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Yes, It's not like any room. OK. Good morning to everyone. I repeat because I were unmuted.

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And then we have this recession on gender inequality measurement. We have for precipitation. And we will have 15Â min for each presentation and finally some minutes after each presentation for discussion and comments.

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Then we start with this message on behalf of a rewang gated. I pay respect to the spirit of country here in Narm where we are meeting.

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I acknowledge the person Listen, Kalination, Urundiri Wurun as the sovereign people here, the cultural knowledge holders who maintain and promote the non Aboriginal low customs and, CASTINANIship responsibilities for country and the people that country continues to sustain generation after generation.

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Into the future for all of us. I recognize the important contribution that First Nation thinkers have made to Theology, including challenges.

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Of our dominant ways of thinking. We thank all First National delegates in the room today to their contribution to knowledge in the discipline.

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Excited respect of the school.

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Okay First percentage. Yeah.

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They are not. Welcome everyone. It's my pleasure to be here. Thank you for the organizers of the session to bring this discussion to be here.

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Thank you for the organizers of the session to bring this discussion on gender equality measure to bring this discussion on gender equality measurement.

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I will be presenting together the work that has been done. I would probably say by multiple people, not to us just listed on this PowerPoint presentation.

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But it was prepared by 2 big consortium European projects. One was Act and the other inspired, but the authors of this presentation who kind of decided to make this reflections are people from my university.

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So, Mara Varapo Nazaka and myself and as well the person who is from the Barcelona York Miller.

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I was talking about measuring gender equality for gender equality plans and this reflection will try to connect the European level policy on gender equality, especially in the connection of this gender equality plan,ability criteria in Horizon Europe with the measurement tool that was designed in the in the project.

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So let's say the kind of the background of what is happening now on the level of the European policy.

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We know that in the last maybe 1015 years there was some development on the level of the European policy.

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We know that in the of what is happening now on the level of the European policy. We know that in the last maybe 1015 years there was some development in the last maybe 1015 years there was some development in regards to policies at the organization, 15 years, there was some development in regards to policies at the organization, in regards to the gender equality.

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But what has been, let's say, And so this all this work, let's say we can say that all the work that we are doing in the project aims at improving gender equality through interventions and the organization level on higher education research research performing organizations and research funding organisations.

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So within this project that we are working, we are trying to develop tools that will support institutions and organizations in developing gender equality.

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And the tool that I will be talking about was in fact designed for the organizations. But before I speak about the was in fact designed for the organizations.

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But before I speak about the tool and about how it was constructed, how it looked like. I want to give this, let's say, overview what is happening also on the European level.

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So as some people know, some might not know because this is a global conference. The European Commission has the huge funding programme which is called Horizon Europe, program which is called Horizon Europe.

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It exists already for multiple years but it exists already for multiple years but the last addition is. It exists already for multiple years but the last edition is between 21 and 27.

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The last addition is between 21 and 27. It is a 95 billion euros that is being targeted towards research and science and innovation.

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So this is really a huge source of funding. And with the European Union priorities on gender equality. The European Commission decided to introduce eligibility criterion for this funding to kind of steer the change in the institution towards gender equality.

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So every institution in the European Union in the associate country needs to have the gender equality plan in their organizations in order to be eligible for funding.

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So it is a really, a let's say a big step for the organizations, a big kind of a forcing, let's say in a sense, enforcement tool for rethinking at least their strategies in the organization.

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And it, this, this eligibility criteria concerns the public bodies, the higher education institutions, research organizations in the in the in the member states and associate countries and it is let's say it's it is obliqueattery from 2,022.

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So there are some requirements towards this enigmatic criteria and there are some mandatory requirements and also recommended areas that are also should be taken into account in this in the strategy at the institutional level.

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So this this gender equality plan or kind of a similar strategy that needs to be introduced in the organisation has to be a public document so has to be endorsed available.

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It has to be communicated through the institution. Has to be like easily find on their somehow main website of an organization.

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So it has to be really a public endorsed by leadership document. It needs to have dedicated resources and potentially also staff resources.

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And what is maybe critical for us in this session, it also the demands from institution that they collect data and monitor some in regards to their institution.

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Yeah, it also has a demand on training and the capacity building. So, so, so, so the fact that there isn't a gender equality plan has to be communicated through institution has to be some training.

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In regard to gender bias or for example teaching how we can include this dimension in the teaching. There are also some 5 recommended Jeb content areas.

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So let's say key thematic areas where the European Commission sees like the more need for action. And they were quite already established, let's say, some of them are quite already established, let's say, in thinking about gender equality in thinking about gender equality in science.

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So for example, work life balance and some interversion in this regard. But what is also important is this element of the transformation of the organizational culture.

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So also not only impacting some, let's say, directly some indicators of recruitment, but also changing attitude somehow of the of the members of the of the organization's researchers but also other people who are actors in the organization like students, administrative staff.

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There is also other content areas like gender balance in leadership and decision making, gender equality and recruitment and career progression, integration of gender dimension into research and teaching content and measure against gender-based violence, including sexual harassment.

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So let's say in the contrast to what is happening now in the EU a little bit before, to what is happening now in the EU.

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A little bit before, let's say, this critical development in the Project Act, in the EU, a little bit before, let's say, this critical development in the Project Act, promoting committees of practice development in the Project Act, promoting committees of practice to advance knowledge, collaborative learning and institutional gender

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change on gender equality to advance knowledge, collaborative learning and institutional gender change on gender equality. We have as a practice to advance knowledge, collaborative learning and institutional gender change on gender equality.

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We have as a projected learning and institutional gender change on gender equality. We have as a project produced a tool to measure gender equality. We have as a project produced a tool to measure gender equality in organization.

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It is called a tool to measure gender equality in organization. It is called the JAM, a tool to measure gender equality in organization.

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It is called the JAM, so it is a gender equality audit and monitoring tool. It is a survey based upon antenna survey for science engineering and technology, but it was decided to be adapted to a more kind of wider European context.

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As we know, the UK context is very specific when it comes to science and higher education. It was adapted, it was it was titled to let's say more European wider audience.

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And it is a survey that was aiming in fact to support institutions in development of some strategies. So you could use it for diagnosing the situation but also later for the monitoring of your general equality plan.

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And it is, this project was quite successful, I have to say. So at the end we had it is available in 14 languages.

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Beyond English and has been used by I think something already like 40 or 36 organisations maybe maybe even more now.

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And it it is, let's say, easy to use because it is just, it has a, maybe show it first.

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It has a full infrastructure. So you can just get an access to the, to an account, customize this service so you can maybe change the things that do not fit your cultural context in your institution or at the structure of your institution.

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And then it almost like automatically generate reports. And I think that's also important when we think about measurement of gender equality because there is a lot of practitioners, not researchers who are in the institutions are actually engaged in this.

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So they often don't have this knowledge and tools how to measure it. So, so this, this tool provides information about individual experiences of employees.

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About work conditions but also about regarding sexual harassment. But also it tackles this aspects of organizational cultures.

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So there's quite a lot of scales and questions that measure perceptions, beliefs, awareness by us.

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The working conditions are, let's say, in the center because it also relates to this key element of careers and there is also such demographic variables related to social discrimination.

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And so this is the, let's say, the structure of this jam tool. You can also, if you just Google JM, you immediately can enter into the all the language versions and you can check the actual questions.

