Yn y cwmhreifft, ddim yn fawr i'r cyfrannu ar y cwmhreifft ar y cwmhreifft. Yn y cwmhreifft, mae'r cyfrannu data ac mae gennym ni'n cyfrannu ar y O&S. Mae'r ystafell yn ymdweud o'r cyfrannu cyfrannu cyfrannu ar y Llyfrgell. A'r cyfrannu cyfrannu ar y Llyfrgell ystafell. Felly, rydyn ni'n gweithio'r cyfrannu ar y Martina ac Petia. Felly Martina Helm yn y cyfrannu cyfrannu ar y O&S, y cyfrannu cyfrannu ar y Llyfrgell a'r Cyfrannu Cyfrannu. A Petia, y cofnodd, yn ymdweud y cyfrannu cyfrannu ar y Llyfrgell ystafell. Rydyn ni'n gweithio'r cyfrannu cyfrannu ar y Llyfrgell ystafell. Ond mae'r cyfrannu sylwagaeth fadeol, yn cyn() o'r cyfrannu cyfrannu cyfrannu. Felly, oherwydd i'r rhan o'r rhan oed yn ydych chi, rwyf wedi'u cyfraith yn 2020, yn ydych chi, sydd ar y cyfraith o'r pwn i'n gweithio yma, ychydig iawn i'r cyfraith a'r gynghwyl wedi'u ddweud ydw i, nid i'r cyfraith o'r cyfraith a'r gyffredinill yn y ddym ni'n gweld yn hynny. Rwyf y gallwn ar y cyfraith o'r cyfraith o'r cyfraith o'r cyfraith o'r cyfraith o'r cyfraith o'r cyfraith. A gael hynny i ddim nesaf o'r ddweud yn gweithio'r gweithio, dwi'n ddwy'r ddweud o'i cyfle gysylltu Gwyrdd LFS yn y Cynllun 2020. Dyma, dwi'n ddwy'n ddweud o'r gweithio bobl pan ddwy'n ddweud. Ond oherwydd mae'n ddweud am y celfon yw'r interview ac mae'n ddweud o'r ddweud o'r ddweud o'r ddweud, mae'n amlwg hynny o'r ddweud o'r ddweud o'r ddweud o'i ddweud o'r ddweud. a we did that through various different ways. Previously, we telemaged only addresses that located north of the Caledonian Canal for telephone interviewing, so we extended that to the entire sample that only gave us about 67% of phone contact details for about 67% of cases. So, we then adapted our advanced material as well and implemented an online portal asking residents of our sampled addresses to provide us with phone contact details through the portal. And then interviews could follow up with telephone interviews. We also got phone contact details through people contacting our survey inquiry line or by contacting our interviews directly, gave us another 67%. But the biggest part of phone contact details we actually obtained through a sort of field tactic that we refer to as Nocturnage. We gained through about 55% to 60% of phone contact details for our cases. So, I wanted to explain a little bit more what Nocturnage is. So, we introduced this in April 2021 after testing it on a couple of our other household surveys, which showed to be quite successful in increasing our response rates. And as part of Nocturnage, we basically sent interviews or face-to-face interviews to cases where we weren't able to obtain any phone contact details and interviews will then make up to three call attempts to obtain a phone number at the doorstep and then follow that up with the telephone interview successful. And we started Nocturnage only on Wave 1 cases to start with, but then extended that to Wave 2 plus cases as well, where these were sort of in the area of Wave 1 cases or where we weren't able to make a successful contact because the phone contact details were perhaps wrong or they were supplied late in the last wave. And Nocturnage, I wanted to show you on the next few slides what success we had with using Nocturnage. So, here's the slide that shows the response rates over time in 2019-2020. So, you can see that before the pandemic, we had a fairly healthy response rate in the mid-50s that obviously then drastically dropped with the start of the pandemic to the mid-20s and stayed around that level until we introduced Nocturnage in April 2021, which we achieved around a 10 percentage point increase at the time and that level then around the late 30s over the summer. You can see a bit of a dip towards the end of last year, but that was not down to Nocturnage losing its effectiveness. That was down to Omicron really playing a part here. Obviously interviewers couldn't go out due to isolation rules and the capacity was stretched for that reason, so that's why we're seeing a bit of a dip there again. But in general, obviously Nocturnage did help bringing our response rate up a bit. Obviously not back up to where it was before as we didn't do it to the whole sample just to a part of the sample where we didn't have any phone contact details or where we failed to make contact. What impact did it have on our achieved samples? So pre-pandemic, you can see here the sample size for the LFS wave 1, again dropping drastically in March 2020. As I said earlier, we then tweaked our sample or doubled it actually with July 2020 and that meant that we actually then had a sample size that was a bit bigger than before the pandemic, so we tweaked it a bit towards the end of the year to bring it down to about the same level. And Nocturnage really helped us to maintain that so far, which was great. Now that showed us at the moment the quantitative impact that Nocturnage had. I also wanted to show you a few slides now with the qualitative impact that it had. So on this slide here, it shows the distribution of tenure, which is normally something where you don't see a lot of movement over time. So the left bars on the side basically showed a distribution of tenure before the pandemic and the green middle bar shows what the distribution of tenure was as we switched to telephone mode only. And we can see here that we got more owner occupiers and fewer renters in the sample in wave 1. And when we then introduced Nocturnage to the mix, this is the purple bar in the middle, we can see that we are getting more of the harder to reach sample members back into the sample, so more renters and fewer of the owner occupiers. And when we look exclusively at those cases that were subject to Nocturnage, which is the right dark ring bar, we can see that the proportion is pretty close to the pre-pandemic level. Similar movement can be observed with individual characteristics such as age, which shows that we got more older and fewer younger people in the sample as we switched to telephone mode and again Nocturnage helped us bring that back into the right direction. Similarly NSSEC, we had more higher skilled and fewer routine workers in the sample and again Nocturnage helped to bring that back to the previous level. Material status, similar picture, fewer single, more married respondents. And nationality, which we had a big debate over as well, we can see that we had more UK and fewer non UK respondents in the sample. And the movement that we observed in tenure as well as the one with nationality and country of birth, both of these led to some adjustment in the waiting. And this is why I hope I can hand over now to Petia to talk us through the adjustments that we made to our samples. So just checking now whether Petia can use her audio successfully. Hi, can you hear me? Yes. Fantastic. Amazing. Thank you. Okay. Thanks Petia. Can you see the slides? Okay. Yes, perfect. Everything else is audio. Fantastic. So I'll go over to the next slide then and over to you. Thank you so much. So yeah, I'm just going to go over the methodology adjustments that we introduced following the changes in the data collection and the pandemic that introduced quite a few biases into surveys as well. So the first thing we did was to add a non-responsive adjustment prior to the change in data collection that LFS did not have a non-responsive adjustment. So what we did now is to adjust the design ways of the household that joined after the change in the data collection. So from week 10 of quarter one of 2020 onwards, we adjusted these design ways by calculating probabilities of responding. We used logistic regression to calculate these probabilities and we used GB region and aggregate level census factors of index of multiple deprivation, output area and open ruler as covariate to calculate these probabilities. Just to note, this was only done for GB as Northern Ireland postcodes were not available last year. So we weren't able to link Northern Ireland to the census factors. We needed the covariate. But now during the 2022 reweighting, which we're going to be starting just recently, we will include Northern Ireland as well. So this was the first thing that we looked at. Next slide, please. The other thing we wanted to see if there's any variables that we can actually use to help us mitigate the biases introduced in the long term. And tenure particularly was key here because what other factors around labour markets or migration or health awareness will eventually cause significant change over time in LFS output. The proportions of the housing stock that are rental or owner occupied are far more stable historically. As you can see on the screen, the distribution for tenure from 2018, the beginning of 2018 up to the end of 2018 is fairly stable. Because it's relatively constant, IA is relatively predictable for us. This is a suitable variable to be used in mitigating action in the short term and the medium term as well that was introduced and introduced biases into the sample. Indeed, we noticed differences in proportions responding to the sample with other characteristics as well such as ethnicity, disability status, nationality, country of birth. So it wasn't just tenure, but we focused on tenure because it was so significantly affected by the changing collection and the pandemic. And because as a variable, it is far less susceptible to large changes compared to some of the other variables we collect. Furthermore, the distribution of other variables of concern are actually correlated with tenure. So if we address tenure, we are also impacting other outputs as well. So what we did is to use the average LFS tenure distribution across the four quarters of 2019 as a basis for waiting in addition to our prior already existing calibration plans. We started doing this from JM 20 onwards. The general effect of the introduction of the tenure waste is that those characteristics that are more present in owned outright housing will be reduced in the weighted estimate. Will those characteristics more prevalent in rented housing will increase? And this will affect not just personal characteristics, but also employment status and type of employment as well. Just to note that we are aware of the caveat that the way it comes from the 2019 survey. And without an external source to update them, they're not really suitable for use in the long term. But in the short term, they did have a good impact. Next slide please. So the impact of the tenure waste was as expected. So once we introduced them, we could see that the unadjusted unemployment rate got higher and the adjusted employment rate got lower as we expected. Now, what we also notice as well is that the introduction of the tenure waste, both other characteristics closer to the long term friends such as the number of people born in the UK, number of people meeting definition of disability, et cetera. So the estimates produced across a range of variables after we introduced the tenure waste became more consistent and credible with other external information and long term expected trends as well. However, we wanted to see if there's anything further we can do. Next slide please. We noticed that country of birth is a particular concern as well. So the tuition rates decreased in the UK born after the pandemic, but they increased in non-UK born. And we observed this across all age bands that we used. The EU born started dropping out of the sample more than the EU, non-EU born as well. Luckily for us, there was actually an admin source that we could use to try to mitigate this. Next slide please. So we identified the RTI administrative source. This is real time information. It comes from the HMRC. And RTI is the complete coverage of payroll and foils including binationality. So this was a good opportunity for us to have an admin source that we can compare to the RTI data and see if first we can actually identify the bias and then if we can mitigate it. So LFS based growth rates for year-on-year percentage changes for non-UK decreased much more between October, December 2019 and July, September 2020 compared to the RTI based growth rates. So this shows us that the LFS estimates suffer from bias stemming from differential non-response between UK and non-UK born. And the additional tenure constraint that we included hasn't really reduced this bias efficiently. Next slide please. So what we wanted to do is to find a way to use the RTI data to estimate year-on-year population growth rates for EU and non-EU born subpopulations. And we needed to get that for each rolling quarter in 2020 onwards. There were very few observations from the RTI data set and it wasn't really suitable to fit a statistical model based on these few observations. So we had to use very simple assumptions to derive an expression of change in the population growth rates in terms of the change in RTI based employee growth rates. We wanted to see if we can track the RTI based employee growth rates and see whether this is going to translate to population growth rate changes. In order for us to actually validate the estimator and validate and prove the assumptions that we made, we need to choose base periods as well. So we looked at October, December 2019 because this is the last period before the pandemic where the population growth is known. The assumptions we made were that any change in the population growth rate of the non-UK