Algorithmic gamblification of work | The workers behind the ‘AI magic’ | New Airbnb regulation

Good day! One minute you get a newsletter in your mailbox every week and the next you have to wait 3 weeks for it. As someone who loves surprises, I go for option 2 😉

Last weeks, I again participated in many events related to platforms in different roles, had the opportunity to contribute to great projects and work with professionals on almost all continents on this globe. How cool are the opportunities to work location-independent with tools to operate in international teams in an accessible way. Although, of course, it’s not about the tools, but how you collaborate and use the tools. Just as with platforms, (online) tools are facilitating and not leading. Should you think: with me it is the other way round, I would have a good discussion with yourself.

And although a lot is possible online and remote, it is also important to continue travelling (as responsibly as possible) and to speak to each other live. So that is the reason I ‘briefly’ took the train up and down to Munich in Germany this week for an interview with the person in charge of the Crowdsourcing Code: a code of conduct between 8 ‘crowdwork’ platforms in Germany. I did this for the WageIndicator’s new podcast: a podcast (and blog) on ‘global gig economy issues’. The first edition of this monthly podcast will go live in mid-April.

Enough introduction: for this edition I have again collected a number of relevant pieces for you and provided them with my interpretation and commentary. Enjoy the read and have a nice day!

The house always wins: the algorithmic gamblification of work | Veena Dubal

The impact of algorithms and technology on the worker: the subject of part two of the European Platform Work Directive. For platforms offering ‘on demand’ jobs (taxi and delivery), the impact of the algorithm on finding, hiring and performing work is great and the worker is paid per job. Where it is often unclear exactly what the returns are.

In the early days of Uber, everyone was excited about the ‘surge pricing’ the company uses. If there is more demand than supply somewhere at a given time, prices rise. With this, demand goes down and supply goes up. At the time, many saw this as a perfect economic model of flexible pricing. The example of the hairdresser was often brought to mind: why do you pay the same for a haircut on Tuesday afternoon as on Friday evening, when demand is many times higher? Now it appears (and this is not overnight) that these ‘smart’ (or: ‘savvy’) technologies are able to root for and entice working people to do more than initially envisaged.

In the article “The house always wins: the algorithmic gamblification of work“, scientist Veena Dubal gives an interesting (and shocking) insight into the algorithms used by Uber to direct workers to be available as much as possible at the platform’s convenience. This is also because the risk of not working is at the worker’s expense: something that, in my opinion, is a very bad idea anyway.

In this article:

“In a new article, I draw on a multi-year, first-of-its-kind ethnographic study of organizing on-demand workers to examine these dramatic changes in wage calculation, coordination, and distribution: the use of granular data to produce unpredictable, variable, and personalized pay. Rooted in worker on-the-job experiences, I construct a novel framework to understand the ascent of digitalized variable pay practices, or the transferal of price discrimination from the consumer to the labor context, what I identify as algorithmic wage discrimination. As a wage-setting technique, algorithmic wage discrimination encompasses not only digitalized payment for work completed, but critically, digitalized decisions to allocate work and judge worker behavior, which are significant determinants of firm control.

Though firms have relied upon performance-based variable pay for some time, my research in the on-demand ride hail industry suggests that algorithmic wage discrimination raises a new and distinctive set of concerns. In contrast to more traditional forms of variable pay like commissions, algorithmic wage discrimination arises from (and functions akin to) to the practice of consumer price discrimination, in which individual consumers are charged as much as a firm determines they are willing to pay.

As a labor management practice, algorithmic wage discrimination allows firms to personalize and differentiate wages for workers in ways unknown to them, paying them to behave in ways that the firm desires, perhaps for as little as the system determines that they may be willing to accept. Given the information asymmetry between workers and the firm, companies can calculate the exact wage rates necessary to incentivize desired behaviors, while workers can only guess as to why they make what they do.”

You don’t have to be an activist to understand that such techniques are far from desirable. This piece includes the experiences of some drivers:

“Domingo, the longtime driver whose experience began this post, felt like over time, he was being tricked into working longer and longer, for less and less. As he saw it, Uber was not keeping its side of the bargain. He had worked hard to reach his quest and attain his $100 bonus, but he found that the algorithm was using that fact against him.”

