A Better Place Employment, Growth and Migrations Report...
Transcript of A Better Place Employment, Growth and Migrations Report...
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A Better Place
Employment, Growth and Migrations
Matteo Bondesan and Marco Odifreddi
Abstract
Our model was been created in order to evaluate what happens in a “representative world” were individuals are created and divided into rationals and irrationals, employed and unemployed, where the areas of the world in which the population-‐growth rate is high are more populated and such higher number of agents are motivated by looking for another area in which the salary (at the beginning it was exogenously fixed, to be endogenized later on) is better, assuming a direct/indirect influence on the salary of the GDP and of the population-‐growth rate ( wage is function of GDP and unemployment-‐rate).
So that, the agents start moving randomly and their decision about “migration” was made only when the agent is placed on the boundary, at this point he decides if try to change State; the decision-‐criterion was though as follow: the agent looks in a cone of radius 3 and angle of 360 degrees (looking around him-‐self), if there is a piece of foreign Nation (patch) which is associated a greater salary (taking into account Unemployment-‐rate and GDP-‐Growth-‐rate), he moves.
This process still works until all the agents in all the areas of the world are able to reach the Country in which the wage is the highest.
Furthermore we want to complicate this simple structure, we have to add heterogeneity into individuals: a group of “Rational” that make the migration’s choice in function of a lot of variables as salary, education, health system, employment rate, competitive advantage of the State, GDP, Public Debt, GDH (Gross Domestic Happiness as the case of Bhutan) ,… ; and a group of “ Adaptive-‐mimic” which make the choice just looking the choice of him neighborhood.
Therefore by modifying the GDP level and the unemployment rate, the wage of different State is modifying and also the agents of the Country having an exogenous highest wage are incentive to move (after an high number of “tick”) in other States with higher GDP-‐growth rate and a relative lower unemployment rate, thanks to the great economic expansion that those Countries are discovering (e.g. BRICKS).
Our aim is to discover, analyze and try to forecast the result of our simulation, hence the structure of the new “world-‐equilibrium”.
Finally we perform some experiments as the implementation of exogenous shocks, crisis, wars … which imply direct consequences on the world’s stability and on the effects of the migration of the agents.
We want to perform such kind of experiments in a “scientific way” in order to make them replicable (probabilities, “seed” is required).
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Comments on the Code
patches-‐own [nation wage taxation_level employment_rate free_jobs employment_growth GDP_growth]
turtles-‐own [nationality employment rationality my_wage]
We have created two sets of “global variables”, the first set is concerning the “patches”, instead the second set in regarding the “individuals” (turtles);
These sets of variables are very important because allow us to establish some variables representing the own features of our agents (patches and turtles), moreover it allows us to use the command “set” in order to define the features of any Countries, while the command “let” is defining a “local variable”.
to setup
clear-‐all
if seed != 0 [random-‐seed seed]
setup-‐patches
setup-‐turtles
reset-‐ticks
end
The “game” starts when the user presses the “setup” button, the game follows the instructions contained inside the command: to setup; therefore first of all it clears what happens before, then the “seed” generates a sequence of random number in order to built a probabilistic structure behind the model; after that the “setup-‐patches” and the “setup-‐turtles” sub-‐commands define a lot of own feature of the patches (e.g. color, nation, wage, unemployment level, GDP-‐growth, real wage and so on) and of the turtles (e.g. nationality, initial-‐level-‐of-‐unemployment); moreover it is asked to the turtles to set “rationality” and “employment level”.
to move
move-‐agent
hire
reset-‐variables
end
This is the most important block of commands of the model because it defines how the agents move, what they are looking for and, finally, what they are able to reach.