population is going to be in the same direction as the change in the RTI employee growth rate. So we wouldn't expect RTI employee growth rates for non-UK born to be increasing while the population growth rates of the non-UK born is decreasing. We also expected that the magnitude of change in the population growth rate does not exceed that of change in the RTI employee growth rate. Because the RTI is only of the employees whereas the population will include employees, self-employed, unemployed, etc. So we wouldn't expect the magnitude to actually be disproportionate. So given these assumptions, next slide please. We selected a very kind of quick method which is three steps and we essentially wanted to check if we can estimate the population growth rate based on the RTI growth rate. So if we let TETA denote the RTI employee growth rate of the EU born population between JS20, so July, December 20, and JS19, we also adjusted RTI year-on-year percentage changes in any subpopulation by differentiating the UK nationals. So in the equations if we take TETA RTI for JS20 for EU for example, the adjusted version of that is the TETA RTI employee growth rate for JS20 for EU minus the TETA RTI employee growth rate for the UK. We removed the UK because we wanted to account for background change in employment in the country. So if we let Y denote the population growth rate of the EU born population, we would actually assume that the population growth rate for EU born in the pandemic, the YJS20 EU adjusted, is going to be proportionate to the growth rate in the RTI. So the population growth rate, the last equation on the slide, the population growth rate for JS20 for EU born adjusted minus that from the base period, which is all D19 for the EU, will be proportionate to the RTI employee growth rate for the same period minus that base period. So essentially we are looking at proportionally estimating this and we have published on our own website proof of this estimation method and we did show that the change of the population growth rate between a base period and a period from 2020 is approximately proportional to the change in RTI employee growth rates. We also choose a proportionality factor so B to be set to 0.5 because this will be a positive number, but we set it to 0.5 to minimize mean prediction errors. Now after we've decided on the method and after we did proof of it, we wanted to compare it to external sources. Next slide please. We first looked at LTIM, so this is the Long Term International Migration Estimates. All the numbers that you see on the slide are presented in the thousands. So we compared the methods and estimates to the LTIM and this is year on year changes. We noticed that the year on year changes between RTI based estimates and the LTIM are actually fairly close. And the mean percentage deviation for the estimate that we produced on just the RTI growth rate is very close to 0 and the mean deviation itself is only 2. So this gave us the confidence that the developed estimator is actually approximately unbiased. The bottom table shows the year on year change in the population total of the UK born and non UK born populations and of the whole population using RTI and then only the LFS without the RTI adjustment. We can see that in the first quarter there is very little difference between the two, however that difference increases with time. The differences between estimates by country of birth however are much larger and they also increase over time. So it is clear that the unadjusted LFS responses under represent non UK born. So in order for us to fix this, the introduction of the RTI based estimates did bring our estimates closer to other external sources. And it fixed that under represents the number present in the data at least to an extent. Thank you. I just wanted to follow up with just a few notes on what's ahead of us now in 2020. So I explained earlier about our field strategy and obviously we're still exclusively telephone interviewing on the LFS. The UNS has started some face-to-face trials end of last year on a small scale looking at the practicalities around that going into people's houses again. And the first findings were quite promising. So a further more large scale trial is planned in spring this year. Although that won't be done on the LFS so it will be done on some of our other surveys and we then will consider the outcomes of that before this might be rolled out on the labour force survey. Petia briefly mentioned earlier about a reweighting over the course of 2022. So obviously we completed the reweighting exercise based on the RTI data over the course of last year and we're planning an update to that based on the same methodology over the course of this year on using the latest RTI data. And as further sources for estimates of the population become available then from the census later this year we'll review the methodology further and further reweighting may take place possibly next year. But plans around that haven't really taken shape yet so there's still a lot of debate over that. And with that at the end of our presentation apologies again for the technical hiccups we experienced but hopefully you were all able to follow the content regardless. Fantastic, great. Well thank you very much and I think we shall move on then to the next talk. Great, so this is the transformation of labour market statistics with James Harris. Okay, and James joined the ONS working for a few years in population statistics on both estimates and projections for moving on to work with local authorities across the country and all types of sub-national statistics, including businesses, trade, jobs, wages, health and demographics for almost a decade. He is now leading the labour force survey team as they work through the transformation to new methods of collecting labour market data and this is what he's going to be updating us on today. Okay, over to you then James. Great. Can you hear me now? Yes I can. There we are, I'm on hooray. Right well hello everybody. So thank you very much for the introduction. Good to see you all. Glad to see so many people here. So my name is James Harris I was previously working in sub-national statistics now head of the labour force survey. Just letting you know a couple of my colleagues on the line as well so obviously you've heard from Martina who's in charge of the LFS production. You also have my colleagues Jason Zawadski. So he's the divisional director of our whole division now. So my manager formally in charge of the census 2021 operations. So very, very aware of what's necessary for the operations of running surveys and data collection. And that's now of course slowly tying up and he's now moved across to work with us in terms of the transformation of labour market statistics. And also all a phrase on the line who's in charge of parts of the transformation. So looking off the labour market statistics transformation aspects in particular looking at the quality and the design aspects of everything. So what I'm going to talk about today is the transformation of labour market statistics. But more importantly, this is what I'm going to cover so our vision for the transformation. So what we're going to be delivering so what you can expect to see and then touching on what to expect to come next. So first off our vision for transforming the labour market statistics where we're going what we're doing why we're doing it. So there's been a very long history of labour market statistics as I'm sure most of you do know we've been running the labour force survey for almost 50 years. Technically we're heading into our 50th year I think now we're producing regular cross section on labour market statistics and estimates. So this has been in production for a very long time and you've already heard from from Martina the developments that we're undertaking to make sure that this continues that we're able to continue producing these numbers. But more importantly than that perhaps enabling a wide variety of articles and analysis of workers and their characteristics and circumstances. So internally with NONS producing outputs and analysis and breakdowns think an awful lot of activity of course to the current pandemic we've been producing over the last few months. Our colleagues all across the rest of government so DWP, DFE looking at aspects of the labour market monitoring the developments and so forth. And perhaps more importantly all of you on the line or use of the data of the information of the available resources that we have to produce your own articles your own analysis like we had presentation from Nye earlier on looking at the breakdowns of the labour market about the population about the different aspects of society going on at the moment. And of course speaking of society we're trying to evolve to over time to the developing changing needs and the shape of society so adding questions subtracting questions when things become important. So, you know, over the course of time that last few years we've had Brexit on the horizon that we've had the COVID pandemic. We've had an awful lot of changes the new industrial classifications new occupational classifications just to example so making sure that we're keeping it and following what's happening over the course of time. But through that through all our engagements through conversations with with you with everybody with colleagues internally recognising all the various questions suggestions issues and is that not only our colleagues but you the users have been raising over the years through the different format through the different forums that we have. The communities and local information partnerships groups and all the different outreach programmes that we have so an awful lot of information that you want want faster and more frequent outputs. Ideally want monthly estimates or indeed the RTI information theoretically it's real time but we'll see what we can do but ideally with monthly as a core target right now having more robust and more detailed data on the character particularly on characteristics of interest. So you know the personal characteristics of who the people and who the workers are and what what what their social is having being able to be more flexible being able to faster to respond to the changing needs of the day. So what the policies on the horizon and changes that are happening what what information do we need about the changing labour market on a you know day to day basis and what's changing over the course of time. Big of course aspect in terms of high quality data quality takes all sorts of different aspects that we will touch on more in a moment but just fundamentally higher quality better confidence intervals so that you're more certain more sure of what we're actually producing. Of course you also want it easier for the public part so in parts to improve the response rates to reduce the respondent burden but fundamentally so that the user the respondents to the survey actually are able to participate were able to understand the questions they're able to respond more easily more quickly and and then feedback their thoughts on what the correction is. And the resilience of what we're doing so making sure that what we're doing is sustainable and resilient so we're able to continue production we're able to continue the collection we're able to do what needs to be done to produce and publish the day. Especially during times of uncertainty like covid so martin has already been through a couple of the approaches that we've had introduced to make sure that the election is is robust and working effectively. And of course we're trying to tell up and change things to make sure that that's consistent in future as well. So what's our broad vision so the transformation fundamentally we're looking at delivering statistics for the public good that's the vision for the whole whole department but we're looking in particular at integrating surveys sensors and administrative data as the broad aim right at the top but beneath that in terms of specifically Labour market statistics we're looking at producing more coherent more granular more timely statistics in particular being responsive to the user needs. So what the what variables people need one information people need what we need to be monitoring in terms of the labour market and of course trying to reduce the costs and the burden both for government for businesses and households and make it easier to respond to. Now I mentioned in here of course trying to add more sources as well so trying to add administrative data trying to add new linked information so that we understand people and the society even better. It's a complicated process trying to link up all the different administrative sources you know the HMRC PAYE data the real time information information of the health records and all these different things trying to look at what what's situation people are in trying to make sure we're in. So that we have as much information as we possibly can from all these different records but of course that that comes over the course of time with an awful lot of research. We're making sure that this is an online first responsive design so we're trying to transform the surveys to make them more effective more responsive so that fundamentally we can improve the quality of them so trying to gather a larger