# Transcript: Will we end up on unemployment benefits?

## Description

Who is Daniel Krištof



Daniel Krištof earned his doctorate at the Faculty of Arts, Charles University in Prague, where he studied work and organizational psychology and clinical psychology. He completed accredited psychotherapeutic training and crisis intervention training. He worked for ten years at Deloitte, where he led transformations of large companies and held top management positions at major companies such as ČEZ Prodej, Česká spořitelna, and Direct pojišťovna. He also managed ČEZ Akademie, which helps people save energy with the support of leading Czech experts.



Since September 2023, he has headed the Czech Republic’s Labor Office and serves as the CEO of the country’s largest aid organization, which is undergoing a major transformation and digitalization of services for its clients.

## Transcript

**[00:00:00]** 🎧🎧🎧

**[00:00:27]** Hello and good day, greetings to you.

**[00:00:30]** Greetings to everyone who made the trip here to Magenta Experience Center.

**[00:00:35]** Greetings to everyone watching us live.

**[00:00:38]** I can now officially announce that the July breakfast was in Brno

**[00:00:44]** and that we will return there occasionally.

**[00:00:48]** So the Magenta center in Brno is open.

**[00:00:51]** You can definitely look forward to us there soon.

**[00:00:56]** Last year, Brain and Breakfast created a kind of clone of those business breakfasts.

**[00:01:02]** You probably know this.

**[00:01:04]** But today we are here in the classic part.

**[00:01:08]** It's appropriate to thank our partners.

**[00:01:10]** Frutissimo, the wonderful breakfast you are enjoying here.

**[00:01:14]** Unfortunately, those of you watching us are not.

**[00:01:16]** We should also thank Slajd, which is a partner of Interactivity.

**[00:01:20]** Make sure to have your mobile phones ready now,

**[00:01:23]** because you will need them shortly.

**[00:01:25]** Not only to interact with us,

**[00:01:27]** but also to ask a question to our guest today.

**[00:01:30]** And we should also thank Magenta Experience Center,

**[00:01:33]** which I already mentioned for hosting us here.

**[00:01:36]** The QR code you see there,

**[00:01:39]** will occasionally appear for those watching us,

**[00:01:42]** and it will take you to the Red Button network newsletter,

**[00:01:46]** which has been behind this whole event for several years now.

**[00:01:51]** So if you want to find out what's new in the network,

**[00:01:54]** just scan it and subscribe.

**[00:01:57]** I won't keep you long,

**[00:02:02]** and I ask you to take out your phones.

**[00:02:05]** You can go to www.slajdu.com,

**[00:02:09]** use the hashtag Brain,

**[00:02:12]** or scan this QR code that just appeared,

**[00:02:18]** or if you are watching us on the Red Button EDU platform,

**[00:02:21]** you can interact directly under the window

**[00:02:26]** where you see me now.

**[00:02:29]** I've already posted the first question, which traditionally is,

**[00:02:33]** have you been to a breakfast before, or is this your first time?

**[00:02:38]** If you know me, you know the result always pleases me,

**[00:02:45]** because when most say no,

**[00:02:50]** I haven't been to a breakfast yet, it means

**[00:02:52]** that we are expanding our scope, that we are bursting more bubbles

**[00:02:55]** and that is very, very good.

**[00:02:57]** So I'm glad that with today's guest we have moved a step further again

**[00:03:02]** and I would like to welcome Dan Kristof.

**[00:03:05]** Dan, welcome, thank you very much for making time.

**[00:03:09]** Thanks for the invitation.

**[00:03:11]** Thank you.

**[00:03:17]** When we titled this breakfast that we would end up at the unemployment office

**[00:03:21]** with a question mark, I won't drag it out.

**[00:03:25]** I feel that you have a great presentation,

**[00:03:27]** that you will convince us that yes.

**[00:03:30]** So here it is, the floor is yours, and thank you very much.

**[00:03:33]** Thank you very much, thanks for the invitation and thanks to all of you,

**[00:03:37]** who came to see this presentation.

**[00:03:41]** You actually have my admiration.

**[00:03:43]** In my previous life, when I was in business,

**[00:03:47]** I wouldn't have trusted anyone from the unemployment office.

**[00:03:51]** Especially people who have been at the unemployment office for more than a year and a half,

**[00:03:57]** employees, clients who have been with us for more than a year and a half,

**[00:04:03]** say sediment.

**[00:04:05]** You will listen to a presentation from the sediment of Břeh,

**[00:04:09]** I have been at the unemployment office for more than a year and a half,

**[00:04:13]** and we will talk about how not to become sediment.

**[00:04:19]** Before I dive into it, so you have a bit of...

**[00:04:22]** I really didn't know much about the unemployment office

**[00:04:26]** before I became the general director.

**[00:04:28]** Before that, I was at Deloitte, doing large transformations,

**[00:04:32]** helping Česká spořitelna with the launch of their healthy finances,

**[00:04:36]** helping ČESU, I founded the energy birth server Šetřím.cz,

**[00:04:44]** where we helped half of Czech households calculate,

**[00:04:48]** how much their property consumes

**[00:04:51]** and what they should do to reduce that consumption.

**[00:04:54]** So I started at the unemployment office relatively uninitiated

**[00:04:58]** and in a world where I spent most of my life really trying

**[00:05:01]** not to end up at the unemployment office.

**[00:05:03]** So before we get into the hard data that now tells us,

**[00:05:08]** we will also show predictive models,

**[00:05:11]** just a quick update on what the unemployment office actually does.

**[00:05:16]** The unemployment office is truly the largest helping organization in this country.

**[00:05:20]** That was what excited me about going to the unemployment office at all,

**[00:05:25]** So, in some kind of midlife crisis, I thought,

**[00:05:28]** helping is good, and actually helping the biggest helping organization,

**[00:05:32]** so it can help more, that's a good mission.

**[00:05:36]** The three main missions we do are,

**[00:05:39]** we help people find good jobs.

**[00:05:42]** If anyone from HR is watching and feels guilty,

**[00:05:46]** because when you fire someone, it's tough.

**[00:05:49]** HR has it tough.

**[00:05:51]** On one hand, they are the employees' buddies,

**[00:05:54]** and on the other hand, they fire them.

**[00:05:56]** That's a difficult role altogether.

**[00:05:58]** So one of the good news is,

**[00:06:01]** when HR fires someone, that person then finds a better job.

**[00:06:05]** Our data shows that when someone is fired,

**[00:06:09]** and they receive unemployment support from us,

**[00:06:12]** typically, measured by the median,

**[00:06:15]** they earn 8% more in the new job the first year and 9% more the second year.

**[00:06:20]** So, the labor office actually pays off for the economy,

**[00:06:25]** because the support we give to people,

**[00:06:28]** returns quickly the next year in taxes,

**[00:06:32]** and at the same time, most companies increase wages by 8%.

**[00:06:35]** So, it's actually good.

**[00:06:38]** It amazed me. When I first saw the data,

**[00:06:41]** I said, aha, I never saw the labor office this way before.

**[00:06:45]** It then made me realize that helping people find good jobs,

**[00:06:50]** giving them perspective, earning more.

**[00:06:52]** That's what we'll talk about today regarding the current labor market,

**[00:06:55]** which is what it is now, but definitely helping in tough situations is a topic

**[00:07:01]** where the labor office has it in its DNA.

**[00:07:04]** It's all those benefits the labor office pays out,

**[00:07:08]** but historically, the labor office struggled to pay benefits on time.

**[00:07:12]** That was one of my big missions,

**[00:07:15]** historically, it managed to pay only two-thirds on time, one-third not.

**[00:07:19]** That means if you don't help people quickly,

**[00:07:22]** when they're in crisis, they typically borrow badly

**[00:07:25]** and get into an even worse situation.

**[00:07:27]** So just to recap.

**[00:07:29]** The labor office, three main missions: find people good jobs,

**[00:07:31]** give them perspective, earn more, and help them in tough situations,

**[00:07:35]** or ideally now, with the super benefit, help them out of the tough situation,

**[00:07:39]** so they no longer need those benefits.

**[00:07:42]** Simply put, 330 thousand clients went through the labor office

**[00:07:53]** and found new jobs.

**[00:07:56]** Many more people are digitally educated with us.

**[00:08:00]** That was one of the big missions I had at the labor office.

**[00:08:03]** And we'll show what data that generates.

**[00:08:07]** The majority of society really didn't want to go to the labor office.

**[00:08:11]** So when we had 50 thousand for digital education for each person,

**[00:08:15]** the money was just sitting there.

**[00:08:18]** When I took over the labor office, when I took over the e-shop

**[00:08:21]** isemvkurzu.cz, after it had been running for a long time,

**[00:08:27]** three thousand people were digitally educated with us.

**[00:08:30]** Now it's more than 140 thousand people who are digitally educated with us.

**[00:08:34]** Precisely because we destigmatized that even someone currently employed

**[00:08:39]** can learn with us.

**[00:08:41]** We don't stigmatize them.

**[00:08:43]** Normally, anyone can log in via bank identity,

**[00:08:46]** choose a digital skills course from basic digital literacy

**[00:08:53]** through using artificial intelligence in everyday practice up to really hardcore

**[00:08:57]** like programming in Python.

**[00:08:59]** We approve the vast majority of courses, so the labor office

**[00:09:04]** isn't so bureaucratic.

**[00:09:06]** And so about 140 thousand currently by the end of last year,

**[00:09:11]** 70 thousand of them currently have jobs,

**[00:09:15]** they just want to earn more, they just want to strengthen their position

**[00:09:18]** in the labor market, so we help them achieve this goal.

**[00:09:24]** We'll see today who actually uses this.

**[00:09:28]** There is a big but, which I think we will all take away,

**[00:09:32]** a bit of a current failure, and I'll show you transparently

**[00:09:39]** where we are not succeeding.

**[00:09:41]** And it holds that right now the labor office approves 94% of benefits

**[00:09:47]** within 30 days, meaning when a person applies,

**[00:09:52]** they typically get the benefit within 14 days, so it doesn't get stuck

**[00:09:55]** and the person doesn't fall into an even worse situation before we pay the benefits.

**[00:10:00]** We pay a lot to first-time applicants or people who end up in that situation by chance,

**[00:10:05]** so it's really important that they get help from us quickly.

**[00:10:09]** So, that's an introduction to the labor office.

