1,721,004 research outputs found
Intergenerational Insurance
Full text linkHow should successive generations insure each other when the young can default on previously promised transfers to the old? This paper studies intergenerational insurance that maximizes the expected discounted utility of all generations subject to participation constraints for each generation. If complete insurance is unattainable, the optimal intergenerational insurance is history-dependent even when the environment is stationary. The risk from a generational shock is spread into the future, with periodic resetting. Interpreting intergenerational insurance in terms of debt, the fiscal reaction function is nonlinear and the risk premium on debt is lower than the risk premium with complete insurance
Optimal Sustainable Intergenerational Insurance
No full textOptimal intergenerational insurance is examined in a stochastic overlapping generations endowment economy with limited enforcement of risk-sharing transfers. Transfers are chosen by a benevolent planner who maximizes the expected discounted utility of all generations while respecting the participation constraint of each generation. We show that the optimal sustainable intergenerational insurance is history dependent. The risk from a shock is unevenly spread into the future, generating heteroscedasticity and autocorrelation of consumption even in the long run. The optimum can be interpreted as a social security scheme characterized by a minimum welfare entitlement for the old and state-contingent entitlement thresholds
Replication Data for: Intergenerational Insurance, https://doi.org/10.7910/DVN/XDBGVY, Harvard Dataverse, V1, UNF:6:Orz4NCbou3GqYAwbkDezmw== [fileUNF]
No full textThis is the replication package for "Intergenerational Insurance," accepted in 2024 by the Journal of Political Econom
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Two models of institutional design in crime and education
Full text linkThis thesis consists of two chapters looking at elements of optimal institutional
design. In Chapter two I present a model of Crime Deterrence.
One of the most ubiquitous aspects of modern society is crime. Adherence to the
law is an important component of the social contract. Not impinging on the rights
of others is the price all citizens pay for the protection of their own rights.
I consider the problem of choosing rewards and punishments to incentivize adherence
to the law and deter crime. From the point of view of the policy maker
this is a moral hazard problem. Whether or not someone has committed a crime
can never be perfectly observed. This necessarily implies that some criminals may
be falsely acquitted and some innocent citizens falsely convicted. This fact must
necessarily temper the magnitudes of both rewards and punishments as well as
the level of suspicion regarding unobserved citizens. This is similar to such classic
moral hazard problems as of providing insurance when the level of caution is
unobserved or of choosing wages for the manager of a firm when their effort is unobservable.
However, unlike most classic moral hazard problems, crime deterrence
pertains to multiple agents whose actions are unobservable.
The first thing I do in this paper is extend the workhorse moral hazard model of
Grossman and Hart [1983] to consider the scenario with many agents. I find that
the optimal contracts chosen in this scenario are identical to those in the single
agent case in every important respect. My framework considers a very simple form
of crime. I use the example of fare evasion on a train. The train firm would like everyone
to buy a ticket however they can’t monitor everyone’s action. They choose
rewards for agents who they find bought tickets, punishments for those caught
evading and rewards/punishments for those whom they are unable to monitor.
These must be such that every traveller would like to buy a ticket rather than risk
getting caught.
Another key difference between crime and many of the other issues studied in the
moral hazard literature is that of externalities. The actions of any criminal agent
induces externalities on other agents. In a society with a large amount of crime,
individual criminals may be hard to find, and vice versa. This may induce various
agents to collude with each other to commit crimes together. Furthermore,
there may be complementaries between different agents, i.e. an agents payoffs
from crime may be increasing in the intensity of crime in society. This too would
induce collusion. If the policy maker fails to take this into account when choosing
rewards and punishments, they may fail to deter crime efficiently.
I explore the implications of this by allowing for collusion in my model. Due to the
monitoring structure I choose, there are very strong externalities that arise from
an agents action. If an agent chooses not to buy a ticket, they make it less likely
that any other agent gets fined and conversely, buying a ticket makes it more likely
for other agents to be rewarded. Given this, it is possible for agents to collude
with each other. I demonstrate that the scope of collusion changes the magnitudes
of rewards and punishments, but that the nature of the optimal contracts remains
unchanged. Implying that, fixing the number of agents, if the scope for collusion
exists, it is costlier to deter wrongdoing.
The above conclusion leads naturally to the question of how the number of agents
affects the nature and magnitude of the optimal incentives. This is the final thing
I examine in this paper. I find that the magnitude of the incentives must increase
as the number of agents increases.
The third chapter deals with the question of how agents make decisions in a dynamic
framework when their preferences are reference dependent.
We consider the case of a student who must choose how much effort to put into
studying for a course that is evaluated on the basis of two exams. The student
chooses how much effort to put in to studying for the mid term, observes the
midterm score and then chooses how much effort to put in to studying for the
final.
The result depends on the ability of the student, the effort they put into studying
as well as luck. The student initially knows only the parameters of the distribution
from which their ability is drawn, not the ability itself. They also know the
distribution of the random component.
We have used a considerably simplified version of aspirations in our model. We
assume that exerting greater effort leads to higher aspirations, i.e. that aspirations
are a function of effort. This seems reasonable on the grounds that someone who
has worked very hard would feel disappointed with a low result.
The utility framework we consider has two components. The first component which
we term the ‘achievement effect’ depends on the score on the exam only. Higher
scores give higher utility. The second component, the ‘aspiration effect’ is reference
dependent, and depends on the ‘aspiration gap’ i.e. the difference between
the aspiration and the actual result. This framework admits two possibilities, that
the student is disappointed (a negative aspiration gap) or that they are elated(a
positive aspiration gap).
This paper looks at what happens in the second period, once the result of the
midterm is observed. We begin by deriving the students newly induced distribution
over their ability. We assume that everything is normally distributed, for
computational ease as well as because this seems like a reasonable assumption. We
use a reference dependent utility function. We then set up and solve an expected
utility maximization problem on the basis of these beliefs.
We find that the optimal effort is unique, given parameter values. We compare
the choice of optimal effort in our reference dependent framework with one that
would arise from a ‘fully rational’ set up with no reference dependence. We find
that the effect of aspirations on the optimal effort depends entirely on whether
exerting more effort grows aspirations faster or (expected) results.
We then investigate the effect of a better midterm result on the optimal effort in
the second exam. We find that it is almost always the case that a better first
result leads to decreased effort. So, in the majority of circumstances, a fear of
failure trumps ambition.
The cases when the opposite is true are quite interesting as well. A better first
result only induces greater effort if two conditions are true:
Firstly, the marginal expected aspirations gap must be positive, i.e. it must be the
case that exerting effort increases the expected result more than it increases the
aspiration. Secondly, the aspiration gap must be small and negative. So, if the
aspiration induced by the optimal effort is slightly higher than the expected result
then a better first result would induce more effort. Another interesting finding is
that there is a magnitude effect. The higher the aspiration, the larger the set of
aspiration gaps that correspond to increased optimal effort
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