salonanarchist | leunstoelactivist

Vakbond

In 1960 hadden 29 Kamerleden een vakbondsachtergrond. Nu nog negen

Na de oorlog had bijna één op de vijf Tweede Kamerleden een vakbondsachtergrond (tot 1956 had de Tweede Kamer 100 leden), maar dat is inmiddels ingrijpend veranderd. In 1960 waren er 29 Kamerleden met een vakbondsachtergrond; momenteel zijn er nog maar negen.1 De grootste daling vond plaats tussen 1960 en 1980.

De positie van werkenden is er sinds 1980 niet beter op geworden - mede als gevolg van overheidsbeleid.2 Het aantal mensen met onzeker werk neemt toe, het sociale vangnet is gedeeltelijk afgebroken en werkenden krijgen een steeds kleiner deel van de opbrengst van hun arbeid. Deregulering en privatiseringen leiden in veel sectoren tot felle concurrentie die wordt uitgevochten ten koste van werkenden. Bezuinigingen ondermijnen de kwaliteit van publieke diensten en hebben veel banen gekost.

De kerntaak van vakbonden is om werknemers te helpen zich te organiseren, zodat ze niet machteloos staan tegenover hun werkgever. Maar de politiek bepaalt voor een belangrijk deel de spelregels op de arbeidsmarkt. Vakbonden mogen zich daarom best wat assertiever met de politiek bezighouden - bijvoorbeeld door hun achterban te mobiliseren om te gaan stemmen bij verkiezingen. Ook is het belangrijk om kaderleden op te leiden voor leidende posities in de bond en in de politiek.

Methode

Voor uitleg hoe de gegevens zijn verzameld en geanalyseerd, zie de Engelstalige versie van dit artikel.

Aanvulling: Kandidatenlijsten 2017

Hieronder probeer ik bij te houden welke personen met een vakbondsachtergrond er op de kandidatenlijsten voor de Tweede Kamerverkiezing op 15 maart 2017 staan. De informatie zal vast onvolledig zijn; aanvullingen welkom.

Concept-kandidatenlijst PvdA

5. Gijs van Dijk. Vice-voorzitter FNV.
9. William Moorlag. Voormalig vakbondsbestuurder bij de FNV.
11. John Kerstens. Tweede Kamerlid en voormalig voorzitter van FNV Bouw.
15. Richard Moti. Vakbondsbestuurder bij de FNV.
32. Mei Li Vos. Voormalig voorzitter Alternatief voor Vakbond.
43. Erik Pentenga. Vakbondsbestuurder Flex bij de FNV.

De hele lijst

Concept-kandidatenlijst GroenLinks

4. Linda Voortman. Oud-bestuurder FNV Bondgenoten, was actief in de schoonmakerscampagne.
7. Zihni Özdil. Bestuurslid Nederlandse Vereniging van Journalisten.
11. Nevin Özütok. Oud-bestuurder FNV Bondgenoten.
13. Lisa Westerveld. Persvoorlichter en lobbyist bij de Algemene Onderwijsbond.
24. Arno Bonte. Is woordvoerder geweest bij ABVAKABO FNV.
De hele lijst

Concept-kandidatenlijst 50PLUS

4. Corrie van Brenk. Sectorhoofd FNV Zorg en Welzijn en voormalig voorzitter van ABVAKABO FNV.
De hele lijst

Concept-kandidatenlijst VVD

11. Dennis Wiersma. Oud-voorzitter FNV Jong.
De hele lijst

Concept-kandidatenlijst SP

3. Lilian Marijnissen. Gaf tot voor kort leiding aan de organising-campagnes van de FNV.
10. Cem Lacin. Vakbondsbestuurder FNV.
22. Ron Meyer. Voormalig vakbondsbestuurder FNV, onder meer bekend van de schoonmakerscampagne.
De hele lijst

Concept-kandidatenlijst CDA

4. Pieter Omtzigt (actief geweest bij jongerenorganisatie CNV).
14. Michel Rog (was bestuurder bij de Unie en voorzitter van CNV Onderwijs).
19. Evert-Jan Slootweg (had verschillende functies bij het CNV).
De hele lijst.