**[00:10:11]** And now, what awaits us today?

**[00:10:13]** What awaits us today regarding the question of who ends up on unemployment?

**[00:10:19]** Let's look at the data.

**[00:10:21]** So for those of you who don't like data at the table, don't worry,

**[00:10:25]** we'll get more into stories later,

**[00:10:28]** but first we'll cut through the data that already shows something.

**[00:10:33]** Some of the threats we said would come,

**[00:10:36]** are already arriving, we can see them in the data and we need to show them.

**[00:10:40]** Then we'll look back at when we experienced

**[00:10:44]** such a big labor market transformation in the past,

**[00:10:49]** how it turned out,

**[00:10:51]** and if there is any lesson to learn.

**[00:10:55]** If there is, in the last point we'll talk about what to do about it.

**[00:11:03]** What tools do we have to handle it?

**[00:11:05]** What does the data say?

**[00:11:11]** The data says the business case for the labor office is really positive,

**[00:11:18]** just to give you an idea, the labor office costs the state over 160 billion.

**[00:11:21]** That means if I were to collect for the labor office,

**[00:11:25]** I would have to collect more than 20,000 from every person of working age.

**[00:11:27]** Every year.

**[00:11:30]** That's a solid amount of money.

**[00:11:33]** My first question as a business person was, is it worth it?

**[00:11:37]** Yes, fortunately the business case is positive.

**[00:11:42]** Besides that, we have really low average wages,

**[00:11:46]** and there is huge potential there.

**[00:11:48]** Poland is already growing faster than us,

**[00:11:54]** and while we can boast about low unemployment,

**[00:11:58]** part of that is because people get wage increases,

**[00:12:01]** typically when they change positions,

**[00:12:05]** and we have the least flexible,

**[00:12:08]** or second least flexible labor market in all of Europe,

**[00:12:10]** only the Greeks are worse off,

**[00:12:13]** only 15% of people change jobs per year,

**[00:12:16]** the EU average is 22%,

**[00:12:18]** Denmark is at 30%,

**[00:12:22]** and changing jobs is a big driver of wage growth.

**[00:12:25]** Because our market is really frozen,

**[00:12:27]** this is a problem here,

**[00:12:30]** and honestly, I would prefer, and we all would be better off,

**[00:12:33]** if people's wages grew more,

**[00:12:35]** and companies would prefer it too,

**[00:12:39]** because companies are ready to pay more for qualified work,

**[00:12:41]** and today we'll show,

**[00:12:43]** that the demand for qualified positions is growing for us,

**[00:12:47]** but it is not fully met.

**[00:12:52]** What I said,

**[00:12:53]** is probably not much more boring at the labor office,

**[00:12:56]** than announcing unemployment every month,

**[00:12:58]** it just varies around 4%,

**[00:13:02]** nothing super interesting happens there,

**[00:13:04]** the unemployment itself is interesting,

**[00:13:07]** we have one of the lowest,

**[00:13:09]** I always think second or the very best in the entire European Union.

**[00:13:14]** What is happening, however,

**[00:13:16]** is that jobs are really changing,

**[00:13:21]** meaning all those low-skilled professions are declining,

**[00:13:26]** agriculture is declining, mining is declining,

**[00:13:30]** IT jobs are growing,

**[00:13:32]** administrative positions are growing,

**[00:13:34]** these lines you see here,

**[00:13:38]** show that in the long term there are areas

**[00:13:42]** where the number of employees is growing

**[00:13:44]** and areas where interest is falling and falling.

**[00:13:52]** Artificial intelligence will now shake this up a bit.

**[00:13:57]** It is clear that a large part of people will have to learn something new,

**[00:14:01]** some positions will disappear, some new ones will emerge.

**[00:14:05]** I will get to that in much more detail,

**[00:14:07]** because there are various predictions on how it will develop.

**[00:14:10]** We already have data showing

**[00:14:12]** who will be the winners of the rise of artificial intelligence,

**[00:14:16]** and we will show that.

**[00:14:20]** Lastly, just in case you got chills,

**[00:14:26]** when I said 20 thousand,

**[00:14:28]** which of you pay annually for the labor office,

**[00:14:31]** including parental benefits,

**[00:14:33]** it really includes everything.

**[00:14:35]** There are housing allowances, unemployment support,

**[00:14:39]** material need benefits, that's the whole package,

**[00:14:42]** but the benefit system overall works well.

**[00:14:49]** It pays off for society, there are big studies,

**[00:14:51]** it's no longer a question of right or left,

**[00:14:53]** there are big studies showing

**[00:14:55]** that in societies with a benefit system,

**[00:15:01]** which allows a person to be born poor,

**[00:15:04]** but not remain poor,

**[00:15:06]** so that initial poverty does not discriminate against them

**[00:15:09]** from education and the future labor market,

**[00:15:12]** they are better off,

**[00:15:14]** because they simply don't miss out.

**[00:15:16]** It still holds that each person's genetics

**[00:15:20]** spin the roulette wheel a bit at the start,

**[00:15:23]** this is one of the greatest injustices in the world,

**[00:15:26]** that intelligence is innate.

**[00:15:29]** Yes, smarter people have smarter children,

**[00:15:32]** but there is still a large degree of randomness.

**[00:15:36]** And by the way, it is the biggest predictor.

**[00:15:40]** Like a predictor of success at work,

**[00:15:42]** a predictor of how much someone will earn,

**[00:15:44]** even a predictor of lifespan, a predictor of health.

**[00:15:47]** So the best predictor psychology has,

**[00:15:50]** is not any personality tests,

**[00:15:52]** the best predictor is intelligence.

**[00:15:56]** Intelligence predicts success the most,

**[00:15:58]** predicts how much people will earn the most,

**[00:16:02]** and through that, also lifespan.

**[00:16:06]** Even lifespan in good health.

**[00:16:09]** And because this is a somewhat random process,

**[00:16:11]** genetics always mix it up,

**[00:16:14]** the fact that a person is born into a poor family

**[00:16:17]** and can succeed again,

**[00:16:19]** those societies that can do this,

**[00:16:21]** are better off.

**[00:16:22]** It comes back in that society.

**[00:16:24]** At the same time, welfare systems obviously stabilize

**[00:16:27]** lower crime rates among those people

**[00:16:30]** who are poor.

**[00:16:31]** So the fact that right now we have

**[00:16:34]** one of the lowest risks of poverty,

**[00:16:38]** is a good result.

**[00:16:39]** That a person can be born poor

**[00:16:42]** and not stay poor is fundamentally good.

**[00:16:46]** That the system can be a bit stricter in some places

**[00:16:49]** and more motivating in others,

**[00:16:51]** so that the person is not just on benefits,

**[00:16:53]** but tries to get out of it,

**[00:16:54]** that's definitely true,

**[00:16:56]** but the super benefit helps a bit.

**[00:16:59]** So that's a quick intro to the labor office

**[00:17:01]** and what we see in the data now.

**[00:17:05]** Now unemployment.

**[00:17:07]** I said showing the aggregated number is boring,

**[00:17:13]** but there are two extremely interesting things.

**[00:17:17]** You see, it's almost noise interpretation

**[00:17:19]** when I show unemployment.

**[00:17:23]** It just recently rose by about two-tenths of a percent,

**[00:17:29]** because school leavers hit the numbers,

**[00:17:32]** some people in education have contracts ending in June,

**[00:17:36]** so they came to the office in July.

**[00:17:39]** Some university graduates register already in July,

**[00:17:43]** so they showed up then.

**[00:17:45]** And generally companies hire less during the holidays

**[00:17:48]** and unemployed postpone starting work until after the holidays in September.

**[00:17:52]** So yes, there is a slight increase,

**[00:17:55]** but the exact same increase happened last year.

**[00:17:57]** So from this perspective, unemployment really

**[00:18:01]** isn't changing much at all.

**[00:18:05]** What is really interesting,

**[00:18:07]** if you look at this graph,

**[00:18:09]** I deliberately left out degrees,

**[00:18:12]** that changes things a bit.

**[00:18:14]** This is the graph of male unemployment.

**[00:18:17]** You can see it's lower than the overall rate,

**[00:18:20]** so if we show women for a moment,

**[00:18:22]** we'll see a different picture.

**[00:18:24]** And you see that male unemployment responds well to seasonal work starting.

**[00:18:28]** Spring begins, construction starts,

**[00:18:31]** seasonal work in winter industries kicks off,

**[00:18:34]** and that absorbs some of the unemployed.

**[00:18:38]** Unfortunately, the most interesting interpretation

**[00:18:42]** is the much higher unemployment among women.

**[00:18:46]** We'll also show today

**[00:18:48]** that the Czech labor market really discriminates,

**[00:18:51]** and we'll keep showing,

**[00:18:53]** which factors are good predictors,

**[00:18:56]** that when a person loses their job,

**[00:18:58]** they won't find another within nine months,

**[00:19:00]** being a woman is one of the strong predictors.

**[00:19:02]** Unfortunately, that's the case.

**[00:19:04]** And you see that throughout this year

**[00:19:07]** female unemployment is rising.

**[00:19:10]** Even though we have childcare groups,

**[00:19:12]** and employers say,

**[00:19:14]** they offer flexible contracts,

**[00:19:18]** but in the aggregated numbers,

**[00:19:20]** there's no happiness to be found.

**[00:19:23]** So, the second point, which is interesting,

**[00:19:26]** and when I first saw it,

**[00:19:29]** that it's no longer just noise, but a trend,

**[00:19:32]** it gave me chills.

**[00:19:33]** It has long been said,

**[00:19:34]** that a gap will open between those

**[00:19:36]** who lose their jobs

**[00:19:39]** and those whom companies are looking for.

**[00:19:43]** That the profiles of people companies fire

**[00:19:45]** and those they seek will differ.

**[00:19:47]** And this shows in a simple fact.

**[00:19:49]** We have long had many people,

**[00:19:53]** about a third,

**[00:19:56]** around 30%.

**[00:19:58]** In 2023, 33%, in 2024, 31% of people who, when they lose their job, find another within three months.

**[00:20:00]** This means the labor market is very liquid and ready.

**[00:20:12]** People who don't fit somewhere are quickly hired elsewhere.

**[00:20:19]** So you see, long-term it's just over 30%.

**[00:20:26]** Now I'll show the current graph. The current graph has dropped below 30%.

**[00:20:30]** It remains relatively stable, and you see that long-term unemployment hasn't changed dramatically,

**[00:20:38]** only the time it takes people to find a job has.

**[00:20:44]** The percentage of people finding work quickly has dropped dramatically.