I think it is important to let platforms take more responsibility in explaining their processes and having this validated by a trusted third party. The fact that platforms like Uber frame complexity as an added value for the worker is evident from this quote:

“If you joined Uber years ago, you will have joined when prices were quite simple. We set prices based on time and distance and then surge helped increase the price when demand was highest. Uber has come a long way since then, and we now have advanced technology that uses years of data and learning to find a competitive price for the time of day, location and distance of the trip.”

When someone takes pride in adding complexity, it should lead to suspicion by default. Because complexity can also be used to hide things. I wonder if upcoming European regulations will lead to less complexity and unclear processes for the worker. It should be a key issue for policymakers anyway.

Video course: Build a successful marketplace | Sharetribe

Starting your own platform: where do you start and what is the route to take? That’s a question you can safely leave to the team at Sharetribe. Sharetribe offers a simple and straightforward tool for putting together your own ‘marketplace’ without any expertise in programming. I have known the company myself for about ten years, and even with the ‘build your own platform’ programme at the The Hague University of Applied Sciences, students with no prior knowledge easily built their own platform via Sharetribe.

Sharetribe invests a lot in content to help their clients successfully launch their own platform. They also have a stake in this: they only make money when their customers are successful. This has resulted in an impressive collection of valuable content. Last week, they added something new to this: an online video course:

This ten-step video course takes you through your marketplace journey all the way from idea to scaling your business. Each step is packed with the latest marketplace facts, actionable advice, and relevant case studies.

In an hour and a half, you will learn a nice foundation of the steps you need to go through to launch a successful platform.

Exclusive: OpenAI Used Kenyan Workers on Less Than $2 Per Hour to Make ChatGPT Less Toxic | Time

It is almost inevitable that you have seen OpenAI’s insane tool ChatGPT pass by or possibly tried it yourself. An impressive chatbot that you can ask any question, only to get a comprehensive and detailed answer. Many sectors, including education, are anxiously considering what to do with such a tool. I have tried the tool myself and it is really impressive. For instance, before the workshop on platform economy and education, I asked what are important topics for education and platform economy. A rather general and vague question. You can see the result in the image at the bottom of this piece.

ChatGPT is yet another development that, as with AI and algorithms, seems like a kind of magic black box. It almost seems like magic: everything happens by itself. But…. is that really the case? Certainly not. Both in training and execution, there are always loose ends. For instance, many tech companies use platforms like Amazon Mechanical Turk: a platform where people all over the world (and especially pieces of the world where you can get by on very little income and where people have little alternative) perform mini jobs of a few seconds via a platform: so-called ‘clickwork’. This involves recognising images, but also resolving loose ends of seemingly automatic systems. Content moderation of platforms like Facebook is also designed according to these principles. Not always pure platform, but similar principles.

Mary Gray wrote a fascinating book on so-called clickwork: “Ghost Work – How to stop Silocon Valley from building a new global underclass“. Timm O’Reily wrote the following about this book: “The Wachowskis got it wrong. Humans aren’t batteries for The Matrix, we are computer chips. In this fascinating book, Gray and Suri show us just how integral human online task workers are to the development of AI and the seamless operation of all the great internet services. Essential reading for anyone who wants to understand our technology-infused future.” Highly recommended.

A long run-up to the piece I want to discuss today: “OpenAI Used Kenyan Workers on Less Than $2 Per Hour to Make ChatGPT Less Toxic.” It describes how OpenAI managed to ‘magically’ make ChatGPT better and safer:

“To build that safety system, OpenAI took a leaf out of the playbook of social media companies like Facebook, who had already shown it was possible to build AIs that could detect toxic language like hate speech to help remove it from their platforms. The premise was simple: feed an AI with labeled examples of violence, hate speech, and sexual abuse, and that tool could learn to detect those forms of toxicity in the wild. That detector would be built into ChatGPT to check whether it was echoing the toxicity of its training data, and filter it out before it ever reached the user. It could also help scrub toxic text from the training datasets of future AI models.

To get those labels, OpenAI sent tens of thousands of snippets of text to an outsourcing firm in Kenya, beginning in November 2021. Much of that text appeared to have been pulled from the darkest recesses of the internet. Some of it described situations in graphic detail like child sexual abuse, bestiality, murder, suicide, torture, self harm, and incest.”