overall sample size with you know more robust processing systems so that we're able to produce the information faster and more effectively trying to build the modular design to integrate more question blocks so you know when users come along when colleagues in the government come along wanting to know more information different information new questions new new policies on the horizon new topics of interest. Trying to build things effectively so that we're able to add in new modules add in more questions in a simpler manner in a faster manner as well and being therefore being more flexible more able to respond to the fast changes that people actually need in particular that the speed aspect to it so that we know it doesn't take massively long to add new questions that we're able to discuss and negotiate and figure out what's necessary from a question point of view and trying to add that in in a matter of months a week. Rather than years that can sometime to happen and of course things I have been changing over the last few years I mentioned the industrial and occupational classifications as two basic examples but making sure that we are updating and upgrading the questions and the response categories that people have so that you know so that we're up to date we're current we know current situation is with the society and population. We're using the correct definitions we're using the harmonized classifications wherever possible changes with things like the census so that we're consistent with other sources as well. And at the bottom of the slide here in particular looking at the improved ability to monitor progress so making sure that the collection is effective and appropriate and the things like the knock to nudge processes are effective and working and moving responses in the right direction. We're building into our longer term aim I think called I packs and perhaps it's an internal term but the integrated population and characteristics survey. Fundamentally trying to build all these sources together into one coherent source of information that we can understand the whole population and society so trying to build a stable core system. All on this this common capability so that all these sources are built in similar fashion that we can link the information together more easily that they're capturing all the relevant variables that need to be that we're meeting the survey needs not just your current needs not just what we're currently producing but also looking at future needs you know the the government's leveling up agenda for example on on on the cards right now trying to collect make sure that we have the information that we need not just now but trying to plan for that. The next six months a year 10 years if necessary. But that that's the kind of the future aspects of what we're going hopefully to be delivering over the coming year few years but specifically what we're working on right now in terms of this particular transformation so moving from the vision to the actual delivery. So we're going to be obviously continuously producing the labour market data continue continuously delivering it we've been producing this information for as I said some decades already but making sure that we're continuing the production as as as normal. Trying to provide similar data sets that you're all currently receiving so it may not be exactly the same. It might be a slightly different format so see it rather than SPSS for example maybe slight differences in the variable names like differences in the content that you're getting. Hopefully when I say differences in sample size hopefully a larger sample size so a larger sample size hopefully leading to larger number of responses, meaning that you're able to do more granular granular analysis larger larger proportions of people in each category that you're then able to draw better conclusions from.  So all of that continues to be produced and it's a part of the transformation designs. We have, of course, refreshed and updated the content of variables for the latest definitions so making sure that we have the correct you know ethnic categories that match up with the census the correct religious categories the correct response categories to the industrial and occupational classifications and everything else. Trying to you know give robust and detailed information about people's character personal characteristics and of course some things will have changed so you know age and sex and ethnicity will not have changed radically over the course of time, but some variables will have come and gone and updated over the course of time. So well we're reviewing the effectiveness of all of them and making sure that we're asking the right questions to get the right answers. And of course, fundamentally this is all leading towards having higher quality data so not just the sample size, but making sure that we have higher quality data. So we have three core targets that we're aiming for and all is on the line if I say anything wrong here please do correct me but fundamentally trying to reduce the bias so achieving a representative sample that we make sure we're capturing the whole of the population in the right way that we're representing representing all the different categories and groups of people. Focusing on both national and local improvements so trying to minimize the variability between different regions between different indices of multiple deprivation categories between different output area classification categories to make sure that we're not mistargeting anything disproportionately. And as I say trying to make sure that we have proportional samples by all the different personal characteristics so age, sex, disability, tenure, ethnicity, the key variables that we know everybody needs. Trying to reduce the attrition as well so making sure that it's not just wave one responses that we're getting but that once you're in the survey hopefully we're continuing to get your continued engagement through wave one to two to three up all the way up to five. So hoping to reduce the drop off in response rates between waves, trying to ensure that we have a sufficient sample size by the time we get to wave five, and whether or not we get a sufficient sample size, trying to make sure that there's no bias in the changes to the sample that it's still the same age, sex, disability groups and so forth that we're representing the population appropriately through the process. And of course hopefully try to improve the response rate throughout the whole process so reducing the operational complexity reducing the respondent burden such that we're still able to meet all these quality targets that we're able to increase the response rates and hopefully not introduce any new biases. And of course, over the course of time, building the capability not just now but for the future so I've touched on what we're building right now that we're building, you know, transformed system building a better survey better way of sampling and so forth. And over the course of time we're aiming to add more functionality to the survey so hopefully, as I said earlier, faster response to emerging needs that we're able to capture new questions and new topics of concern as they come up engagement with users. Such as yourselves, trying to add the functionality for additional modules making trying to make things more frequent. So we may not necessarily be delivering that in the coming few months or year or so, but adding the capability that we will be able to add modules and add more frequent data and trying to better integrate things into different production systems. I won't give you all the acronyms, but we have a bunch of different production systems within on s and across government and I'm sure some of you have your own production systems that use the LFS data as well. So trying to make things more more effective more more tech friendly as well. And working with our publishing colleagues so the web dissemination side especially trying to build more exciting ways of viewing and interacting with the data. New means of accessing the information new means of presenting the information new data visualizations for example new data science techniques trying to look at the information in new and exciting ways. So over the course of time trying to build these capabilities. I mentioned in terms of the questions so looking from a first principles approach so not just taking a question at face value and just sticking on the survey, making sure that we're answering the right question that we're meeting the user need. It's not just a definition of a variable. It's actually meeting the what people need to know that we're asking the right question or multiple questions to get to that final requirement at the end, and engaging in an awful lot of cognitive testing with respondents with people answering the survey. To make sure that we're getting the responses that we expect that they understand it that it's received in the right way that it's all understood. And we have extensive systems and flow testing to make sure that the questionnaire is designed in the right way that the questions are asked in the right order that the routing is correct and that we're getting the right responses that we're expecting and the people aren't randomly missing questions or skipping questions or whatever. So making sure that we have all these extensive systems and testing all in place and wherever possible trying to maintain consistency with ons standards with gds standards with office for stats regulation standards with standards set for for example with the authorities office, all the different international standards as well in terms of industrial and occupational classifications. So wherever possible, trying to maintain consistent with all the different standards that are available, and indeed hopefully with the time series so you don't see a much radical change over the course of all the different time series in terms of the data sets that we produce. One touching briefly on the data collection flow because this is one change that's rather significant so currently or this is changing a little over the course of the pandemic but the currently the process is that we issue the survey to the respondents, and either they respond face to face or they respond by telephone obviously over the course of the pandemic that slightly changed so this is a very simplified model here, but fundamentally, somebody turns up at the door knocks the door you know. Hi, we're from the ons. We'd like you to engage in this survey, and before the pandemic and hopefully after the pandemic, either we have a little face to face interview there. With the person so we get all the information as accurate as possible, or as it has been over the last few months, engaging in the telephone capture so making sure that they're able to answer the telephone survey by the telephone, making sure that they have all the information that they need, making sure that, you know, encouraging them to engage with the process. And then, once we've got that that telephone capture, we either have a response or a refusal or a failure out of the end of it but trying to actively promote engagement with the survey and with the collection. Over the course of time it was normally with the LFS it used to be face to face first in wave one and then following up by telephone, but now we're going for an online first approach. So, again issuing the survey, hoping that people respond online, hoping that we get the right responses. But if they're not responding online going through the process of telephone capture, whether it's the telematching that Martina mentioned, or whether it's a knock to nudge process to find people's telephone numbers. Maybe it's they responded in wave one and then we're phoning them up for wave two just to make sure that they understood and they're able to respond. But some sort of process here trying to get an online response or a telephone response possibly not with a knock to nudge process trying to encourage that. And in the course of time, hopefully we're aiming to add active field interviews into the process as the final option available to respondents if they wanted to. Fundamentally trying to get a response out of users. So, essentially the same process, but if I just skip back previously there wasn't an online option it was just feel a face first and then telephone first, but now it's online first telephone second and field third. So, that's what we're hoping to deliver, but what to expect next what's coming so obviously we're delivering this transformation so we're going to make iterative improvements over the course of the year. So, we have an online mode that's already live, we're adding telephone mode coming up very soon so in the next month or so hopefully adding the telephone mode into the process, then adding more additional questions, then adding a field mode in later 2022, whether that's just not not or an active field mode we're still working through the exact details, but that's what's happening over the course of this year. And of course throughout that but fundamentally aiming at an upgraded production system that we're able to produce these statistics robustly and quickly and accurately, and building with the new design that we have coming forward. And of course it doesn't end there this ongoing