**[00:20:49]** This means it's true that employers are laying off more.

**[00:20:55]** Low-skilled workers are in higher demand.

**[00:21:01]** You can clearly see this among qualified workers in the research we did

**[00:21:04]** with employers, where 70,000 employers responded

**[00:21:10]** to a questionnaire on many topics.

**[00:21:20]** This is one of the most interesting charts.

**[00:21:23]** Every fox praises its own tail, so of course I was most pleased

**[00:21:28]** that 65% of employers are satisfied with the labor office

**[00:21:32]** and that 25% say it has improved over the past year.

**[00:21:36]** I'm glad about that, but the second most interesting chart for me is actually

**[00:21:42]** the huge demand for qualified employees.

**[00:21:46]** It varies a bit by sector, but basically everyone says in unison,

**[00:21:51]** the main problem is that we can't find qualified employees.

**[00:21:55]** We do some internal training, but they are scarce on the market,

**[00:21:59]** specialists are rare, and they don't care much about qualified workers,

**[00:22:05]** and managers are promoted internally, so the demand isn't that high.

**[00:22:09]** So this is a very strong chart that says

**[00:22:12]** that qualified people are still in demand on the labor market.

**[00:22:17]** Especially in qualifications that are sought after,

**[00:22:20]** while the demand for low-skilled workers is decreasing and they remain on the labor market.

**[00:22:32]** I spend a lot of time at branches talking to clients,

**[00:22:36]** asking them how we help them.

**[00:22:46]** Many people say, I was with one company for a long time,

**[00:22:51]** Czechs tend to stick with their employer,

**[00:22:54]** a typical Czech holds on to an employer even if they're not satisfied,

**[00:22:59]** they're paid poorly, they complain about it in the pub, but they stay there.

**[00:23:04]** And these people are why only 15% change jobs,

**[00:23:08]** although objectively, data shows that when they do change jobs,

**[00:23:11]** they get an 8% higher salary, which their current employer won't give them.

**[00:23:18]** But it's true that employees in the Czech Republic stay with one employer for a long time,

**[00:23:28]** so when they lose their job, they really need even basic counseling,

**[00:23:33]** which for you is automatic, and you might be surprised

**[00:23:36]** that such a service might be needed.

**[00:23:39]** But yes, part of that counseling is helping them navigate the labor market,

**[00:23:43]** explaining how to look for a job,

**[00:23:46]** helping them write a resume, and then, and this is the most interesting part,

**[00:23:50]** because it takes people longer now to find a job,

**[00:23:55]** there is also a part of human support where many of those,

**[00:24:00]** mainly those stubborn men, said, I actually feel ashamed about this,

**[00:24:03]** I can talk about it at home, but here I come and someone supports me,

**[00:24:07]** so I can gather the courage to go to the 21st interview after sending

**[00:24:15]** 100 resumes and often not even getting a reply, and going to 20 interviews

**[00:24:22]** where either they didn't respond or rejected me.

**[00:24:25]** And this human contact is actually important.

**[00:24:28]** This humanity is what large studies in psychotherapy show,

**[00:24:36]** Did you know that in psychotherapy there are many approaches, a person can be a Freudian,

**[00:24:42]** Jungian, Rogerian, or do CBT, and when big studies are done,

**[00:24:47]** it turns out it doesn't really matter what the psychotherapist has in mind,

**[00:24:51]** because there are some universal therapeutic factors,

**[00:24:55]** that work across all approaches in the end.

**[00:24:58]** One is universality, that a person understands others have the same problem,

**[00:25:03]** so the problem stops feeling so unique.

**[00:25:06]** Another very strong factor is providing hope.

**[00:25:10]** And actually, providing hope through the frustration of failures

**[00:25:14]** is one of the things people clearly need from products, services, or employment offices,

**[00:25:19]** especially when the time people spend looking for a job gets longer.

**[00:25:26]** So, we've covered 20 minutes. For those of you who don't really like numbers,

**[00:25:31]** good, you survived it, it's behind you now.

**[00:25:34]** Now we'll talk more about concepts.

**[00:25:42]** I've seen tons of predictions about what will happen with the rise of artificial intelligence.

**[00:25:47]** Which jobs people will lose. Just yesterday I was reading,

**[00:25:51]** something from Bill Gates again, who survived three professions.

**[00:25:55]** But before we dive into this, it's good to look at how past big technological revolutions turned out.

**[00:26:00]** I took two and two quotes that I really like.

**[00:26:03]** One is from an economist who says,

**[00:26:09]** during the industrial revolution, remember what we learned in school,

**[00:26:14]** many people who wouldn't have had the chance,

**[00:26:18]** got new opportunities, broke social barriers,

**[00:26:25]** people who otherwise wouldn't have gotten rich became wealthy.

**[00:26:29]** This happened because wealth was then accumulated by the nobility,

**[00:26:32]** and suddenly the industrial revolution gave the bourgeoisie

**[00:26:36]** and the working class a chance to get rich, precisely because they adopted technologies faster.

**[00:26:41]** I famously say, this is how we learned it,

**[00:26:48]** and in the context of artificial intelligence, it sounds pretty good for one person.

**[00:26:51]** It would be great if it turned out that way.

**[00:26:57]** But I remind you, power and wealth were concentrated in the nobility,

**[00:27:00]** and the industrial revolution broke this barrier,

**[00:27:06]** because the nobility, mostly just enlightened nobles,

**[00:27:10]** adopted steam and electricity quickly.

**[00:27:14]** Most did not, and it was the bourgeoisie who used it,

**[00:27:18]** which created a new social class,

**[00:27:22]** and it was strong enough that it couldn't be ignored,

**[00:27:27]** leading to the whole society getting richer.

**[00:27:30]** Then there is the second big revolution, the internet revolution.

**[00:27:39]** And here even from a former US Secretary of Labor,

**[00:27:43]** it's about what simply happened in America with the advent of the internet,

**[00:27:48]** and it's simple. The poor got poorer, the rich got richer.

**[00:27:52]** No positive impact.

**[00:27:57]** On the contrary, it excluded many people,

**[00:28:01]** and it disproportionately concentrated wealth in large corporations.

**[00:28:07]** You know now that these big corporations have more wealth,

**[00:28:14]** than some countries do.

**[00:28:16]** So these are two perspectives looking back.

**[00:28:20]** We have one revolution, or it was a sequence of several industrial revolutions,

**[00:28:25]** that ended well.

**[00:28:27]** We have objectively one relatively recent revolution.

**[00:28:31]** We don't really remember that a system change was coming with it.

**[00:28:35]** At that time, capitalism was also being built here and entrepreneurship was emerging,

**[00:28:39]** but in America, it really didn't unite society.

**[00:28:43]** On the contrary, it divided it.

**[00:28:46]** So these are two quotes.

**[00:28:50]** And if you look at current quotes,

**[00:28:57]** I probably won't name authors because there are many,

**[00:29:02]** there are simply people saying,

**[00:29:05]** that this will continue with artificial intelligence.

**[00:29:09]** By the way, the fact that relatively few companies own it,

**[00:29:13]** kind of suggests that we have a problem brewing.

**[00:29:17]** Because artificial intelligence can sometimes help people,

**[00:29:21]** acting as an assistant to humans,

**[00:29:25]** augmenting human skills,

**[00:29:28]** there are studies and specific cases,

**[00:29:31]** where it helps people who are low-skilled,

**[00:29:35]** for example with lower intelligence, to leap beyond inherited intelligence

**[00:29:41]** and boost it with artificial intelligence.

**[00:29:44]** There are studies showing one thing, and studies showing another.

**[00:29:49]** It always depends on the use case of the artificial intelligence involved.

**[00:29:54]** Now, I'll show you my absolute favorite quote.

**[00:30:00]** I'll let it sink in for a moment,

**[00:30:04]** because I've been focusing a lot on intelligence towards you.

**[00:30:07]** It simply says that the strongest don't survive,

**[00:30:10]** the smartest don't survive,

**[00:30:13]** those who survive are the ones who adapt the most.

**[00:30:16]** It's Darwin, and for me,

**[00:30:20]** now I'll show you data that most strongly confirms this.

**[00:30:24]** It seems so, remember, dinosaurs roamed here,

**[00:30:27]** they were food specialists.

**[00:30:30]** It really seems that in times of stability, specialization pays off,

**[00:30:33]** but when big change happens,

**[00:30:36]** specialization stops paying off.

**[00:30:39]** And actually, remember, dinosaurs were big,

**[00:30:42]** and alongside them ran small furry creatures,

**[00:30:45]** mammals, the mammals survived,

**[00:30:48]** and dinosaurs didn't; we evolved from mammals,

**[00:30:52]** because mammals were much more adaptable.

**[00:30:57]** By the way, birds were successful because of their beaks,

**[00:31:01]** which also allow complete flexibility.

**[00:31:05]** And here you can see it in a nutshell.

**[00:31:08]** Even Darwin said,

**[00:31:12]** that in history, those who survive are the ones who can cooperate and improvise.

**[00:31:17]** And these are our data.

**[00:31:21]** In cooperation with Masaryk University,

**[00:31:26]** we took the largest dataset you can imagine,

**[00:31:31]** unemployed people over the last two years,

**[00:31:35]** and asked what we knew about them from labor office data,

**[00:31:39]** what we could use as a predictor of whether,

**[00:31:44]** they would end up on unemployment benefits, since that's the lecture's title,

**[00:31:49]** and stay there for more than nine months, or find a job within nine months.

**[00:31:53]** Most people find a job, as you saw, within six months.

**[00:31:57]** We gave a nine-month buffer,

**[00:32:01]** because some who get a good severance package,

**[00:32:03]** treat it like a vacation, so that's why we set nine months.

**[00:32:07]** And we enriched the data with various Czech Republic specifics,

**[00:32:13]** because from an employment perspective,

**[00:32:18]** the Czech Republic breaks down into three regions.

**[00:32:21]** First is Prague, which is a separate chapter,

**[00:32:25]** then there are excluded localities: Karlovy Vary region,

**[00:32:29]** Ústí nad Labem region, and Moravian-Silesian region,

**[00:32:33]** and then the rest of the country.