OpenAI considers this work very important: “Classifying and filtering harmful [text and images] is a necessary step in minimising the amount of violent and sexual content included in training data and creating tools that can detect harmful content.” The article’s authors are rightly critical after their research: “But the working conditions of data labelers reveal a darker part of that picture: that for all its glamour, AI often relies on hidden human labor in the Global South that can often be damaging and exploitative. These invisible workers remain on the margins even as their work contributes to billion-dollar industries.”

ChatGPT remains an impressive tool, but surely the magic is a lot less (clean) than the tech optimists try to make us believe. Andrew Strait describes it powerfully in the piece: “They’re impressive, but ChatGPT and other generative models are not magic – they rely on massive supply chains of human labour and scraped data, much of which is unattributed and used without consent”

What can we learn from this?

While the insights from this story alone are interesting on their own, I think it’s important to look further. What can we learn from this case study.

For one thing, it shows that these kinds of tools feast on the work of others: scrapping content created by others and low-paid moderators and workers. It is the bright minds who devise and build systems to do this and get away with the credit and money, but I think it is important to (re)recognise more that this content does not fall from the sky and there may be a necessary discussion about how fair and desirable this is.

I would also like to broaden that discussion a bit. I regularly speak to very committed scientists with a clear opinion about what is ‘fair’ who meanwhile use Amazon Mechanical Turk for their research. I understand that this is incredibly convenient, but then of course you also have butter on your head. A good conversation about fair treatment and remuneration of everyone in the chain is something that is missing from many innovations. A conversation that, as far as I am concerned, could be had more often. People who perform clickwork are a kind of ‘disposable labour’. The moment they are no longer needed, no one will care. And because of this, it is only right that the authors of Ghostwork and of this article point out the facts to us.

NEW AIRBNB LEGISLATION MAKES ENFORCEMENT RULES MUCH EASIER

Airbnb and regulation: it is an issue that has been around for quite a few years. Earlier, national regulations were introduced in the Netherlands and now this is being extended to European regulations. I think a good step for everyone.

The regulations will also be accompanied by a European tool: “The Commission is coming up with a single European data tool for exchanging information on holiday rentals between platforms and local authorities. Platforms will now have to share, in places where rules apply, data every month on how ma
ny nights a house or flat has been rented out and to how many people.”

Ultimately, each city will continue to set its own holiday rental rules. That too is a good step, although Amsterdam’s case study teaches us that it is not as simple as it seems.

It would also be good to secure the knowledge and research in a central location alongside this tool, so that not every city has to reinvent the wheel itself. Furthermore, I am (very) curious about the implementation of this. Connecting the platform and the central European tool: that’s probably fine. But what about the translation to the individual systems of the municipalities (a link with the land registry in the Netherlands, for instance, seems light years away, and we are a country in the digital vanguard…) and what are the privacy risks involved? I will keep following this.

About and contact

What impact does the platform economy have on people, organisations and society? My fascination with this phenomenon started in 2012. Since then, I have been seeking answers by engaging in conversation with all stakeholders involved, conducting research and participating in the public debate. I always do so out of wonder, curiosity and my independent role as a professional outsider.

I share my insights through my Dutch and English newsletters, presentations and contributions in (international) media and academic literature. I also wrote several books on the topic and am founder of GigCV, a new standard to give platform workers access to their own data. Besides all my own projects and explorations, I am also a member of the ‘gig team’ of the WageIndicator Foundation and am part of the knowledge group of the Platform Economy research group at The Hague University of Applied Science.

Need inspiration and advice or research on issues surrounding the platform economy? Or looking for a speaker on the platform economy for an online or offline event? Feel free to contact me via a reply to this newsletter, via email ([email protected]) or phone (0031650244596).
Also visit my YouTube channel with over 300 interviews about the platform economy and my personal website where I regularly share blogs about the platform economy. Interested in my photos? Then check out my photo page.

English newsletter Ghost-EN

11,000 GigCV downloads! | Will the platform directive make a difference? | NYC Platform Cooperative | How India tries to ‘democratize’ shopping

Good day! Last week, I posted my newsletter about the platform economy for the first time in a long time and I received a lots of nice reactions. Although I have not yet decided what the frequency of this newsletter will be, you will receive this next one a week later. In this edition, I have again compiled a number of articles that have appeared in the media in recent weeks and provided them with my interpretation and comments. And I take a closer look at the 1st anniversary of GigCV: a data-sharing agreement with platforms in the Netherlands enabling more than 50,000 workers to access their data on reputation and transactions that has already been used more than 11,000 times in its first year! Have a great day and enjoy reading.