  1. In sommige Europese landen wordt de relatie tussen politiek en vakbeweging vooral gezien als een zaak van de sociaal-democratische partijen. In Nederland zijn er daarnaast veel christen-democratische Kamerleden met een vakbondsachtergrond. Hun aantal laat een vergelijkbare ontwikkeling zien als de sociaal-democratische Kamerleden met een vakbondsachtergrond. De huidige Kamerleden met een vakbondsachtergrond zijn Harm Brouwer (PvdA, FNV), Sjoera Dikkers (PvdA, CNV), Fatma Koser Kaya (D66, FNV), John Kerstens (PvdA, FNV) Jesse Klaver (GroenLinks, CNV), Pieter Omtzigt (CDA, CNV), Michel Rog (CDA, CNV), Paul Ulenbelt (SP, FNV / NVV) en Linda Voortman (GroenLinks, FNV). Update december 2016: In dit overzicht ontbreekt Mei Li Vos, voormalig voorzitter Alternatief voor Vakbond (een organisatie waarover uiteenlopend wordt gedacht maar die zichzelf omschrijft als vakbond).

  2. Daarmee beweer ik niet dat het overheidsbeleid een rechtstreeks gevolg is van de achtergrond van Kamerleden - waarschijnlijk is de relatie complexer.

In 1960, 29 Dutch MPs had a trade union background. Today, nine

After the Second World War, almost one in five members of the Dutch Lower House had a trade union background (in 1956, the Lower House expanded from 100 to 150 members). Then change set in. In 1960 there were 29 MPs with a trade union background; today nine.1 The largest decline was between 1960 and 1980.

The position of workers hasn’t gotten any better since 1980 - partly as a result of government policies.2 More workers have precarious jobs, the social safety net has been reduced and workers receive an ever smaller share of the proceeds of their labour. In many sectors, deregulation and privatisations have produced cut-throat competition, at the expense of workers. Austerity has deteriorated the quality of public services and destroyed jobs.

The key task of unions is to help workers organise so they’re not powerless vis-a-vis their employers. But in many ways, politicians set the rules that govern the labour market. Therefore, Dutch unions should probably engage more actively in politics - for example by mobilising their members to vote in elections. Further, it’s important to train union members for leading positions within the union and in politics.

Method

The analysis is based on the resumes of post-WWII members of the Lower House published on Parlement.com. I counted occurances of the following union federation names: 'FNV', 'CNV', 'NKV', 'NVV', 'EVC', 'RKWV', 'KAB'.

Some notes:

  • I didn’t count mentions of unions affiliated to these federations - that would hardly be feasible given given how many there are and the changes that have occured over time;
  • I manually excluded a number of cases where names of union federations occured in resumes. Reasons include: the reference was to an organisation with a name that is identical to one of the union federations’ names; someone merely sat on a joint committee of a political party and a trade union; etcetera;
  • I did not include the small unions / union federations that represent high-educated professionals, but including them would have had a negligeable effect on the outcome.

I recorded the start and end date for each period any of these persons was a member of the Lower House. Then I defined periods using all those dates as partitions (I ended up with over a thousand periods). For each period, I checked how many people with a union background were members of the Lower House during that period.


  1. In some European countries, the relation between politics and the union movement is dominated by the social-democrat party. In the Netherlands, there are also many christian-democrat MPs with a union background. Their number shows a similar development as the number of social-democrat MPs with a union background. The current MPs with a union background are Harm Brouwer (PvdA, FNV), Sjoera Dikkers (PvdA, CNV), Fatma Koser Kaya (D66, FNV), John Kerstens (PvdA, FNV) Jesse Klaver (GroenLinks, CNV), Pieter Omtzigt (CDA, CNV), Michel Rog (CDA, CNV), Paul Ulenbelt (SP, FNV / NVV) and Linda Voortman (GroenLinks, FNV).

  2. That’s not to say that the background of MPs directly influenced government policy - the relationship may well be more complex.

Assignment 2-4

In previous assignments I’ve looked into the association between union membership and political participation among paid employees, using the Outlook On Life surveys dataset. I found that respondents who have a union member in their household are more likely to have engaged in political participation over the past 2 years. This was consistent with what I expected on the basis of a study by Kerrissey and Schofer.

In the present assignment we’re to check for a potential moderator. The study by Kerrissey and Schofer found that the association between union membership and political participation is stronger for lower educated respondents, possibly because they have fewer other sources of political capital at their disposal.

Against this background I decided to test the association between union membership and political participation for different subgroups based on education. The OOL dataset has a variable with four education levels (less than high school; high school; some college; bachelor’s degree or higher). Since there are relatively few respondents with less than high school, I decided to lump together the first two categories.