• We start analyze the first sub-‐command, “move-‐agent”:
In this block the agents are divided into two groups: Rationals and the Irrationals;
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They differ because of their different moving decision-‐criterion; indeed the Rational ones are smarter with respect to the Irrationals and they take into account “ifelse any? patches in-‐cone 3 360 with [wage * employment_rate > comparable_for_employed]”, where comparable_for_employed = my_wage = gainable * employment, where gainable = [wage] of patch-‐here;
Notice that the rational agents can look for a better standard of living in a greater “cone” with radius 3 and angle 360 degrees and realize that the expected wage is not simply the wage but the wage weighted by the probability to get it, while the irrational individuals (when are not only mimicking other agents) are only able to look in a cone of radius 2 with angle of 30 degrees and that they evaluate only differences in wages;
Furthermore we introduce relevant differences between “Rational and employed agents” and “Rational and unemployed agents”, and also between “Irrational and employed agents” and “Irrational and unemployed agents”;
Finally we add an important task that all the individuals have to do, [set expost_wage [wage] of patch-‐here * employment set Dwage expost_wage -‐ exante_wage], this is useful in order to plot the “Delta Wage (Dwage)” in the “Wage vs. Unemployment” chart.
• The second sub-‐command is “hire”:
An important variable is introduced: “free_jobs”, which is function of the GDP-‐Growth; then another variable is introduced, “max_existent_possible”, with the aim of updating the number of “free_jobs” available for every Country.
The unique particularity was reserved for Chinese workers who are ever get ready for working because of their worst wage conditions; this was the motivation of the line of code: ask n-‐of max_existent_possible turtles with [nation = "china" and employment = 0] [set employment 1].
• The third and final sub-‐command is “reset-‐variables”:
We initially define “GDP_growth_variation” of every Countries that we fixed between -‐1% and 1% in order to restrict the range of fluctuation, the idea is to try to isolate the results standing in the tails of the Normal Distribution, so our preference go to the more usual results;
Then for every State we ask “patches” to update the GDP_growth, by taking into account the Country_GDP_growth_variation, all this mechanism works correctly only if the switch “GDP_growth_variability” is set ON.
Finally, the most important lines of code:
ask patches
[ if endogenyzing_the_wage
[ set wage wage * (1 + GDP_growth) / (2 -‐ employment_growth)
set employment_growth employment_rate * (1 + GDP_growth) ]]
ask patches [if wage < 0 [set wage 0]]
tick
in these simple lines is encased the “magical” tool which is reliable of the “endogenyzation process of the wage”,
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if the switch endogenyzing_the_wage is set ON, the wage is directly proportional to the GDP_growth and inversely proportional to the unemployment_growth [i.e. 1-‐ employment_growth, but since we want the denominator is (1+ unemployment_growth), we have (1+1-‐ employment_growth)= (2 -‐ employment_growth)];
furthermore we endogenyze (i.e. “we define”) the employment_growth as function of the employment rate and the GDP-‐Growth.
The code ends with a technical line: ask patches [if wage < 0 [set wage 0]], in order to avoid the wage can assume a negative value.
Finally, once the agents (patches and turtles) have unrolled the command “to move”, the “tick” works and so the “time” spends (remembering the mean of the Time in NetLogo: “The time is represented by “the repetition of the procedures”).
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We will now introduce two sets of experiments: the first one more related to variation of outcome due to change in the economy and the second one more related to variation of outcome due to variation in rationality.
First set of experiments
Switches:
Experiments # Endogenyzing_the_Wage GDP_Growth_Variability 1 on on 2 off off 3 on off 4 off on
# 1
Seed: 1 Rational people: 0.9 (irrationality-‐type = complete irrationality) Countries’ sliders set on proxy for “real” values, e.g. Initial level of unemployment: Italy 0.36, Brazil 0.25, China 0.13, US 0.14, Morocco 0.24; and level of initial population close to the realistic ones. Switches ON-‐ON (tick 50 / 907):
We immediately notice the fall of all rate of unemployment, follows by the fall in the American unemployment rate too (which react later because of the immigration mitigating effects); moreover we have to signal a “logarithmic” improvement in the Delta-‐Wage (grey line) counting the number of people improving their situation and a relevant improvement in the world’s welfare value (green line), represented by the decreasing of the number of people which wage is smaller than the world’s average wage, which means that the number of people
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which wage is smaller than the world’s average wage is decreasing, hence people (on average) feel better; and rational people are able to increase their status quite quickly these are two of the main implication of the model. Before explaining second part of the second chart we give a fast comment on the third since is necessary to understand what is going on.