GigCV 1 year anniversary!

Exactly one year ago, GigCV went live. GigCV is a project I set up after spending a year researching the issue of ‘reputation and transaction data portability for platform workers’. Going live, 4 platforms participated: Charly Cares, Helpling, Roamler and YoungOnes. They integrated the GigCV data sharing agreement system (and its API and legal documentation) into their platform, giving workers completing jobs through their platform access to their free PDF digital resume. They also agreed to participate in independent scientific research around data sharing with platform workers.

Now it was already quite a step that it had succeeded in putting data portability into practice: from the start, more than 50,000 workers had access to their data. This is because this topic usually remains stuck in a conceptual vacuum. That’s different with GigCV. Through an extreme focus on simplicity and avoiding complexity, we managed to get this project off the ground in a short time, without a business model. And then it’s especially nice when it’s used! Two weeks ago I announced that 11,000 resumes were downloaded in the first year! That’s a number I never would have dared mention at the start.

There is still plenty around GigCV in store for 2023:

connecting new platforms;
extending the standard;
research into the impact of the inclusion of data on the position of the worker;
research into the value of this data outside the platform market;
securing the long-term continuity of GigCV.

Upon reading the list of participating platforms, you will also see the name of Helpling. This platform, depending on how the bankruptcy ends, will disappear from the Netherlands. An incredibly shame (and pointless), but what is nice to see is that after the bad news was announced, the number of downloads of resumes from Helpling cleaners skyrocketed. This indicates that workers are eager to secure their data now that their profiles will most likely be taken offline soon. Which provides me with another interesting insight for a piece of “exit by design” I am currently working on.

P.s. GigCV is currently only running on Dutch platforms. Some of these platforms also are active outside the Netherlands. This makes GigCV also available to those working in England, Germany, Belgium and France via affiliated platforms.

Improving conditions for gig workers splits MEPs | EU Observer

The ‘Platformwork Directive’: a European law specifically for platform workers, has been under discussion in Brussels for some time. This directive proposes reversing the legal presumption (employee, unless) and imposing certain transparency obligations on the automatic decision-making processes used on platforms.

It is an exhaustive process and at every (mainly online) meeting I attended on the subject, I did not get the feeling that people actually believed themselves that this was ever going to make a difference. Recently it became clear once again how laborious the process is:

“In December 2022, the council failed to reach an agreement on its position, so it remains divided between those who advocate a pro-worker directive, and those who do not. And the European Parliament, the third axis in this relationship, was supposed to vote this Thursday (19 January) whether the report of the employment committee voted upon last month will be the institution’s position in the trilogue negotiation. However, MEPs are divided, and the text could be rejected in a plenary vote that has now been postponed for two weeks (until 2 February), after 71 MEPs who disagreed with the committee report wanted more time to insert amendments before the trilogues started.”

Still, there is some progress. Today there was an agreement in the European Parliament. “The European Parliament votes to adopt the text on the Platform Work Directive. 376 in favour, 212 against, 15 abstentions”. A good step, but there is still a long way to go. Also read the very good read on this by Brave New Europe: “Gig Economy Project – Defeat for the platform lobby: European Parliament backs stronger Platform Work Directive.”

During the kick-off of the Dutch NWO NWA PlatWork-R research, led by Utrecht University, a presentation by Hanneke Bennaars further highlighted the complexity of the issue. She explained that IF a final agreement comes out of the EU, each country is then responsible for the implementation itself. After all, labour laws are national, not European. And, if I understood correctly, when a country itself has a ‘better’ alternative, this is always leading. It is not for nothing that platform work (the ‘gig economy’) is also called ‘lawyers paradise’.

Meanwhile, lawsuits against platforms also continue in several countries in Europe. For example, the court in France recently fined Uber 17 million euros “in damages and lost salaries to a group of drivers who argued they should have been treated like employees rather than self-employed.” Uber has indicated it will appeal. You wouldn’t expect it. Cases against Uber Taxi and Deliveroo are also in their final stages in the Netherlands. You could say that legislation (which I have a hard head in believing is ever going to happen) is lagging behind the lawsuits. And I keep repeating: where is the voice of the worker in this debate and who is the first to acknowledge that the gig economy is not a loose planet or silo and that we need a proper debate on the value and valuation of work in general anyway.