First of all, here’s a grouped bar chart showing what percentage of respondents have engaged in political participation, by union membership (at household level) and by education level. Political participation levels appear higher for higher educated respondents, which will not come as a surprise. More surprisingly, the association between union membership and political participation appears stronger for higher educated respondents.

So let’s take a look at the chi squares for the different education levels. The entire Python script for my analysis can be found here. Below I copy some of the output from the code:

measure: "political_participation", group: "employees"

Results for "low"
union                     No  Yes
political_participation         
0.0                      158   36
1.0                       87   24

chi-square value, p value, expected counts
(0.24815891922850686, 0.61837443272471648, 1, array([[ 155.83606557,   38.16393443],
       [  89.16393443,   21.83606557]]))

Results for "medium"
union                     No  Yes
political_participation         
0.0                      130   26
1.0                      124   41

chi-square value, p value, expected counts
(2.7736422284672679, 0.095827887556796373, 1, array([[ 123.43925234,   32.56074766],
       [ 130.56074766,   34.43925234]]))

Results for "high"
union                     No  Yes
political_participation         
0.0                      157   27
1.0                      154   60

chi-square value, p value, expected counts
(9.5760783080978147, 0.0019712903653131314, 1, array([[ 143.77889447,   40.22110553],
       [ 167.22110553,   46.77889447]]))

The results show that the chi square value is smallest for the lowest education group and largest for the highest education group; and only significant for the highest education group (note that a post-hoc tests is not required because the explanatory variable has only two levels).

This comes as a surprise. Based on the study by Kerrissey and Schofer, I expected that the asssociation between union membership and political participation would be stronger for the lower educated respondents. However, using the OOL data, the association is only significant for the highest education level.

Note for students reviewing this assignment: the elaboration below isn’t strictly speaking part of the assignment. I wouldn’t want to waste your time so feel free to skip the rest of the article and make your assessment based on the text above.

I can’t really explain why my analysis leads to a result that seems at odds with the Kerrissey and Schofer study, but here are some considerations.

First of all, it’s entirely possible that I made some silly mistake in my analysis. And if that’s not the case, the method applied by Kerrissey and Schofer is different in a number of ways from my analysis. For example, they did regression analyses taking a number of relevant background variables into account. Further, they found a significant interaction between union membership and education in two different datasets. One could argue that Kerrissey and Schofer’s analysis is superior and their finding therefore more credible. Even so, it would be nice to be able to explain why a simpler model results in an opposite outcome.

Second, characteristics of respondents might play a role. I have the impression that union members may be overrepresented in the OOL dataset, but I don’t immediately see how that would explain the different outcome. More importantly, I did my analysis on a subset consisting of respondents with paid employment. It’s entirely possible that paid employees tend to be higher educated than unemployed and retired respondents. I guess it wouldn’t hurt rerunning the analysis on the entire group of respondents.

Third, it may matter how you define and measure political participation. I used a measure that includes contacting an official, participating in a protest or march and signing a petition. Kerrissey and Schofer found an interaction for voting, protest and membership. It would be interesting to see what happens if I use just the protest variable instead of the composite measure.

All respondents, composite measure

When I run my analysis on the entire group of respondents rather than just paid employees, the outcome changes in that there’s now a significant association between union membership and participation, not just for the highest education group, but also the medium education group. For the lowest education group, there’s still no significant association. So this doesn’t really explain the difference.

measure: "political_participation", group: "all_respondents"

Results for "low"
union                     No  Yes
political_participation         
0.0                      466   73
1.0                      279   60

chi-square value, p value, expected counts
(2.4819670432296759, 0.11515814971338957, 1, array([[ 457.35193622,   81.64806378],
       [ 287.64806378,   51.35193622]]))

Results for "medium"
union                     No  Yes
political_participation         
0.0                      262   32
1.0                      279   82

chi-square value, p value, expected counts
(14.963425544693942, 0.00010961532600357433, 1, array([[ 242.83053435,   51.16946565],
       [ 298.16946565,   62.83053435]]))

Results for "high"
union                     No  Yes
political_participation         
0.0                      237   34
1.0                      307   95

chi-square value, p value, expected counts
(12.134008533047874, 0.00049510583539001945, 1, array([[ 219.05497771,   51.94502229],
       [ 324.94502229,   77.05497771]]))