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So this is quite a white tool and the indicators are used in this way that there is like a core questioner that is proposed for all the institutions and then there is also modules that you can add on if you want more questions.

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So it is really like quite adaptable. And I can say from my experience I have used it in my university.

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It was, it was successful and we have also added some questions, some, some questions, also form relations of questions to kind of feel better to the institution situation.

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So now I would like to connect these 2 discussions about what is happening in the level of the European Union policy in regard to gender equality and the usefulness of this tool that was created beforeheads and but also looking critically how it could be useful and what are also from our experiences of using this tool, what are our reflections in regards to measurement

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of gender equality. So to what extent this tool is useful for the design of gender equality class. So to what extent this tool is useful for the design of gender equality plus and does it really fit this vision of commission?

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To what extent it is useful for designing institutional solutions in regards to inclusive gender equality and does Jam provide effective indicators to capture implicitly biased behaviours and act.

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So do we have enough indicators not only to measure more For example, work conditions, contracts are available, but also the change in the attitudes in the institution.

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Because then this will be also important, for example, to measure change. So I evaluate if actually this this this policy intervention so introduction of this gender equality plan LGBT actually had impact on institutions.

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Because I think that's the most interesting to see if this policy tool is actually affected, effective or not.

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But to be able to capture this, we really need to capture also what's happening in the institutions.

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This is I think we need those tools. So, so we did some analysis. I don't need to go in details here.

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But I hear Mark, I put here on the left this mandatory areas and the I put here on the left there's mandatory areas and recommended areas and we try to reflect how it is matching areas and we try to to reflect how how it is matching.

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And as you can see it is quite well marked the green area like the well matched area and the red is the area that is not covered with some areas that maybe need some development.

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But in general we can say that this tool directly answered the need of data collection and monitoring. And, but maybe it doesn't cover so much data on this mandatory elements of of ofipping criteria so the knowledge about this policy development and the engagement also of the members of the community in this jab.

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But it is well covering this let's say key areas of European Union policy. So the careers of the researchers, it is very well covered in regards to recruitment, promotion, career progress, mentoring and the work of distribution.

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And it also well covered work-life balance. It's a very well-covered area, but also organizational culture has is proposing quite some tools like for example bleak masculinity content scale or ISSP work and conflict too so it's also using what is already also introduced.

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So it's also using what is already also introduced in the field to also have some benchmarking.

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But the policies of the European Union are developing further. And the next steps are an introduction of this intersectional perspective in gender equality plan and this is already quite an active discussion also in in this field so we know that just measuring gender equality is not sufficient.

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So this we we try to also introduce already when this tool was designed, this intersectional perspective in the tool.

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So, and so what we can say about JM is that it actually covers kind of broad, very broad socio-demographic section.

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So there is really an opportunity to see how different. So there is really an opportunity to see how different categories intersecting in relation to see how different categories intersecting in relation to gender equality.

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So it can allow different categories intersecting in relation to gender equality. So it can allow us to understand in relation to gender equality.

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So it can allow us to understand these complexities and have this maybe we can allow us to understand these complexities and have this to understand these complexities and have this complexities and have this very more complex vision, a vision of inequality.

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But it also raises challenges because we know from the field that people have this very complex vision, a vision of inequality.

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But it also raises challenges because we know from the field that people in the organizations have a vision of inequality.

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But it also raises challenges because we know from the field that people in the organizations have little knowledge about intersectionality.

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And also as researchers we don't have so many tools and so much experience on how to analyze this intersection and interactions of the different characteristics of such as gender, age, but also disabilities or origins, migration background, all these are included into the JAM.

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And actually it raised it maybe for the measurement also it raises maybe for the measurement also some issues maybe for the measurement also some issues because as it covered such a broad range also some issues because as it covered such a broad range of some issues because as it covered such a broad range of these characteristics When we were doing this study, we got some

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feedback that people thought it was like a little bit maybe too invasive to ask about every dimension on their life and they had a fear a bit of losing also anonymity because there is having all these characteristics being gathered about one person that in a especially smaller institution this person can be identified.

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So that's a little bit of challenge also for the measurement of intersectionality. And the second and the last, and the third and the last issue I will talk now is the question of discrimination and gender-based violence in JAM because I think that's really a strongly appearing issue also in the policy level and being now introduced

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to institutions. And what is I think valuable in this in this tool is the fact that it measure microaggressions.

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So not only like discrimination and really the experience of bullying and harassment. But also this covered discrimination. So some kind of an environment for of gender equality.

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And we thought that this part was particularly important. What was also, how much minute I have? Because to know how much I should summarize it.

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Thank you. So I think it is, it provides quite a good knowledge on this area. And it always allows also the refusal of the answer so it's quite in the sense it's it's not very invading and what we found very important when we were also analyzing this part it was the qualitative in fact open answer open

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open answer questions so somehow the the heart indicators of occurrence or not occurrence when they were met with qualitative open analysis of qualitative open open questions.

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They really provided much more insight. So we thought that was actually a strength and maybe that should be should be reflected upon because people have different definitions of bullying of harassment and and they describe diverse things which are, for example, they don't know maybe necessarily know how to categorize, yes?

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But in the open question, when the ability to speak up about different experiences, it becomes much more, let's say, inclusive.

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And when it comes to challenges, the challenges were to build trust in the Serbian anonymity when it's conducted and the organizational level.

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So confidentiality of data and impossibility of a. Yeah, that was that was raised by the by the people who were participating also in some comments to us as organizers.

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So from our organizational perspective we can say. And I think this has to be really strongly underlined in the conduct of this kind of studies.

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And there is also a need of adaptation of questions to student respondents because this is really a work-oriented survey and very much researchers oriented.

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So I think when we study organisations and I think that might be a difficult challenge for our indicators, it's different situations of work and also for example of administrative stuff that might have a different experience for example of hierarchies and this has to be captured.

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And, and we actually it doesn't address the perspective of bystanders. And I think this is important because at least if you compare to this the sense that there is this policies also the culture of 0 tolerance towards gender-based violence.

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So if there is a change in this culture of 0 tolerance towards gender based violence. A culture of 0 tolerance towards gender based violence.

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And that here it might be also important that there were some more advancement in this area in Europe, in Europe, and there was a UNICEF project that was directly involved in gender based violence and created also their own tools.

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So it's possibility to do one market. So just to conclude. We think it's quite well adapted for the diagnosis of Sato's core interface for initiated and for monitoring institutional change in regards to this requirement.

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So, so if the institution would like to use it as element of their of their strategy and element of their job.

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We think it is quite a way tailor. But in order to, let's say, face this geologically criteria, it should be matched with this geologically criteria, it should be matched with institutional level data.

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So it does not directly deal with institutional level data. So it does not directly deal with structural questions.

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For example, it does not directly deal with structural questions. For example, a gender pay gap. There is a question about how much do you earn? For example, a gender pay gap.

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There is a question about how much do you earn, but of course it is a question about how much do you earn but of course it is a question about how much do you earn but of course this data is difficult to use later on because it's subjective and we don't know people actually used like their basic income or also edited all

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their like top things that they get on top of their main salary. So here, for example, institutional level data would be more effective to measure the change.

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But we know from experience that it is difficult to get into this data. So this data from the therapy maybe is at least something that's available.

