woensdag 29 november 2017

Stata command labelmerge to label values from an Excel file

Download the code here:
https://drive.google.com/open?id=0B3AYa3V7lxmoeXhlR2dIT0lNV00

Syntax

labelmerge varname using excelfile.xlsx [, nomerge nolabel]

Your excelfile has one column varname, and the other varnamelabel. Varname could be nace, for instance, and your file could be called nacerev2english.xlsx.

The code adds the label to the values of varname. If you want to disable this, use the option nolabel.
The code also adds the label as a string. If you want to disable this (and gain some speed and space), use the option nomerge.

Don't forget you need -labmask-. Install this first from SSC.

It's as simple as that.

Cheers




From the ado:
___________________

Labelmerge
___________________


The program does two things:
1) it adds a variable that holds the label as a string
2) it assigns the values of that string as variable labels

You can decide to not maintain either of both, but not both as that would be
senseless.


TO DO
-----
1- Perhaps it could be handy to have the option to select a label from different
options (e.g. languages) in the dataset.

2- Maybe trimming some labels is needed if -labmask- doesn't do it already.


HOW SHOULD YOUR LABELFILE LOOK LIKE
-----------------------------------
It should have just two columns:
a) the numeric variable itself, labeled in the main file exactly the same
b) the value labels as a string variable which is labeled as the main variable
with suffix 'label'

The filename of the matrix could be anything, this is to be specified.


WHY IS THIS PROGRAM USEFUL?
---------------------------
Because you can use label files easily between different syntaxes and projects.
It is better than 'label save' which creates do files, because you have the
labels nicely adjustable in a matrix.


INSTALLATION INSTRUCTIONS
-------------------------

You need to have -labmask- installed.

To install: put the contents in the folder

`c(sysdir_personal)'/m/

for example, in Windows this is generally:

c:\ado\personal\m\

- OR -

Install the file through adopath + "folder"
Like this:

adopath + "c:\random_ado_folder\"


donderdag 9 november 2017

Running Python on Mac

Most rookie guides are still to complex for a fairly easy install.

macOS has Python 2.7 pre-installed, you don't need Python 3.X (yet).

Additionally, install (in order):
  • Xcode (including command line tools)
  • Homebrew (a package manager)
  • Sublime Text (or TextWrangler)
Writing the syntax in Sublime Text, you need to select the build system (python), and build (=run) the code. The output is giving in the console. Code is straightforward, but graphic capabilities are poor.

Source: https://www.macworld.co.uk/how-to/mac/coding-with-python-on-mac-3635912/

vrijdag 12 mei 2017

Simulation panel-invariant variable: OLS versus fixed effects

Below is a simulation of the consequence of controlling for fixed effects. In some cases this is desirable, in others not.

In the wage equation below, the wage depends on gender (b = 2), effort (b = 10), and ability (b = 3). There is no unobserved heterogeneity. A straightforward OLS estimation will return the correct b's.

However, as gender and ability are panel-invariant, the fixed effect regression does not yield any effect -- although the effect of effort is unbiased.

On the other hand, say that ability and effort are correlated, and ability is not observed, the estimated beta if effort in OLS would be biased, but not in a fixed effects estimation. The latter, however, would not allow estimating the beta for gender.

Good riddance.



clear
gene byte gender = .
gene float effort = .
gene float wage = .
gene float ability = .
gene long id = .

forvalues i = 1/1000 {
local a = runiform()
local g = runiform() > .5

set obs `=`i'*5'

replace ability = `a' if missing(id)
replace gender = `g' if missing(id)
replace id = `i' if missing(id)
}
replace effort = runiform()
replace wage = 2*gender + 10*effort + 3*ability + .5*runiform()


regress wage gender effort ability
areg wage gender effort ability, abs(id)


/*
. regress wage gender effort ability

      Source |       SS           df       MS      Number of obs   =     5,000
-------------+----------------------------------   F(3, 4996)      >  99999.00
       Model |  50282.7806         3  16760.9269   Prob > F        =    0.0000
    Residual |  104.339671     4,996  .020884642   R-squared       =    0.9979
-------------+----------------------------------   Adj R-squared   =    0.9979
       Total |  50387.1202     4,999  10.0794399   Root MSE        =    .14452

------------------------------------------------------------------------------
        wage |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      gender |   1.999089   .0040916   488.58   0.000     1.991067     2.00711
      effort |   10.00101   .0070513  1418.33   0.000     9.987187    10.01483
     ability |   3.004343   .0071066   422.75   0.000     2.990411    3.018275
       _cons |   .2498592   .0058902    42.42   0.000     .2383118    .2614065
------------------------------------------------------------------------------


end of do-file

. do "/var/folders/p2/2v2ckxtd2794655ypfbmfg9w0000gn/T//SD31177.000000"

. areg wage gender effort ability, abs(id)
note: gender omitted because of collinearity
note: ability omitted because of collinearity

Linear regression, absorbing indicators         Number of obs     =      5,000
                                                F(   1,   3999)   = 1602779.17
                                                Prob > F          =     0.0000
                                                R-squared         =     0.9983
                                                Adj R-squared     =     0.9979
                                                Root MSE          =     0.1450

