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.

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.

Official Central Bank Interventions in the Foreign Exchange Markets: A DCC Approach with Exogenous Variables

This paper assesses the impact of official central bank interventions (CBIs) on exchange rate returns, their volatility and bilateral correlations. By exploiting the recent publication of intervention data by the Bank of England, this study is able to investigate fficial interventions by a total number of four central banks, while the previous studies have been limited to three (the Federal Reserve, Bundesbank and Bank of Japan). The results of the existing literature are reappraised and refined. In particular, unilateral CBI is found to be more successful than coordinated CBI. The likely implications of these findings are then discussed.

Modelling Breaks and Clusters in the Steady States of Macroeconomic Variables

Macroeconomists working with multivariate models typically face uncertainty over which (if any) of their variables have long run steady states which are subject to breaks. Furthermore, the nature of the break process is often unknown. In this paper, we draw on methods from the Bayesian clustering literature to develop an econometric methodology which: i) finds groups of variables which have the same number of breaks; and ii) determines the nature of the break process within each group. We present an application involving a five-variate steady-state VAR.

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.

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.

Using VARs and TVP-VARs with Many Macroeconomic Variables

This paper discusses the challenges faced by the empirical macroeconomist and methods for surmounting them. These challenges arise due to the fact that macroeconometric models potentially include a large number of variables and allow for time variation in parameters. These considerations lead to models which have a large number of parameters to estimate relative to the number of observations. A wide range of approaches are surveyed which aim to overcome the resulting problems. We stress the related themes of prior shrinkage, model averaging and model selection. Subsequently, we consider a particular modelling approach in detail. This involves the use of dynamic model selection methods with large TVP-VARs. A forecasting exercise involving a large US macroeconomic data set illustrates the practicality and empirical success of our approach.

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.

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.