upgrade so it's not just that we're producing this thing now we're going to have to make sure that the systems are still working the questions are updated and upgraded and make sure that everything's functioning appropriately. And of course actively monitoring the progress and effectiveness of this overtime so looking at the response rates looking at the quality of the information coming forward. So we'll be dual running both the new design and the current LFS collection dual running those over the course of a good number of months that they're both live concurrently at the same times that we're able to compare the two sources and make sure that they're delivering what we expect them to deliver and continuing the engagement with the regulatory bodies so for the office for stats regulation so making sure that we're still complying with all the requirements all the needs all the badging necessities to make sure that we're collecting the right information in the right way that it can be still retain the badge if possible. And of course we have the intention to release the indicative results in late 2022. So hopefully later this year you should see some initial results from this newly transformed system so that we understand what what the label market looks like and that the systems are all functioning as we expect them to. And the target is that all of this so the online mode telephone mode field mode everything is all in place come autumn 2023 so all these improvements will hopefully be in train in action working by autumn next year. And what's happened with the LFS during this time you might ask well nothing radically is changing we're continuing production of the LFS data until at least mid 2023 you know all the production all the collection is still continuing as normal. No substantive changes to the content or design so all the variables in the LFS are all staying there we're not adding or subtracting anything in particular. No big changes to that the collection you know not to nudge process or the exact sample size might change a little bit as as Martina mentioned but that's for the purposes of making sure that we're collecting what we need to collect. Continuing to maintain all the materials so the user guidance and the data sets all being produced and published and updated as they normally are on a regular basis. And of course continuing that our customer contact services, if any of you have dealt with the Darts team for example, making sure that we're still maintaining the continued access through that process and archiving the current data and so making sure it's in the current system so that the ways of accessing the data in the data archive and other places that you're still able to retain the access for future analysis. Of course we're all taking this journey together it's it's nice talking about the transformation but you're a part of this as well so making sure that we're continuing to engage with with all of you all of our users and producers as well, continuing the engagement events including our labour market working groups. There'll be a feedback exercise in the spring regards that how how the transformation is going will be publishing blogs and updates and I mentioned the experimental results coming hopefully later this year.  You'll be getting updates as and when we have information to disseminate. And of course engaging in conference events like this for example, but giving updates and developments as they come along when when there are conferences to attend and making sure that over the course of time you have sufficient guidance and materials and information so all the user guidance or the documentation snippets of code if we have them available, making sure that you have all the information you need about the transformation and about what's coming that you're able to take this on board. And remember we're further developing the 50 year legacy so we have a long history of good quality good good labour market statistics all there in the public domain for the for the public good and we're making sure that we're developing this improving this making sure that you have the information you need and trying to bring it all up to date with the modern technologies and modern descriptions and definitions. Just on the final slide here so you know making sure that you're part of the engagement. So if you have questions if you have issues if you have things things you want to know more about if you want to be kept in touch with through the process. The email address here social surveys at ons.gov.uk so do get in touch if there's something that you want to know or something that you want to follow up on and I'd welcome any any additional questions. So I see number five in the Q&A section so I'll stop there and head towards questions. Thank you very much.


Good morning, thank you. I'm very glad to have a lightning talk at this confide. I'm Sora Sugami, a Japanese high school student and a blockchain protocol designer. So today, I would like to introduce a new protocol to share the ability among the year 1 blockchains. Maybe you have already know what the code is. A chain depends on its value chain. But layer 1 blockchains such as Bitcoin and Ethereum have equivalent ratios. It can't use the existing protocols. So I designed a new one. In this protocol, a chain which imports security doesn't need to depend on other chains. And a chain which exports security doesn't need to be changed. The name of the protocol is Proof of Units. It's a kind of signal control mechanism like proof of work or proof of stake. It can integrate multi-mechanisms. Abstractly treating an element such as password or stake of token as unit, summing up costs for shields to produce blocks, thanks to an abstract model of signal control mechanisms. This has some abstract variables and functions. These are two described signal control mechanisms. For example, to describe proof of work, tasks become sealed data for binding. I think products become produced as hash, work can binding, and verify become confirming with the hash's recent target. And also, they are to combine multi-mechanisms. The most important value in this model is unit. Any kinds of products are transformed into units, and they are used as both power in the consensus algorithm. So it can integrate multi-mechanisms. However, if one unit is generated from one product, attackers can get the majority of units by performing easy tasks with small costs. It has a problem from the viewpoint of security. So it adjusts the amount of generated units. To explain this adjustment system, I also defined two variables, a base task and its base cost, and one function to compute the value of units and products. This is an identical equation to adjust the amount. It means when a consumed cost is x times as azure's base cost, x units are generated per unit. It guarantees the equivalent amounts of cost has to be consumed to generate the equivalent amounts of units, no matter which task is performed. In this way, 244 units describe civil control mechanisms abstractly and integrate them securely. Then, integrating the mechanisms of external chains, it can sum up the consumed costs and increase the total amounts of units. More units are generated, more cost attackers have to be consumed. So it can implement the security. It has more advantages. A chain which imports security doesn't push information like block hatch to the azure. It just puts information and security. So the imported chain doesn't need to be accepted by other chains. And the chain which exports security doesn't need to be changed. This is why this protocol can share security and layer 1 blockchains. In the case of importing security, its preferred mechanism is described like this. It enables us to import the cost consumed for Bitcoin mining. I would like to continue this research and design more complete models. Thank you very much.


Yes. Well, I think they are.  I think that South Africa is right up there in one of the highest inequality countries in the world, as is Brazil, but Brazil's drifted down the world table. South Africa stayed right up there and obviously has a very distinctive history. Well, it was profound. I think that's the first point. It was profound in the sense that it was structural. It wasn't as though it was just a norm. When people think about South Africa apart from their think of racial discrimination as a sort of a norm. But it was profound. That influenced education expenditure, for example. It influenced spatial where people lived. Put the majority of the population in rural peripheries far away from the labour market. Gave them very bad education. And it's these legacies that continue to the present day. They take a long time to overcome those legacies, especially in the climate of what has been the world economy since then, since 1994. The world economy has been one in which competitiveness is the name of the game, fairly good skills, ability to create your own work. The legacies of apartheid were exactly the opposite. What dominates inequality? So the technical exercises that show that labour markets, the earnings distribution and employment creation dominate the overall household inequality story in South Africa. And that's easy to understand in the sense, again, of our legacy. If you think about South Africa with its mining legacy on which we built a manufacturing base. So quite a formal industrial structure, but one that was exclusionary or only used the bulk of the population to the extent it needed the population. Now, that's proved a huge problem in the country. The informal sector was actually stifled under apartheid. So it was very small. Then you've got this formal sector in which the mining industry, the manufacturing industry, when you open up to the world economy, they haven't grown. They haven't been the sectors that have grown. It's been the financial sector and the services sectors in South Africa that have grown. Again, the formal side of the economy, which are quite capital intensive, as you said. And these other sectors have not flourished and have hired less and less workers. And so the story of employment in the post-apartheid period has been very sluggish employment creation in the formal sector. The informal sector has started to grow a little bit, but hasn't sort of mushroomed. It's still very small by world standards, especially African standards, where it's almost like this residual sector picking up whoever's not hired in the formal sector. As a consequence, we have world-beating unemployment rates, as measured by the ILO, for example, because we don't have this informal sector that just picks up the slack with underemployment. And it's a problem. The growth of the formal sector has been towards capital intensive, higher-skilled employment and away from the population as a consequence, massive, massive. And so the two most vulnerable sectors of the economy, well, from the workers' point of view, are domestic work and agricultural work. And domestic work hasn't shrunk necessarily, and there's been some quite good legislation, extending basic labour market protection to domestic workers, et cetera. But it's not like a driver of employment creation. Nobody would think that. The agricultural sector has declined dramatically. So coming back to the Brazilian experience, what really started to turn inequality around in Brazil was the fact that the previously vulnerable or excluded started being included in the labour market and being employed and earning fairly decent wages. That's what we're battling to get right in South Africa. So government has taken on the notion, contentiously I think, that they're the employer of last resort, if you like. There has been quite a large expansion of their government public works programs. But measured in a climate in which government is trying to do evidence-based assessment and monitoring and evaluation, and they haven't been uniformly successful. There's been some real successes. For example, programs in rural areas to clear out alien vegetation have been remarkably successful, surprisingly. Everybody thought that was tinkering on the margins. Well, those are the ones that have really had some impact. The type of public works programs where you build roads with spades and rural labour rather than engineering companies, they happen, but they haven't been that successful at all. Well, that's what we've been talking about, I think. It's the legacy of apartheid to some extent. And then the way the economy, the world economy has played out with the skills twists. South Africa has been subject to a skills twist in a classic American or European sense. And that's what you get. You get the population. You know, government has done a few things right. Education levels have expanded. They were higher than Brazil to use a counterfactual. Even in 1994, they were higher than Brazil on average. They were around about seven years of schooling whereas Brazil was around about five. Since then, they've gone up to ten, just over ten years on average. That's a good thing. That's a success. If you look at water, electricity, those sorts of government services, they've rolled them out. But at the end of the day, unless the return on that in a narrow sense, in an income sense, is employment. If you don't get the employment, you don't see the return on that. And that's the problem.