------------------------------------------------------------------------------
        wage |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      gender |          0  (omitted)
      effort |   10.00054   .0078993  1266.01   0.000     9.985056    10.01603
     ability |          0  (omitted)
       _cons |   2.810143   .0044419   632.65   0.000     2.801435    2.818852
-------------+----------------------------------------------------------------
          id |      F(999, 3999) =    401.808   0.000        (1000 categories)
*/

woensdag 22 maart 2017

Negotiated pay levels

ECB

Aggregate indicator for the Eurozone ("Indicator of negotiated wage rates")
https://sdw.ecb.europa.eu/browse.do?node=9691595

TURI

CAWIE

Eurofound

Eurostat minimum wages


dinsdag 28 februari 2017

Gross domestic product

Introduction

The gross domestic product (GDP) is arguably the most important economic indicator, drawing a picture of total output and economy produces in a given year. The GDP can be approached in three ways:

  1. Production approach
  2. Income approach
  3. Expenditure approach
All three are equivalent but have different components. What is produced is equal to what is earned, and this is equal to what gets spent. I will address the components of each GDP definition, and briefly discuss how to practically deal with it.

Production approach

GDP is the gross value of domestic output of all economic activities (GDP at market prices. This is the value of the total sales of goods and services plus value of changes in the inventories. In the production approach it consists of gross value added (GDP at factor cost) and the value of intermediate consumption (i.e., the cost of material, supplies and services used to produce final goods or services).

Gross value added = gross value of output – value of intermediate consumption.

GDP at factor cost plus indirect taxes less subsidies on products is the "GDP at producer price".

Income approach

GDP = COE + GOS + GMI + [T_PM – S_PM]
compensation of employees (COE) + gross operating surplus (GOS) + gross mixed income (GMI) + taxes less subsidies on production and imports (T_PM – S_PM).

Expenditure approach

Y = C + I + G + (X − M)
consumption (C) + investment (I) + government spending (G) + net exports (X – M)

Often Y is used in this definition instead of GDP.

Gross value added

Gross value added (GVA) is the measure of the value of goods and services produced in an area, industry or sector of an economy, in economics. I.e. of output.

GVA = GDP - intermediate consumption (production approach)
GDP = GVA + taxes on products - subsidies on products
GVA = GDP + subsidies - (direct, sales) taxes

If taxes > subsidies, GDP > GVA, which is the case for Belgium.

Gross value added is used for measuring gross regional domestic product and other measures of the output of entities smaller than a whole economy (for which taxes-subsidies are aggregated).

Over-simplistically, GVA is the grand total of all revenues, from final sales and (net) subsidies, which are incomes into businesses. Those incomes are then used to cover expenses (wages & salaries, dividends), savings (profits, depreciation), and (indirect) taxes.

More on the difference in interpretation between GVA and GDP on Quora.

GDP, GNP, GNI, and HDI

GDP can be contrasted with gross national product (GNP) or gross national income (GNI). The difference is that GDP defines its scope according to location, while GNP/GNI defines its scope according to ownership. In a global context, world GDP and world GNP/GNI are, therefore, equivalent terms. Most often, we care about GDP, but GNI might indicate that an economy is colonized or colonizing.

The Human Development Index (HDI) was created by the United Nations to emphasize that people and their capabilities should be the ultimate criteria for assessing the development of a country, not economic growth alone. The HDI can also be used to question national policy choices, asking how two countries with the same level of GNI per capita can end up with different human development outcomes. These contrasts can stimulate debate about government policy priorities. The Human Development Index (HDI) is a summary measure (geometric mean) of average achievement in key dimensions of human development:

  1. Healtha long and healthy life (life expectancy)
  2. Education (years of schooling)
  3. Standard of living (log of the GNI per capita)

In practice, GDP and the HDI correlate very strongly, although there are notable differences that appear even sharper in other 'happiness' indicators. For instance: if two persons decide to stay at home to clean the house and care for the children, nothing is registered in the GDP. If each cleans the house of the other and pays him or her for the job the same wage, we have precisely the same utility, but two wages are added to GDP. I nevertheless favour the use of GDP as an objective measurement for economic development (in the example given, a market for cleaning opens up), but keeping in mind that we are more interested in utility than in the mere quantity of goods and services provided.

GDP figures from Eurostat

GDP figures can be downloaded from Eurostat (key: namq_10_gdp). Below are a few tips to select the correct indicators:
  • The European System of Accounts (ESA) gives a guideline for the measurement of production in sectors. If you want to have a good laugh, look at what happened in Ireland in 2015: 30% GDP growth because of a change in the guidelines! Depending on the source you will find longer time series in one or the other system, but for recent years in Europe, you should take the most recent ESA.
  • The broadest GDP in nominal terms is 'GDP at market prices'. Mostly you want it to be in EUR or USD, not in the national currency, unless exchange rates are what passionates you.
  • GDP in real term is found in the 'chain linked volume' series. Basically, chain linking output means output is expressed in the prices of the previous year, but as it goes this boils down to using a base year.
Note that ESA/Eurostat use the code B for GDP components using the production approach, code D for components using the income approach, and code P for the expenditure approach (which are extensive).

Together with Sebastien Fontenay I have made the -eurostatuse- command in Stata to fetch data from Eurostat. To make indices, I have made the -reindex- command you find elsewhere on this blog or if you send me an email.

vrijdag 17 februari 2017

Politieke overtuiging naar studierichting

Deze post is een klein onderzoek. Vaak hoor je economische opinies, voornamelijk over de arbeidsmarkt, maar naar voor gebracht door politici of zakenlui die geen arbeidseconomen zijn. Dat is natuurlijk niet verboden, maar het bevuilt soms het beeld dat mensen van economisten hebben.