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And we find it also as this I think it's also a discussion how we can include gender equality into some wider topics.

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So how actually in introduction of gender equality plan doesn't only bring change in relation to gender equality cut but can bring some more reflections about war conditions.

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So there's so many questions about precarity, flexibility or overwork and it for example in our data it appeared very strongly this this over this overworking there's really like a burden of academic work and I think this is why this this survey was quite well perceived also as employees to let's say have they saying also on the work conditions.

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So this is why we think there was quite a high participation also in that. And we think it is important to include also this invisible work within academia as an important part of this gender equality, not just like care understood more like motherhood and parenthood.

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And maybe the last point to administrative and technical stuff and students are not very well covered. So if you have to be, I think we have to think if we need separate maybe tools to measure the other groups.

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That is finished. Thank you much. Thank you very much. And here's a website of the of the tool and we are happy to use it and also bring comments and anything.

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And now they got us on it. Any comment or questions? Just.

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It's okay or not. You can come here because if you come here, here is better because people can see you for, thank you.

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Thank you very much. Really, really interesting your project. Very, very interesting your project, very, very near from a thing that we are developing.

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Trying to develop and it's really really useful and interesting and according to the European plans for gender equality.

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I would like to ask you about the They underlying things like lime survey and you put this all the data that generate this project are centralized or each each enterprise has his own server and accumulate the data.

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This is really interesting because it connected with the thing that you you speak about the confidentiality. This is one question and the second question is how do you manage the confidentiality?

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Thank you. Yes, this is a very important question. It is, yes, that each institution is has access to their own survey and only the manager of the platform is an additional person that that has access so no that information is not accumulated.

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After after some time the information is deleted and after some time the information is deleted and the institution is the owner of the of the information is deleted and the institution is the owner of the of the data and the owner of the data.

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And this is very much discussed also because we have this experience that we cooperate with. This is very much discussed also because we have this experience that we cooperate with different institutions in our region of Central and Eastern Europe and we cooperate with different institutions in our region of Central and Eastern Europe and we wanted to do some comparison with different institutions in our region of Central and Eastern Europe and we wanted to do some comparison.

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So the fact that every institution has the data of Central and Eastern Europe and we wanted to do some comparison.

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So the fact that every institution has the data and it's not cumulated it doesn't allow this inter institutional comparisons and we think it will be a very valuable to compare it also and to have some, I mean it would be fantastic to have a wider data set of what's happening in the institutions but right now no it is because of these reasons

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it's every institution has their own take care of the data and I think it has to be also, it's not enough I think just to say there is confidentiality.

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I think the survey in the is very important I think who in the institution is the manager of this data.

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And so is it for example an equality office or is it like the human resources office or some rector office?

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And I think that's really changes. I think it's better if this is like the gender equality office or I don't know maybe some plenty potent sharing for workers rights or maybe the trade union that depends on the really on the cultural context where it can be built trust to people that this data is used confidentially.

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So I think that's really important not just to asure it. So I think that's really important not just to assure it let's say by all the standards that we have like GDPR and so on, but also, but also through kind of building trust that this is going to be used for.

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For purposes for purposes of and it be just used as aggregated data. And the second thing it allows also always to refuse an answer.

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So we have seen this strategy in some maybe smaller organization that there was more refuse to answer and people deliberately like kind of just decide which data they want to pass and and which and which not which I think it's also good because people can then have as they're say for example in evaluation of what this institution is doing but not want to

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maybe pass their personal situation to the data set somehow. So it gives maybe more. But I think it has to be really well explained.

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And I still need to really also be very welcome with they clearly communicated to the organization what's happening with the data and was one.

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Yes.

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Okay, thank you very much for the. I'm part of a gender equality pan group in my university and so I have some experience and some slides.

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It is a European directive. And of course, universities in particular are very interested in preparing general equality plans otherwise they could not access to European funding.

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So rectors and organs are pushing the preparation of gender equality planes. But we found some sort of resistance from the university personnel, some even some professors at the university do not find the gender equality plan so useful at the end.

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And it is very fatigating to work with gender equality plans at least in my institution. So of course not all the professors are not involved or not willing to prepare this document but there is some resistance.

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So the first question is this one. If you found some resistance similar resistance in the other universities and situations.

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And the other question is a little bit more technical is what is if there is a sampling schema.

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Because if understood correctly they are self selected that is you send an mail a link and then who response is willing to respond and so there is a selection bias in the data that you collect.

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So this is the second question. Thank you. Maybe I start, maybe I start with the second question.

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Yes, I mean the institution decides by themselves how they want to sample the data. How to choose the sample for the for this survey.

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Most of the times it was self-selected, yes. But we had also institutions that used like the sampling and the emails.

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Let's say they put the emails of the people and the system was sending the reminders and so on.

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So there was some kind of a sampling strategy. I was sending the reminders and so on so there was some kind of a sampling strategy but most I think of the times it was some kind of a sampling strategy but most I think of the times it was self-selected.

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But most I think of the times it was self-selected. And yes, of course there is some bias.

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And yes, of course there is some bias but also it gives us some bias but also it gives us freedom of participating or not let's say there is some bias but also it gives us some bias but also it gives us freedom of, but also it gives us freedom of participating or not, let's say.

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The freedom of participating or not, let's say. This is to be discussed and I think this is really to be reflected what bias is in the sample, for example, yes.

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So if there's more women men from which faculties and administrative staff are researchers so you can know in your institution you can see who actually participated or not for But in ours there was quite a wide participation from different faculties and more research a wide participation from different faculties and more researchers and administrative staff more women than men.

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That was more or less how I think that's quite common trait. When it comes to resistance, yes, this is this is very debated if actually this top enforcement is actually the good way to change organizational setting and of course there are resistances and you can imagine in different contexts and in the context of Central An Eastern Europe

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is also very specific because we have quite political also a lack of backing up of these policies so also on the political national level there's no support for such activities so and there is resistance also on the leadership level but also from the employees.

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I think students are I think a rather supportive group and in some organizations they were they were chosen as a group that was a support for example like a good group to find support let's say but what is I think also another danger in this in this process of gender equality plans and we are because having been introduced so quite fast.

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It makes the institution to create this Gentric Party plans really fast, you know, and very often they were just not co-created within, say, a community not discussed, not communicated, but just, OK, this is a document and and that's it, you know.

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And so I think that's really about also how it is done about the process and if it is really like backed up by maybe data because we have seen because I was also in the project on gender equality in physics in Genera project and we were talking to physicists and they were also very resistant but when they saw a very resistant but when they saw I've seen the data

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they say okay we can see the data they say they say okay we can see there are some issues that we have to say okay we can see there are some issues that we have to like deal with and that that was some issues that we have to like deal with and that that was I think data is and this good diagnosis is very important because I

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think we should not, and this good diagnosis is very important. Because I think we should not also say that we have to correct everything.

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Because I think we should not also say that we have to correct everything in an institution. I think we should not also say that we have to correct everything in the institution. There are different institutions. They have different institutions. They have different challenges. So why heavy data?

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We can also choose something that's connected. For example, now with situations like working conditions, if this comes strongly.

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Then this is maybe the area you can work and you can persuade people that it will also impact their working conditions, not just gender equality and find might be something more synergies within just more general problems that scientists face today in the institutions.