All respondents, protest measure

Using the protest measure rather than the composite participation measure, the association is once again only significant for the highest educated group.

measure: "protest_demo", group: "all_respondents"

Results for "low"
union          No  Yes
protest_demo         
0.0           695  119
1.0            49   15

chi-square value, p value, expected counts
(2.9184647984510526, 0.087571143400387977, 1, array([[ 689.76765376,  124.23234624],
       [  54.23234624,    9.76765376]]))

Results for "medium"
union          No  Yes
protest_demo         
0.0           499   99
1.0            43   15

chi-square value, p value, expected counts
(2.5743436452143076, 0.10860914232973193, 1, array([[ 494.07926829,  103.92073171],
       [  47.92073171,   10.07926829]]))

Results for "high"
union          No  Yes
protest_demo         
0.0           487  104
1.0            52   23

chi-square value, p value, expected counts
(6.5436232099967668, 0.010526075473228987, 1, array([[ 478.3018018,  112.6981982],
       [  60.6981982,   14.3018018]]))

After these additional analyses, it’s clear that it makes a difference whether you include respondents who are not paid employees, but I don’t think that fully accounts for the difference between the analysis using the OOL dataset and Kerrissey and Schofer’s analyses. Using a ‘protest’ variable instead of a broader composite measure of political participation also didn’t help clear things up. I’m afraid I still don’t really have an explanation for the different outcomes.

Assignment 2-2

I’m using the OOL Surveys dataset and I’m interested in the association between union membership and political participation in the US (more specifically, between union membership at the household level and having engaged in at least one out of four forms of political participation over the past 2 years).

In the current assignment, we’re asked to run a chi square test of independence to figure out whether two categorical variables are related. If the outcome is significant and the explanatory variable has more than two levels, we’re required to carry out and interpret a post-hoc test. This would mean carrying out comparisons between all pairs of categories for the explanatory variable and dividing the required significance level (for example, 0.05) by the number of comparisons.

I’m in a bit of luck this time. First, my original research question concerns the relation between two categorical variables, so there’s no need to recode quantitative variables to categorical ones or to look for other variables. Second, my explanatory variable has only two levels (respondents either do or don’t have a union member in their household), so there’s no need to do a post-hoc test.

The entire Python script for my analysis can be found here. Here’s an excerpt from the script:

# contingency table of observed counts
ct1=pandas.crosstab(sub2['ANY'], sub2['W1_P8'])
print (ct1)
print()

# column percentages
colsum=ct1.sum(axis=0)
colpct=100*ct1/colsum
print(colpct)
print ()

# chi-square
print ('chi-square value, p value, expected counts')
cs1= scipy.stats.chi2_contingency(ct1)
print (cs1)

And here’s the relevant output:

W1_P8   No  Yes
ANY           
No     445   89
Yes    365  125

W1_P8         No        Yes
ANY                       
No     54.938272  41.588785
Yes    45.061728  58.411215

chi-square value, p value, expected counts
(11.559955638910083, 0.00067387460877846761, 1, array([[ 422.40234375,  111.59765625],
       [ 387.59765625,  102.40234375]]))

Among respondents with union members in their household, the percentage who have engaged in political participation is higher (58%) than among other respondents (45%). There are 125 participants who have a union member in their household and who have engaged in political participation; had there been no relation between the two variables a lower number (102) were to be expected. For other answer categories, the observed values also differ from the values that were to be expected if there were no relation between the variables.

The chi square value is 11.6 and the p-value < 0.001. In other words, the outcome of the test is that there is indeed a significant relation between union membership (at household level) and political participation.

Assignment 2-1

In this assignment, we’re asked to run an analysis of variance and then conduct post hoc paired comparisons. In earlier assignments I looked into the association between trade union membership and political participation, using the OOL Surveys dataset. The variables I considered are not suitable for the present assignment, so I’ll pick a different issue for now: possible regional variation in opinions about unionised workers (summary statistics about the latter variable here).

Background

Based on anecdotal evidence about successful union campaigns in the US - from Justice for Janitors to the Fight for 15 - I have the impression that unions are more active in some places (e.g. LA, San Francisco, Seattle, New York) than others. If this is correct, then I assume it’s possible that other aspects of trade unionism, such as union density and opinions of unionised workers, may also show regional variation.

By way of initial exploration, I created a map that shows the share of workers who are union members by state (I wrote Python scripts to scrape the data from a Wikipedia page and to modify this svg-map). Hover your mouse over the map.