The GDP-‐Growth chart displays a fast increasing long-‐run tendency for the Brazilian GDP (a sort of stochastic trend: random walk with drift) and an increasing one for the Moroccan GDP, while it shows contemporaneously a recessive cycle for US faced by an expansionary one for Italy which are going to finish; in the end it shows a quite “stationary” trend (e.g. Autoregressive process) for China.
Now we are able to understand why we have (around tick 250/300) a temporary worse off situation in the society: rational agents, which were suddenly stabilized in the richest countries (China and US) know have to face the costs of migration and worker are stressed by the problem of matching a job
Indeed we can see that the results in term of migration at the 50th and at the 907th tick confirm our predictions: agents from US and China finally move to Brazil: the country with the higher rate of GDP-‐Growth and compensation (wage rate weighted by the opportunity of working) .
# 2
Seed: 1 Rational people: 0.9 (irrationality-‐type = complete irrationality) As usual, countries’ sliders set on proxy for “real” values, e.g. Initial level of unemployment: Italy 0.36, Brazil 0.25, China 0.13, US 0.14, Morocco 0.24; and level of initial population close to the realistic ones. Switches OFF-‐OFF (tick 141):
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The rates of unemployment fall, except the American one which increases following a logarithmic growth because people of all Countries in the world move to US because the wage is exogenous higher than in the other Countries, with the consequence that in the end also for US citizens is more complicate find a job because of the competition with “new” foreign workers.
The number of people which wage is smaller than the world’s average wage remains flat, implying none improvement in the standards of living; moreover we notice that of course the GDP-‐Growth rates remain flat since the switch is set OFF, an d people go to US.
Finally we have to observe that the Delta Wage is following a “strange” path, it fluctuates a lot with short intermediate periods in which it remains flat (because of the relative high unemployment rate); if we’ll be Central Bankers we do not like this kind of relevant fluctuation because it does not have positive consequences on the consumption, investment and saving paths of economic agents.
The improvement in the world’s welfare is also relatively small and it remains at unacceptable level (the green line stabily too high).
# 3
Seed: 1 Rational people: 0.9 (irrationality-‐type = complete irrationality) Again countries’ sliders are set on proxy for “real” values, e.g. Initial level of unemployment: Italy 0.36, Brazil 0.25, China 0.13, US 0.14, Morocco 0.24; and level of initial population close to the realistic ones. Switches OFF-‐ON (tick 200):
The result is pretty similar to the previous one (in terms of Delta Wage too), the difference is about the GDP-‐Growth rates: the Italian, Chinese and Brazilian look like a Moving Average or an Autoregressive process (stationary one, because they seem fluctuate around their mean value), while the US’s GDP-‐Growth rate is trend decreasing and the Moroccan trend increasing. The point here is that our rational agents knows they do not need to revise their expectation since they are anticipating the fact that the economy are not internalizing their changes on the national welfare (in particular on the wage rate). It is the reason why the demand on free jobs in US increases while decrease on the rest of the world attracting the foreign workers which go to the US. As we know foreign agents are also working for the benefit of people leaving in US; which is still having the greater wage (exogenously fixed since the switch “endogenyzing_the_wage” is set OFF) but the situation is unsustainable on the long run.
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# 4
Seed: 1 Rational people: 0.9 (irrationality-‐type = complete irrationality) Let’s get the last information using countries’ sliders set on proxy for “real” values, e.g. Initial level of unemployment: Italy 0.36, Brazil 0.25, China 0.13, US 0.14, Morocco 0.24; and level of initial population close to the realistic ones. Switches ON-‐OFF (tick 114):
Such result is due to the action of the unemployment rate as determinant of the wage; since the Chinese unemployment rate is the smallest among all the unemployment rates, and since it acts negatively (inversely proportional) in the functional form of the endogenous wage, the China’s wage is increasing, hence China attracts the foreign workers which go there.
The GDP-‐Growth rate does not enter in the functional form of the wage because the switch “GDP-‐Growth-‐variability” is set OFF.
Finally we have to notice the tendency of the Delta Wage: after an initial phase of fluctuation, it increases (in logarithmic form), pointing out how the wage of workers is increasing.
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Second set of experiments
As anticipate know we are more interested in knowing the effect on outcome of different behavior of agents (that our model is prepared to face)
In all the experiments we will set slider and bars at the same level changing only percentage of rational ant level of irrationality.
