Network effects and the choice of mobile phone operator

This paper explores the role of network effects in the consumer’s choice of mobile phone operators in the UK. It contributes to the existing literature by taking a new approach to testing for direct network effects and by using individual-level data, which allows to analyse the impact that the immediate social network has on consumer choice in network markets. For our empirical analysis we use two sources of data: market-level data from the British telecommunications regulator OFCOM and micro-level data on consumers’ usage of mobile telephones from the survey, Home OnLine. We estimate two classes of models which illustrate the role of network effects. The first is an aggregate model of the comparative volume of on-net and off-net calls. This finds that the proportion of off-net calls falls as mobile operators charge a premium for off-net calls, but even in the absence of any price differential between on-net and off-net, there is still a form of pure network effect, where a disproportionate number of calls are on-net. The second is a model of the individual consumer’s choice of operator. This finds that individual choice shows considerable inertia, as expected, but is heavily influenced by the choices of others in the same household. There is some evidence that individual choice of operator is influenced by the total number of subscribers for each operator, but a much stronger effect is the operator choice of other household members.     

Effect of public subsidies on farm technical efficiency: a meta-analysis of empirical results

Investigating the impact of public subsidies on farm technical efficiency is becoming a critical issue in applied agricultural policy analysis. This article presents a meta-analysis of empirical results on this issue, based on data gathered from a systematic literature review. We find that, in the empirical literature, subsidies are commonly negatively associated with farm technical efficiency. Meta-regression estimation results show that the direction (significantly negative, significantly positive or non-significant) of the observed effects is sensitive to the way subsidies are modelled in the empirical studies.     

Understanding Price Variation Across Stores and Supermarket Chains: Some Implications for CPI Aggregation Methods

The empirical literature on price indices consistently finds that aggregation methods have a considerable impact, particularly when scanner data are used. This paper outlines a novel approach to test for the homogeneity of goods and hence for the appropriateness of aggregation. A hedonic regression framework is used to test for item homogeneity across four supermarket chains and across stores within each of these supermarket chains. We find empirical support for the aggregation of prices across stores which belong to the same supermarket chain. Support was also found for the aggregation of prices across three of the four supermarket chains.     

THE EFFECTIVENESS OF PRIVATE BENEFITS IN FUNDRAISING OF LOCAL CHARITIES*

This article provides an empirical analysis of the role that private benefits play in explaining charitable donations to large cultural and environmental organizations. We develop a multiple discrete choice model with differentiated products. We estimate the model using a unique data set of donor lists for the 10 largest cultural and environmental charitable organizations in the Pittsburgh metropolitan area. We find that some private benefits such as invitations to private dinner parties and special events are effective tools for fundraising. Our policy simulations suggests that the composition of private benefits has a potentially large impact on donor behavior.     

RATE‐CUTTING TAX REFORMS AND CORPORATE TAX COMPETITION IN EUROPE

While there is a large and growing number of studies on the determinants of corporate tax rates, the literature has so far ignored the fact that the behavior of governments in setting tax rates is often best described as a discrete choice decision problem. We set up an empirical model that relates a government's decision whether to cut its corporate tax rate to the country's own inherited tax and taxes in neighboring countries. Using comprehensive data on corporate tax reforms in Europe since 1980, we find evidence suggesting that the position in terms of the tax burden imposed on corporate income relative to geographical neighbors strongly affects the probability of rate‐cutting tax reforms. Countries are particularly likely to cut their statutory tax rate if the inherited tax is high and if they are exposed to low‐tax neighbors.     