Arbeidseconomie

Maarten Goos
Joep Konings
Eddy Omey
Filip Abraham

Bedrijfskunde / toegepaste economische wetenschappen / management

Johan Van Overtvelt
Marc Descheemaecker (+ Europese economie Europacollege), voormalig CEO NMBS,  voorzitter RvB Brussels Airport
Donald Trump (Wharton, bachelor, vastgoed)

Overige economische wetenschappen

Koen Schoors
Gert Peersman
Geert Noels
Freddy Heylen
Andreas Tirez, Liberales
Luc Denayer, secretaris CRB
Herman Van Rompuy, voorzitter Raad van Europa
Yannis Varoufakis (speltheorie)
Jeroen Dijselbloem (landbouweconomie)
Luc Versele, CEO Crelan
Frank Vandenbroucke, voormalig voorzitter SP.a
Peter De Roover, voorzitter Vlaamse Volksbeweging

Geschiedenis

Bart De Wever, voorzitter N-VA
Mark Rutte, VVD, eerste minister

Geneeskunde

Maggie De Block (huisarts), minister van volksgezondheid, regering Michel I
Daniel Bacquelaine (huisarts), minister van pensioenen regering, Michel I

Communicatiewetenschappen

Freya Van den Bossche, Vlaams minister, SP.a

Politieke wetenschappen

Kristof Calvo
Jan Peumans
Willy Claes, socialistisch politicus, voorzitter NATO
Caroline Gennez, SP.a

Rechten

Gerolf Annemans, Vlaams Blok
Jo Libeer, voormalig CEO VOKA
Kris Peeters, minister van werk, voormalig voorzitter UNIZO (+ kandidaat wijsbegeerte, + postgrad boekhouden/fiscaliteit bij Vlerick)
Geert Bourgeois
Gwendolyn Rutten (+ politieke wetenschappen)
Charles Michel, eerste minister
Luc Van den Bossche (doctor), SP.a
Johan Vande Lanotte (prof), SP.a, burgemeester Oostende
Yves Leterme (+ politieke wetenschappen), CD&V, eerste minister
Bert Anciaux, VU-ID21/Spirit/SP.a
Jean-Luc Dehaene (doctor)
Wolfgang Schäuble (+ economie)
Nicolas Sarkozy
Rik Torfs (kerkelijk recht)
Guy Verhofstadt, Open VLD, eerste minister
Etienne Davignon, zakenman
Danny Pieters, N-VA

Taal & letterkunde

Siegfried Bracke (germanist), Han Vermeer / Valère Descherp, Vlaams-nationaal politicus, voormalig journalist
Louis Tobback, SP.a, burgemeester van Leuven

Sociologie

Hans Bonte (+ politieke wetenschappen)
Jan Denys, arbeidsmarktexpert Randstadt
Jos Geysels, voormalig voorzitter Agalev
Kathleen Van Brempt, Europarlementslid SP.a

Psychologie

Daniel Kahneman, nobelprijswinnaar economie, Thinking Fast and Slow

Filosofie

Overige

Jesse Klaver (sociaal werk), voorzitter GroenLinks
Karel Van Eetvelt (sportwetenschappen), voorzitter UNIZO
Geert Wilders (bachelor verzekeringen)
Meyrem Almaci (vergelijkende cultuurwetenschappen ~ antropologie)
Filip Dewinter (bachelor journalistiek)
Jan Jambon (licentiaat informatica)
Jean Jacques De Gucht (culturele agogiek), fractievoorzitter senaat
Tom Van Grieken (bachelor communicatiemanagement)
Guy Van Hengel (onderwijzer), Open VLD
Elke Sleurs (gynaecologe), staatssecretaris N-VA
Alexis Tsipras (ingenieur)
Elio Di Rupo (chemicus)
François Hollande (haute école)
Albert Frère (ongeschoold)
Theo Francken (licentiaat pedagogie)
Marc Coucke (farmacie)
Steve Stevaert, socialist, burgemeester van Hasselt, voorzitter SP.a, gouverneur Limburg (horeca)
Karel Dillen, oprichter Vlaams Blok (humaniora)


maandag 6 februari 2017

Making sense of measuring effect size (eta-squared)

Introduction: it's impossible

We like to know whether effects are significant, or more significant than others, but generally avoid the issue of whether effects are substantial. On rare occasions, when an OLS regression suffices, a statistician may want to report R-squared values. However, do not expect much enthusiasm, as R-squared can only increase when adding additional variables even if they do not make sense. Adjusted R-squared, AIC and BIC measures correct for this. Also, R-squared has to be computed differently depending on whether or not there is a constant in the model (see Hayashi, 2000, p. 20). Worse, if your model is not OLS, you will need to define some pseudo-R-squared.

Needless to say, if you want an R-squared value for any subset of explanatory factors, statisticians will hate you. This is not possible, because of collinearity, they will say. You can stepwise add or delete variables and check the change in R-squared, but this will include or remove variance that is due to other variables. Sadly!

Objection: it may be possible

The people at UCLA, in fact Philip B. Ender, thought differently. They have a Stata package called -regeffectsize- for grabs which partitions the variance explained by the separate explanatory factors, giving a good indication of how substantial effects are. The help-file is not very instructive, so I deciphered the .ado myself, and here's the report. I'm sorry this is not (yet) in LaTeX, but speed over quality for now.