Percentage of workers who are union members (Wikipedia); darker green represents higher density

The map suggests that union membership tends to be higher in states along the west coast and in the northeast of the US. Note that there could be overlap between these states and states where metropolitan areas are concentrated.

Code

While I think it’s more practical to link to a separate code file, the assignment says we should paste the code into the article, so here it is:

# Import relevant libraries
import pandas
import numpy
import statsmodels.formula.api as smf
import statsmodels.stats.multicomp as multi

# Read data & print size of dataframe
data = pandas.read_csv('../../Data Management and Visualization/Data/ool_pds.csv', low_memory = False)
print (data.shape)

# Only variable W1_N1H contains missing values that need to be recoded
data['W1_N1H'] = data['W1_N1H'].replace(-1, numpy.nan).replace(998, numpy.nan)

print('ANOVA to compare means by MSA [metro] status')
model1 = smf.ols(formula = 'W1_N1H ~ C(PPMSACAT)', data = data)
results1 = model1.fit()
print(results1.summary())
sub1 = data[['W1_N1H','PPMSACAT']].dropna()
grouped = sub1.groupby('PPMSACAT')
sub2 = grouped['W1_N1H'].agg([numpy.median, numpy.mean, numpy.std, len])
print(sub2)
print()

print('Explore state-level data')
sub1 = data[['W1_N1H','PPSTATEN']].dropna()
grouped = sub1.groupby('PPSTATEN')
sub2 = grouped['W1_N1H'].agg([numpy.median, numpy.mean, numpy.std, len])
print(sub2)
print()

# Create subset including only respondents from states with at least 50 respondents
counts = dict(data['PPSTATEN'].value_counts())
include_states = [state for state in counts if counts[state] >= 50]
sub3 = data[data['PPSTATEN'].isin(include_states)]
sub4 = sub3.copy()
recode = {21: 'NY', 22: 'NJ', 23: 'PA', 31: 'OH', 33: 'IL', 34: 'MI', 43: 'MO', 52: 'MD', 54: 'VA', 56: 'NC', 58: 'GA', 59: 'FL', 62: 'TN', 63: 'AL', 74: 'TX', 93: 'CA'}
sub4['PPSTATEN'] = sub4['PPSTATEN'].map(recode)

print('ANOVA to compare means by state for states with at least 50 respondents')
model2 = smf.ols(formula = 'W1_N1H ~ C(PPSTATEN)', data = sub4)
results2 = model2.fit()
print(results2.summary())
print()

print('Post-hoc test [HSD] for state means')
sub5 = sub4[['W1_N1H', 'PPSTATEN']].dropna()
mc1 = multi.MultiComparison(sub5['W1_N1H'], sub5['PPSTATEN'])
res1 = mc1.tukeyhsd()
print(res1.summary())
print()

print('Print summaries per state')
grouped = sub5.groupby('PPSTATEN')
sub6 = grouped['W1_N1H'].agg([numpy.median, numpy.mean, numpy.std, len])
print(sub6)
print()

Analysis

All the code output can be found here.

First, I carried out an ANOVA to see whether opinions about union members tend to vary between metro and non-metro areas. There is a statistically significant difference (F = 7, p < 0.01), although the variance explained is small. Opinions about unionised workers are somewhat more favourable in metro areas (mean score 62 out of 100) than in non-metro areas (57). Since the metro variable has only 2 levels, there’s no need for a post-hoc test.

Exploratory analysis of the state variable reveals that a substantial number of states have very few respondents. I decided to create a subset consisting only of respondents in states with at least 50 respondents (I’ll admit I’m not sure whether this threshold makes sense and how to decide this).

Note that this is a somewhat unbalanced subset: most states that are included are from the eastern part of the US, with California and Texas as major exceptions. This probably makes sense if you’d look at the population size of states, but still it’s something to keep in mind when interpreting the findings.

The conclusion of an ANOVA testing differences in opinions between states is that there are indeed differences in mean opinion about unionised workers per state (F = 2.4, p < 0.01), although again the variance explained is quite small.

A post-hoc test (HSD) reveals that the differences can be attributed to the divergent position of Florida within this sample of states: respondents from Florida on average have less favourable opinions (a mean score of 54) than those from Illinois (67), Michigan (68), Missouri (69) and New York (66). The average rating by respondents from Texas (57) turns out not to be different from other states from the sample - at least not at a statistically significant level.

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