She's starting to learn when to move away from a cow and when to stay towards it Which was a little bit of a challenge when I first started at least for me She kind of wanted to stay against the cow all the time Instead of getting away from it ever.  There's no question and stay against the cow and stop it but there's also a place Where a horse needs to get away from a cow so they can draw it to them and to me it's probably more important that the horse stays between your feet then Whether you're staying against the cow or getting away from it as long as that horse stays between your feet And that sort of sort of serves the purpose of holding the line If you can ride that horse To the cow or against the cow whenever you want And then also get away whenever you want You're a lot more likely to a little bit sticky feeling right there and not terrible But just a little bit like to ask her across that cow just a little more but as close as that cow is pure She's probably being wise for For not really zipping out there But right there I wanted to get across just a scotch more That's where she


What's up everybody, Jason from JaspiesCaseBrakes.com. We just sold out 2019-20 Panini Immaculate Basketball five box case break pick your teams number five And again guys we had a couple R&Bs to sell this out. We had a 21 teamer. We had the Grizzlies R&B randomizer the Pelicans R&B randomizer I know it took a little longer than usual guys, but we got it done and let's pull some fire So these are the customers that are part of the 21 teamer That definitely includes actually let me So let's do this really quick Print out a new sheet, but I want to include all 21 teams So you guys can see the team that are part of that one on one second guys Yeah, this this 21 teamer does have a lot of teams guys Printing it out guys. Well in the meantime, here's the customers that are part of the Pelicans R&B They got their random numbers there Customers that are in the Grizzlies R&B and then here are the customers that are part of the 21 teams And then here they are as well from the Lakers down to the Magic Keep that list up here and only a few customers bottom straight up, but good luck Gasser Peter Derek Chris Kurt Derek and Kurt so those hits for those teams will go Direct to the owner of course of the teams But everything else is round on our lock style guys, and it's a full five box case We got a Zion yesterday On Zion a day keeps the doctor away Let's find out. All right. We got Lou Williams to 99 for the Clippers 41 out of 49 Clippers part of the R&B 21 Team R&B is going to spot one and so men we got the Marcus Alders 69 out of 99 for the Spurs Part of the R&B as well 69 Spot 9 gonna Robert Wolf We have a Sudco Dumboya. Look at that 45 out of 50 jersey numbers for the Pistons Detroit Pistons that is also part of the R&B 45 That is going to Alyssa We got Kevin Knox 41 out of 50 for the Knicks Patch Auto spot one going to some in we have a Luca Samonich 66 out of 99 There you go. You got double sixes Ryan, and we got Larry Bird 23 out of 49 autograph for the Celtic spot three That is going to Robert Ronkel and spot three in the R&B Very nice Our next one The Angela Russell one out of 99 Timberwolves. That's R&B spot one One is so men We got Kevin Love 46 out of 49 spot six for the Cavs Going to Ryan Harrell. This must be your super heater here. We got massive materials memorabilia 10 out of 25 Chuma O'Keeke Nice four color patch I'm just joking We have Jordan Bone 7 out of 75 for Detroit. Hey, what's up Mojo? Thanks for watching brother. You bring on Mojo 7 Detroit Pistons Cameron there you go, man. We have a Goga Bitatze 62 out of 75 2 Or it actually Indiana Pacers are actually bought straight up Derrick there you go a little bogey and a 5 out of 10 Carl Anthony Towns little three color patch 5 out of 10 T Wolves 5 Alyssa I box 3 So far Larry Bird and Carthony Towns You have a thick one and blockchain Dual autographs Carson Edwards Romeo Langford sets both to the Boston Celtics and So Boston Celtics has to go to spot zero gonna Robert we have Jackson Hayes to 99 Pelicans R&B 86 6 pelicans Going to Chen We got Dwayne Wade 20 out of 99 Bulls edition of Dwayne Wade 20 it's gonna spot zero and Robert Ronco. It looks like we got a piece of the shoe and It's Shaq diesel Wow It's a signature shoe logo right there Souls of the game and that's Suns edition out of all the additions you can get Suns Chris who actually bought the Suns straight up Wow Six out of 25.  I told you man Over here like I've got right on my six. Yeah, six is fired. That's pretty cool Archie Clark 75 for the Philadelphia 76ers R&B one What is so men? Yeah, you probably good Matthew. Honestly We have a redemption right here guys and Chris Bosch three out of 11 all-star 11 time all-star It's Chris Bosch Hall of Famer. Yeah, right? three out of 11 and That is Raptors edition So three we'll be going to Robert Ronco. There you go, man All right, we got a redemption guys See who this is Rookie patch autographs of red. I have no idea what red is you guys want to look that up car number 114 No, is he a future Hall of Famer Matthew? No, not if he's not Romeo Langford for the Boston Celtics Going to Robert Ronco and spot zero. I would want to say no to but I mean 11 time all-star Pretty pretty good I just felt like his last few years of course kind of dropped off because of his heart disease, right or his blood I believe he was getting blood clots Which kind of sucks kind of derail his career, but that was towards the end of it, right? Want a couple chips of Miami? Great player for Toronto Maybe not a first baller, right? Maybe at some point. I would think he gets him. All right. Next one At a 25 Yeah, that was a patch all right there. Very nice Another thick one it looks like or at least it feels like it actually Maybe not. Maybe yes No, maybe not Nicola Vusevich. I pulled the true RPA yesterday Matthew 27 out of 99 Orlando magic Seven and it's going to Cameron. We got Marcus all 15 out of 49 spot five Going up listen Yeah, it's pretty nice looks very similar to the one that sold for like 11 G's a couple days ago Hopefully we can pull another one in these last two boxes. It was pretty sweet 22 out of 99 four out of 50 Jersey numbers Cody Martin Four going to Jordan Hope last mom. Oh, Joe. We have drew good in 28 out of 75 cleaning Cavaliers Eight going to Steven. We got a patch auto here of fion do Eight out of 25 red for the Clippers spot eight going to Steven the last one is 49 out of 49 Alan Iverson the answer is to love him in 2k3 49 Spot nine going to Robert Wolf. All right guys Last one good luck Demption it looks like right at the bottom Donovan Mitchell 16 out of 25 Utah Jazz Kurt Dylan Windler to 99 spot five Cavs going to Alyssa Kauai Leonard 79 out of 99 Toronto Raptors spot nine going to Robert Wolf Here comes the autos We got Troy Brown Jr one out of 25 for the Washington Wizards one But it's so men Looks like we got a little Boris DL sneaker swatches 47 out of 40 49. Sorry. It's pretty good. Utah Jazz edition Put that in a slider box or not ever one more and then we have a redemption Do not do not know what this redemption is So we're about to find out We got ourselves patch autographs Okay Card number 58 Anthony Davis. Wow. Is that like the card though? Is that like the Celsius card? I? Don't know what numbering this would be out of But card number 58 Anthony Davis. I'm assuming that it's it's this card right here We pulled the Celsius card right patch autographs It's probably what's gonna look like very nice that it's gonna go to the Lakers R&B Zero going to Robert Ronkel and there you go, man very nice NBA champ Anthony Davis Well, Ryan, you got some sixes. I mean you gotta look at some odd and tricky autograph It's just that the fact that the other sixes that came out were like for customers. I bought it straight up So you didn't entirely skunk or anything like that. Well, there you go guys Anthony Davis Alan Iverson beyond the patch auto Roman Langford patch auto redemption Bors. Sorry Chris Bosch to 11 we got car Anthony towns to five Patch auto two more Kiki path to 25 Larry bird to 49 some monitor right there as well Bors deal will put that in the slider box right now Got a souls of the game Shaquille. Neil Manning is heavy I'm sure his shoes size is much heavier. Yeah guys. There you go So unfortunately, there was really no grizzlies in this and just one pelicans card But everything else did go to the R&B 21 and again guys This is immaculate picker team number five in the books We have more mac at it if you guys want to give it a shot I don't think we pull anything crazy out of the first two boxes of the case I know we're a little ways to go guys But hey, you never know me tonight or tomorrow pick your team one box break you buy two or more You have a chance at the pelicans a lot of teams remaining are in the 50s 60s and 70s can easily get this trim down to under single digits So I appreciate you guys Jaspys case breaks


I'm Zeke and welcome to the dream green show this week. We're gonna be taking $100 and do option trading I'm gonna use this never lose strategy called put credit spread. We're gonna be doing it on SPY three times this week to see what kind of return can we get on $100? I'm shooting around 10% on one week So if I can make $10 off of this $100 in the next week 10% in one week.  Let's get straight into the video Welcome back YouTube. I'm gonna turn this $100 into a little profit this week I'm gonna be using the token symbol SPY and do put credit spreads on SPY over in the Robin Hood app So let's pull up the Robin Hood app right quick to explain to you guys exactly what I'm going to be doing over the next week All right guys here. We are it is Friday My account is up 1.4 percent up $209 on Robin Hood over the last week I'm up 2.13 percent for hundred and thirty three dollars in the last month. I'm up 11 percent Thanks to Tesla. I'm up 2123 dollars and 30 cents. So my Robin Hood account is doing pretty good so far But let me explain to you guys exactly the strategy I'm going to be using this week in order to try to get a 10 to 15 percent return on my investment The token symbol I'm going to be using this week is SPY Because they have three options every single week that you guys can trade and I'm gonna be doing a quick credit spread on each one The reason I'm doing this is because SPY took a little dip this week It's down point zero six percent after being up almost one point seven percent SPY follows the S&P 500 and it does is not very volatile to it kind of trade sideways even on a great month It's only up two point four seven percent So it's pretty safe that this stock is not going to go dramatically up and it's not going to go dramatically down So I'm going to be doing put credit spares on SPY the lowest SPY have been over the last month is around 300 three around three hundred dollars. Okay, so if we're going to trade hit trade options And then the one that's ending on Monday is July 27 Then I have one ending on Wednesday July 29th, and then I have one ending on Friday July 31st I'm going to be doing an option