**[00:32:35]** So geography plays a role, and Czechs rarely work with it,

**[00:32:39]** so we included a lot of geographic data,

**[00:32:42]** and these are the importance rankings in a huge model

**[00:32:45]** using artificial intelligence,

**[00:32:48]** a predictive model with great reliability,

**[00:32:51]** which can simply identify a third of the people,

**[00:32:53]** which has twice the chance that they won't find a job within nine months.

**[00:32:59]** These are the strongest predictors.

**[00:33:02]** I led one part of my career

**[00:33:08]** at Deloitte Team Data Science.

**[00:33:10]** We helped banks predict,

**[00:33:13]** for example, which client will take a loan,

**[00:33:15]** or who will buy insurance,

**[00:33:17]** so they could call them even before the client might know themselves

**[00:33:19]** that they want to buy it.

**[00:33:21]** And the strongest predictor was always that

**[00:33:24]** the person had bought a loan before,

**[00:33:26]** or had bought insurance before,

**[00:33:28]** that was always the biggest predictor of that behavior.

**[00:33:34]** It's the same with our predictive model

**[00:33:38]** for previous long-term unemployment.

**[00:33:41]** Clients who have a lot of activity on their loyalty card with us,

**[00:33:46]** our loyal clients, this is the strongest predictor.

**[00:33:51]** Simply put, if a person couldn't find a job for a long time in the past,

**[00:33:55]** the risk is significantly higher.

**[00:34:00]** The second strongest predictor is

**[00:34:03]** when a person has a qualification for which demand is declining.

**[00:34:08]** Simply when demand is falling in that region,

**[00:34:10]** and they are not ready to relocate.

**[00:34:12]** This is the second predictor.

**[00:34:14]** And by the way, fortunately, we can address this.

**[00:34:17]** We offer retraining, which we fund 100%,

**[00:34:21]** last year it grew by 100%, this year it grew by another 50%,

**[00:34:26]** and interest in it keeps increasing.

**[00:34:29]** I will talk more about who takes retraining.

**[00:34:32]** Primarily, people take it because the time allocation for retraining is longer,

**[00:34:37]** so retraining is really targeted.

**[00:34:40]** Even when we offer it to people who already have jobs,

**[00:34:42]** typically, those who take it are people who lose their jobs.

**[00:34:47]** Actually, typically only after they lose their job.

**[00:34:49]** I spoke with many people, for example when Liberty was closing.

**[00:34:55]** Liberty stopped paying employees' wages; we paid them.

**[00:34:59]** That's how the state system works, because people pay insurance,

**[00:35:03]** and if the employer doesn't pay, the labor office steps in.

**[00:35:09]** At that time, we paid wages for nearly five thousand employees

**[00:35:14]** and also provided them with counseling and told them,

**[00:35:17]** Look, even if it turns out well, it's still better to retrain.

**[00:35:23]** It's always better to look at the job market,

**[00:35:26]** Liberty hired people around forty, there were positions on the job market,

**[00:35:31]** there are many, but for half the pay.

**[00:35:37]** And if the family wants to maintain their income, the way is,

**[00:35:42]** to retrain, for example, as a welder, machinist, CNC, metalwork,

**[00:35:45]** start now, even if nothing happens, we're paying you now,

**[00:35:50]** you don't have to work, you have time, come on, we'll pay for it all.

**[00:35:55]** Still, almost no one retrained even when things were going downhill,

**[00:36:03]** until they had the ratio, they started dealing with it only when they lost the job.

**[00:36:10]** But here the state has a solution. We'll show education, education is magic,

**[00:36:16]** I know there are many hypotheses now about how,

**[00:36:21]** that more people have university degrees, so its quality is declining.

**[00:36:28]** You can't see it in salaries or unemployment, it's still much better

**[00:36:33]** than university, or at least a proper high school.

**[00:36:38]** We'll show how it is.

**[00:36:40]** And work history, people who have longer employment periods,

**[00:36:43]** stable jobs, are better off.

**[00:36:46]** And now we get to things where a person can actually do something about it.

**[00:36:55]** Yes, age is a discriminatory factor, simply 50+.

**[00:37:00]** I can't believe that employers, when they know,

**[00:37:05]** they will have to learn to work with this group,

**[00:37:08]** so it's still a problem.

**[00:37:10]** But simply age 50+, it's a problem.

**[00:37:13]** Clearly in the data, that's where it breaks.

**[00:37:16]** People without a driver's license.

**[00:37:19]** Just a warning here, we even subsidize driver's licenses,

**[00:37:22]** like the labor office, for example for vans.

**[00:37:26]** But it's not necessarily like that.

**[00:37:29]** Here it's a predictor, so if a person has lived a life

**[00:37:33]** without getting a driver's license until now, they're worse off in the job market.

**[00:37:39]** Likewise, if they don't have email, and again it's not like,

**[00:37:42]** if I set up a Gmail account for them, everything will be fine.

**[00:37:45]** It's that if they've lived their whole life until now

**[00:37:48]** without needing email, they're worse off.

**[00:37:51]** This is digital illiteracy.

**[00:37:53]** Here in the data you see the impact of digital illiteracy,

**[00:37:56]** exclusion from the job market.

**[00:37:58]** By the way, also older people,

**[00:38:01]** we teach them, for example, how to write a CV,

**[00:38:04]** how to apply for a team interview, because they had never done it before.

**[00:38:07]** Or sometimes the employer wants the first round of interviews

**[00:38:10]** to be conducted as a team interview, and they are doing it for the first time in their life,

**[00:38:13]** so they are completely overwhelmed, nervous.

**[00:38:16]** And that nervousness then plays a role, affecting the quality of their performance.

**[00:38:20]** Well, I promised there would be women,

**[00:38:23]** and you can see they are there too.

**[00:38:26]** So these are the main factors.

**[00:38:29]** A somewhat satisfying feeling a person can have,

**[00:38:32]** is that at least some of these factors are influenceable.

**[00:38:36]** Simply yes, it’s worth supporting digital literacy,

**[00:38:39]** because otherwise people fall out of society.

**[00:38:42]** Yes, it’s worth retraining them.

**[00:38:45]** Yes, it’s worth even supporting their driver’s licenses.

**[00:38:48]** Although when I started at the labor office, I was surprised.

**[00:38:51]** I promised education in more detail.

**[00:38:59]** I’ll jump back a bit to give you some context first.

**[00:39:06]** I went to the labor office

**[00:39:09]** because I was looking forward to the agenda,

**[00:39:12]** that it’s simply 10 thousand... Well, it was 12 thousand people in the district,

**[00:39:16]** when I started, now it’s just over 10 thousand.

**[00:39:19]** That I would enjoy the transformation,

**[00:39:22]** that the labor office can do it with fewer people,

**[00:39:25]** that it can do it better.

**[00:39:28]** But then I was really excited about the data.

**[00:39:31]** That I would get access to data I would never have gotten otherwise.

**[00:39:34]** It’s one of the things I was really interested in.

**[00:39:39]** And of course,

**[00:39:42]** access to data at the labor office is excellent,

**[00:39:45]** and also, for example, when we optimized branches,

**[00:39:48]** we bought data from mobile operators,

**[00:39:52]** since we are here at magenta,

**[00:39:55]** and we closed branches based on seeing

**[00:39:58]** how people actually move around in the region,

**[00:40:00]** so when mayors told me what a disaster it would be,

**[00:40:04]** I said, no it won’t, people still travel to that city for shopping or to the doctor,

**[00:40:08]** and 90% of them do, so because of the 18 clients left there,

**[00:40:14]** there’s no reason to keep the branch open, here’s the data, and actually they saw for the first time data

**[00:40:19]** showing how people move in their region.

**[00:40:22]** I had similar luck when we decided,

**[00:40:28]** that we dig a bit into education, because when we are evaluated in the European Union,

**[00:40:37]** they say, hey, you have great employment, you actually have quality education,

**[00:40:41]** but the worst part is that these two things don't communicate,

**[00:40:46]** that these two things are not aligned at all, and I said, ok, let's take a look at it.

**[00:40:53]** The best thing would be, and that is our goal, I believe we will achieve it this year,

**[00:40:59]** to start saying how much graduates from individual schools earn,

**[00:41:04]** to start giving price tags to those schools and fields of study.

**[00:41:08]** I'm looking forward to that the most, we don't have the data yet,

**[00:41:11]** because it requires linking data not only from the labor office but also from education,

**[00:41:16]** so we thought about what other indicator would be good,

**[00:41:20]** and in the end, we used the same one as in that predictive model,

**[00:41:24]** where we looked at which schools generate long-term unemployed people.

**[00:41:29]** Because it's interesting that a person changing several jobs right after school,

**[00:41:35]** doesn't matter at all, it can happen that the job doesn't fit them.

**[00:41:39]** At the same time, in Prague, even if someone studied floristry,

**[00:41:44]** the labor market is such that it absorbs everything,

**[00:41:47]** so we looked at which fields and schools generate long-term unemployed.

**[00:41:54]** And what's interesting is that we published this exactly when

**[00:41:58]** parents are deciding where to send their children.

**[00:42:01]** They decide very blindly.

**[00:42:03]** It's actually a double blind experiment where the child has some preferences,

**[00:42:07]** which are childish, and the parent tries to protect them from that.

**[00:42:12]** And then they have some feeling about the school.

**[00:42:15]** That's very little.

**[00:42:18]** I experienced at the labor office what it is like when you put data into the system.

**[00:42:22]** That suddenly 90% of branches can process benefits within 30 days,

**[00:42:27]** I didn't do that with any force.

**[00:42:29]** I did it so that every employee knows how they are doing.

**[00:42:32]** And suddenly, when you put data into the system, people start behaving like,

**[00:42:35]** yeah, a clerk may not want to be first, but definitely doesn't want to be last.

**[00:42:38]** When they have data, they try to be in the 90%.

**[00:42:43]** So I said, I'll do something similar in education.

**[00:42:46]** And one thing is that we looked at it by fields.

**[00:42:51]** And it turns out that those craft fields, qualified,

**[00:42:56]** auto mechanic, welder, machinist, CNC, years,

**[00:43:00]** these are things where the market immediately takes people back.

**[00:43:06]** And then there are some fields where you only send your child if you don't love them,

**[00:43:11]** or if you want to keep them on the mommy hotel for a long time,

**[00:43:14]** like ironworker, textile, but surprisingly also salesperson.