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Yes. Okay, okay, so we do have the Thank you so much. And I think in the online is also my colleague Mara Varad here.

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I don't know if you want to add something but I cannot hear it. There's a lot.

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Yeah, thank you. This is very interesting. I just wondered, you know, you you said This is a useful tool because it is so flexible.

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Adaptable to every. Institution. And I wonder, you know, how, what is a good criteria for judging such an instrument.

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I mean you could think about maybe response rates or so you could also think is the information really taken up in decisions because this is what is actually intended.

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And now when I think about This being taken up in decisions, if you have a very, very, very, very, very broad Set of indicators.

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Not sure if if this is helpful for decision making because Some I mean very often it is useful to have let's say a standardized set or at least a set of key indicators where say, okay, these are the crucial indicators.

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I would like to hear your thoughts on that.

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Yeah, this is I think the usefulness is a big question and it relates to what you were talking about and also to kind of like.

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Transferability between context of these tools. Yes, so we know that our systems and also cultures in academia differ.

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So they were while translating for example we faced quite lot of challenges in translation and and I remember that some questions in regards for example to recruitment where I think these are like very difficult to transfer between countries because there is different procedures in recruitment, different ways you hire people.

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And so that's definitely the question and about the judgment maybe yeah that made this very kind of a general statement it's useful in a sense Maybe it's.

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It's useful in a sense that it is already prepared, so it is an easily accessible tool, especially if you're working as a practitioner on these issues.

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So in the sense it is useful for you because you don't need to design your own tool and you don't need to decide which indicators to include or not or at least you can have already some based knowledge on which indicators are commonly come only measured.

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So in the says I think it is useful. I have seen people for example from engineering who are doing it and they are not sociologists so they were happy to have this prepared by experts and kind of trust that they have more knowledge than engineers on how to prepare a service.

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So in the sense I would say this is this is useful as this process of JAP introduction is not very often steered by sociologists or people who know how to do service.

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On the other hand, because of these issues of transferability, it might be not useful in a sense.

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It might create actually cultural barriers and you might decide that these questions are not adaptable to your context. But there is at least something you can decide upon or change.

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And the usefulness in relation to policy, I think this is an important question. And we had some cases that it has been actually used for some.

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And actually it was not used like I think it's selected indicators. We had this issue in our institution that it was so much that it was so much that it was so much that it was almost difficult to grasp.

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And we will do now the second edition and we'd be definitely much smaller. Because we think it's like it's difficult to deal with with so much data.

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So I think and also this jam service is developing and now I think it is the third version but the second version was much shorter.

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Than the first one. Because of course we have ambitious, we have very good measurement tools and we want to measure end masculinity and toxic masculinity and the bias.

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And the microaggression and that becomes too much because every scale has to much because every scale has 20 questions because every scale has 20 questions and then you have to calculate it.

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That becomes too much because every scale has 20 questions because every scale has 20 questions and then you have to calculate it.

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The people don't understand how you calculated it, you have to explain it. So sometimes really like more easy, like something like, did you experience harassment and bullying?

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And then you have the percentage. And then that is something that is striking and you can think about it and one case in one case of institution that I remember was the most important indicator.

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You know, that made people think, okay, this number is. Something go high or not high or are we surprised by this and what we get also in this qualitative answers.

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So I think it's a very good point that it can be too much. Thank you so much.

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Okay, we moved the second presentation and we got has been working.

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Okay, thank you. Good, good morning to everyone, also the ones who are connected online. So this is a presentation about a project that is ended and I'm presenting this work but the project was done with a lot of people who was in this room with Sandra with Pedro and with Christian because we we did this a European

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Ajamuni project in 2,02122. And it was about gender equality measurement at regional level.

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The idea of a project is born of course starting from the international literature that is moving towards a measuring of gender equality.

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Of course, gender studies respect to 90, 80, sorry, the twentieth century, but we have some some experiences or some discussions about even earlier.

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However, measurement of gender equality only started approximately 20 years ago, 2030 years ago. So a complete set of indicators of a tentative to compare countries and to understand in which fields we have that equality is higher or lower is very recent.

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And one of the most important tentatives that have been done was for the millennium development goals that we see in the top left.

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In which one of the goals was to, to guarantee to promote gender equality. Then we had a lot of experiences and at the international level in my opinion one of the biggest one of the most important ones is the gender, the global gender gap report by the World Economic Forum.

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Whereas in Europe, and we, of course, we are coming from Europe and Europe as a strategy on gender equality as we have seen in the first presentation.

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We have in office that is the European Institute for Gender Equality that is proposing an indicator that is I think the most complete in terms of a number of indicators domains that are used to measure gender equality.

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So for example, while we have that the global gender gap report is made of 14 indicators divided into 4 domains.

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The gender equality index is as we we will see sorry later here is made of 31 indicators and the 6 domains plus 2 additional domains.

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So in Europe as we have seen before we have a strong policy for gender equality. And, but there is, I think, also a notational issue or a definition issue because we talk about gender mainstreaming gender indicators and that is the goal but most of the indicators that we have are sex.

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Based. Okay. So most in the most cases we talk about the gender indicators but in the area in the reality what we can measure is simply a sex indicator.

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This is relevant and this is also political because we have some European countries in which gender is not so a nice word to be used.

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It is better to use maybe sex. Or not agenda. However, we placed this project inside a big strategy that it is now even been updated recently by the European Union that is connected with a gender mainstreaming that is coming from a Beijing platform about however it is the vision that every decision must be evaluated in all its possible impacts on gender

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or sex on gender. And so, sorry, I have to change my glasses.

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Because I can't follow well with the light.

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Okay, much better, sorry. So the basic principles of gender equality strategy in a Europe is to give equal treatment legislation according to general mainstreaming to integrate gender perspective into all other policies and having specific measures for advancement of women.

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So this also includes some legislative issues of course as you know because for example in in in our constitution we have 2 different forms of equality.

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That is the formal equality and the substantial equality. Formal equality is what we should achieve. It is our goal.

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But the constitution recognises that there are situations in which This is not true. And so it is possible to take actions that are unbalanced the is you can introduce incentives for women rather than men in order to reduce vision balancing.

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So, all this is very important in Europe and it is growing in importance in all the member states with some resistances.

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So it is not just a the actual gender equality strategy is the 2,000 22,025 and the goal is a union where women and men, girls and boys in all their diversity are free to pursue their choosing path in life have equal opportunities to thrive them and they can equally participate in and lead our European society.

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So now we are doing a step forward. We do not only want to measure equality between men and women.

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But we wanted to measure positive opportunity, but this is something different. It is something different because we are we really in an ideal world if everyone is playing soccer and the nobody is playing basketball or is it better if anyone plays soccer and basketball as they prefer.

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So the point is that the equality of opportunity is difficult to measure. So it is really difficult to measure.

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And what we try to measure is if women are equal to men. And this is I think a problem because what is it written that there should be exactly the same values.

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The point is that each person, male-female, a girl, a boy, should be free to make his her own decisions without external convictions, without cultural biases, without influences in this.

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However, in this framework that is very wide we have to to go down to reality and to understand which are the data that we can really use.

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Because as we said, when we collect data at any level, we ask for sex or we are recorded in the administrative data sets according to our sex not on our gender or it is difficult some universities are now taking account of gender and with the double career but it is however something complicated experimental and not well spread and diffused.