The structure of the program is as follows:
  • From the regression, they take:
    • TSS, total sum of squares in Y, which is RSS+MSS
    • RSS: residual sum of squares
    • MSS: the model sum of squares
    • DFR: residual degrees of freedom, which is n-k (n: sample size, k: number of variables in the model)
    • If you divide RSS by DFR you get the mean summed error MSE
  • From the F-test (simply -test-), they take:
    • The F-value
Obviously, eta-squared as we know it is ETA2 = MSS/TSS and it is generally decomposed in a within and between effect. Typically we get an ETA2 for the second level of a multi-level model, to indicate how much variation exists between versus within the second level units. This is also one formulation of the intraclass correlation coefficient ICC.

So how do they get to the (semi)partial eta-squared formula? There are three formulations, which are more or less the same and build up. The basis is the effect sum of squares, which is ESS = F*MSE.

Semipartial eta-squared

ETA2 = ESS/TSS = F*MSE/TSS

A glimpse of genius, if you ask me, to use a distribution unit (F) into the equation for the partitioning of variance. You might think that if F is really key, then our partitioning will be little more than a comparison of significance levels. The higher ESS, the more important the explanatory variable – for this it needs to be significant. In Hayashi, 2000, p. 53, I found some intuition that may be helpful. The F-test is a monotone transformation of the likelihood ratio.

Recall that MSE = RSS/DFR = RSS/(n-k) and add that F = (n-k)*(RSSr/RSSu-1) when there is only one test variable (#r = 1) to compare with the null. It is easy to see that n-k drops from the RHS and we are left with:

ETA2 = [ RSS*(RSSr-RSSu)/RSSu ] / TSS

RSSu are the unrestricted errors when beta is as calculated. Hence RSSu = RSS (the model residual sum of squares). RSSr are the restricted errors under the null (i.e. beta = 0). We can rewrite the equation to:

ETA2 = (RSSr-RSS)/TSS

Say there is only one variable in the model, then RSSr = TSS, and ETA2 = R2 = MSS/TSS. In general, the better the prediction, the lower RSS will be and the higher ETA2. Note that by definition RSS > RSSr, unless there is nothing explained, and then ETA2 = 0. What matters is the difference if we 'exclude' a variable: then RSSr rises proportionally to the importance of that variable.

Is this a good metric? I would tend to think so. The advantage of this approach in my view is that the beta's of other variables are unbiased by the omission of one variable, so MSS is unchanged and RSSr will be correct. There is also no difference in DFR in the models to compare, as it would be with stepwise deletion. To me, this looks useful as an approach to explain how much of total variance is explained by one explanatory variable, although I may overlook something. In that case, let me know.

Oh, I don't know why it would be semi-partial – the .ado uses the macro `spe2', so it's just a guess.

Percentage eta-squared

%ETA2 = 100*ETA2/R2

I think that is obvious enough. The output says 'change eta-squared', but there is no change involved.

Partial eta-squared

Here we have a little change, to not include the variance explained by other variables in the denominator. This will always be larger than the semipartial eta-squared, and will be exponentially higher when ESS approaches MSS. It could be a solution to avoid the issue of having much shared variance in MSS which is not in ESS, I'm not sure.

Part.ETA2 = ESS / (ESS+RSS)

References







Stata user command -norm-

Normalizing a variable is not so hard to do. The formula is:

normvar = (oldvar - minimum) / (maximum - minimum)

Hence you get a long way with the -summarize- command and r(min) and r(max).

However, in some cases you want to normalize to a wider range than appears in the data, e.g. rescale a variable that was coded 1-5 to 0-100, despite there not being units with either value 1 or 5. Maybe such cases exist in another pool, so you want to transform the variable in the same way. Again this is easy to do, but it may get tedious and make your syntax look ugly and confusing.

This is where the -norm- command may be of use.

Syntax

norm var [, replace generate(newvarname) scale(integer) maximum(integer) minimum(integer) [no]label]

The default scale is 1, hence range: 0-1.
By default value 0 gets the label 'Minimum' and the maximum gets a label too. You can wipe the label using the option nolabel.

Examples

norm q89, scale(100) replace
norm q93, scale(20) max(5)

Installation

Download the files and put them in your ado-folder (plus/personal) or refer to the containing folder by adopath + folderpath.

maandag 30 januari 2017

Economics for European feminists

Just when I thought I was out, they pull me back in. I was recently consulted to co-author a discussion paper on the Belgian gender pay gap law. We plan on adapting and translating the note to Dutch, but following up on the conference on gender inequality set up by a high-panel commission supported by the ILO, I want to reflect on a few economic ideas. Indeed, my area of interest is inequality, and I regard gender inequality as another expression of how social forces at the micro level shape our labour markets and societies.