#* In general we have: Initial level of unemployment and population respectively : Italy 0.36 and 14, Brazil 0.25 and 33, China 0.13 and 74, US 0.14 and 40, Morocco 0.24 and 24; the switchs Endogenyzing_the_Wage and GDP_Growth_Variability are active and set equal to one to leave wages and GDP vary over time.
# I / II
As we can imagine these experiments lead to the trivial results we will reported below. With complete rationality and (seed1 tick 185) we have results comparable with experiment 1 while with complete irrationality (seed1 tick 330) we have no hope to find any message from the model.
Experiments # rational_people Irrationality_type I 100% Undefined (complete-‐ irrationality) II 0% complete-‐irrationality III 0% only-‐imitation-‐for-‐irr IV 0% bounded-‐rationality V 85% only-‐imitation-‐for-‐irr VI 15% only-‐imitation-‐for-‐irr
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# III
This experiment, even if do not own a great economic meaning in term of reaction of agent to economic variables, shows an interesting points (which can be used also in economics), agents if move miming can be studied as planets reacting to the gravity force, which if gravity is strong enough (in our program if the cone is big) are attracted one close to the other and compressed into small spaces (until if g-‐force became really strong hypothetically we came back to a unique point). On the other hand as the force became weaker and weaker (of course g force is standard and the relevant point is the distance but socially could be possible to change the force per se) planets became more disperse (or remain disperse for more time).
Above results from seed1 at tick 280
I give one possible application to be more clear: while selling a good if agent are miming each other instead of advertize a big pool and attract it in a specific taste space, we can increase the information exchange (or check that it is strong enough, our g-‐force) then advertize strongly a small set of consumers to force them in the previously mentioned small taste space and wait that they bring the others to that point for us.
# IV
By bounded rationality we mean that agents are both able to imitate and to look at place close to them in search for better places. Their problem while doing so is that they are less able with respect to rational to calculate real remuneration (because of they do not see the relation with the probability to find a job) and to anticipate expectations.
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As we can see looking at the plots , we learned to use, unemployment lines and welfare indicator of these first charts shows that agents with bounded rationality are able to improve their situation but the process is quite slow, incomplete and unfortunately reversible in front of a minimum shock.
Comparing also the migration situation of tick 145 and of tick 376 our hypothesis seems to be confirmed. Till the situation evolved in a quite standard way (GDP are close to each other and following the initial trend) they behave quite optimally moving to the best countries (China and US), but at the point in which situation became less stable (GDP start to diverge heavily with new trends) they reacts slowly and continue to migrate also in the now less valuable China.
# V
I wanted to create these last experiment to talk about the rationality of imitation.
As we can see looking at the world’s welfare and Delta Wage line of wage vs. unemployment chart (taken at tick 2147 of seed1 because in such a way we passed over many migrations) when we have a big subset of rational people (which in our cases is made by 85% of the population) combined with people moving only by imitation also these last one are quite always able to reach the best possible outcome even in front of a fast changing environment.
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Indeed looking at the GDP growth chart we see that the best nation where to live (which usually is the one with the highest GDP growth) changed many times and probably is still changing now since Italy is performing better and will probably be able to offer a better welfare system with respect to US which is the place agents are still living.
# VI
This last experiment is to show that the previous point still perform better (even if we see some errors in the wage vs. unemployment chart even in this quite static world), but on the other hand we see that it takes more time now that we have a lower percentage of rational people (note results are of tick 1643 of seed 1).
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As we can see miming in our model can be quite rational; in particular it can become even more rational if in our model we had considered the existence of agents evaluation made by the people leading them to follow mostly actions of agents of which they have the best valuations. If we would have done it, for example making agent judging if miming agent x at time t improved its situation (with a +1 0 -‐1 indicator), probably we would have strong rational results also with only one two per cent of rational agents (since in the end agents probably will find themselves to follow either a rational or an irrational miming a rational or an irrational miming an irrational miming a rational and so on or in some few cases a really lucky agent).
Indeed also in the real world we know that follow agents/friends can be a good strategy in particular if we are able to understand who we can believe in (maybe not in all situations but at least in a subset of them).