except for the children from poorer socio‐economic backgrounds.

The Spillover Effects of Monitoring: A Field Experiment.

We provide field experimental evidence of the effects of monitoring in a context where productivity is multi-dimensional and only one dimension is monitored and incentivised. We hire students to do a job for us. The job consists of identifying euro coins. We study the effects of monitoring and penalising mistakes on work quality, and evaluate spillovers on non- incentivised dimensions of productivity (punctuality and theft). We .nd that monitoring improves work quality only if incentives are large, but reduces punctuality substantially irrespectively of the size of incentives. Monitoring does not affect theft, with ten per cent of participants stealing overall. Our setting also allows us to disentangle between possible theoretical mechanisms driving the adverse effects of monitoring. Our .ndings are supportive of a reciprocity mechanism, whereby workers retaliate for being distrusted.

Partner Selection into Policy Relevant Field Experiments

This study investigates the issue of self-selection of stakeholders into participation and collaboration in policy-relevant experiments. We document and test the implications of self-selection in the context of randomised policy experiment we conducted in primary schools in the UK. The main questions we ask are (1) is there evidence of selection on key observable characteristics likely to matter for the outcome of interest and (2) does selection matter for the estimates of treatment eff ects. The experimental work consists in testing the e ffects of an intervention aimed at encouraging children to make more healthy choices at lunch. We recruited schools through local authorities and randomised schools across two incentive treatments and a control group. We document the selection taking place both at the level of local authorities and at the school level. Overall we nd mild evidence of selection on key observables such as obesity levels and socio-economic characteristics. We find evidence of selection along indicators of involvement in healthy lifestyle programmes at the school level, but the magnitude is small. Moreover, We do not find signifi cant di erences in the treatment e ffects of the experiment between variables which, albeit to a mild degree, are correlated with selection into the experiment. To our knowledge, this is the rst study providing direct evidence on the magnitude of self-selection in fi eld experiments.

Data-based priors for vector autoregressions with drifting coefficients

This paper proposes full-Bayes priors for time-varying parameter vector autoregressions (TVP-VARs) which are more robust and objective than existing choices proposed in the literature. We formulate the priors in a way that they allow for straightforward posterior computation, they require minimal input by the user, and they result in shrinkage posterior representations, thus, making them appropriate for models of large dimensions. A comprehensive forecasting exercise involving TVP-VARs of different dimensions establishes the usefulness of the proposed approach.

Model Uncertainty in Panel Vector Autoregressive Models.

We develop methods for Bayesian model averaging (BMA) or selection (BMS) in Panel Vector Autoregressions (PVARs). Our approach allows us to select between or average over all possible combinations of restricted PVARs where the restrictions involve interdependencies between and heterogeneities across cross-sectional units. The resulting BMA framework can find a parsimonious PVAR specification, thus dealing with overparameterization concerns. We use these methods in an application involving the euro area sovereign debt crisis and show that our methods perform better than alternatives. Our findings contradict a simple view of the sovereign debt crisis which divides the euro zone into groups of core and peripheral countries and worries about financial contagion within the latter group.

Model Uncertainty in Panel Vector Autoregressive Models.

We develop methods for Bayesian model averaging (BMA) or selection (BMS) in Panel Vector Autoregressions (PVARs). Our approach allows us to select between or average over all possible combinations of restricted PVARs where the restrictions involve interdependencies between and heterogeneities across cross-sectional units. The resulting BMA framework can find a parsimonious PVAR specification, thus dealing with overparameterization concerns. We use these methods in an application involving the euro area sovereign debt crisis and show that our methods perform better than alternatives. Our findings contradict a simple view of the sovereign debt crisis which divides the euro zone into groups of core and peripheral countries and worries about financial contagion within the latter group.

Large Bayesian VARMAs

Vector Autoregressive Moving Average (VARMA) models have many theoretical properties which should make them popular among empirical macroeconomists. However, they are rarely used in practice due to over-parameterization concerns, difficulties in ensuring identification and computational challenges. With the growing interest in multivariate time series models of high dimension, these problems with VARMAs become even more acute, accounting for the dominance of VARs in this field. In this paper, we develop a Bayesian approach for inference in VARMAs which surmounts these problems. It jointly ensures identification and parsimony in the context of an efficient Markov chain Monte Carlo (MCMC) algorithm. We use this approach in a macroeconomic application involving up to twelve dependent variables. We find our algorithm to work successfully and provide insights beyond those provided by VARs.

Prior selection for panel vector autoregressions

There is a vast literature that specifies Bayesian shrinkage priors for vector autoregressions (VARs) of possibly large dimensions. In this paper I argue that many of these priors are not appropriate for multi-country settings, which motivates me to develop priors for panel VARs (PVARs). The parametric and semi-parametric priors I suggest not only perform valuable shrinkage in large dimensions, but also allow for soft clustering of variables or countries which are homogeneous. I discuss the implications of these new priors for modelling interdependencies and heterogeneities among different countries in a panel VAR setting. Monte Carlo evidence and an empirical forecasting exercise show clear and important gains of the new priors compared to existing popular priors for VARs and PVARs.