The kick-off discussed earlier also raised the question: shouldplatforms, like employment agencies in the Netherlands, become a separate category? While I want to delve again into why in the case of employment agencies this has been done (tips and links are welcome), I think this is not a good idea in the case of platform work. This is because I expect the grey area will only grow in the coming years and we really need to start seeing platforms as an organisational mechanism rather than silo organisations. Then you also get to the most interesting and relevant questions about really new issues. And then it would be nice if, in the meantime, the real reform of the labour market will also take place one day, so that there is no (or less) competition on employment conditions between the different contract forms. Because everyone (except the lawyers, that is ;-)) is done with that now too.

New York cleaners create new path to entrepreneurship – BBC News

It has come up many times in my publications: the platform cooperative Up&Go. Here is a nice piece on BBC News about this concept, in which cleaners, who are only members of local worker cooperatives, together form the board and ownership of the app they depend on.

It remains a great example, but the question is whether this is a ‘platform first’ initiative, or a way for existing worker cooperatives to better organise and market their existing work. I go for option two. A lot less exciting and spectacular perhaps, but ultimately it’s about the impact on workers and how they have been helped.

India aims to ‘democratise’ online shopping with ecommerce network – Financial Times

It is a question that keeps coming up: how to ensure that the power of platforms is more equitably distributed. And what is the government’s role in this? In this article, an interesting angle from India.

“India is preparing to launch a government-backed ecommerce initiative to “democratise” online shopping, in an ambitious attempt to challenge the dominance of companies such as Amazon and Walmart-owned Flipkart in one of the world’s fastest-growing markets. Open Network for Digital Commerce, a non-profit company set up by India’s commerce ministry last year, is holding trials in more than 85 cities including the tech hub of Bangalore, ahead of a nationwide launch next year. While companies such as Amazon run proprietary services controlling everything from vendor registration and delivery to customer experience, ONDC is an “interoperable” network, where buyers and sellers can transact regardless of the apps or services they are using. The open-source network would allow a customer using one app, such as fintech services provider Paytm, to find and order groceries from a vendor registered to another platform, such as small business hub eSamudaay. This can then be shipped by whichever alternative platform, such as delivery service Dunzo, that is able to do it at the fastest and lowest rate.”

Interesting about this initiative:

Open source;
Scale through interoperability (interchangeability);
From the government;
A way to get existing retailers online.

Will this work? No idea: in the end, “the proof of the pudding is in the eating”. The question is whether a purely technical infrastructure is enough to generate enough reach and whether the platform as a ‘private regulator’ can ensure that all suppliers can keep their promises to consumers. Because ultimately, this is the group that will determine whether this initiative will be a success. That “only 0.1 per cent of the country’s 12mn retail outlets are digitally enabled” gives little hope. And besides, the government is a co-investor, but it is not clear exactly what that means. But who knows, it might still succeed. Or at least deliver some interesting lessons and insights that others can take forward.

About and contact

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How Helpling’s bankruptcy exposes the flaws of the debate on the gig economy.

Good day! It has been silent for a while around the English version my newsletter on the platform economy. Whereas the Dutch edition appeared weekly and is now on its 320th edition, it was difficult to publish an English version in parallel. Meanwhile, I have the processes more in order and I am making a new attempt to get more continuity in the English version.

You will no longer receive this newsletter from Revue: the platform I used to send my newsletters. Less than eight months after Twitter bought Revue, the company closed its doors. After a long search I ended up at Ghost: an open source initiative organized by a foundation. I had no desire to become another puppet of Venture Capitalists.

In this newsletter, as before, I will discuss various publications in the platform economy and will regularly share long blogs of my own. In this first edition via Ghost, I am sharing a piece I wrote about the level of debate around the gig economy based on the bankruptcy of the Dutch arm of the platform for domestic cleaners Helpling.

Enjoy the read and have a nice day,
Martijn

How Helpling’s bankruptcy exposes the flaws of the debate on the gig economy.

After a long legal battle, platform for domestic cleaners Helpling in the Netherlands filed for bankruptcy. The bankruptcy marks the end of a not uncontested battle with Dutch trade union FNV, which claimed the bankruptcy as a “belated victory” in the Dutch media.