The gender issue can be approached in different ways: a) as a power conflict, b) as a cultural issue, c) as a major economic flaw, and d) as minor economic flaw. Let me be brief on the first two: it was said before and repeated at the conference that "men are the greatest kartel in the world". That, for me, is hate speech. We also hear that "men are beaten by women in the field of education - which is a reason for optimism". That, again, is sexism. The aim should always be fairness and equality, not a battle of genders.  Secondly, it is of course true that most societies appear paternalistic. It suffices to listen to the discourse by Trump and Putin, and the ideas among conservative voices in Europe, to understand that this is a fact. Appearance and reality are slightly different though. It was said that women make 90% of the household consumption decisions - a figure which I highly doubt, but below the surface, the power must be larger than how it appears in the discourse. I also believe this is the case in the Arab world, and one reason why we overact in the current rhetoric of a clash of civilizations. Or put otherwise, if it wasn't so that women have more leverage than it is portrait, then by all means women should take the arms and fight for it to happen. More important than social norms and culture, which are always oppressive, is the individual liberty to adhere to them or not. In that sense, the feminist fight for equal rights is exactly the same as the liberal individualism that developed after the French Revolution. Morally, we're on the same line. However, I do not believe this is the core of the debate today in developed welfare states.

Another approach to gender inequality is to regard it as a major economic flaw. There are two opposing narratives on this which I find equality ridiculous. The first is the often quoted figure based on a study by McKinsey that if women have an equal labour market position than men, global gap would increase by 26%. In other words: we would produce a quarter more than we do today. I do not share the general criticism of gdp as a limited metric. Of course this criticism is true, but the limits are in its interpretation. Yet I don't like the McKinsey figure because, as much as it may be true, we know that there will be a substitution of informal by formal work, so that we will not produce a quarter more. Moreover, how will this quarter be distributed. Feminists regard the female labour force participation as an unambiguous gain, but if you think about it: in the traditional paternalistic single breadwinner model, the income of men sufficed to provide for the family needs. If that wasn't the case, our race would have disappeared. Yet today, many families need dual incomes, and if work intensity is low, rely on social benefits. This means that the real income of men has decreased - in part because of a labour supply increase, but likely more because of additional pressures - and that there is a larger producer welfare surplus that would have to be shared. However, we know that this does not happen, hence the inequalities in the world today. As such, the feminization of the labour markets is not a guarantee for welfare maximization, let alone for a better position for workers. The McKinsey figure carries in it a danger.

The second narrative that addresses the gender issue as a major economic flaw is the debate on value. In fact, the definition of value is one of the greatest economic puzzles, and I will not pretend to have solved it. Yet, let's falsify the argument as it is put. It is said that the work of women is undervalued, because the market only desires profit. I would say those are two different things. It may be true that the work of women is not valued on the market, because externalities are not accounted for. For instance, care and education have important social and economic multipliers, but the direct consumer does not want to pay for this. A very simple solution is to publicly provide these services of general interest, as even Smith would agree on, and as is the case in most welfare states. Neoliberalist politics wants to commodify these services and privatize the provision. If we're not interested in the sell-out, but want to value this type of work, that women prefer to do more than men for whatever reason, it should be pulled out of the market. The market itself, in so far as it is not perverted because of negative externalities, will still create and assign value by the invisible hand. It is manifestly impossible to deliberately measure and compare all aspects of a job, including all tasks, skills, and social value. It is even useless and to live in such a world would be the end of humanity. Still, it has been argued that a 'value portfolio', an instrument that apparently exists, would highlight undervalued assets, of which women should have more. It can only be that the test of this measure is seriously confounded: indeed, some skills may be undervalued, but not its social contribution. This feeds back into my first point: if the market does not value something, it should be solidarized. I will relax this position slightly in what follows, but by no means implying that because we love the person that does the cleaning, and because cleaning is societally important, the wage of the cleaning personnel should be way above average. Why not? Demand would, of course, plummet. This is not even a marxist argument: there is no morality in Marx definition of value: the only thing he suggests is that a) the value is ultimately based on labour, which is human time (but in a competitive spirit - there is no more value if you have been wasting time), and b) that part of the value created is stolen by the capitalist, who takes away the surplus profit, which is the difference between the pay for the worker, needed to provide subsistence at best, and attract enough personnel at worst, and the market value of the product. In a perfectly competitive market, this may all be the same, but when there is some monopoly power, the social relations enter the distribution of productivity gains. In short, it does not in any way support the feminist argument. If you bring the points together, though, you may propose a state-organized economy where you exclude market checks, and assign income based on (democratic) valuation. Most likely this kind of economy will underperform, because it does not provide the right incentives.

Hence we are left with the fourth approach: gender inequality is a minor economic issue. That is to say: it is easy to solve, and for this we need not change the laws of economics. Hurray! To begin, we know that in most countries, the state sector does not have an important gender pay gap. Yet this may reflect the first argument: the state sector does not have the same competitive pressures as the market, because it's primary locus of control is democratic, not financial. In the previous point, I suggested it is unlikely that it is desirable or possible to organize the full economy democratically through the state. Hence, what to do with the market economy? One option is to fully deregulate the labour market. This is where inequalities arise and therefore also gender inequality. It is a standard case of discrimination, and not well understood. The default take on this matter is that discrimination is irrational and inefficient. Firms that discriminate against female workers (or any other specific group, be it young people, migrant workers, etc.) would perform worse, an as a result disappear over time. On the other hand, sometimes discrimination is needed (e.g. mannequin), or it could be profitable. For instance, if a workplace is perfectly homogenous in terms of ethnic origin, religious conviction, or gender, and if the subculture around it are important social constructs, then worker heterogeneity may come at, at least, a short term cost of adaptation. In the long run, companies that rely on inequality to grow, cause inequality itself to grow, and will be less resilient for challenges (i.e. a sudden preference for dark haired waiters instead of blondes, or the necessary employment of a few men to do tasks that cannot be undertaken by the women, so that the homogeneity is broken either how). We cannot, however, impose quota easily, pretenting to know what the gender ratio should be in every single enterprise, nor can we risk to allow entirely free wage negotiations, as irrational forces may play out as well.