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So, considering this, the best European index that we have on gender equality. Is made of 6 core domains that are work money. Maybe I have another picture here.

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No, sorry. Took it away. Work, money, knowledge, time, power and health and men and female are compared according to these values in order to understand if there is a difference or not.

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I cannot go in detail but it is a published and well known indicator I suppose. And this is the situation in in a Europe with a Scandinavian countries and some countries in central Europe that are closer to, thank you, closer to equality, but we are still very far from gender equality.

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So, the idea of this project was not only to look at national values, but to move at regional levels.

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Because having values at national level is very interesting to compare countries but it is merely descriptive. It is difficult to suggest or identify policy action if you have a unique national value.

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Because we need to identify the critical areas on which we need to intervene or we have to understand how the phenomenon is distributed in the territory.

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So the first study that we did was published on regional studies some years ago was to repropose the gender equality index on a regional level trying to see how many indicators that are used by the gender equality index could be used for a regional level indicator and which had to be integrated.

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Because there are some indicators that are easily transferable, can be interpreted easily also at the regional level.

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For example, female participation to the labour market. Other indicators are not. So percentage of women that are part of boards of listed companies that makes sense at the national level but at the regional level it is complicated because the headquarters of one company are in a city button.

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So you need also to change some indicators. Some are easier to change. For example, instead of considering the percentage of women in a national assembly, you can use local or regional assemblies.

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So some indicators can be directly taken from the definition of a gender equality index. Some indicators may be adopted.

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Some have to be substituted if possible. At the end of this process we found that for Italy we could out of 31 indicators we could perfectly substitute 10, find 14 close alternatives.

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And 7 were missing. So, for example, this is the result for for Italy. Maybe if any of you knows a little bit to Italy.

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Southern Italy is culturally different from northern Italy because of the prairie union period and a lot of things.

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So this points out immediately that considering a national value is averaging the scores of indicator and probably this may help into identifying areas where to intervene or communication or culture action because most of the issues related to gender equality are due to cultural issues.

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That is, there is a anti female culture that is persisting and the culture is long to change. The project had these research activities.

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So first of all we consider the 4 countries. So first of all we consider the 4 countries. Italy, Spain, France and Germany, Italy, Spain, France and Germany.

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And we wanted to find all these different countries if we could replicate it, Italy, Spain, France and Germany.

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And we wanted to find all of these different countries if we could replicate what it has been done for Then we also considered the role of gender responsive local fiscal policies.

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Fascal policies are a good tool to enhance or to reduce sorry gender disparities because they are compulsory.

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So I do not have to talk to you. I oblige you to do something that reduces gender disparities.

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Then we did a sociological analysis of regional gender disparities in the study region that is basically what Sandra will present later.

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And then we created a tool to analyze how aggregating data could influence the results. The main deliverable is this website that is online now if you want to go and see it it is working that is a region.

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. IT that is my university but the site website is online. And there is also the working dashboard is online.

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There is also the working dashboard in which you can see the website is online. There is also the working dashboard in which you can see according to the different indicators and domains that we have seen, how is it different?

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The value of the indicator. The site is still quite a visited. This is an information also for my co-workers of a project.

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We are having more than 100 visits per month. That is not bad for a project because we are having more than 100 visits per month that is not bad for a project like a measurement project.

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But the main deliverable is a book that will, be published this, this year. We hope there are some administrative issues that we have to solve but the book is ready.

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But we've been done in 2 parts and it will be published in the social indicator research series.

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The book is measuring gender equality and multidisciplinary analysis of some EU countries and for what we know this is the very first time that it is published a book on measurement or gender equality.

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We have a lot of grey literature that is a lot of reports and I would say technical reports but no books.

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So we have a very first part on gender studies and indicators for measuring gender equality. So the first chapter is about a brief theory of gender equality, equity and equal opportunities.

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Of course there is a vast literature but you cannot do everything in one single book. Then the problem of the complexity of social phenomena and how to construct indicators, then a review of the main indicators of gender inequality and then gender impact evaluation in European projects because we have seen it is important now to include the gender indicators in European

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projects. I will go to conclude. The second part is on the project and it is the story of what we have done and why it is important for us in the sub national level of analysis to measure gender equality.

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The part about the results of the project and then some Applicatory aspects about data driven policy making gender responsive regional high school policies.

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In a European perspective and in the labour market. What are and I'm almost concluded. What are the main findings of this project?

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Well, first of all, we found that the regional level is as predictable as expected a very important way of looking at this kind of data.

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But what was also interesting is that this is true not in all the countries. For example, France, it is less, has less regional differences than Italy or Spain for example.

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Probably because it is an older country, an older nation, and so it is more homogeneous while Spain is very old as well but it has some differences while Italy has a lot of differences.

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Another point is, that, Data, the availability of data at multi-level territorial levels is very complex despite the existence of Eurostatum.

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For example, Germany is not releasing data at regional level for privacy issues, privacy reasons. So for Germany for some indicators we cannot have information lesser than at counter level, not 0.

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Some indicators at level, that's one that is Macrarian. While for our countries we are also to nuts 4.

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Now, nuts 4 is very small so we can start talking about the statistical significance of using data at that level.

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But however, at least the NASA 3 that is regional level, we so we have Italy, France and Spain with indicators for NASA 3 level Germany not one or not 0.

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So this is quite an interesting issue to analyze. Moreover, the European the European service that we used are the well known, for survey, serving on income living conditions.

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Hitos about the time and such economic survey. There are different problems here. We in some cases with a statistical unity is the household sometimes is the worker.

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Or we have, early or semester die data or we have a measurement every 4 years or 5 years.

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So the statistical unit changes from one server to the other so it is not so easy always to integrate this data.

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However, I think we think that this is very interesting. What is very difficult is to substitute the indicators that cannot be derived from this.

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So there is a lot of work to do for each country to find the most appropriate indicator. So, then Sandra will go more in detail on the aspects on the regions and theitorial differences.

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But this is an outline of a project that we wanted to share with you because we think it was in a nice project and also because we are going to publish this book in a few months.

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2. Okay, maybe it's a good idea to present this following presentation. This is a pack and then comments.

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Good morning. Thank you so much. We will present the part of the sociological chapter of this book.

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And we have to Wait. Okay. Well, I passed some part of this presentation very quick because we know the Jay's is a very quick because we know the Jay's, a very quick because we know the Jay, a very quick because we know the Jay, a very quick because we know the Jay's is the index that we are using as a base and the

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concept we are using as a base and the conceptal framework of the index is the institution of European institutions that construct this index and as Enrico has explained has a 6 domain workman acknowledged this index and as Enrico has explained has a 6 domain workman acknowledged time power and health then we have a 6 domain workman acknowledged time power and health then we have not to the time in

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this topic but just to say that we have not to the time in this topic but just to say that we have in 21 in 68 points to equality, that gets us 60 years to get equality in this more or less if we continues in this in this trend in in 60 years now we can reach the equality then we have to do

280
01:01:52.000 --> 01:02:22.000
something more active in order to resent. The thing that we have done is to to to apply our methodology a very well known but in separate way that this is the factorial analysis, the PCA and then a cluster analysis in order to understand how the the different countries and then as an observational level and performs this

281
01:02:26.000 --> 01:02:36.000
index. No. Then the first things that we have done is to test the already measure that we have.