The big question that remains is: a victory for whom? In a blog in 2018, I predicted that a ruling in FNV’s ‘favour’ would result in the company going bankrupt. And I already put quite a few question marks and exclamation marks on this case. In this blog, I outline the context of the case and Helpling’s activities, discuss what is (not) new about a domestic cleaning platform and give an overview of what changes the platform has made over the years. To conclude with a proposal for a debate that honours both workers and society.

The rise of Helpling

Helpling, a startup from Germany’s Rocket Internet, entered the Dutch market in June 2014. The platform where individuals can book a cleaner for their home is ambitious and is quickly seen as one of the big promises in the rise of the gig economy, a.k.a. ‘gig economy’, in the Netherlands. That the platform will not just stick to cleaning becomes clear to me when I interview then-Director of the Netherlands Floyd Sijmons at their Amsterdam office in 2015. When I ask him if it will stick to cleaning, he replies, “if we were to stick to cleaning only, we would have called our platform Cleanling”. Crystal clear.

Yet the platform fails to expand its services. It is looking at window cleaners, but that ultimately does not get off the ground. The added value of the platform also turns out to be a lot less than with, say, Uber. It involves a pre-scheduled transaction (nobody wants a cleaner ‘on demand’) and the worker and client stay connected after an initial successful gig and can easily work around the platform. Due to the nature of the transaction, no high-level algorithm is needed and the benefits of data mining are limited. Therefore, there is no strong ‘lock in’ as with Uber and the platform has to work hard to offer an appropriate service to worker and client to bind them to the platform.

Retaining clients within national markets was more difficult than potentially expected at the outset. Where it was first thought that one strategy would suffice, it soon became clear that each country needed its own strategy. This is reflected in the recently published paper “Platform adaptation to regulation: the case of domestic cleaning in Europe“, written by Koutsimpogiorgos, Frenken and Herrmann. For this paper, the authors compare Helpling’s general terms and conditions in France, Germany, Ireland, the Netherlands and the UK and analysed the changes over the years. Their conclusion is that although Helpling starts with a general strategy, it soon has to adapt to the institutional conditions per country. Over the years, Helpling regularly adjusts its model in the Netherlands. For instance, the platform changes its commission structure and introduces a model where cleaners are allowed to set the price for a cleaning job themselves. A minimal fee should ensure that there is no ‘race to the bottom’.

Home services arrangement

Helpling has never worked with freelancers. The platform, like other platforms where consumers hire services in and around the house, makes use of the ‘regeling dienstverlening aan huis’ (‘service at home scheme’). This is a national regulation in the Netherlands that exempts private employers, which is what you are when you get a cleaner to come for your home, from a large set of employer obligations. However, a private employer must pay the minimum wage and 8 per cent holiday pay, continue to pay the cleaner in case of illness and provide a safe and healthy workplace.

In practice, little comes of this regulation in the private cleaning market in the Netherlands due to lack of enforcement. Or, as I wrote in an earlier blog: “So it looks like we have it well regulated for our domestic workers, but it is not. They fall into an ‘excuse category’, so to speak.” Helpling, by using this arrangement, joins the standard in the Netherlands.”

The end of Helpling

The fact that the private employer finds, books and pays a cleaner through a platform is reason for FNV to take the platform to court in 2018. FNV believes that the platform is an employer and must employ the cleaners. In the first instance, the subdistrict court ruled in 2019 that Helpling is not an employer but an intermediary. Two years later, however, the Amsterdam court of appeal sees things differently and rules that Helpling must treat the cleaners as temporary workers, with corresponding rights and obligations.

Helpling is aware that a temp model will not work for the platform: consumers will never want to pay these rates. The platform flips the model for the last time, rigorously discontinuing its original model where it deducts a commission on the hours paid out and introduces Helpling Select (where you pay a one-off fee to find a cleaner and then take care of everything yourself) and Helpling Premium (where a professional cleaner comes to be employed by a cleaning company).

In the end, the platform did not live up to expectations: the receiver’s response stated that 10 employees were working for the platform on the day of the bankruptcy.

Looking back: what was new about Helpling?