trade on every single one. The one that we're going to look at first is July 27th I'm going to hit sell put and scroll down So the share price right now is three hundred and twenty one dollars and nine cents since SPY went down this week It's a good time to put in a put credit spread because it might not go down any lower The option that I have selected now is called the sale put if I click on the option 319 sell put right there. That means I'm obligated to buy a hundred shares of SPY at three hundred and nineteen dollars That means if it falls below three hundred and nineteen dollars I have to buy one hundred shares of SPY at three nineteen But they were creating my account one hundred and four dollars because you're obligated to buy one hundred shares If it stays above three nineteen, I would just keep the one hundred and four dollars and keep it moving But let me show you guys why I can't do that in order to do that You need a minimum collateral of thirty one thousand dollars of over thirty one thousand dollars So I can't do that so the strategy I'm going to use is called the put credit spread and let me show you guys Exactly, how do you do that? You want to hit the select button at the top right and Make sure that you have sell put Selected I'm going to scroll down. I do not think that SPY is going to fall below on by Monday below Three fifteen so the lowest has been over the last month was three hundred I don't think it's gonna go all the way down to three hundred over the next two days It took a whole month in order for it to recover from one hundred dollars So I'm going to go around the three fifteen mark actually I'm gonna go around the I don't think that SPY is gonna fall Below three fifteen actually so I'm going to select three fifteen right here and Let's play safe. I'm gonna select three fourteen and then what you want to do is hit buy and Then select three thirteen right below it and then that is going to make it a put credit spread And I'll be getting the difference in between twenty cents and twenty seven cents so around seven dollars I'm going to hit continue and there you go if you did it right It should say put credit spread if you did it wrong. It might say something Ridiculously crazy, maybe a put debit spread or something like that, but this is a put credit spread. I'm going to hit review hit one review and then they're going to hold a collateral of my $100 right here. They're gonna hold a collateral and I'll be credit credit it into my account immediately $7 if this contract goes through so I should get $7 for this contract Why they're holding $100 collateral if it falls below three fifteen three fourteen? They're going to take my $100 and keep it, but I do not think that SPY will fall below Three fifteen by Monday and once the Monday day ends and it's above three fifteen I'm gonna they're gonna give me my $100 back. They're gonna give me my $100 back I keep the $7 premium and now I have a hundred seven dollars And then I'm going to repeat that on Wednesday, and I'm going to repeat that on Friday, so I'm going to be doing this three times this week in order to hopefully grow my account if I could get $7 each time That is a 21% gain in a single week That is insane a 21% gain in that single week. So let me go ahead and swipe up so this contract can go through There it goes. Let's give it some time. It should be placed within the next couple of minutes I'm going to be back Friday and let you guys know exactly how this goes how this plays out and hopefully I turn this $100 to around $121 and we're back YouTube as you guys can probably tell with a smile on my face. We successfully turned $100 into $120. I had a 20% gain 20% gain in one week from doing put credit spreads. It's not the safest option trade of all time But it works and you can make it and you can be risky with it if you want to or you can make it safe So I'm going to show you guys right quick all the trades that I made on SPY on doing put credit spreads I'm gonna show you again. How did I set it up and what percent chance of Profit did I take in order to have a 20% gain in one week with just $100 on SPY, so let's go ahead and dive into robber hood right quick. So here we are on robber hood SPY Did go up 1.5% in a week So that is up that is a big growth for SPY to go up with 1% in a week, but it did also drop down Half of press almost half a percent this week Which was good for me because I'm gonna tell you guys why it was good for me in a little bit But that was good for me. I got in a good price and went back up So to show you guys my profit right quick. Let's do show more Alright, so here we are on all of my history. I actually profited $23. I just didn't have $3 land around the house So my profit was $23. I did a sell on Monday, which I had made a profit of $6 in credit I did a sell on Wednesday, which I had another profit of $8 in credit And then I did a sell on Friday, which I had a profit of $9 in credit, which comes out to be $23 So I'm up 23% in a single week just from doing put credit spreads Now, let me show you guys the strategy that I use in order to accomplish this So we go back to SPY right now is at $326 and 44 cents.  So they have one inspiring on Monday the 5th Wednesday the 7th and Friday the 10th If we go to Monday in order to do a put credit spread I usually look for 80% chance of profit last week. I was doing 90% chance of profit So you can make a lot of money if you want to but it's a little bit more risky I was playing this safe and I still came up 23% in one week So I look around at 80 84% chance of profit and what you'll do is you'll click select at the top right Here goes one with the 84% chance of profit right now It ends Monday. So I don't think it's going to go down to $320 over the next day or so All you want to do is click on Sell put option right there and then you want to switch over to buy or put and put the buy put right below that So you buy an option at 70 cents and then one at You're selling an option at 70 cents and then you're buying one at 58 cents. Then you're going to swipe up All I have to do is hit review is going to give me a credit of 12 cents You hit one You hit review And then it's going to ask for a collateral of $100. That's the $100 I was telling you guys about It'll take my collateral of $100. It's going to credit my account $12 immediately And if SPY stays above $320 by Monday They're going to give me my $100 back I still keep the $12 and then I'm going to do another 87 to 90 chance profit put credit spread for Wednesday Take that profit and do another one on Friday So that was the strategy that I used to come up 23% in a single week Now the reason I say this one is not the safest is because if it do somehow have a flash crash And falls below $320 I will still be credited the $12. I will still keep the $12 but Robin Hood would take my $100 and basically keep it or I could sell it before the option ends and try to make Most of that money back But it's going to take that $100 if it spires below $320 they're going to keep my $100 for collateral They're going to keep that and hold on to it. Well, they're just going to keep it I ain't giving it back. So that was the strategy that I used in order to come up last week on Doing put credit spreads on robin hood Now the safer way to do this will be to use the wheel strategy Or you do your buy you'll just sell puts on SPY let's say if you sell one at 80 percent Now now you'll keep the $84 For profit that send you $84 straight to your account And if it falls below the $321 strike price, you will have to buy 100 shares of SPY Which is not bad to hold on to 100 shares of SPY because once it goes back up you can just sell it But as a put credit spread if it goes below that price You're stuck like chuck. You're out of money. You're you're not going to get your $100 back But if you just do a sell put in the falls below you'll end up with 100 shares of SPY You'll just wait for it to go back up and then you'll sell it. All right, guys So that was how I came up on SPY last week. Am I going to continue to do it? You bet I am Yes, I'm going to continue to do it. But I'm gonna be a little safer with it I'm gonna wait for the price of SPY to drop a little bit and once it has that drop That one day where it drops I'm going to do a put credit spread on it because It it helps me out in the long run when the price is low because the chances of it going back up Is higher than the chances of it going back lower. It could go down But I would get more bang for my buck when the price of SPY is lower If you guys want me to show you how to do the wheel strategy Make sure that you go down and hit the thumbs up button It really helps out the youtube algorithm a lot. You pay just live that mode And then I'll be able to do the wheel strategy and show you guys How to do the wheel strategy on robber hood But if you don't want to miss out on any future videos, make sure you scroll down Hit that subscribe button But other than that, I'm Zeke bringing you the dream green show and I'm out. Peace Oh, I get that peace slow and I'm out. Peace


All right, can you all see my screen? Because I can't see. OK, good. Awesome. Good morning. So I too enjoyed your theme and took advantage of that in my slides, so we'll have a little fun with that. We're a little short on time, I think. So this is going to be a little interesting, but I'm going to make sure that we cover as much as we can before we move you all on to the 10 o'clock sessions. But also, remember how to use this little thing that I brought along with me. Don't you love, like, speaker gifts? This was a speaker gift, and I don't know how to use it. There we go. So make sure it works before I move forward. So all right, so I'm really excited to be here in Asheville. This is my first time, and I have to say, you'll have an amazing town. I've been here since Wednesday, and I really enjoyed it. I've eaten some amazing food. I ate at 12 Bones, I think it was called, which was, oh my gosh, that was really good. That potato salad was, like, life force. Really good potato salad. And there's a coffee shop on every corner here, which is, like, really great for me, because I'm a freelance remote worker. And so there was always, like, 40 options to go sit and work for a couple of hours. I had a really good meal downtown at Kurate, which was, oh my god, that was good, right? And I saw the drum circle last night. So I've had a really great Asheville experience. So who am I? My name is Rachel Cherry. And for those who I have not had the pleasure, I am a freelance software engineer. I do a lot of WordPress work. I get to build websites for companies like Disney, which is a lot of fun. I also do a lot of higher education work. I am also the director and founder of WP Campus, which is this shirt that I'm wearing. We're an organization for people that use WordPress in higher education. And so we do a lot of great work with that. Yesterday, we celebrated our three-year anniversary. And last month, we had our fifth conference. And so if you're anybody working higher ed, if you're not familiar with us, we'd love to have you get involved. Our website is wpcampus.org. I also have tons of swag. So find me later. I love our swag. And I would love to give you said swag. You don't have to be in higher ed to come take the swag. We have a great WAPU that have stickers and pens and buttons galore. So come find me later. So I love video games. This is me being a Super Mario World expert as a young child teaching my uncle how to play and how to beat Bowser. So I wanted to throw that out there. And so, for the internet. So I will say that I'm not old enough to be, I guess the internet, I'm trying to say the internet was invented before I was born.  