282
01:02:36.000 --> 01:02:49.000
In order to compare 2 times that are important that is the first time that the hay appeared, that is, 2,013.

283
01:02:49.000 --> 01:03:16.000
And the last year that we have in the moment that we finish the project that was 2,021 now we have 2,022 ready And we use the At the first part of this work, the J of 2,010 that is published in 20,013 for that reason we say, 2,013.

284
01:03:16.000 --> 01:03:33.000
The data published in 2,019 published in 2,021 because they use a different It takes much time to construct the and get information on that.

285
01:03:33.000 --> 01:03:58.000
But the general thing when we see the distribution in the space about the this 6 domain. We can find that between 2,010 and 2,019 more or less more or less the picture of the inequality or the equality measure is more or less the same.

286
01:03:58.000 --> 01:04:10.000
We get one space of the egalitarian part in front of the less egalitarian. Distribution.

287
01:04:10.000 --> 01:04:30.000
And we have also a little more distribution of the power more in the in the north part and health morning that interchange you see but more or less the picture and the space is more or less the same in both years.

288
01:04:30.000 --> 01:04:39.000
Then the society changed but not changed too much. We have a more or less a picture. When we apply cluster, we can see 3 groups.

289
01:04:39.000 --> 01:04:52.000
One group is the more equal, that is the one, and 2 groups are more countries more unequal.

290
01:04:52.000 --> 01:05:14.000
And we distributing this space of equality all the countries and which is the picture that We can find we're going fine So this part that is the social space of the countries more egalitarian are obligated to the different countries that are here.

291
01:05:14.000 --> 01:05:35.000
People risk here. The Mali egalitarian to the right part. And 2 and a less part of the of the space we have the countries that are the more Inegali in less egalitarian countries.

292
01:05:35.000 --> 01:06:02.000
And we have a very difference that we can see. We can divide this space in 2 but. These are the countries less egalitarian, one more related with power, other more related with money or health but we have in the left part of the of the graph the less egalitarian countries.

293
01:06:02.000 --> 01:06:18.000
Then he said the European, is not our indicator but this is the picture that we have when we see this indicator that we have when we see this indicator that we have when we see this indicator that construct a European Union.

294
01:06:18.000 --> 01:06:45.000
When we see the Mar result of this indicator. We can see the thing that I told before that is the things are more or less the same between 2010 and 2,019 that with a little changes for example Malta and Luxembourg were little changes for example Malta and Luxembourg were in.

295
01:06:45.000 --> 01:06:46.000
For example, Malta and Luxembourg were in group 3. And are now in Group One and Poland was in group 2 and moved to group 3.

296
01:06:46.000 --> 01:06:58.000
But more or less the picture more or less the picture is the same.

297
01:06:58.000 --> 01:07:25.000
The would be lessIGERial. And there's more power in This is that the right, the left side, yes, and there are the less egalitarian but one more near 2 point related with power another more near 2 point related pneumonia health.

298
01:07:25.000 --> 01:07:31.000
This is the second factor, sorry, I didn't say. The second, far, to, divide, the less, the less egalitarian country between 2 parts.

299
01:07:31.000 --> 01:07:55.000
The is that that are more related one with with a power another with money. This is the it's a detail of are more related with this is a detail of are more related with this is the why is the because this is the why is the because they divide in 2 but are the less egalitarian countries.

300
01:07:55.000 --> 01:08:12.000
We move now yes to our work about the rational analysis with 6 domains. We use as a and recorcel and that's 2 because we have rational part.

301
01:08:12.000 --> 01:08:23.000
That's 2 is a division that we go a little more down that the macro rations in each country.

302
01:08:23.000 --> 01:08:52.000
And we have 3 Spain, France and Italy because Germany remained in the macro level and we cannot go too much too with Germany then we present this this analysis when we move to the rational analysis as Enrique told us we use 22 indicator in instead 31 because we have 2.

303
01:08:52.000 --> 01:09:08.000
Play with the differences and trying to complete some indicators that are not at the same level and national level or we have to do all the work that Enrique explained.

304
01:09:08.000 --> 01:09:20.000
Then We can see here if we use the air shade that is rational J then we move from J to rational J.

305
01:09:20.000 --> 01:09:50.000
And we see all the Russians that we have that are 62 Russians in total. And we do this analysis we we calculate the race in the way that the is done but with Rationals we can Do quite still square tiles And we can see some differences.

306
01:09:50.000 --> 01:10:01.000
And we can see that the dark areas are the more egalitarian. And the clear areas. The more No egalitarians, okay?

307
01:10:01.000 --> 01:10:31.000
More unequal. Then when we use and do some metrics and then we can we can observe that the thing that that Enrico said that is a the, the France has the, the more the less dispersion, there is a coefficient of variation is is the most the coefficient more how do you

308
01:10:38.000 --> 01:10:48.000
say No, thank you. Marlowe. Then you you can see that Francis more or less more uniform than the other Russian.

309
01:10:48.000 --> 01:11:00.000
But you can see that Italy is a greatest internal disparity, then you will see more clearly. But Italy has a very, very clear dual reality.

310
01:11:00.000 --> 01:11:28.000
Yes? And then when we can see the This will have to be before, no. This is the space that all of the 62 ratio-hour duplicated and this is the result of that we have in terms of the 62 Russians.

311
01:11:28.000 --> 01:11:41.000
Together and we have this result finally in the in the terms in index. Then we have made another analysis.

312
01:11:41.000 --> 01:12:11.000
We have made 3 analysis. First one is just to present this that is the HSA directly measure and inquiries and we extract this this conclusion but then we do a technological Again, we work with all the, all the, all together, put it in a social space.

313
01:12:16.000 --> 01:12:44.000
That is that. That space. And we do again the same analysis that we have done with countries. And when we have done the same analysis with that we have done with countries of all Europe but now applying to the 62 Russians we find this social space this allocation of the 6 domain.

314
01:12:44.000 --> 01:13:09.000
With this 6 domain we obtain a picture that Also, we have as a nationalists at the countries we have to the right the more egalitarian countries now a little with an internal division this is the type one and the type 2 Russians.

315
01:13:09.000 --> 01:13:13.000
More egalitarian and in the left part the type 3 and the type 4 less less egalitarian.

316
01:13:13.000 --> 01:13:37.000
Again, the first factor is the most important and the division in the second factor that is less important is more related with power again and with work in this in this analysis because this analysis is about rations.

317
01:13:37.000 --> 01:13:52.000
It's not about country. Okay? We put all the ratio here. Then we can understand and we can find this picture with this 4 type of Russians.

318
01:13:52.000 --> 01:14:21.000
And now you can see again. The duality of Italy that is more clearly separate now as less equality in the south in the north more equality and you also can see some Russians as the dark Russians that are the more egalitarian.

319
01:14:21.000 --> 01:14:31.000
We move, sorry, that we confuse you. We move to unaffected analysis with another analysis.

320
01:14:31.000 --> 01:14:41.000
You know, to see how the Russians do a performance. If we apply this analysis, this topological analysis to each country separate, not the 62 Russians.

321
01:14:41.000 --> 01:14:53.000
Now, then When we apply this analysis to each country, real appear differences, more difference appear. Okay?