In the aforementioned blog from 2018, I asked the question: what’s new about Helpling? I wrote:

“Platform Helpling enables households to find a domestic cleaner through an online marketplace. In addition, the platform supports with quality control (by phone intake, ID check and reputation system), back office (complaints, queries, replacement help in case of holidays) and payment module. The company makes money by skimming a margin from each transaction. A common used model.

Helpling is certainly not unique; this kind of service has been around for years. Take the company HomeWorks. A company that has been doing exactly the same thing as Helping for 25 years. The only difference is that this is not an online marketplace, but the company manually matches supply and demand. It also has service coordinators who oversee quality and even go with the initial cleaning. All this makes it an expensive model: the commission the company charges is therefore around 39% (compared to 23% at Helpling).”

The platform competes with intermediaries with a similar model and strategy that have been operating in the market for many years. It is therefore questionable why the platform was then suddenly seen as an undesirable player.

Where was the informal market in the debate?

In the lawsuit against Helpling, FNV pretends that the platform competes with cleaning companies. That, of course, is nonsense. The only thing Helpling was competing with is the informal market. My prediction in my earlier blog: “if FNV wins and Helpling has to employ domestic workers, the price of help for the customer will go up to a minimum of 25 euros. Practically no individual is willing to pay this amount per hour. No one will book through the platform anymore and Helpling will be bankrupt in no time. Does that solve the problem? No. That cleaning will continue to be done afterwards. But then through the informal market.” And that is exactly what will happen now.

And that is a shame. The case concerning Helpling could have sparked a debate on how we as a society value work in and around the house. That is a discussion we should have in society that should culminate in a political choice. Because that’s what it is: a choice. That politics can indeed do something to improve the situation is proven by the countries around us: Belgium, France, Scandinavia. There, the government subsidises this kind of work, in Belgium, for example, through service vouchers. I talk to many people who recognise that the current Dutch rule is worthless and we should introduce a system like in Belgium. But they fail to get the issue on the agenda. Apparently, a structural debate is less interesting than a random court case.

Towards a broader view of ‘mediation’ for home cleaning

With today’s knowledge, I want to dig a little deeper into what was new about Helpling. The fact that it is a platform made Helping distinctive from other ways for private employers to find domestic cleaners, but the question is what impact this has on workers’ autonomy, satisfaction and income. The moment something is new, my first question is always: how was it done before? And in what areas does the ‘new’ make things better, worse or stay the same. In the case of Helpling: what are the conditions of domestic cleaners who find their private employers through other channels? There are always different ways to find a domestic cleaner: via via via, a note in the supermarket, via social media, mediation websites and through platforms.

Quite coincidentally, on the day of Helpling’s bankruptcy, I attended an academic workshop on ‘platform work‘ organised by Koen Frenken of Utrecht University. One of the speakers was Juliet Schor, economist and Sociology Professor at Boston College. Schor has some very interesting research in the sharing and clustering economy to her name and presented a study in Utrecht where she interviewed workers in private home care who find their clients via a Facebook group or via a platform. She was curious whether these workers would experience their work and conditions differently.

She found that the outcomes between the two ways of finding work were very similar. The only differences she discovered were traceable to differences in the workers’ circumstances, not the way they had found work. Workers’ income was higher through the platform than through a Facebook group and “our interviews found reveal high levels of work and platform satisfaction among these workers”. To which Schor did add the note that the ‘market conditions’ for this target group are predominantly positive.

Her conclusion: “We conclude that factors external to the technical functioning of platforms, such as market and regulatory conditions, workforce composition and the nature of the work, should be more systematically examined to understand what forms of control platforms themselves can be subjected to.”

Conclusion and proposal debate

As predicted in 2018, Helpling is now out of business and cleaners have to find their way back into the informal market. The fact that there has never been a serious attempt to explore how a central platform can improve the situation of a vulnerable group of workers without becoming an equal employer is a missed opportunity (and responsibility). Not only of FNV, but also of all the institutions that are responsible about this agenda and have not spoken out. That FNV is claiming a belated victory is sour, because you have to wonder who won. Certainly not the cleaners and certainly not the debate.

I hope that this case (and this blog) will encourage policymakers, but also trade unionists, to still take up the important questions surrounding this group of workers. That research is now done into the differences that the different ways of ‘matching’ entail and to see how to really do something for this group of workers. This starts with (re)recognising the context, to make (political) choices along these lines. Perhaps the closing of Helpling’s chapter will now free up space for this. Let’s hope so.

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