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Volledige samenvatting Economie van de Publieke Sector (boek & hoorcolleges) - bestuurskunde jaar 2
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Volledige samenvatting Economie van de Publieke Sector (boek & hoorcolleges).
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[TEST BANK] PUBLIC FINANCE IN CANADA 5TH EDITION BY ROSEN, GAYER, SNODDON.
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Economie van de publieke sector aantekeningen + samenvatting boek
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Complete Summary of Economics of the Public Sector (including lectures)
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Week 1Chapter 1 – Introduction
Public Finance: the field of economics that analyzes the taxing and spending activities of the
government. Also known as public sector economics or public economics.
Er wordt gekeken naar de micro-economische functies van de overheid, namelijk de manier waarop
de overheid haar middelen verdeelt en het inkomen verspreid. Ook de macro-economische functies,
zoals belasting, uitgaven van de overheid en monetair beleid om werkloosheid en prijzen.
Beleidsdoelen kunnen behaald worden door zowel uitgaven en belastingen van de overheid, als
regulaties. Zo kan de overheid het aantal bedrijven limiteren door middel van het invoeren van
belastingen op grote bedrijven, of het illegaliseren van bedrijven van een bepaalde grootte. Dit
maakt de grens van public finance soms onduidelijk.
Twee stromingen hoe de overheid moet handelen
1. Organic view of government:
Een samenleving is een natuurlijk organisme, en hoort ook zo te werken. Er is niet zoiets als
een individu, en alles heeft een bepaalde waarde tot de samenleving/staat. De doelen van de
samenleving worden bepaald door de staat.
2. Mechanistic view of government:
Overheid is niet een organisch deel van de samenleving. Het is eerder een vernuft die
gemaakt is door individuen om hun doelen beter te realiseren. Het individu staat centraal
i.p.v. de groep. De overheid hoort het goed van de individuen te behartigen, maar hoe
bepaal je wat goed is? Op z’n minst het verdedigen van haar burgers van geweld.
a. Libertarians geloven dat de staat niet goed de sociale welvaart kan verbeteren, en pleiten
daarom voor zo min mogelijk invloed van de staat.
b. Sociaal democraten aan de andere kant geloven dat de staat nodig is om een zo goed
mogelijke samenleving te creëren, en de staat moet dus ook vaak invloed uitoefenen.
De grootte van de overheid
De grootte van de overheid kan gemeten worden door het volume van de jaarlijkse uitgaven:
1. De aankoop van goederen en diensten. De overheid koopt een grote verscheidenheid aan
dingen.
2. De verplaatsing van inkomen naar mensen, bedrijven of ander overheden . De overheid
neemt inkomen van bepaalde individuen en bedrijven, en geeft het aan anderen.
3. Interest betalingen. De overheid leent geld om haar activiteiten te bekostigen, en zij moet
daarover interest betalen.
Het is echter onmogelijk om met één cijfer de gehele impact van de overheid te meten. De overheid
probeert dit wel te doen met een unified budget = the document that includes all the federal
government’s revenues and expenditures.
Sommige mensen vinden echter dat de overheid een regulatory budget moet publiceren.
Regulatory budget = an annual statement of the costs imposed on the economy by government
regulations.
,Chapter 2 – Tools of Positive Analysis
Mensen zullen vrije tijd benutten zolang de voordelen boven de kosten uitkomen. Stel dat iemand
$10 verdient. Zodra de overheid een belasting invoert van 20%, dan maakt hij maar $8 per uur. Nu is
het gebruiken van vrije tijd goedkoper geworden. Dit is niet geheel realistisch maar het is wel
simplistisch en makkelijk te begrijpen.
Dit heet het Substition effect = Het idee dat mensen meer gaan consumeren van één goed en minder
van het andere als gevolg van een prijsverandering in een van de twee goederen.
Normal good = Een goed waarvoor de vraag groeit als het inkomen groeit, en de vraag vermindert als
het inkomen daalt.
Dit leidt tot een ander effect:
Income effect = Het effect van een prijsverandering op de hoeveelheid die wordt gevraagd doordat
het inkomen van een consument veranderd is. Hierdoor zou een persoon meer werken als hij minder
verdient.
Causation vs. Correlation
These effects show the importance of establishing causal relationships between government policy
and an outcome of interest. So for X to cause Y, three conditions must hold:
1. X must precede Y.
2. The cause and effect must be correlated. Two events are correlated if they move together.
This can be positive or negative.