**[00:43:18]** Obviously, being a salesperson is one where a person goes right after elementary school,

**[00:43:22]** or after school, where they learn to be a salesperson.

**[00:43:28]** But even more interesting is this, which you can barely see,

**[00:43:33]** so I'll tell you about it.

**[00:43:35]** When we looked at how much better off a person is,

**[00:43:41]** when they don't just finish elementary school.

**[00:43:45]** If a person only finishes elementary school, they have a 37% risk

**[00:43:51]** of being long-term unemployed.

**[00:43:53]** That's quite a big risk, more than a third.

**[00:43:59]** Then, if they have lower secondary vocational education,

**[00:44:03]** if they have some school but don't finish it,

**[00:44:06]** that risk drops by half.

**[00:44:07]** It's still better if they at least enroll somewhere,

**[00:44:09]** at least study for a while.

**[00:44:12]** So it drops to about half, around 16-17%.

**[00:44:16]** And then it's really interesting when you look at secondary schools.

**[00:44:21]** The fact that gymnasiums perform worse compared to other vocational schools,

**[00:44:27]** take that as noise, because these aren't gymnasium graduates,

**[00:44:30]** these are gymnasium graduates who didn't continue to university.

**[00:44:34]** So this comparison of gymnasiums is unfair,

**[00:44:37]** because it's the subset who didn't get into university.

**[00:44:40]** But the comparison of individual secondary schools is very interesting,

**[00:44:45]** whether it's better to have a high school diploma without an apprenticeship or to be apprenticed.

**[00:44:51]** And the absolute winner is an apprentice with a high school diploma.

**[00:44:55]** That means I really know something, but I have a broad range.

**[00:45:00]** And again, yes, you remember Darwin.

**[00:45:02]** People who are more versatile are better off.

**[00:45:06]** You can see it here.

**[00:45:07]** Or then at the school level, lyceums scored great.

**[00:45:10]** Again, I really know something, I have some real expertise,

**[00:45:13]** because, I remind you, employers desire qualified people,

**[00:45:17]** so I have qualifications, but I'm not narrow-minded,

**[00:45:21]** I'm versatile and broad.

**[00:45:24]** That's the view on education.

**[00:45:26]** And then, of course, you see that the risk of unemployment

**[00:45:32]** disappears with a university degree.

**[00:45:34]** Miraculously, only 4% of people with a university degree.

**[00:45:38]** I even looked closely at those,

**[00:45:41]** if you're following the political campaign against some fields,

**[00:45:44]** I looked at them.

**[00:45:46]** They also do well, yes, even historians and philosophers.

**[00:45:49]** Obviously, higher education qualifies a person

**[00:45:52]** to a certain level of thinking,

**[00:45:55]** and that level of thinking then matters in the job market,

**[00:45:58]** even if people don't work in their original field,

**[00:46:03]** the level of thinking that higher education brings

**[00:46:06]** is something the job market appreciates.

**[00:46:09]** I'm almost at a third, that's good.

**[00:46:15]** Now we have the last twenty minutes ahead.

**[00:46:20]** It's a bit depressing, so I'm preparing you for that.

**[00:46:28]** There's nothing to be done, yeah.

**[00:46:30]** When I started at the labor office,

**[00:46:33]** I intended to be transparent, yeah, I actually talk with the opposition,

**[00:46:36]** with the coalition, and I tell them how things really are,

**[00:46:40]** and also to the public.

**[00:46:43]** Now I'll show you a perspective: if I said

**[00:46:46]** that forty thousand people are digitally learning with us,

**[00:46:51]** and that we subsidize it,

**[00:46:54]** typically with eighty-two percent,

**[00:46:57]** they pay eighteen percent themselves,

**[00:47:00]** they choose courses on the e-shop,

**[00:47:03]** jsem vkurzu.cz.

**[00:47:05]** That's already a solid sample of people,

**[00:47:08]** it's basically a nice regional city.

**[00:47:11]** We still don't have granular data,

**[00:47:15]** so one bank decided,

**[00:47:18]** instead of creating some smart foundation,

**[00:47:21]** it would be better to help one office,

**[00:47:24]** and that the biggest helping organization would be best,

**[00:47:27]** so they did an analysis based on bank data,

**[00:47:30]** which is great, so we know

**[00:47:33]** that people who train in technical professions

**[00:47:36]** see their salary increase by twenty percent.

**[00:47:39]** Most other trainings

**[00:47:43]** lead to a salary increase of two to four percent.

**[00:47:46]** And of course, when people train in social fields,

**[00:47:49]** their salary decreases.

**[00:47:52]** Mine dropped to a third,

**[00:47:55]** when I went to the labor office,

**[00:47:58]** but generally the standard is a four percent decrease

**[00:48:01]** when someone goes into social work.

**[00:48:04]** But now the question is, who even takes the plunge.

**[00:48:07]** Who is the one who, without losing their job,

**[00:48:11]** because when things get bad,

**[00:48:14]** people already know they have to do something about themselves.

**[00:48:17]** And when they see there are no offers for them,

**[00:48:20]** they retrain. That happens.

**[00:48:23]** That's why retraining is growing by one hundred percent

**[00:48:26]** and another fifty percent this year.

**[00:48:29]** But the question is, who does it before they hit rock bottom.

**[00:48:32]** Before they get fired.

**[00:48:35]** Here are some interesting charts.

**[00:48:38]** This one is the worst.

**[00:48:41]** Mostly university graduates.

**[00:48:44]** Or people with high school diplomas. Or bachelor's degrees.

**[00:48:47]** Only one percent of people with just basic education.

**[00:48:50]** It's just completely bad.

**[00:48:53]** We see that when a person with basic education trains,

**[00:48:56]** their income increase is double

**[00:48:59]** that of a university graduate.

**[00:49:02]** Even in retraining, you have these stories.

**[00:49:05]** You have stories of a worker who retrains

**[00:49:08]** to become a machine operator or CNC technician,

**[00:49:11]** or a welder,

**[00:49:14]** and then earns double the salary,

**[00:49:17]** so you have even extreme stories.

**[00:49:20]** But those people don't go for it at all.

**[00:49:23]** When you ask them why,

**[00:49:26]** I read it in some foreign study,

**[00:49:29]** which is spread from education.

**[00:49:32]** They are actually glad they don't have to go to school anymore.

**[00:49:35]** Their surroundings also don't engage in lifelong learning.

**[00:49:38]** No one wants to do lifelong learning.

**[00:49:41]** It's such an ugly word.

**[00:49:44]** No one wakes up in the morning and says,

**[00:49:47]** I'm going to do lifelong learning.

**[00:49:50]** They don't believe in the system and don't believe

**[00:49:53]** that things could get better for them.

**[00:49:56]** www.hradeckesluzby.cz

**[00:49:59]** They don't believe the system will help them

**[00:50:04]** and they don't believe in themselves.

**[00:50:08]** That's why they don't go for it.

**[00:50:10]** We thought

**[00:50:12]** that about 83%

**[00:50:14]** in this segment

**[00:50:16]** don't pursue education

**[00:50:18]** because they don't want to.

**[00:50:20]** We have courses in the evenings,

**[00:50:22]** on weekends.

**[00:50:24]** No, they don't want to.

**[00:50:26]** And the education is usually

**[00:50:28]** paid for by the employer.

**[00:50:30]** That's why employers should co-fund education.

**[00:50:32]** Employers

**[00:50:34]** push people into it.

**[00:50:36]** On the contrary, it's quite mild that

**[00:50:38]** young and old really study with us,

**[00:50:40]** even the oldest who study with us,

**[00:50:42]** I think is 87.

**[00:50:44]** Often these people are

**[00:50:46]** part-timers,

**[00:50:48]** helping someone,

**[00:50:50]** doing interesting things

**[00:50:52]** even in retirement.

**[00:50:54]** But you see, the age curve

**[00:50:56]** is good. So we have

**[00:50:58]** done a lot. We partnered

**[00:51:00]** with libraries so that a librarian,

**[00:51:02]** when recommending a good book,

**[00:51:04]** also tells the older person,

**[00:51:06]** "Franta, why don't you learn computer skills

**[00:51:08]** here at the employment office,

**[00:51:10]** you can take a course for that."

**[00:51:12]** I said people don't believe

**[00:51:14]** that success is possible.

**[00:51:18]** When you ask people,

**[00:51:20]** usually someone

**[00:51:22]** recommended it to them.

**[00:51:30]** The good thing about data is,

**[00:51:32]** we simply see,

**[00:51:34]** the people who have trained with us,

**[00:51:36]** say, yes, it helped us

**[00:51:38]** find a better job.

**[00:51:40]** Primarily, this

**[00:51:42]** specifically helped us find

**[00:51:44]** a better job. We often use,

**[00:51:46]** those skills.

**[00:51:48]** We use them at work and at home.

**[00:51:50]** Obviously, those people,

**[00:51:52]** I don't have that,

**[00:51:54]** let me tone down the enthusiasm a bit.

**[00:51:56]** It's not just the expertise

**[00:51:58]** of the course.

**[00:52:00]** It's that people meet

**[00:52:02]** a different group of people there,

**[00:52:04]** make contacts, then continue

**[00:52:06]** to support each other,

**[00:52:08]** recommend jobs, and prefer to

**[00:52:12]** succeed in the job market.

**[00:52:14]** It's often

**[00:52:16]** something in psychology called

**[00:52:18]** an initiation ritual,

**[00:52:20]** where a person overcomes a boundary.

**[00:52:22]** Our

**[00:52:24]** subsidized course is an initiation ritual,

**[00:52:26]** where the person then continues learning,

**[00:52:28]** no longer needing us,

**[00:52:30]** but we are the first step, helping them cross

**[00:52:32]** that boundary.

**[00:52:34]** So.

**[00:52:36]** Well,

**[00:52:38]** I'm starting to wrap up.

**[00:52:46]** I'll return,

**[00:52:48]** before I get to this quote.

**[00:52:52]** I am...

**[00:52:58]** One of the advantages of

**[00:53:00]** being the head of the labor office

**[00:53:02]** is that between labor offices

**[00:53:04]** There is no competition in the European Union.

**[00:53:06]** So we meet

**[00:53:08]** every six months, all the directors

**[00:53:10]** of the labor offices and exchange experiences,

**[00:53:12]** showing each other the smart things we're doing.

**[00:53:16]** It's great.

**[00:53:18]** It's really like

**[00:53:20]** I've never experienced it in any big corporation.

**[00:53:22]** It's amazing.

**[00:53:24]** And I met

**[00:53:26]** in Greece

**[00:53:30]** the general director

**[00:53:32]** of the labor office,

**[00:53:34]** Spiros, that's his last name, but I can't pronounce it,

**[00:53:36]** who

**[00:53:38]** was

**[00:53:40]** in Barack Obama's administration,

**[00:53:42]** and then, when

**[00:53:44]** the crisis hit Greece,

**[00:53:46]** the prime minister called him

**[00:53:48]** to help

**[00:53:50]** fix the labor office.

