Learning by fund-raising

From experience, fund-raisers become more efficient solicitors. This paper offers a full characterization of the optimal solicitation strategy when there are learning economies. The characterization is recursive, and does not require equilibrium computations. Our main results are that (1) the fund-raiser may now solicit a “ net free-rider”—a donor whose contribution falls below the marginal solicitation cost; (2) the number of solicitations is non-monotonic in the learning rate; and (3) a solicitation technology with high overhead but low marginal costs is likely to be optimal if income distribution in the population is relatively homogenous. The complementary case with a decreasing returns to scale technology is also considered.     

ASSESSING THE EFFECT OF PUBLIC SUBSIDIES ON FIRM R&D INVESTMENT: A SURVEY

This survey examines the empirical literature on the relationship between public R&D subsidies and private R&D investment over the past five decades. The survey reveals a considerable heterogeneity of empirical results that cannot be explained fully by methodological issues. We aim to provide further explanations of the possible causes of that heterogeneity. In particular, we emphasise a set of issues that, in our view, are critical to understanding the potential effect of public R&D subsidies on private R&D spending. Special attention is paid to the dynamic aspects and composition of firm R&D, the constraints faced by the firm (such as financial constraints), and the amount and source of public subsidies. None of these issues have been investigated in depth. We formulate a set of research assumptions to guide future empirical research in this field.     

A critical overview of privacy-preserving approaches for collaborative forecasting

Cooperation between different data owners may lead to an improvement in forecast quality—for instance, by benefiting from spatiotemporal dependencies in geographically distributed time series. Due to business competitive factors and personal data protection concerns, however, said data owners might be unwilling to share their data. Interest in collaborative privacy-preserving forecasting is thus increasing. This paper analyzes the state-of-the-art and unveils several shortcomings of existing methods in guaranteeing data privacy when employing vector autoregressive models. The methods are divided into three groups: data transformation, secure multi-party computations, and decomposition methods. The analysis shows that state-of-the-art techniques have limitations in preserving data privacy, such as (i) the necessary trade-off between privacy and forecasting accuracy, empirically evaluated through simulations and real-world experiments based on solar data; and (ii) iterative model fitting processes, which reveal data after a number of iterations.     

Modeling and Measuring Economic Reality: Reply to the Reviews

To begin with, we'd like to express our appreciation to our three commentators for their thoughtful and helpful reviews. Like the founders of the subject, we believe, and our reviewers seem to agree, that structural econometrics has the potential to enable us to develop serious working models of how different economies actually operate, and also to tell us something about the changing patterns of their growth and transformation. But both we and our reviewers agree that there is a great deal wrong with the way econometrics is practiced today.     

The Great Collapse in Value Added Trade

This paper studies the great collapse in value added trade using a structural decomposition analysis. We show that changes in vertical specialization accounted for more than 40% of the great trade collapse. Second, we find that the drop in the overall level of demand accounted for roughly a quarter of the decline in value added exports while just under one third was due to compositional changes in final demand. Finally, we demonstrate that the dichotomy between services and manufacturing sectors observed in gross exports during the great trade collapse is not apparent in value added trade data.     

Gender and ethnic earnings gaps in seven West African cities

In this paper, we measure, compare and analyse gender and ethnic earnings gaps in seven West African capitals using data from an original series of urban household surveys. Our results show that gender earnings gaps are large in all the cities in our sample with significant variations across cities. Cities with large gender earnings gaps are also where gender education gaps are wider and where the female labour market participation is highest. Decomposition of the gender gaps shows that differences in characteristics explain around 40% of the raw gender gap on average, but this varies somewhat across cities. The results of the full decomposition of the gender earnings gaps suggest that differences in sector allocation contribute, on average, to one third of gender earnings gaps. Gender gaps are very wide in the informal sector and differences in micro-firm characteristics also account for differences in self-employment earnings. In contrast to the large gender earnings gaps measured in the seven cities, majority ethnic groups do not appear to be in a systematically advantageous position on the urban labour markets in our sample of cities, and observed gaps are small compared with gender gaps. Looking at more detailed levels of ethnic disaggregation, ethnic earnings differentials are found to be systematically smaller than gender differentials. Moreover, none of the minority “favoured” groups seem to have any relation to the ethnicity of the Head of State at the time of the survey. Holding productive characteristics constant, some unexplained differences persist however.     

Restricted Hodrick–Prescott filtering in a state-space framework

The current paper extends previous results on Hodrick–Prescott (HP) filtering and shows that it is possible to implement the judgement-augmented, or restricted, HP filter within the state-space framework. The implementation entails augmenting the vector of measurements and altering one of the system matrices of the state-space model for the HP filter. Restrictions can thereby be incorporated in the HP filter, making, e.g., estimation more accessible. An application to US GDP gap estimation illustrates how the restricted filter could be usefully applied in an empirical macroeconomic setting.     

Designing a medicalized wellness service: balancing hospitality and hospital features

Medicalized environments around the world are challenged with making trade-offs between the clinical nature of the service and the customer service elements needed to deliver the service. Many medicalized wellness services have yet to achieve an effective balance between their hospitality and hospital features to generate loyalty (repeat patronage). We present a case study of a blood service organization in a developed country that, at the time of data collection (2011), was working to resolve tension between clinical goals and expectations of Millennial donors. The results identified seven principles: ‘control over booking and service interactions’; ‘build social connections’; ‘offer a luxury, indulgent experience’; ‘build relationship with customer beyond the “medical” procedure’. The three remaining principles related to hospital-like features: ‘hide the functional/medical features of the service experience’; ‘demystify the “hidden” processes’; ‘ability for the physical service environment to be modified by the customer’.