We need, then, a regulatory framework within which individual liberties exist. Collective bargaining is such a model. In collective agreements wage floors are set based on characteristics required by the function. All else is economically irrelevant, but may be psychologically tempting - from the old boys networking to the attraction of young women. It may indeed still be that the views also exist amongst representatives of employers and employees, but it is much easier to challenge this collectively than it is to impose morals and bargaining skills on every individual worker. Collective agreements save us the trouble of having a thousandfold of negotiations. Yet, the main purpose is to define a sectoral lower limit, which should still be economically feasible for well-performing firms (if not, they should not sign the agreement), livable for the worker, and adaptive to specific needs of firms. If you think about it, and look into the hundreds of agreements countries with collective bargaining have, this system is not nearly as rigid as it was viewed, and indeed very sensible. More importantly: it protects weaker groups on the labour market that are at risk of having wages being pushes below equilibrium, or just outright too low.

The system I described, which is the most common system of institutional wage bargaining, is sectoral. One direct effect is that collective bargaining creates more equality for workers that are covered. This may imply a transfer from workers with a higher earnings potential to workers with a lower earnings potential - for instance when not in the monopsony case. Another direct effect is that in sectoral negotiations, it is straightforward to distribute the rents to workers. However, the rent distribution creates inequalities between sectors, and hence if the same function is done in different sectors, unequal pay for equal work. The alternative is a guild system, where wages are strictly determined by occupation. I see this as an exchange of general equality for specific gender equality. It may ultimately result in a higher share of female workers in precarious position - but equally much as male workers. My conclusion would be on the one hand pragmatic - share the rents among workers - and more open, as we should perhaps rephrase 'equal pay for equal work' or 'equal pay for equally valuable work' to: 'equal pay for equally valued work'. Take the example of an accountant working for a theatre company, and another working for a multinational arms dealer. Depending on personal views, the motivation to work in the cultural sector may express a value that is not reflected in the wage, but clearly taken up by the worker in his assessment of total utility. So, again, this is a minor economic issue.

Let me conclude this point by another economic issue that collective bargaining may solve. A fairly new point of view. It is possible to have a limitation or an upper margin for wage growth over all economic sectors, like in Belgium. What happens then is that when productivity increases beyond that rate, in a way profits go up. However we have seen that increasing wage floors imply that workers with with a low earnings potential need to find compensation elsewhere in the wage distribution, if not employment is threatened. No many sectors are in fact strongly related's and dependent upon each other. For instance accommodation and catering depend on tourism, but also on meetings conferences etc. So when at first there is division of economic activity over sectors and in particular sectors wages gets Notes because of non-increasing productivity, self-evidently enter sectoral wage differences increase. With such a system as a wage increase ceiling or away each norm as it is: Belgian, what we have is that extra profits are separated and may be transferred to precisely those industries that do not have the productivity increases. It is ultimately depends on the demands inelasticity for those services or goods but what happens is that demand does not fall back as would have in case costs would have increased in the sector at hand. The result of the system is that's into industry which differences remain limited and social relations remain reflected is within the wage distribution. This echoes a fascinating statement by Hicks (1955), quoting Clay, in which he says that there is some sentiment of just wage differences. more precisely his point is that when wages are changing there is a tendency for convergence. Simply put: if there is an increase of revenue it is generally perceived as morally correct to at least pay the lower wages more. What collective-bargaining does is institutionalising these attitudes. In absence of it, by means of distraction, and through psychological irrationality, we would continuously have to adjust, criticize, and debate inequalities and worry, such as the concerns of the feminist movement.

To end, even if collective-bargaining is really importance in institutionalising social relations, this is still within the legal and regulatory framework. Some issues do you have to be dealt with at that level. Probably the best example is maternity leave which are paradoxically plays against women. It  interrupts careers so that at around age of 30 we see a divergence is of the wages of the women and men. Worse even, because of the new certainty that's female employments within the firm Will creates uncertainty, the employer me rationally choose not to invite women on a recruitment interview. The only option is done also mandatorily require men to take up paternity leave for exactly the same amount of time as the woman. I do not know in what way two day legislation a wireless maternity leave to one of the partners in a male homosexual relationship when they adopt a child or have one through a carrying mother. It would only be fair that both partners enjoyed you seen rights. Of course this would also imply that male wages for the paternity leave. Hence in two ways, to lower wages for men and an increasing chance to get invited on a job interview, paternity leave for simple economic reasons restores gender equality.

As economist Lucie Davoine correctly says it: the gender pay gap is not complex. However, bringing together insights from many different fields leads to many misunderstandings, and often a debate about the approaches and not about what to do. To play the economics card is then often an unpopular option, and mostly regarded as the very cause of the gender pay gap, which, after all, is an economic metric. Strangely, many feminists then conclude to avoid tackling the roots in the field of economics, and propose a plethora of other measures, often political and normative. This goes beyond the structural cause of inequalities. I recognize that norms and feelings are essential to the create the economic world, but I would say that the idealist view that values change the world is totally flawed, and here Marx is very correct in inverting the relationship: value follow from structures. After 50 years of soft feminism, and probably little success beyond having libraries full of literature and a topic for discussion around the table amongst those women with the time for exploits - which I believe is very perverse - it is perhaps time to really do something for equality.

donderdag 5 januari 2017

Minimum wages increasing gini?