**[00:53:52]** And he asked himself the same question,

**[00:53:54]** that we all probably would have asked in his place,

**[00:53:56]** which is, where did all the European Union money go?

**[00:53:58]** And he says,

**[00:54:00]** he did an analysis and it showed

**[00:54:02]** the same information

**[00:54:04]** as the data I just showed you.

**[00:54:06]** He says, 90% we

**[00:54:08]** wasted, we educated people

**[00:54:10]** who were already educated

**[00:54:12]** and they didn't even use it in the labor market.

**[00:54:14]** But then there was 10%

**[00:54:16]** the few

**[00:54:18]** educated ones, and they

**[00:54:20]** paid for the business case of the whole

**[00:54:22]** education.

**[00:54:28]** So

**[00:54:30]** that's a challenge.

**[00:54:32]** Yes, it is,

**[00:54:34]** the question is, we actually have

**[00:54:36]** a group of people here who are not educating themselves,

**[00:54:38]** while such a big

**[00:54:40]** change is happening in the labor market. They don't educate themselves because

**[00:54:42]** they wouldn't want to,

**[00:54:44]** because they couldn't,

**[00:54:46]** and if we don't help them,

**[00:54:48]** if I said 20 thousand from each,

**[00:54:50]** that I would collect here,

**[00:54:52]** I would collect more.

**[00:54:54]** Not me, but

**[00:54:56]** more of these people will end up on benefits.

**[00:54:58]** Because

**[00:55:00]** the sequence is, when a person is

**[00:55:02]** long-term unemployed, they have to stop,

**[00:55:04]** we start paying unemployment benefits at some point,

**[00:55:06]** and then the welfare system kicks in.

**[00:55:08]** And it's always easier

**[00:55:10]** to get back from unemployment

**[00:55:12]** than to get out of the welfare system.

**[00:55:16]** I looked into

**[00:55:18]** what tools

**[00:55:20]** exist across the European Union,

**[00:55:22]** and we are now preparing to implement some of them.

**[00:55:24]** One of the things is,

**[00:55:26]** when we subsidize

**[00:55:28]** courses, there is a minimum subsidy

**[00:55:30]** per hour,

**[00:55:32]** it used to be 16 hours,

**[00:55:34]** now, according to new regulations,

**[00:55:36]** we will require 40 hours

**[00:55:38]** for a specific course.

**[00:55:40]** It seems that people who are

**[00:55:42]** low-educated

**[00:55:44]** and don't want to educate themselves, find this too big a

**[00:55:46]** commitment, they are afraid of the obligation,

**[00:55:48]** even the rules,

**[00:55:50]** unfortunately, since it is paid for

**[00:55:52]** by money from the European Union, or thank God,

**[00:55:54]** that it is paid for by money from the European Union, but unfortunately the rules

**[00:55:56]** say that if the person does not complete it,

**[00:55:58]** they have to pay the money back.

**[00:56:00]** That's what they fear. So we

**[00:56:02]** in the new program,

**[00:56:04]** in course 2,

**[00:56:06]** will have an obligation for educational companies,

**[00:56:08]** to offer a sample, some specific

**[00:56:10]** skill within 5 to 40

**[00:56:12]** minutes

**[00:56:14]** online on that e-shop,

**[00:56:16]** so that ultimately even the person,

**[00:56:18]** when applying for that specific course,

**[00:56:20]** can try out whether the approach

**[00:56:22]** of that particular educator suits them,

**[00:56:24]** or if there are also secondary schools and universities,

**[00:56:26]** offering that education.

**[00:56:28]** So the selection process is transparent,

**[00:56:30]** but

**[00:56:32]** at the same time, we will be able to offer these courses,

**[00:56:34]** which will be normally available online with us.

**[00:56:36]** Microsoft recently made a similar

**[00:56:38]** course and it seems

**[00:56:40]** that the interest in it is great.

**[00:56:42]** The second thing we will do

**[00:56:44]** is that in the end digital,

**[00:56:46]** as you saw earlier, discriminates,

**[00:56:48]** so we are opening

**[00:56:50]** 70 educational

**[00:56:52]** centers across the country, where we will

**[00:56:54]** invite people, and we have already

**[00:56:56]** piloted this in

**[00:56:58]** the Ústí region. The interest

**[00:57:00]** in basic literacy courses

**[00:57:02]** is simply huge,

**[00:57:04]** people actually

**[00:57:06]** are ashamed to ask for it, and when the offer is there,

**[00:57:08]** they gladly come

**[00:57:10]** and they are satisfied with the courses.

**[00:57:12]** And then we see that where we

**[00:57:14]** have run the courses, more clients

**[00:57:16]** communicate with us digitally,

**[00:57:18]** which saves us capacity. So that's good.

**[00:57:22]** But the last mile still remains,

**[00:57:24]** so

**[00:57:26]** we approach education

**[00:57:28]** during such a big labor market transformation

**[00:57:30]** for people who are stuck in certain behaviors,

**[00:57:32]** with some employer,

**[00:57:34]** they are unhappy, poorly paid,

**[00:57:36]** but they are afraid to make a change,

**[00:57:38]** so we will try to lower the entry barrier

**[00:57:40]** by having educational

**[00:57:42]** hubs, by offering them tasters here,

**[00:57:44]** and by sending them miles so they can try something.

**[00:57:46]** At the same time, we are preparing

**[00:57:48]** an app that will

**[00:57:50]** help people

**[00:57:52]** and if they have a resume, so that,

**[00:57:54]** when you ask Przones, everyone will tell you

**[00:57:56]** that they have five seconds for a resume,

**[00:58:00]** and that the decision is not made only

**[00:58:02]** in the first two seconds,

**[00:58:04]** you say, okay,

**[00:58:06]** and that

**[00:58:08]** for the first screening,

**[00:58:12]** not only the candidate's quality matters,

**[00:58:14]** but also the quality of the presentation.

**[00:58:16]** So people who

**[00:58:18]** are discriminated against by the quality of their

**[00:58:20]** resume, we will try to help with an

**[00:58:22]** app. We want the app to be like

**[00:58:24]** Tinder, where we will show it,

**[00:58:36]** what current positions are available

**[00:58:38]** and that people can say what

**[00:58:40]** they want. They can say what they don't want, so they will

**[00:58:42]** just swipe a lot

**[00:58:44]** on which positions they want their resume

**[00:58:46]** to be prepared for. We try with all this

**[00:58:48]** to lower the threshold. Still,

**[00:58:50]** there remains one last mile

**[00:58:52]** and that last mile

**[00:58:54]** is actually

**[00:58:56]** and it basically says,

**[00:58:58]** intellectual elites have to help those people.

**[00:59:00]** There is no other

**[00:59:02]** way, intellectual elites

**[00:59:04]** must always have those people

**[00:59:06]** around them,

**[00:59:08]** in their

**[00:59:10]** community, in their

**[00:59:12]** municipality, in their family

**[00:59:14]** simply onboarded into these things. This is simply the strongest

**[00:59:16]** tool, it is,

**[00:59:18]** which is why we actually see that, for example, librarians work great for us,

**[00:59:20]** that

**[00:59:22]** the person who

**[00:59:24]** is with the employer where they are unhappy,

**[00:59:26]** complains about it, is poorly paid,

**[00:59:28]** has low education,

**[00:59:30]** so the last

**[00:59:32]** mile simply has to be done by another person,

**[00:59:34]** who tells them, hey, so

**[00:59:36]** what if you tried

**[00:59:38]** some first course here. That's great, now you have

**[00:59:40]** nothing to lose.

**[00:59:42]** And this recommendation,

**[00:59:44]** when we talk to those people, is actually

**[00:59:46]** the strongest.

**[00:59:48]** And this challenge with the arrival

**[00:59:50]** of artificial intelligence,

**[00:59:52]** which people are afraid of,

**[00:59:56]** even at the employment office

**[00:59:58]** by the way, people are afraid of artificial intelligence.

**[01:00:00]** because they fear it will take their jobs.

**[01:00:02]** And I tell them, no, Jesus, social networks release dopamine,

**[01:00:08]** there's nothing artificial about that yet, it's like wanting the like and watching

**[01:00:15]** how many likes you've collected and if someone appreciates you,

**[01:00:18]** or watching those stories, that releases dopamine.

**[01:00:20]** It's a nice hormone, it creates that instant happiness,

**[01:00:23]** but it can't release oxytocin. Oxytocin can only be released so far.

**[01:00:28]** Technology can't do it yet, only human contact can.

**[01:00:31]** It's the love hormone.

**[01:00:33]** For example, when you hug someone for more than ten seconds,

**[01:00:36]** it starts to release.

**[01:00:38]** And it releases during long, deep contact.

**[01:00:44]** That's how human contact helps,

**[01:00:46]** it also adds an experience, like a sense of life.

**[01:00:50]** People are still irreplaceable there.

**[01:00:52]** It's like, I failed 20 times, and now I go there to get advice,

**[01:00:56]** so someone can put me back together and send me out again,

**[01:00:59]** to try for the twenty-first time.

**[01:01:01]** Employees at the employment office will be irreplaceable.

**[01:01:04]** I believe this human contact will remain.

**[01:01:07]** On the contrary, some tasks will be done that will actually help us.

**[01:01:11]** Still, employment office workers are afraid

**[01:01:14]** that digitalization and the rise of artificial intelligence will take their jobs.

**[01:01:18]** Even though they went to the employment office to help people and are actually unhappy,

**[01:01:22]** burdened with administration, with artificial intelligence they say

**[01:01:28]** that those who learn it are better off than those who don't.

**[01:01:33]** We are glad, I didn't put this graph in,

**[01:01:36]** I could have, but the absolute number one among the 140,000 people

**[01:01:40]** who learned digitally with us is artificial intelligence.

**[01:01:44]** Before AI arrived, it was websites,

**[01:01:48]** data analysis, tax and accounting software,

**[01:01:54]** basic digital literacy, and then AI came

**[01:01:58]** and it completely changed everything, and it's like the most popular

**[01:02:03]** courses are in this area.

**[01:02:05]** Still, if we don't want to leave a large part of society behind,

**[01:02:11]** I think, unlike the Industrial Revolution,

**[01:02:14]** which was a welcomed opportunity for many people,

**[01:02:17]** here it is still more of a threat for most of society with lower education.

**[01:02:23]** and onboarding them into it can only be done through human contact

**[01:02:29]** and in the end, an American philosopher quite clearly says that here.

**[01:02:35]** So that's my message, the data already tells us that,

**[01:02:40]** because if it weren't true, more than one percent of people with only elementary education would be learning with us.