322
01:14:53.000 --> 01:15:06.000
The interesting, I, we thought that the intelligent thing is to put in relation the analysis all durations.

323
01:15:06.000 --> 01:15:26.000
But even though we have to show the diversity and we would like to do this this analysis and we can see that effectively there are difference and the space the social space of equality change when you perform this analysis separately.

324
01:15:26.000 --> 01:15:41.000
And then the result as you can see is in this part. This is the analysis 3 and the other is the analysis that I already show you.

325
01:15:41.000 --> 01:15:59.000
Then you can see that when you apply separately and here France have has more diversity internally yes than Italy that has only 2.

326
01:15:59.000 --> 01:16:17.000
Only 2 needed there are a clear clear situation when you only analyze for separate for example the duration of Italy you can see 2 They are not doubt that you have 2 parts.

327
01:16:17.000 --> 01:16:36.000
And then in Spain you have 3 groups, a clear group and France is more diverse but also have more diversity and we can see more things that we can understand and we can try to work.

328
01:16:36.000 --> 01:16:49.000
But We thought that the most interesting analysis is when you put in relation the 62 rations in order to compare.

329
01:16:49.000 --> 01:17:00.000
Then, just to conclude. We can say that the disaggregated analysis at the regional level and rich research and show the diverse realities.

330
01:17:00.000 --> 01:17:30.000
In the behaviour of gender equality indicators. This is one of course a first point. And the most important we we consider that the construction or regional gender index in Spain, France and Italy provide valuable information to inform and monitor policies aimed at reducing gender gaps at both local and national level because we need this information we need this differentiation of race in

331
01:17:32.000 --> 01:17:56.000
order to and to orientate policies. For that reason, the relevance of this analysis in climbing the importance of the persistent rational disparities in climbing the importance of the persistent rational disparity observed in climbing the importance of the persistent rational disparity of the persistent rational disparity of self in Europe because we analysed that and we saw that there are some indicators not relations

332
01:17:56.000 --> 01:18:04.000
with in Europe because we analyzed that and we saw that there are some indicators not relations with gender that also show this diversity this rational adversity and we also found this.

333
01:18:04.000 --> 01:18:18.000
Or regional diversity in gender. The national analysis of differentiating 3 group of countries in both periods And racial analysis taking into account 62 racial nuts, 2 of Eurostat.

334
01:18:18.000 --> 01:18:33.000
And the data from the HSA calculate with 22 indicators. A lot to conclude that on an aggregate basis the highest level to conclude that on an aggregate basis the highest level of gender equality that on an aggregate basis the highest level of gender equality are found in a aggregate basis the highest level of gender equality are found in France followed by

335
01:18:33.000 --> 01:18:40.000
Spain, the highest level of gender equality are found in France followed by Spain and firstly by Italy. And however, internally rational realities are quite diverse.

336
01:18:40.000 --> 01:19:10.000
And the significant polarization in some territories as Italy we have to to highlight. And finally to show you that they say that the official or between countries is this number and we can obtain our rational shape or relationship almost almost near and in the same ranking of the Shea index.

337
01:19:11.000 --> 01:19:23.000
Thank you very much.

338
01:19:23.000 --> 01:19:33.000
Related with this 2 presentation. If you have comments or questions.

339
01:19:33.000 --> 01:19:38.000
Yes.

340
01:19:38.000 --> 01:19:48.000
Thank you for this interesting presentation. I wonder if you had, it made me wonder what are the determinants of the original differences.

341
01:19:48.000 --> 01:19:54.000
So did you, it made me wonder what were the determinants of these original differences. So did you, to some analysis or reflected what is impacting this diverse level of gender equality.

342
01:19:54.000 --> 01:19:57.000
So did you, to some analysis of the regional differences? So did you, to some analysis or reflected what is impacting this diverse level of gender equality on regional level?

343
01:19:57.000 --> 01:20:08.000
Is I don't know, level of organisation of these regions or did you have any like hypothesis about where these differences come from.

344
01:20:08.000 --> 01:20:14.000
Because when looking at it, Italy would think that urbanization maybe or maybe the education level of citizens is impacting.

345
01:20:14.000 --> 01:20:24.000
So I wonder what, because my question comes from the fact what more it gives us this this index. What we learn.

346
01:20:24.000 --> 01:20:45.000
How you really think this, in what way it can impact policies. So did you imagine or maybe did you have your party in your project that was like application part that we actually you were thinking about what what interventions because for example for labor market I mean most policies on the national level so how this index is useful actually to intervene on

347
01:20:45.000 --> 01:20:58.000
the labor markets. I was thinking about maybe it's a host code connected to the usefulness question from my presentation and also like usefulness on this policy level of this indexes and did you have any thought about this?

348
01:20:58.000 --> 01:21:08.000
Thank you so much. We like these.

349
01:21:08.000 --> 01:21:11.000
Just stops you doing.

350
01:21:11.000 --> 01:21:28.000
Okay. Okay, so I start because Italy is the most relevant country in this sense. Well, We should consider that we are in Europe, so we do not have so large differences in the indicators.

351
01:21:28.000 --> 01:21:34.000
So for example also about the results that the Sandra has shown before. We have different levels in France but of course the spread is not so high.

352
01:21:34.000 --> 01:21:49.000
Probably Italy has only 2 groups also because the variability there is higher so it is easier to detect 2 groups.

353
01:21:49.000 --> 01:21:57.000
In France has more groups for that. I am sure there is an issue relating the participation of female participation to the labour market.

354
01:21:57.000 --> 01:22:12.000
In southern Italy it is much lower. And all cities because at the end of the gender for what I realised the general inequality is basically related to cultural issues.

355
01:22:12.000 --> 01:22:23.000
And this is very low to change. So, there is a family model in southern Italy that is very traditional.

356
01:22:23.000 --> 01:22:42.000
In which a female should stay at home and of course here we are going we risk to face some ecological fallacy okay but just to give some stereotypes to give ADM of course as always stereotypes they are not 100% correct.

357
01:22:42.000 --> 01:23:10.000
So participation in female participation to a market labour is very low. And education level is not so relevant. And this is in general a problem that we found looking at the data about gender equality because education level is too wide because the standards of education in Europe are more or less the same, okay?

358
01:23:10.000 --> 01:23:27.000
The point is in watch, in what subjects, in what topics you are educated. So a big problem we have in Italy is about the stem preparation stem that is a science technology environment and mathematics, the scientific part.

359
01:23:27.000 --> 01:23:47.000
We have a very low participation. I female in this kind of courses. So the problem is basically cultural and this is reflected in many aspects of social social life.

360
01:23:47.000 --> 01:24:05.000
For example, we have a higher attachment to traditions, to religion, to a traditional family model in the South in which the role of the woman is a part one step behind.

361
01:24:05.000 --> 01:24:18.000
Then there is a part of population from the south that is moving to the north. So we have internal migration because of the also work opportunities.

362
01:24:18.000 --> 01:24:30.000
And who is moving is more productive. So the most active and who remains is probably more even more linked to the traditions.

363
01:24:30.000 --> 01:24:39.000
So this is asentrating these aspects. So basically the reasons that we found are this kind about Spain.

364
01:24:39.000 --> 01:24:45.000
I do not know the characteristics of those areas.