SECULAR MEAN REVERSION AND LONG‐RUN PREDICTABILITY OF THE STOCK MARKET

The empirical financial literature reports evidence of mean reversion in stock prices and the absence of out‐of‐sample return predictability over horizons shorter than 10 years. Anecdotal evidence suggests the presence of mean reversion in stock prices and return predictability over horizons longer than 10 years, but thus far, there is no empirical evidence confirming such anecdotal evidence. The goal of this paper is to fill this gap in the literature. Specifically, using 141 years of data, this paper begins by performing formal tests of the random walk hypothesis in the prices of the real S&P Composite Index over increasing time horizons of up to 40 years. Although our results cannot support the conventional wisdom that the stock market is safer for long‐term investors, our findings speak in favor of the mean reversion hypothesis. In particular, we find statistically significant in‐sample evidence that past 15‐17 year returns are able to predict the future 15‐17 year returns. This finding is robust to the choice of data source, deflator, and test statistic. The paper continues by investigating the out‐of‐sample performance of long‐horizon return forecasting based on the mean‐reverting model. These latter tests demonstrate that the forecast accuracy provided by the mean‐reverting model is statistically significantly better than the forecast accuracy provided by the naive historical‐mean model. Moreover, we show that the predictive ability of the mean‐reverting model is economically significant and translates into substantial performance gains.     

Foreign Direct Investment and Technology Transfer Under Uncertainty in a Liberalizing Host Economy

This paper tackles the issue of investment and optimal ‘choice’ of market structure for a foreign multinational enterprise (MNE) in a newly liberalized economy under uncertainty and in the presence of sunk costs. A minimalist duopolistic model is developed whereby a foreign investor's subjective belief about the probability distribution of policy uncertainty is endogenized as a function of the aggregate output in the tradable goods sector. The main propositions derived from the model are consistent with some unconventional empirical findings in the literature on Foreign Direct Investment (FDI), technology transfer to and crowding out of domestic firms in LDEs.     

Entry strategies into China: The choice between Joint Ventures and Wholly Foreign-Owned Enterprises: An application to the Italian manufacturing sector

We investigate the entry choice between Joint Ventures and Wholly Foreign-Owned Enterprises in the Chinese market. The analysis is based on Italian firms, thus allowing to evaluate the behaviour of also small-medium sized companies — usually less focused on by the literature on entry modes. The data, mainly from a questionnaire, supply detailed enterprise-level information and make possible to properly measure and evaluate the impact on entry modes of several variables such as innovation, internationalization, and, new in the empirical literature on this issue, corporate capital structure.     

Deriving and Testing Efficient Estimates of WTP Distributions in Destination Choice Models

Estimation of welfare measures is often a dominant driver in the empirical literature on nonmarket valuation. To this end, qualitative choice models based on random utility theory have been widely employed in outdoor recreation studies. A frequent goal of applied studies has been the estimation of welfare changes associated with site attribute changes at recreation sites in order to inform regulatory policy and resource management. We review the evolution of the methodology of random utility theory in this field with a focus on taste heterogeneity models and then focus on the recent proposal of specifying utility in the WTP-space (Train K, Weeks M (2005) Discrete choice models in preference space and willing-to-pay space. In: Scarpa R, Alberini A (eds) Applications of simulation methods in environmental and resource economics, chapter 1. Springer, Dordrecht, pp 1–16). Our empirical application is on outdoor alpine recreation data. We emphasize the efficiency and direct testing that using the maximum simulated likelihood estimator affords to practitioners using the WTP-space approach, and illustrate these with examples.     

University IPRs and knowledge transfer: is university ownership more efficient?

This paper addresses an issue that has been largely ignored so far in the empirical literature on the role of patents in university–industry knowledge transfer: does it matter who owns the patents on university research? We observe that especially in Europe, many patents in which university researchers are listed as inventors are not owned by the university. From a literature review, we conclude that private ownership of university patents may reduce the efficiency of the knowledge transfer process. This hypothesis is put to an empirical test, using data on patents in six European countries. Specifically, we assess whether university-owned patents (in Europe) are more often applied, and/or more economically valuable, than university-invented (but not-owned) patents. Our results indicate that, after correcting for observable patent characteristics, there are only very small differences between university-owned and university-invented patents in terms of their rate of commercialization or economic value.     