**[01:02:51]** and it's true that for me about 20 thousand at the labor office is still OK,

**[01:02:59]** when you include parental benefits, but whether it's worth it,

**[01:03:04]** I wouldn't want it to be any more than that.

**[01:03:07]** So for me, the message is that most of you sitting here,

**[01:03:13]** and those watching us online, probably won't end up at the labor office,

**[01:03:17]** because you are learning, because you are the signal that you are working on yourself,

**[01:03:22]** but I think our task is to help others not end up at the labor office,

**[01:03:29]** because otherwise we will pay for them ourselves.

**[01:03:32]** So, that's a good point for me to open the floor for questions.

**[01:03:37]** Is that right?

**[01:03:38]** Is that right? Great. I'll just bring the chairs here.

**[01:03:48]** Thanks so much for the really interesting data.

**[01:03:53]** There are obviously plenty of questions.

**[01:03:57]** Before I look at them, I was actually thinking,

**[01:04:05]** and we also talked about it, that the labor office has such a burned-in stigma,

**[01:04:11]** it's basically a place, it's a huge stigma.

**[01:04:14]** Why do we Czechs have it like that?

**[01:04:16]** Or when you meet like this in Europe, is it just our specific thing?

**[01:04:21]** Or how do people perceive it?

**[01:04:26]** Well, people really try not to end up at the labor office.

**[01:04:30]** And this stigma of the labor office exists.

**[01:04:33]** That's why, for example, and I wasn't involved in choosing the name,

**[01:04:38]** the e-shop with digital courses isn't called the labor office,

**[01:04:44]** but it's called Sem v kurzu.

**[01:04:46]** Probably trying to find a better brand.

**[01:04:48]** My goal for this year is to find a better name for lifelong learning.

**[01:04:53]** For me, the current name is off-putting, so I prefer to talk about retraining,

**[01:04:58]** digital courses, and some micro-courses.

**[01:05:01]** And I think it's a combination of two things.

**[01:05:05]** First, yes, when a person physically comes to the labor office,

**[01:05:09]** they meet people they might not meet anywhere else,

**[01:05:15]** except at the labor office.

**[01:05:17]** Which creates a certain impression.

**[01:05:20]** And that's how it is.

**[01:05:22]** That we have a loyal client base there,

**[01:05:26]** who unfortunately lose their jobs more often,

**[01:05:29]** and that we have people there who are long-term on benefits

**[01:05:33]** and they don't even manage to get off them, so it just pays out.

**[01:05:36]** So the impression is, yes, when you come to the labor office,

**[01:05:39]** you will see a lot of such people there.

**[01:05:43]** And secondly, Czechs really don't engage in lifelong learning.

**[01:05:47]** We are completely bad at it.

**[01:05:49]** And historically, the labor office hasn't really played that role much.

**[01:05:52]** And that's actually the third fifth of the labor office.

**[01:05:55]** We help people gain the perspective to earn more,

**[01:05:58]** which was somewhat neglected

**[01:06:01]** and which people actually like.

**[01:06:03]** People want to earn more, companies want people to earn more,

**[01:06:06]** because they are ready to pay more for qualified positions.

**[01:06:09]** And we have actually talked little about that.

**[01:06:11]** That it has been somewhat destigmatized.

**[01:06:14]** I am a pronounced introvert, I had to learn to go on TV

**[01:06:17]** or do appearances like this to talk about it,

**[01:06:20]** that we are open about this, we approve most of it,

**[01:06:23]** and thanks to that, 140 thousand people, that's a huge number,

**[01:06:26]** are learning digitally with us.

**[01:06:28]** That wouldn't have happened until recently.

**[01:06:31]** So I think some destigmatization has happened.

**[01:06:34]** I even dealt with maybe one last thing,

**[01:06:37]** whether the labor office should be rebranded.

**[01:06:40]** Sometimes labor offices have better names, sometimes nicer ones.

**[01:06:46]** But we are still the office.

**[01:06:48]** Besides that, we are really the ones who decide,

**[01:06:51]** who gets benefits and who doesn't.

**[01:06:54]** And I think work is open.

**[01:06:57]** The way out of benefits is through work, so the labor office

**[01:07:00]** would probably still be called 'pracák' (work office) by us.

**[01:07:03]** So I try to wrap it by saying we are the biggest slow organization.

**[01:07:07]** I actually first saw you somewhere at Kreativní bydokracie

**[01:07:11]** and then I noticed you a lot at Čekítas.

**[01:07:14]** You are in very close contact with them.

**[01:07:17]** There are many questions about educating those women.

**[01:07:21]** Why? And people want to return those charts, what's the reason.

**[01:07:26]** And how do you contribute to making sure it isn't like that?

**[01:07:29]** So it isn't a discriminatory factor.

**[01:07:33]** Is it true that more than 60% of participants,

**[01:07:37]** Two-thirds of the participants in that digital education,

**[01:07:41]** are women.

**[01:07:43]** We don't really contribute to that, it just happened.

**[01:07:46]** So I'm just reporting it as objective data.

**[01:07:49]** It's roughly the same percentage as,

**[01:07:53]** what comes out of universities, since universities

**[01:07:56]** actually produce more women with degrees than men.

**[01:08:02]** So at the education level, it actually looks good.

**[01:08:06]** Returning to work after maternity leave.

**[01:08:09]** Full-time jobs or part-time jobs, women with children, and so on.

**[01:08:13]** Now the children watch each other.

**[01:08:17]** The barrier is set very low there.

**[01:08:20]** There is no hygiene control like we have.

**[01:08:23]** There are controls, there are windows, a bathroom.

**[01:08:27]** Maybe I'll just ask,

**[01:08:31]** we also try this through Brain&Breakfast,

**[01:08:34]** because by broadcasting it to companies, many people,

**[01:08:37]** who normally wouldn't come into contact with this,

**[01:08:40]** are able to watch, get data, get information.

**[01:08:44]** They are equipped with technology, so those people will certainly be able to come

**[01:08:47]** and try everything on our technology.

**[01:08:50]** I was really pleasantly surprised because you said,

**[01:08:54]** you plan about 70 educational centers.

**[01:08:57]** What kind of places will they be? Will they be connected to libraries,

**[01:09:00]** or will something be built from scratch?

**[01:09:03]** We had this discussion about whether it should be at the labor office or elsewhere.

**[01:09:08]** Then we thought, if we want to improve the labor office's reputation,

**[01:09:11]** it has to be with us.

**[01:09:14]** So it's always with us, it's a room,

**[01:09:17]** a room we can keep open in the evening,

**[01:09:21]** open on weekends, which also requires some security adjustments in our situation.

**[01:09:24]** But it's a room equipped with IT technology,

**[01:09:28]** a room where our employees will conduct

**[01:09:31]** basic digital literacy courses.

**[01:09:34]** Then each educational center has a partnership with a secondary school,

**[01:09:37]** which will provide some micro-certificates there.

**[01:09:41]** And we try to involve employers,

**[01:09:45]** because that might be another point,

**[01:09:49]** if I said that the poor, uneducated, don't trust,

**[01:09:56]** that they could be better off, it actually works quite well,

**[01:10:00]** when they see, hey, here is an employer who would be ready to hire me if I complete a series of courses.

**[01:10:07]** So we want to allow employers, and by the way even the army, to present themselves and also offer a taste of what

**[01:10:16]** it's like to work somewhere specific and actually help people think through in their heads,

**[01:10:22]** whether they should go to a retraining course, for example to become a CNC metal machinist, because they have a spot for them there.

**[01:10:29]** This is really like a scale-up Čekítas, that's exactly it, maybe you know, it's great, this will definitely come in handy.

**[01:10:37]** What caught my attention when you were talking was that I realized how education and work

**[01:10:45]** actually relate to each other, that we have a Ministry of Education and what kind of KPIs and parameters we have there,

**[01:10:50]** that it should be exactly what you said. That means we prepare here for something,

**[01:10:56]** and if we don't prepare well, then we haven't done our job well.

**[01:11:00]** And what you said about giving those amounts to schools,

**[01:11:06]** that seems to me like a really good motivational factor, and likewise, if I know,

**[01:11:12]** as a person who is a welder somewhere in a company where he's just bullied,

**[01:11:16]** that I really have a chance to get 10, 15, 12% more somewhere. And if that actually comes up,

**[01:11:22]** then I say, well, I'll probably try it. Because there is a concrete amount, that's the motivation.

**[01:11:28]** Exactly, exactly. We really focused a lot on what the main motivation is,

**[01:11:32]** and the motivation to earn more. We probably keep coming back to that.

**[01:11:36]** And education doesn't have it easy. I am really absolutely lucky,

**[01:11:42]** that I run an office with 10,500 people, but I run it through a simple,

**[01:11:48]** flat structure, I go through one KPI, everyone has those KPIs on their mobile,

**[01:11:54]** it's a simple central office. Education, you know, it's not managed

**[01:11:58]** from the ministry, nor is it a central office, the regions decide there.

**[01:12:02]** It's a very difficult system to manage. And even the ability, yes, when I decide something,

**[01:12:10]** I am even empowered, so as a civil servant I can really decide,

**[01:12:16]** and I am responsible for that decision, but the decision happens.

**[01:12:22]** No one can really interfere much. In education, the ministry

**[01:12:27]** can issue some directive, but how it filters down to the regions, there are so many

**[01:12:31]** levels in between, that it's much harder. Students have it much harder

**[01:12:35]** in how the system is managed compared to a centralized labor office.

**[01:12:39]** By the way, some labor offices are also decentralized, it's a nightmare.

**[01:12:42]** Everyone who has it like that complains about it. I really believe exactly

**[01:12:46]** what you said, to put those price tags there. Simply, the average graduate

**[01:12:51]** of this school earns this much in five years, this much in ten years.

**[01:12:55]** And I really believe that the system will start to self-regulate. I really believe,

**[01:12:59]** that when you put that information into the system and it's objective information,

**[01:13:04]** there's nothing you can do about it, then both the regions and the parents will start deciding based on it.