But I am, I guess, getting my description backwards. But I do know what it was like before the World Wide Web was invented. The World Wide Web was invented about five years after I was born. So I grew up, I did grow up a little bit. So to put it in perspective of how young the web is, I don't mind saying this in front of you all, I'm 33 years old and I'm older than the World Wide Web. So I do remember what it was like, back when HTML first got started. And who remembers what websites were like before CSS was invented? Like, I remember what that was like. And people got really creative with HTML tables and to lay things out. I always love, if you don't know what this map is that I'm showing, it's basically the internet. If you don't know how the internet works, it's lots of cables that run under the ocean floor. And I highly recommend finding videos that talk about it because it's really cool to check out. But I remember what it was like to get AOL CDs in the mail. I had a, we got our first computer when I was like nine years old. We had one computer, the whole house shared. We had dial up internet.  I remember what that was like. So I grew up with the web, which is really great to say. This was also the first, this was the first website ever released, by the way. You can go to that link I have in the bottom right if you wanna pull it up on your phone or something. I imagine this responsive because websites were just so basic at that point that it has to be responsive. But I grew up with the web and I like to consider myself and all of us in this room as humans of the web. The web itself and the, it was invented by this one guy, this medium of the web. But over the past, almost three decades, the web has become what people like you and I have put into it. And a lot of us in this room, myself included, we have this privilege that we have the skills needed to be architects of the web. And so we have this advantage that we can truly make the web what it will be, what it has been and what it is now and what it will be in the future. And as cheesy as what I'm about to put on the screen is, that it's very true. We have a power when it comes to the web and WordPress, using systems like WordPress and the whole democratizing the web philosophy is that it's giving us this power to change the web and to change what is on the web, what we consume on the web, what we share, what knowledge is available. If you've been following along this past week, there's been a lot of kind of WordPress drama on the web about free speech. And this is, there are pros and cons to open web, things like that. And so it is what we make of it, it is how we use it, what we put into it. And so today I wanted to talk a little bit about how to level up on the web as humans, as web professionals. I care about everyone here in this room and I care about, we're thinking about our careers and our futures in the web as professionals, A, but we're also just talking about our personal lives. The web has had this influence on our lives and our society more than any other communication method. And so it does, it's extremely important. And so I do think there's a lot of value in how we grow with the web and what we learn from it, what we put into it, and then how we grow in our careers so that we can put what we learn back into the fold. And so I did have a lot of fun with these slides to kind of go on the game. The game theme, which is really great. So I have just like a few really high level, I'm gonna make sure you watch the time. I have a few high level bullet points that I hope as I talk that you'll put these into your day to day and kind of wrap your brain around these and how you can use these and kind of put them back into your life. So number one, never stop learning. And basically what I mean by this is professional development is super important. And it's kind of weird how a lot of people don't really put that much emphasis on it. A lot of my employers never really have, but there's so much value in it. And it kind of depends on what you're doing. I'm a developer, so for my purposes, like maybe an hour is not really enough time for me to maybe really dive into a code base or something, but I could spend an hour in the morning reading articles and studying theoretical type things.  What you're doing here is professional development.  So you're already placing a focus on it already, but I hope that you take that forward and always how important it is to keep learning, trying new skills. This will also only better your career options and open up this world to you. I wear a lot of hats. I am a developer, but I'm also an event organizer and a planner, I lead a community, and that only has all these things enriched in my life. I've made a lot of friends. I've organized camps, word camps on top of WB campus. I've organized several higher education conferences, but it's also given me this new skill that's made me more desirable to an employer because I have all these great project management skills on top of my developer skills. I also have a bachelor's degree in graphic design. So because I play around, I'm always learning, I taught myself how to code when I was in college or going to college, there wasn't programs. And so I had to teach myself. And so if you want to teach yourself a new skill, that would be really, that's really great. Like for me, I would find projects and experiment, play around, and that's how 15 years later or however long it's been. Here I am taking it day by day. Question everything. I encourage everyone on the internet to just question everything that happens. That's kind of like a pro in a con. There are pros and cons of the open web, one being that anyone can put anything on the internet. And we've learned that a lot. And when there are new tools and frameworks that come out, don't just look at new and shiny and think that it's the answer to your problems. Question what it's doing and how it's built. And what people are doing on the back end, things like this. I have a friend that she likes to say, I'm gonna get it wrong. Tools before strategy equals tragedy or something like this. And the point she's trying to make is that a lot of people will go with new and shiny tools and instead of going with what's best for your project, things like this. And so what she's trying to say is that, don't go with new and shiny just because it's new and shiny, but go with things that fit into your strategy. You should have goals before you pick tools. So just being a human on the internet, question everything that's going on. And it's not necessarily, you have to be this super negative thing about what you're questioning, but it's just more also to learn. If you are questioning things, you are learning and you're growing. You're growing as a web professional because you're taking in more about what's going on. If you're a developer, always look at the code. The first, anytime someone comes to me and asks how I can learn WordPress better, I'm like, just look at the core code. Just break it, look at the code, play around with it. Don't hack core code, but break it and learn with it and that's really the best way, get down to it, figure out how it's loading, things like this. They're really important in your growth. All right, we're gonna have to hurry up along here. All right, have a side project. This is, I have another great talk that I love, a talk that I love to give called, Imprase of the side project because I do think they're really valuable, especially like I mentioned earlier, if you're wanting to learn a new skill. So has anyone in here ever heard of the Hiroy Club? I know a few of you have. So I have a fun side project called Hiroy. You can go to the website right now. Holly recommended Hiroy.club, tweet at Roy. That's all you really do on the website is tweet at my friend Roy. And this is a side project that I started and on the surface it's really silly and there's really not much to it. But on the back end, I've built all these things because it's my playground. I've built an Alexa skill and a Google custom action, they call them. And I've built APIs. There's an API you can ping that will play audio files of people saying Hiroy. Like, this is, like, you know. And you know why I built it? Because I wanted to play around my Raspberry Pi and I wanted to, so I wrote this script that I hooked into Twitter's streaming API and anytime someone used the Hiroy hashtag, it would hit my API and it would play the audio file and I hooked that up to speakers. And so I never did this, but my plan was to hide the speakers under Roy's desk. And it was a pretty awesome idea.  And so I also built a jQuery plugin. You know, like these, it's just an excuse really for me to test things out and have a little fun at the same time. But maybe you find a project that like actually serves a purpose other than me innocently trolling Roy on the internet. And so find the need, find a project and then that can be your excuse to learn a new skill to try something out you've never done before and then go with it. So contribute to open source, you know, pretty much that's what we're all doing in here right now, right? And open source is really important on the internet because it does drive this collaboration and the sharing of knowledge that's really valuable. And so if you can take the skills and the things that you've learned and you can give it back to a project, it can become, it's just, it can be really enriching for you and it can be really enriching for someone else. Now how many people probably wouldn't be where they are right now if someone else hadn't shared what they'd done online? Like, right? Like I wouldn't be a developer if there weren't websites online where some previous person had come along and shared what they learned because, you know, there wasn't tons of curriculum. And even now, like it's getting better and better but, you know, how many of us like Google 40, you know, 500 times a day to find the answer to something.  