Aggregation and long-memory: An analysis based on the discrete Fourier transform

Datasets constructed via temporal aggregation or skip sampling are widely used by empirical studies in economics and finance, which leads to substantive discussion and debates on the effects of temporal aggregation and choice of sampling frequency. This paper studies a key feature of data aggregation by deriving the representation of the discrete Fourier transform (dft) of the aggregated series considering the aliasing effect. Analyses are not limited to the spectrum of the stationary series under aggregation, but extended to the periodogram of the non-stationary series. We further apply our results of the dft to a particular example of fractional processes under aggregation. We show that the estimates of the long-memory parameter are the same for the temporally aggregated series and the original one if the same bandwidths are used, regardless of the stationarity of the series. The theoretical findings are empirically verified by the analysis of S&P 500 volatility from 1928 to 2011.     

Human Capital Theory and Education Policy in Australia

The human capital model is the basis of neo-classical analysis of labour markets, education and economic growth. However, education policy in Australia has been influenced by models based on screening theory and public choice theory which yield the policy implication that reductions in education spending are generally desirable. In this paper, the competing models, and the evidence supporting them, are surveyed. It is concluded that the human capital model is strongly supported by the available evidence. The predictions of screening models are generally not supported by empirical tests, except where they coincide with those of the human capital model. The main evidence supporting the public choice model, derived from the literature on educational production functions, is shown to be deficient. The principal policy conclusion of the paper is that the negative effects of recent cuts in education spending will outweigh any benefits achieved through reductions in public debt.     

Sudden stops and output: an empirical Markov switching analysis

Sudden stops and their negative effects on GDP have recently received increased attention because quantitative easing has led to substantial capital inflows into emerging economies. We extend the empirical literature on the impact of sudden stops on GDP by proposing an alternative econometric approach which is multivariate, nonlinear and uses a novel way to identify sudden stops. We estimate a Markov switching vector autoregression with a latent variable indicating whether the economy is in a sudden stop regime. We use the maximum fraction of forecast error variance approach for partial structural identification of the vector autoregression model. Beyond confirming findings from the existing empirical literature on sudden stops, our results additionally show that (i) sudden stops are associated with regime switches (i.e., breaks in the behavior of economic variables), which have significantly negative and permanent effects on GDP; (ii) impulse responses to net capital inflow shocks are regime dependent with economies being more vulnerable to shocks during the sudden stop regime; and (iii) there were different main drivers of the output decline in historical sudden stop episodes.     

Endogeneity and panel data in growth regressions: A Bayesian model averaging approach

Bayesian model averaging (BMA) has been successfully applied in the empirical growth literature as a way to overcome the sensitivity of results to different model specifications. In this paper, we develop a BMA technique to analyze panel data models with fixed effects that differ in the set of instruments, exogeneity restrictions, or the set of explanatory variables in the regression. The large model space that typically arises can be effectively analyzed using a Markov Chain Monte Carlo algorithm. We apply our technique to investigate the effect of foreign aid on per capita GDP growth. We show that BMA is an effective tool for the analysis of panel data growth regressions in cases where the number of models is large and results are sensitive to model assumptions.     

Forecasting volatility in the Chinese stock market under model uncertainty

Volatility forecasting is an important issue in empirical finance. In this paper, the main purpose is to apply the model averaging techniques to reduce volatility model uncertainty and improve volatility forecasting. Six GARCH-type models are considered as candidate models for model averaging. As to the Chinese stock market, the largest emerging market in the world, the empirical study shows that forecast combination using model averaging can be a better approach than the individual forecasts.     

Cost thresholds,cut-offs and sensitivities in stated choice analysis: Identification and implications

Within the discrete choice literature, there is growing recognition that some respondents do not process all attributes when evaluating their choice outcomes. Worryingly, the cost attribute is often among those attributes that are likely to be ignored by respondents. We use probabilistic decision process models (similar in form to latent class models, but where we define the classes to describe specific heuristics) to facilitate situations where respondents adopt cost thresholds and cut-offs. We further develop this model to address the potential confounding between preference heterogeneity and processing heterogeneity by simultaneously allowing for a segmentation of respondents based on their sensitivities to cost. Results, based on an empirical dataset on the existence value of rare fish species in Ireland, provide further confirmation that a share of respondents did not attended to cost. Importantly, however, when heterogeneity to cost levels is accounted for the inferred incidence of complete non-attendance is markedly lower, to the extent that when cost thresholds and cut-offs are also accommodated it almost disappears. This modelling approach leads to significant gains in model fit and has important implications for welfare analysis.     