This is a remark I made at the presentation of the PhD of François Ghesquière. I am not sure it is 100% correct and to be found in reality, but let me explain the paradox of increasing observed inequality when minimum wages increase.

We got used to thinking about minimum wages being fairly harmless to employment in monopsony markets. The minimum wage increases marginal costs above labour supply, so that the monopsonist limits demand for labour, but still pays the wage on the supply curve. Now suppose the supply curve is fairly flat (elastic). For a small amount of money offered, a great deal of extra workers will be available. Hence in this case, an increase in the minimum wage may increase the number of low-paid workers, and shift the tenth percentile more to the left than it does the median, thereby increasing lower tail (and overall) wage inequality, e.g. the gini coefficient.

Of course, the increase of the minimum wage appears to only have advantages in this case, as more workers entered the workforce and the market failure of the monopsonist is attenuated. The apparent increase is therefore due to the selection bias.

Moeten vrouwelijke sporters zoveel verdienen als mannen? En pornoacteurs zoveel als actrices?

In een andere post, in de taal van Joyce, heb ik mijn twijfel uitgedrukt bij de conventionele verklaringen voor de hoge lonen in de sportwereld. Kort samengevat: er zijn andere redenen dan het superstar-effect waarom de veiling de prijs van sporters kàn doen oplopen tot ver boven hun marginale productiviteit en boven de aanbodscurve. De voornaamste lijken mij witteboordcriminaliteit en oligopoliemarkten.

Hier wil ik een andere dimensie van de sportloonproblematiek aansnijden: de ongelijkheid. Men kan loonongelijkheid als een perfect allocatiemechanisme zien en daarmee de discussie sluiten. Anderen zien er een onrechtvaardigheid in, niet zelden door het verschil tussen mannen en vrouwen, die 'toch gelijkwaardig zijn'. Ik hou van deze discussie, omdat ze op basis van een terecht sentiment paradoxale conclusies trekt.

Men gaat namelijk uit van een binnengroepshomogeniteit van mannen en vrouwen. Dat is ongelofelijk binair denken dat grenst aan racisme. Ook tussen 'rassen' zijn er biologische verschillen, maar niet elke Latino, Aziaat, zwarte, of Caucasiër beantwoordt aan de norm van hun groep. We zouden het aanstootgevend vinden het zo voor te stellen. Het onderscheid tussen mannen en vrouwen is trouwens niet eens gemakkelijk te maken - het 23ste chromosoom is geen afdoende criterium, vaak gaat men ook fenotypische klassen maken. De gemiddelde man heeft niet de longinhoud van Vincenzo Nibali en niet de motoriek van Dries Mertens. Hij zal ook ernstige letsels oplopen als hij het opneemt tegen Ronda Rousey, topvedette in de MMA. Er zijn dus véél relevantere divisies te verzinnen dan man/vrouw, die ook binnen sporten gelden. Het voorstellen alsof vrouwelijke sporters, omwille van de gelijkwaardige inspanningen die ze doen, recht hebben op een loon dat overeenkomt met de mannen in de sport, laat veel mannen in de kou staan. Zo is het ook in de politiek: de gemiddelde vrouw lijkt helemaal niet op de gemiddelde vrouwelijke politicus. De stiel vereist een zeker haantjesgedrag, een sociaal-economische positie, opleiding, en verbetenheid die redelijk uitzonderlijk is. Je kan enkel tijdelijk quota rechtvaardigen, om alvast uit te sluiten dat een meisje dat opgroeit zichzelf zou wijsmaken dat haar gender haar gedroomde carrière in de weg zou staan. Eens het structuratie-effect werkt, zijn quota overbodig en zelfs hinderlijk voor het gelijkheidsdenken.

In de filmwereld is het trouwens soms omgekeerd: een pornoactrice verdient het tienvoud van wat mannen opstrijken, wellicht voornamelijk door een simpel spel van vraag en aanbod. Dit brengt ons bij de kern van de zaak: wat willen we laten meespelen aan de vraagzijde? Het is mogelijk om meer aandacht te wijten aan vrouwensport en misschien is het zelfs een zeer goed idee, gezien de dreiging van obesitas in België. Het is echter ook discriminatoir en niet-meritocratisch. Er zijn veel vormen van ongelijkheid, en ze zijn wat mij betreft allemaal problematisch: naar afkomst, tussen jong en oud, tussen hoog- en laagopgeleiden, tussen mannen en vrouwen, tussen grote en kleine mensen, en op basis van seksueel kapitaal of uiterlijk. Zelfs een onbenoembare ongelijkheid kan problematisch zijn, vanuit het rechtvaardigheidsdenken en de veil of ignorance. De genderloonkloof is slechts het topje van de ijsberg.