And so that's not necessarily always open source but you get my point. Who all contributes to something open source besides WordPress? Yeah, a few people. There's tons of projects online so you doesn't have to be just WordPress. So I also encourage everyone to find your people, when I'm trying to give advice on how to like get involved, a lot of people will come to me and ask how to like become a developer, things like this. And one thing I do encourage is to find your people and we're doing that right now, aren't we? We've found our WordPress people. And there's a lot of value in that if you're trying to get familiar with a piece of software because you've found people that you can network with and you can ask. And when you're having a really bad day or you're stuck on a problem you know where your people are that you can go to and you can talk to them and you can talk it out. So a lot of us can struggle with this whole idea of being on the internet and being home alone. And I work for myself, I'm remote. So if I don't have my people I'm sitting by myself in my bedroom on my computer all the time. And so I need my people even if it's just to get out of the house and to not go crazy. So find your people whether they're in person at a meetup. So if you find a local meetup and there's obviously like there's a WordPress meetup here there's other meetups. You don't have to be WordPress and maybe you're like a huge React fan or something like some other kind of framework or I'm a big accessibility advocate so I like to find accessibility meetups things like this.  And so you can find these other meetups that will also allow you to find a network of people to talk with and to share your ideas. All right, so let your voice be heard. And what I mean by this is kind of going back to what I mentioned earlier about sharing what you learn. It's really important to share everything. Maybe you've gotten up and speaking at a word camp. Maybe you're in a situation like I am right now sharing these experiences. But also if you really believe in something, talking about it and sharing, we have, there are a lot of things like imposter syndrome are really real and stuff like that, that it can be a struggle, but I really encourage you to do what you can to share. Maybe blog about something. Maybe if you've learned a new technology or a skill, you write the blog about it. Share it with somebody else. That person is gonna come along behind you and now they need to learn too. And you may not think that you have something to teach but I promise you you do. You have insight and experience that is very unique to you. And you have no idea what that will mean to someone else. Even if it's just one person who will, it turned on a light bulb, I promise you it's worth it. A lot, you know, I talked to a lot of people that do struggle with imposter syndrome and that's really what they come down to.  Like I said, what light bulb you might turn on. And so these things are really important to share your experiences, what you're learning and share it with everyone else, especially lovely people in this room. So as we all are hardworking people on the internet, it is really important to balance your time. And so what I wanna talk about this for, you know, phrases like work-life balance that people use. I'm not a super fan of this word but I am a fan of the ideology, I guess, behind it in the sense that we need to focus on our priorities. And we don't need to spend all of our time like doing one thing. And because I struggle with this too, I struggle with it a lot. I like what I do for a living, I like coding, I like organizing my events, I do all these things. And so, but you know, sleep is important. And apparently we need sleep. And so I struggle sometimes with getting enough sleep. And so for me, my work on balancing my time is having these priorities that are not work and making sure that I am spending the time that I need for those things. So for me, my priorities are like my health. So I just kind of include sleep in that. And exercise, I like to move and be around. So for me, it's like making sure that I move my body a few times a day. I do actually, I'm not kidding, I have a reminder on my phone that reminds me like once an hour to get up and move. Because otherwise I will get so in the zone of my work that I will forget. And then like six hours later, I stand up and you know, you have that feeling or you've been sitting there for a long, that happens way too much to me. So I need to be reminded to get up and move.  You know, these two are also very important. So, you know, maybe on a Friday night, like I really want to sit there and code for a few more hours, but I need to go hang out with my friends because that's very important. So this is kind of like what we mean by work-life balance and managing our time. It's really easy to get sucked into the web and our jobs on the web, especially if you like your job like I do. And so kind of keep these priorities in mind. Consider the consequences of what we do on the web. To go back to earlier, we do have this power as architects on the web that you know, what we do matters and what we put on the web matters. The products that we create and the effect they have on people's lives, they matter. And so it's really important to ask yourself these questions when you're, you know, building something that you're going to put out there, when you're sharing something that you're going to put out there.   And, you know, why is it important and what effect could this have on the world? This is, you know, check your stereotypes. This is, if you follow me on Twitter, you know, I talk a lot about stuff like we have, we have a lot of, you know, diversity issues in our industry.  And I had someone come up to me and start explaining to me how websites work.  And, you know, did you even plan to ask me what I do for a living?  It happens at our camps. And so I just encourage you, we all have them. We all have bias, whether it's conscious or unconscious. And so I just encourage you, if you're sitting there and you're about to, you know, interact with someone in our industry, just to kind of think for yourself for a second and check what you're thinking. You know, I do it too. I have to check myself because I have stereotype and bias. And so it's just really important as we move forward, if we want, you know, if we want the dinner diversities and all the other diversity issues in our industry to change, then we have to start with ourselves and we have to, you know, we have to do something about it.  And, you know, that's how we grow. And sometimes it's uncomfortable and sometimes it's really hard. Sometimes you fail, sometimes you succeed. And there's nothing wrong with failure as long as you learn from your mistakes. And, you know, it's okay. And so I just encourage you to, you know, think outside the box and whatever other cliches we can throw in in the next couple of minutes. But it's super important as we, you know, to grow as even just humans in general, but also as web professionals, you know, I challenge myself to, you know, learn new pieces of technology.  You know, things like this, they were a challenge to me, but they made me better and they made me smarter. And so I encourage you to do this in your own day to day. I wanted to list out the game credits that I used, you know, give them some heads up. I got kind of bummed because I ordered a t-shirt that was my favorite game and I was gonna wear while I gave my talk. And I was gonna give like Kudos to anyone who actually knew what the game was from my shirt, but has anyone ever played the game missed? Yes! Come talk to me later, I'm a big fan. A huge missed nerd. When Sian, like, did their kickstart a couple years ago for the abduction game, like I was the person that gave enough money to like sit in their hangouts and like talk to them. Like that was me. And so, but thank you very much. I hope you have a wonderful camp. I'll be around today and tomorrow and I'll be at the party the night. I hope you'll come and say hi and we can talk about missed and we can talk about WordPress stuff all you want. So thank you.


कोई ultimately, you would get very little budget. If you liked this video, then don't forget to like, share, and subscribe to our channel for more


Can you teach without a preparation? A lesson plan is a detailed description of outlining instructions, various steps, and activities of what is to be thought in a lesson. Lesson plans can take on various styles based on the topic, type of activity or the learning objective of the teacher. Each teacher will have their own unique way of structuring their lesson plans. Details How detailed a lesson plan is, can be dependent on a number of factors. These factors may be, the length of the lesson, the objective of the lesson or whether the teacher is new or experienced. When writing a lesson plan, one should ensure that it is clear and easy to read. A good lesson plan should also incorporate a good mix of skills throughout the lesson. There are various schools of thought on the issue of lesson planning. While some theorists suggest that having a lesson plan is not a good idea as this can make lessons rigid and stops flexibility. For inexperienced teachers, this may not be so. Importance So, why is it important to have a lesson plan? There are a number of answers to this question. One of the most important reasons for having a lesson plan is that it helps with the entire planning process and acts as a guide for teachers in the classroom. A lesson plan needs not to be elaborate and detailed. There is no special way that a lesson plan must be written. Having simple jottings and points can serve as a guide to help in the flow of the lesson. Lesson record It's also important for teachers to have a written or typed lesson plan, as this acts as a record of what the class has covered. Teachers can use these lesson plans as a source for future references. The beauty about this is that if a teacher has a new class with little or no time to prepare something on a topic that the teacher had already covered in the previous year. The teacher can simply draw for the previous lesson plan, tune it up as he or she likes and utilizes it again. This can be very useful and effective, especially if that lesson plan worked well in the previous lesson. Having a lesson plan helps you to remember what you intend to do and helps you to think about where you are going. Teachers set the tone for their classroom, and organized teacher will show his or her students that they know what they are doing. Having a lesson plan helps teachers to achieve this level of structure and organization. If a teacher is in the classroom and is constantly stumbling or unsure of what should come next, this can create confusion for both the students and the teacher. Students may lose confidence in their teachers as a result of this and this can impact on classroom management. Do you want to prepare perfect lessons? Both experienced teachers and new teachers can benefit from having a lesson plan to guide them throughout their lessons, ensuring a smooth flowing lesson. Needless to say, a lesson plan should not be rigid and inflexible. Teachers should be able to adjust their lesson plans to foster student learning and flexibility. With that being said, it is better to have a guide rather than having nothing at all. Speak with an ITTT advisor today to put together your personal plan for teaching English abroad. Send us an email or call us toll free at 1-800-490-0531 to speak with an ITTT advisor today.