3. Other explanations for any observed correlation must be eliminated. This is tricky but can be
managed through the:
a. Treatment group = The group of individuals who are subject to the intervention
being studied. They receive the “treatment”.
b. Control group = The comparison group of individuals who are not subject to the
intervention being studied. They are used to rule out any other changes.
Correlation does not have to prove causation.
Experimental studies
Say you are testing unemployment benefits. The treatment group gets higher benefits than the
control group. This leads to shorter unemployment times. We do however not know if there are any
other explanations, like a higher motivation. This is a third influence, factor Z. We are speaking of a
biased estimate = an estimate that conflates the true causal impact with the impact of outside
factors.
In order to rule out other factors, we would like to know the counterfactual = what would have
happened to members of a treatment group had they not received the treatment.
Experimental (or randomized) study = An empirical study in which individuals are randomly assigned
to the treatment and control groups. With this, the people have the same characteristics averagely
speaking. Because they are chosen randomly, it is less likely that other factors influence the
correlation or causation.
,Pitfalls of experimental studies
Ethical issues = For example, a policymaker wants to know how many fewer illnesses would result
from a given reduction in pollution. Is it ethical to expose a treatment group to higher levels of
pollution?
Technical problems = What if you perform a research, but some of the people who were randomly
selected to not actually attend?
Response bias = Failing to respond to follow-up surveys requesting their information. For example,
job-training actually increase wages, but low-wage workers are less likely to report their future
wages, then the average of the post-treatment wages of the control group is artificially high.
Experiment may not behave the same way if the entire society were subjected to the policy.
This leads to a more general concern: can experiments be generalized to other populations, settings,
and related treatments? This is kind of a black box aspect of experiments, that they are contained in
themselves.
Observational studies
Experimental studies are simply out of the question for many important issues. Even if some studies
were legally and politically possible, we would still have the problem of people knowing they are
participating in a study.
For this, economists rely on observational studies = an empirical study that relies on observed data
that are not obtained from an experimental setting. Data can be collected from telephone surveys or
surveying people.
Econometrics = the statistical tools for analysing economic data. This is used to estimate causal
relationships in economic data. For example, with regression analysis, estimates can be made.
(Drawing a regression line through a scatter diagram)
Conducting an observational study
Suppose we are interested in estimating the effect of a reduction of the income tax on annual hours
of work. A change in the income tax changes the net wage rate that a person receives.
, L = Labour supply
w = wage
So if the taxes are changed, is there an observed correlation between changes in w and L.
Independent variables = variables that are thought to be causal. Variables that we think have an
effect on the dependent variable. In this case it would be wage.
Dependent variables = A variable that is thought to be an outcome. Variable that we think are
influenced by the independent variable. In this case it would be labour supply.
Regression line = the line that provides the best fit through a scatter of data points. The slope of this
line, known as the regression coefficient, is an estimate of the relationship between after-tax wages
and labour supply. Suppose the coefficient is 1.5, that means that an increase in net wage by $10 is
associated with an increase in labour supply by 15 hours per year.
Standard error = a statistical measure of how much an estimated regression coefficient might vary
from its true value. It indicates the reliability of the estimated coefficient.
There are also several types of data:
Cross-sectional data = data that contain information on different entities at a given point in time. It
relies on variation across different individual entities in order to estimate the regression line.
Time-series data = data that contain information on one single entity at different points in time.
Panel data = data that contain information on individual entities at different points in time.
Combines features of cross-sectional and time-series.
Pitfalls of observational studies
Because the data is collected in a nonexperimental setting, it is difficult to ensure that the control
group forms a valid counterfactual. One way to address this is to include other independent
variables.
Quasi-experimental studies
Experimental studies have excellent properties when it comes to eliminating bias, but they may be
difficult to perform. Observational studies have knotty problems with bias, but the data are relatively
easy to obtain.
Quasi-experimental studies (or natural experiments) = Used by empirical economists to estimate a
causal relationship. An observational study that relies on circumstances outside of the researcher’s
control to mimic random assignment.
The main difference between experimental and quasi-experimental is that experimental explicitly
randomizes people into a treatment or control group, whereas a quasi-experiment makes use of
observational data but relies on circumstances outside the researchers’ control to naturally lead to
random assignment.
Conducting a quasi-experimental study
A successful quasi-experiment hinges on whether the researcher has identified a situation in which
assignment to the treatment group is random. This can be established through several ways:
Difference-indifference = an analysis that compares changes over time in an outcome of the
treatment group to changes over the same time period in the outcome of the control group. (p. 28
gives a great example)
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