Willen we iets aan ongelijkheden doen, dan moet niét gefocust worden op de categorieën waarin ze tot uiting komen, maar aan het proces van de loonvorming. Op de arbeidsmarkt gaat het om het creëren van producten en diensten met het oog op handel. Men dient dus zo efficiënt mogelijk te werken, hoe of met wie maakt weinig uit. Soms kan het product of de dienst wél samenvallen met de creator: zo gaan we bijvoorbeeld meer dan de marktprijs betalen voor een kaarsenset die in een verzorgingstehuis zijn gemaakt, omdat we sympathie hebben voor de ouderen. We zullen misschien liever naar een masseuse gaan dan naar een masseur, of vice versa naargelang geaardheid, omdat een ruwe hand en een zachte een andere dimensie toevoegen aan de verbeelding - en relaxatie het uiteindelijke doel blijft. Voor de meeste producten echter maakt het evenwel geen bal uit. Loonongelijkheden komen dan door toevalligheden tot stand: de mate waarin iemand door een familiale situatie gedwongen is om wat voor job dan ook op te nemen, of toe te geven op het loon om meer flexibiliteit te bedingen. Sommige mensen zijn slechte onderhandelaars, makkelijker te manipuleren. Die psychologische en sociale component heeft niets te maken met de waarde van de werknemer in het arbeidsproces. Vandaar dat men over zulke lonen collectief moét onderhandelen. De werkgever zal een voor de omzet correct loon aanvaarden, mogelijks met dezelfde gemiddelde vergoeding als wanneer individueel onderhandeld wordt. Het verschil zit echter in de spreiding, die zonder collectieve onderhandeling vaak nodeloos groot wordt. Dat zien we in landen die geen sterk (gecentraliseerd) sociaal overleg kennen. Daar zien we zelfs de trend naar een omgekeerde loonkloof bij jongeren, waarbij mannen minder gaan verdienen dan vrouwen.

Dit is ook de keuze voor de sportwereld: willen we dat ploegen een loon uitbetalen dat in verhouding staat tot de sportieve prestaties, een perfect meritocratisch gegeven, of willen we een personencultus vieren en in marketing investeren? Door te focussen op individuele kenmerken om kloven te dichten, zal men paradoxaal genoeg de algemene ongelijkheid doen toenemen, bijvoorbeeld tussen voetbal en andere sporten. Maar nogmaals, dat is een voorkeur zoals een andere.

Football inflation

The wages of football players are astonishingly high and we do not precisely know why. In this tentative post I explore a few possible explanations. The main argument is that we have a market inefficiency and it is built on a simple intuition: you do not have to pay unschooled labour very much to make them hit a ball all day. Yes, the talent is rare, and the very best will not refuse more money if you throw it at them. And yes, you will loose the bidding process if you don't show the money, so the exchange value of elite football players may be very high, but no, there are no efficiencies gained during the wage escalation that justify the wages.

The standard way of thinking is that it is all propensity to pay, and due to a superstar effect of millions of people watching just one team that has the world's most famous player, there is enough at stake to raise wages sky-high. However, what profits does such a team make? Not a lot, as there is a finite capacity in the stadion, and you cannot ever have enough markups on some letters printed on a shirt. Let's discard that road. Broadcasting rights then, yes, amount to many millions that are injected in the world of sports. Again, if the broadcasting rights were cheapish, or non-existing, would that be a game-changer? My guess is no: I was watching all games in any league in the 80s when broadcasting rights were negligible, and players were equally motivated as today. The main difference is that back then there were no ads during the games, so we actually got more sports for less money, in fact for free. Broadcasting rights are very similar to the author's rights in the music and movie industry. They are actually bad calculations. To have your actors, players, musicians, show up and do all the necessary recording, training, and coaching, a known sum of money has to be paid. You can then figure the audience and the expected returns at any price level. At some equilibrium price level, welfare is optimal, and a monopolist will go above that equilibrium, restricting the quantity and increasing prices. But what is more: long after all wages and input costs have been paid, the intellectual (well...) rights still generate returns. As a consumer, you get no value for your money, because production is at zero marginal costs. Now it could be me, but paying more for nothing extra seems very ... inflationary.

What is worse is that the price effects may be endogenous. Say the market for sports products is not very competitive, a paradox indeed, but probably true. Marketing defines what brands are in the market, and multiple brands have the same shareholder structure. I'm no specialist in the field, but I read for instance that Puma and Adidas come from the same family business, and they largely avoid competition, with Puma focussing on the expensive stylish segment of the market, and Adidas on more specialized sportswear. Suppose also that there is a fairly inelastic demands for those fashionable goods, in fact the goods from the companies that decide what's fashionable. Then they can bid whatever amount of money to sponsor the teams that pay the players, and likewise, any other sponsor can further inflate prices by sponsoring the television channels for the broadcasting rights. It is an orgy of inelastic demand and price raises. Interestingly, in Belgium there are many ads for cheese and ham with a local label of origin. By law, these companies have no competitors in the same market. So these companies cause inflation, with a peculiar consequence: inflation leaves room for inequality. The first to benefit are the players, of course, and the last are the spectators that need to ask for a pay rise to their bosses. With some luck, when working at one of the sponsoring companies, that is not too hard, but others will lag behind.

If this sounds like a bubble, that is what it is. It is remarkable that organizations that pay extraordinary salaries, like football clubs, have equally high debts. This means the banks recognize the value of the players as real assets. This is foolish, as everyone know that one tackle suffices to end a player's career, but probably they insure against this. Anyway, it certainly is economically dangerous and also suspicious. A lot of money is flowing in from Arabic states, from oil sheiks and the like. Yes, they produce inelastic goods, but they also excel in money laundering, as do the teams they employ.

In sum, football is inflationary, very literally: thin air blowing up a bubble.