**[01:13:09]** The reason it wasn't like that before was precisely because the data didn't exist.

**[01:13:13]** Data transparency, whether in healthcare or elsewhere, is the alpha and omega.

**[01:13:17]** Very often, when I say labor office, I encounter,

**[01:13:22]** how it is with voluntary unemployment, that is people of productive age

**[01:13:26]** and maybe they aren't even on unemployment benefits, but simply have no taxable income,

**[01:13:30]** they have resources and therefore don't have to work.

**[01:13:35]** So, when comparing unemployment, or

**[01:13:40]** the inverse indicator to unemployment is the percentage of the productive population,

**[01:13:44]** that is working, in that we are comparable to Europe,

**[01:13:48]** except for one difference: here, women are partly on parental leave,

**[01:13:53]** that's the only difference, so in that we are comparable to Europe.

**[01:13:57]** There is a part of the population that simply works in some gray economy

**[01:14:02]** and could work, or they are in the benefits system.

**[01:14:07]** I have positive expectations from the super benefit.

**[01:14:11]** I believe that the super benefit will finally widen the gap

**[01:14:16]** between those people who receive benefits but try to do things

**[01:14:21]** so they won't have to rely on benefits in the future,

**[01:14:25]** because there is roughly a 400,000 CZK work bonus.

**[01:14:29]** So there is a bonus for fulfilling a support plan,

**[01:14:33]** meaning that the person makes positive progress in at least one area per quarter,

**[01:14:37]** so they try to reduce debt, send kids to school,

**[01:14:41]** try not to qualify or do public service.

**[01:14:45]** Basically, if they make positive progress somewhere, they receive a higher benefit,

**[01:14:49]** than if they don't try. So I believe this will push

**[01:14:53]** some people from this client segment back into the regular labor market.

**[01:14:59]** We were just talking here, try to comment,

**[01:15:03]** how big of a change this actually is?

**[01:15:07]** The labor office is surprisingly tuned for big changes.

**[01:15:11]** We came during the floods, we were there the very first day,

**[01:15:15]** the next day the first extraordinary immediate aid was already being paid out,

**[01:15:19]** we managed it at Liberty too, now we have a new employment system,

**[01:15:23]** of course every IT system has some teething problems at the start,

**[01:15:27]** but that settled down.

**[01:15:30]** The super benefit is really the biggest change in the history of the labor office

**[01:15:34]** since it was established, in that on the same day

**[01:15:38]** the legislation changes, meaning the benefit is paid

**[01:15:43]** under different rules, and the IT system changes as well,

**[01:15:47]** and the labor office experienced black screens, so the nervousness

**[01:15:51]** of our employees around this is naturally very high,

**[01:15:55]** if the IT changes had failed, but luckily they are working now,

**[01:15:58]** but people still have historical memory.

**[01:16:01]** And on top of that, it's a change in the way of working, because now we work

**[01:16:06]** with back-office, things are processed regardless of

**[01:16:10]** where the benefit was applied for, so it's a huge change

**[01:16:14]** and at the same time, it's a change through which we actually process

**[01:16:16]** about half a million clients, so it's a change for a large part of Czech society.

**[01:16:19]** How successful is the labor office in placing applicants

**[01:16:23]** compared to private agencies?

**[01:16:27]** I think we don't really compete, but we have tougher clients.

**[01:16:32]** To put it the other way around, we have clients

**[01:16:37]** who are not attractive to agencies.

**[01:16:41]** And that's the main difference. That's why it's worth paying for the labor office,

**[01:16:45]** because yes, for a person who quickly finds a job

**[01:16:49]** and agencies compete for them, it doesn't make sense

**[01:16:53]** for us to spend taxpayers' money on that.

**[01:16:57]** On the contrary, for those whom agencies don't want,

**[01:17:01]** we have to take care of them so they find their way to the labor market. That's the difference.

**[01:17:05]** Is there actually, when we talk like this, some kind of transparent measure

**[01:17:09]** like placed versus unplaced, showing how society benefits?

**[01:17:12]** Like some data that clearly shows

**[01:17:15]** that paying for these services simply has huge long-term value.

**[01:17:18]** It's true that the breakeven is in the second year.

**[01:17:22]** Basically, if you take all the people who lost their jobs,

**[01:17:26]** we paid them benefits, plus retraining,

**[01:17:30]** plus sometimes subsidized jobs, since we sometimes subsidize those jobs

**[01:17:33]** at the start, by the second year it's already paid back.

**[01:17:36]** It's an investment that, if I offered it as an investment,

**[01:17:40]** it would be a good one.

**[01:17:46]** Does my wife, returning from maternity leave to work, have the right

**[01:17:50]** to counseling and retraining?

**[01:17:54]** Sure, everyone does.

**[01:17:57]** The labor office doesn't yet have segmentation that I'm fully satisfied with.

**[01:17:59]** We already have a new plan, but with all the changes now,

**[01:18:02]** I didn't want to release it yet.

**[01:18:05]** Right now, we have counselors divided into four segments,

**[01:18:08]** we have counselors for people who are physically or mentally handicapped,

**[01:18:10]** counselors for young people, for middle-aged, and for older people.

**[01:18:14]** Age is not a barrier.

**[01:18:20]** There are also projects set up by the European Union.

**[01:18:24]** In the future, I want to have a separate segment that takes care

**[01:18:27]** of mothers on parental leave or caregivers.

**[01:18:30]** We are already building a segment of counselors focused on helping Ukrainians,

**[01:18:42]** to get into qualified work.

**[01:18:45]** So we will improve in that.

**[01:18:47]** Right now, in every former district town, a person can find an advisor

**[01:18:52]** for their segment.

**[01:18:54]** They are not in the micro-branches, but they are in the larger ones.

**[01:18:57]** By the way, the fancy name is 'project'.

**[01:18:59]** For some reason, the labor office, since it is funded by European Union projects,

**[01:19:03]** uses the term in their vocabulary that the person wants to be included in a project.

**[01:19:09]** So they ask, 'Do you want to be included in a project?'

**[01:19:11]** And the person doesn't know what that means.

**[01:19:14]** 'Do you want to be included in a project?'

**[01:19:16]** Because then they get assigned to an operator who takes care of them.

**[01:19:19]** Who conducts the research? Where does this research come from? Who does it?

**[01:19:23]** I have replaced two-thirds of the labor office management.

**[01:19:33]** One of the great things is that I managed to hire a third of the people from outside.

**[01:19:36]** People from retail who wanted to do meaningful work.

**[01:19:40]** And I am very glad about that.

**[01:19:44]** And one of the very strong data competencies is that

**[01:19:46]** we hired a person from a bank who now manages data for us.

**[01:19:49]** And at the ministry, there is a great data specialist who manages the data warehouse.

**[01:19:54]** So fortunately, we already have this competence.

**[01:19:58]** At the labor office, at a real level, this person previously managed

**[01:20:00]** Controlling at a bank.

**[01:20:04]** And do you have that person with you or are they in the digital agency?

**[01:20:05]** They are not in the digital agency.

**[01:20:07]** Data Warehouse, a strong competence at the Ministry of Labor and Social Affairs,

**[01:20:09]** really best in class on Databricks,

**[01:20:14]** really top-notch, because they built it from scratch,

**[01:20:17]** so they went straight to the best, while some corporations

**[01:20:21]** struggle to get there, and we have strong business

**[01:20:25]** data competence that we simply brought in from outside.

**[01:20:31]** And then we usually have some partnerships, so we have partnerships

**[01:20:38]** with ČSOB, with Masaryk University,

**[01:20:41]** and now we are working with the Academy of Sciences, so we always have

**[01:20:45]** some expert partner for that.

**[01:20:50]** Mother-in-law 60+, three years before retirement, was fired from a position

**[01:20:53]** where she had been for over 20 years. What are the right first steps

**[01:20:56]** to succeed in finding a new job?

**[01:21:02]** The first step is to go to the labor office and get included in a project,

**[01:21:09]** where it's not an obstacle, they get the basic counseling.

**[01:21:13]** What I see in the data, for those people, most often there are

**[01:21:18]** two or three scenarios. One scenario is,

**[01:21:22]** that the person retrains for a job,

**[01:21:28]** so we see a lot of retraining into trades,

**[01:21:31]** where people who used to do some managerial position

**[01:21:35]** or administrative work suddenly work with their hands and enjoy it.

**[01:21:39]** Then we see huge interest in helping professions,

**[01:21:44]** and we also subsidize retraining into social work.

**[01:21:49]** Although there isn't a direct increase there, I say that upfront.

**[01:21:52]** It's just that it's deeper.

**[01:21:54]** Four percent less, so it doesn't really matter.

**[01:21:57]** But the person has a better feeling, a sense of purpose in their work.

**[01:22:00]** And lastly, we quite solidly subsidize

**[01:22:06]** children's groups so that older people can also look after

**[01:22:10]** children and others.

**[01:22:12]** And it's really low-threshold, the neighborhood children's group is basically

**[01:22:16]** like in my living room, I remove sharp objects,

**[01:22:22]** a lady from the labor office comes from the office,

**[01:22:25]** it's 20 thousand income that a person otherwise wouldn't find.

**[01:22:31]** That's great, that's great.

**[01:22:33]** I enjoy talking about these concrete things.

**[01:22:37]** And now something a bit more personal.

**[01:22:39]** What was the biggest wall you hit when transitioning from the private sector

**[01:22:42]** to the public sector?

**[01:22:46]** No, it's terrible.

**[01:22:48]** It's like you're about to run a marathon

**[01:22:52]** and then you find out your legs are tied.

**[01:22:55]** I had several huge strokes of luck,

**[01:22:59]** which I didn't even know when I started.

**[01:23:01]** One is that most of the management expired,

**[01:23:04]** on five-year contracts, then they re-competed.

**[01:23:07]** And everyone re-competed just as I did.

**[01:23:09]** It was divine how I could choose my own management team.

**[01:23:12]** Only a third of the original team remained.

**[01:23:15]** I found a third at the office,

**[01:23:17]** promoted them to higher positions,

**[01:23:19]** talented people from the office,

**[01:23:21]** and I brought in a third of people from outside.

**[01:23:23]** That's great.

**[01:23:25]** Without that, I wouldn't have those landings at all, no way.

**[01:23:27]** And that created great chemistry.

**[01:23:29]** Good retail skills,

**[01:23:31]** talented people from within,

**[01:23:33]** and they call themselves dinos,

**[01:23:35]** the people who survived Kristof's era,

**[01:23:39]** that's the type.

**[01:23:41]** So he puts it all together into a nice chemistry.

**[01:23:44]** That was luck I didn't even know I had.

**[01:23:46]** The second luck I didn't know about,

**[01:23:48]** is the strong data competence at the ministry,

**[01:23:51]** because right now thousands of people

**[01:23:53]** look at the report every week to see how they're doing.

**[01:23:56]** And that started to self-regulate the office.

**[01:23:59]** Those were two huge risks that, if I didn't have,

**[01:24:02]** I would have been stuck out of necessity.

**[01:24:05]** I have to say what bothers me internally

**[01:24:08]** is how long the laws take.

**[01:24:12]** For example, I have three employee segments now.

**[01:24:16]** I travel to branches a lot, as I told you.

**[01:24:20]** People working in employment,

**[01:24:22]** they are very proud

**[01:24:24]** that they help people earn more, it's awesome.

**[01:24:26]** But they experienced the rollout of the new system,

**[01:24:28]** so they have dark circles under their eyes,

**[01:24:30]** because the new system rollout really hurt.

**[01:24:32]** It was like half a year,

**[01:24:34]** even the loyal women cried.

**[01:24:37]** And for me, that was a quality rollout of the new system.

**[01:24:40]** In a retail organization, it wouldn't be any different.

**[01:24:42]** In a bank, it wouldn't be different either.

**[01:24:44]** Then I have employees on super benefits,

**[01:24:47]** they have double dark circles under their eyes,

**[01:24:48]** because you know they're expecting

**[01:24:50]** what they experienced from employment.

**[01:24:52]** And then I have people on care allowance.

**[01:24:54]** And I really feel sorry for those people.

**[01:24:56]** Because they know that on the first of July

**[01:24:58]** it will come to the Czech report.

**[01:25:00]** So that's kind of an external change,

**[01:25:02]** that will happen to me.

**[01:25:03]** It's actually correct,

**[01:25:04]** because it's simply better,

**[01:25:05]** if it's under one organization.

**[01:25:06]** Right now, we're passing this benefit

**[01:25:08]** between us and the Czech report.

**[01:25:10]** That's stupid.

**[01:25:11]** That's organizational disruption.

**[01:25:13]** It takes way too long.

**[01:25:14]** The person might die before

**[01:25:15]** we pay out the benefit.

**[01:25:17]** That's really terrible.

**[01:25:18]** This is one of the benefits

**[01:25:19]** that failed to speed up.

**[01:25:21]** The decision is right,

**[01:25:22]** but the fact that it takes a year

**[01:25:24]** means that in this client segment,

**[01:25:26]** I have double the employee turnover,

**[01:25:27]** you can't hire there.

**[01:25:28]** Women are desperate.

**[01:25:29]** Now I've finally decided

**[01:25:31]** to clearly sort out

**[01:25:32]** who will come and who won't,

**[01:25:33]** so at least they know what they're getting into.

**[01:25:34]** But actually, many of these things

**[01:25:36]** are some externalities

**[01:25:37]** that happen to me.

**[01:25:39]** And then the second thing is,

**[01:25:41]** that with every change,

**[01:25:43]** it's always the first clerk

**[01:25:45]** who messes it up.

**[01:25:47]** Simply, flood clerks pay out incorrectly,

**[01:25:48]** they protect the market,

**[01:25:50]** clerks mistreat the disabled.

**[01:25:51]** Now with the super benefit, I'll definitely hear

**[01:25:53]** that we're processing it wrong.

**[01:25:55]** That's always the case,

**[01:25:57]** the clerk is the scapegoat.

**[01:25:58]** So I guess I'll wish a lot of luck

**[01:26:05]** and best of luck,

**[01:26:07]** whether the data and curves flatten out

**[01:26:09]** or grow depending on

**[01:26:11]** what kind of curve it is.

**[01:26:12]** Thanks a lot, Dan,

**[01:26:13]** for taking the time.

**[01:26:14]** Thank you.

**[01:26:15]** Thanks a lot.

**[01:26:16]** Thank you for your attention.

**[01:26:21]** As usual, we kindly ask you

**[01:26:24]** to give us feedback,

**[01:26:27]** which you should slowly see on the slide.

**[01:26:30]** Please rate our breakfast

**[01:26:33]** and write a word or two

**[01:26:35]** that came to mind

**[01:26:36]** while listening to Dan today.

**[01:26:39]** What’s important is,

**[01:26:41]** please try to

**[01:26:43]** discuss what you heard

**[01:26:44]** and what maybe surprised you

**[01:26:46]** with people around you.

**[01:26:47]** Whether it’s your team,

**[01:26:48]** your family,

**[01:26:49]** or your company.

**[01:26:50]** These things really help

**[01:26:52]** and we spread data,

**[01:26:55]** which is important.

**[01:26:57]** We won’t be an exception,

**[01:27:00]** and on the second of September,

**[01:27:01]** if you have the interest and time

**[01:27:03]** to dive deeper into this topic,

**[01:27:05]** I will invite a few more people

**[01:27:07]** and together with Dan we will look

**[01:27:09]** more in depth.

**[01:27:10]** And you can join through the platform

**[01:27:12]** Red Button EDU,

**[01:27:13]** so I warmly invite you on the second of September.

**[01:27:17]** Well, business breakfasts continue as well.

**[01:27:20]** So it’s the usual habit building,

**[01:27:23]** digital mindset, AI in everyday work

**[01:27:25]** with Dita, who definitely had ringing in her ears today

**[01:27:28]** and greetings to Italy.

**[01:27:31]** So it will be on Friday.

**[01:27:33]** Life School with Ján Košturek

**[01:27:36]** continues as well.

**[01:27:38]** We keep recommending books,

**[01:27:41]** so definitely check rbkniha.cz

**[01:27:43]** for the latest ones.

**[01:27:46]** Well, if you need anything,

**[01:27:48]** just press the red button.

**[01:27:51]** And now, the next breakfast.

**[01:27:54]** The next breakfast will be on the 18th.

**[01:27:58]** Where there's a will, there's a way,

**[01:28:00]** Honza Tománek will come.

**[01:28:02]** If you don't know Honza,

**[01:28:03]** just google him.

**[01:28:04]** This breakfast will definitely be worth it.

**[01:28:08]** So come on 18.9. here to Magenta,

**[01:28:11]** or watch us live.

**[01:28:14]** It will be great.

**[01:28:15]** Once again, thank you, Dane.

**[01:28:17]** Take care.

**[01:28:18]** Bye and see you.

**[01:28:20]** Thank you for the invitation.