The Salient Issue of Issue Salience

This paper proposes a model where the set of issues that are decisive in an election (i.e., the set of salient issues) is endogenous. The model takes into account a key feature of the policy-making process, namely, that the decision-maker faces time and budget constraints that prevent him from addressing all of the issues that are on the agenda. We show that this feature creates a rationale for a policy-motivated decision-maker to manipulate his policy choice in order to influence which issues will be salient in the next election. We identify three motivations for the decision-maker to manipulate his policy choice for salience purposes. One is to make salient an issue on which he has an electoral advantage. A second motivation is to defuse the salience of an issue on which he is electorally weak, which is accomplished by either implicitly committing to a policy outcome or triggering a change of salient issue for the challenger. A third motivation is to induce the opposition party to nominate a candidate who, if elected, will implement a policy that the incumbent decision maker finds more palatable.     

Approval of social security disability appeals: analysis of judges’ decisions

This article is an empirical analysis of decisions by judges regarding requests by individuals for disability benefits. Applicants for disability benefits who are twice denied through the normal process can appeal to one of the Social Security Administration’s administrative law judges, who hold appointed positions. The data for this study are taken from decisions made by approximately 1000 judges for cases heard from 2010 through 2012. Using each judge as a unit of observation, the data reveal the number of cases heard and the number of approvals granted. We augmented the data with additional information on the presiding judge, and with data from the state in which the court resides. The purpose of the study is to determine whether a simple model can explain, first, the volume of decisions rendered at the judge level and, second, the proportion of approvals. Results indicate that the volume of decisions can be explained in part by the judge’s recent record of leniency. Evidence also supports the hypothesis that approval outcomes depend on judges’ professional tenure and economic factors in the state of jurisdiction.     

A Bayesian approach to aggregation in demand systems: smoothing with perfect substitution priors

Demand analysis requires aggregation of commodities. Some are imposed at the data collection level, leaving some for the estimation level. When data are collected, the implicit assumption underlying the aggregation is perfect substitutability: one gallon of gasoline is viewed by consumers as equivalent to another gallon; hence, the two are added together. While such aggregation can be carried out further by the data analyst, it is difficult to incorporate perfect substitutability into the estimation of direct demand systems. Perfect substitution in that context implies discontinuous demand functions, which are not nested within standard empirical demand systems. Perfect substitution is much more easily handled in a system of inverse demands, though an empirical method to impose perfect substitutability in an inverse demand system has not previously appeared in the literature. In this article, we develop such a method, which allows perfect substitutability to be imposed as a prior restriction. We use Leamer’s information contract curve as a tool to flexibly impose the substitution restriction and to investigate consistency between the data and prior. We illustrate the method with an application to inverse demands for fish in Korea.     

Inattentive agents and disagreement about economic activity

This paper evaluates empirically the (in)consistency of disagreement in survey forecasts with the prediction of sticky information models à la Mankiw-Reis, in which only a fraction of agents update their information sets at every period. To address this issue, a dynamic stochastic general equilibrium (DSGE) model that features agents’ infrequent information updating as well as nominal rigidities is fit to U.S. data. We find that the survey disagreement shares two pivotal characteristics with its model-based counterparts: (i) disagreement can be predicted by agents’ average forecast revisions reflecting the arrival of shocks; and (ii) disagreement exhibits a U-shaped relationship against the deviation of output growth from its steady state. These features arise because the arrival of new information elevates disagreement among informed and uninformed agents. Our findings indicate a substantial degree of infrequent information updating in the survey disagreement. The existing literature often uses survey disagreement as a proxy for macroeconomic uncertainty, but our finding suggests that it is unlikely to be an appropriate measure.