365
01:24:45.000 --> 01:25:10.000
Well, we We tried to answer this question to using some correlation between the factor one that we have in our analysis of the 62 ration in order to understand a little more but of course this is to move in the council, explanation that we have not moving.

366
01:25:10.000 --> 01:25:19.000
I must in this war, but we try to find some correlation that is not the same of course as we all know.

367
01:25:19.000 --> 01:25:44.000
And then we have done an analysis using a 6 indicators in order to show if there are any correlation with the factor one that we found and we use gross domestic product the European Rational Competitiveness Index all made by this made by by Europe the E.

368
01:25:44.000 --> 01:26:07.000
So shall the EU European Social Progress Index that is made by also by Europe that tried to go beyond the gross domestic product and trying to incorporate another things trying to show the progress is interesting also because it includes some social and environment indicators.

369
01:26:07.000 --> 01:26:37.000
And things like that that we can see and we can go a little more further to the GDP as as usually and also we saw and we tried to measure the European quality of government because we We understand that it's very important the European quality of government and also as Enrique said we incorporate education trying using an indicator of

370
01:26:42.000 --> 01:27:02.000
the tertiary education as a measure. Trying to find this relationship with the 62 Russians and also we understand that maybe the population density could be something because the very Urban areas in relation to the rural.

371
01:27:02.000 --> 01:27:23.000
And we thought all these things. But the result shows that the population density is not important. Then we have not to because it's the less the coefficient that has 23 or the most low coffee coefficient of the Bearson coefficient of relation.

372
01:27:23.000 --> 01:27:51.000
Then in order to understand the European quality of government that surprise a lot as tertiary education has only a point 40 of correlation is important but is not so important then is for us it was a little surprise because we expect in that area for example in equality of government that the indicator is not.

373
01:27:51.000 --> 01:28:15.000
Low because point 40 is okay but it's not is not so as we expect. We have a little more 43 the correlation of the It education that is L speed that is the Social Progress Index.

374
01:28:15.000 --> 01:28:26.000
Well, okay. Is related, he connected with her she, then we can expect some relation but not so high, 4.4 3.

375
01:28:26.000 --> 01:28:55.000
Then we move to a more important correlation point, 56 that is the European national competitiveness then No, we go near to the to take some economical Explanation and then we find the most important explanation that is the GDP I'm so sure.

376
01:28:55.000 --> 01:29:05.000
And point 66. Then we have not all the explanation and of course it's not so big but it's the biggest and in this all this indication.

377
01:29:05.000 --> 01:29:29.000
Then we have conclude that in some countries as Italy, the cultural things are very important, but also we can add the importance of the development because we as we know the GDP has relation with the economic development.

378
01:29:29.000 --> 01:29:51.000
Then we think that the If we would like to impose some policies, we have to know and we have to see above the poverty of the region of the region and then we can do something because this poverty or this rich richness of this race are related with culture also.

379
01:29:51.000 --> 01:30:16.000
Then there are 2 mechanisms there. And then we can we can find all these 6 exploring this 6 indicator but we have to move on and try to go further and try to take more explanation because This as you see is a descriptive of the situation.

380
01:30:16.000 --> 01:30:37.000
It's not We not explain. We just try to find here in the in this chapter for example some relations not Kavanaugh

381
01:30:37.000 --> 01:30:54.000
An additional comment to this result that Sandra said is that if you analyze the same indicators at particular territorial analysis in the case of the French analysis, the Italian analysis.

382
01:30:54.000 --> 01:31:03.000
This conclusions are in some aspect different. In the case of France, the GDP is not so important like in in Italy is more important.

383
01:31:03.000 --> 01:31:14.000
Spanish case is in the middle. Then when you have to elaborate comparison international comparison, this is a good instrument.

384
01:31:14.000 --> 01:31:37.000
But when you elaborate particular policies in a local dimension, It's important to have this another vision and you can complement the general comparison with a particular comparison in encountering in a local vision that is so important because policies are half hard.

385
01:31:37.000 --> 01:31:42.000
Hannah Dimensian, obviously I have local dimension and policies are local and and then you can combine results and analysis and conclusions in details.

386
01:31:42.000 --> 01:32:07.000
Thank you because thank you to Buntal like that because for example a tertiary education that is not in general because for example tertiary education that is not in general in the 62 areas because for example tertiary education that is not in general in the 62 areas important in Italy as an indicator of cultural things.

387
01:32:07.000 --> 01:32:20.000
Is getting important then we can move in this and turn in in the chapter you can move at to each country and see how is the weight of this coefficient in each country.

388
01:32:20.000 --> 01:32:38.000
And as Pedro, I thank you because I am all speaking about the general results and then we can move speaking about the general results and then we can move and speaking about the general results and then we can move and go specific of each country and then we can move and go specific of each country and then we can see which of each country and then we can see which of these indicators

389
01:32:38.000 --> 01:32:44.000
has his own weight.

390
01:32:44.000 --> 01:32:49.000
Don't hear.

391
01:32:49.000 --> 01:32:57.000
Yeah, thank you. This is really interesting. I have 2 questions.

392
01:32:57.000 --> 01:33:19.000
So one would be, let's say, provision of childcare or something like that is that included in the GEI is that part of the GI okay yeah so this means okay so this is something that needs to be explained as well okay so this is something that needs to be explained as well okay so this is something that needs to be explained as

393
01:33:19.000 --> 01:33:25.000
well. Okay, so this is then probably to be explained as well. Okay? So this is then probably why I don't know something like

394
01:33:25.000 --> 01:33:53.000
Worlds of welfare, capital, world of welfare, something like that as a topology cannot be used for explaining your pattern is that Is it because I wondered why you don't refer to let's say existing topologies for trying to explain but maybe it's also not useful to do that or it's overlapping so it would

395
01:33:53.000 --> 01:34:03.000
be tautological. So this is one question and the other would be like if you refer to culture.

396
01:34:03.000 --> 01:34:13.000
I mean I don't see what you actually, how you would PIN it down and but there are You could think of how.

397
01:34:13.000 --> 01:34:21.000
How pinning that down like referring to long-term structures in terms of

398
01:34:21.000 --> 01:34:35.000
I don't know, feminist movements or I mean you could think of measures maybe.

399
01:34:35.000 --> 01:35:01.000
We have not used a typology of well-being but perfectly we have to put in in each classification in a in a already done classification, we can read this indicator in order to the to each country and we will see if there are And if there are some relationships.

400
01:35:01.000 --> 01:35:11.000
We have done the typology and we didn't use because we have the indicators of the shade already done already.

401
01:35:11.000 --> 01:35:22.000
We have not to do a new classification for example. This is another another topic. We can now propose a new classification of J.

402
01:35:22.000 --> 01:35:32.000
But our objective in this in this analysis is only go down with the same indicator Go down. That's all.

403
01:35:32.000 --> 01:35:44.000
We is not to do another analysis, but we can do another analysis. For example, when we explain this in Latin America, we say, oh yes, we can do that, we can do that, we say, oh yes, we can do that, we can do that, we can use this indicator and say, okay.

404
01:35:44.000 --> 01:35:50.000
yes, we can do that, we can do that, we can use this indicator and say, okay, yes, but we have not to use another indicator that are very, very interesting for us to take into account.

405
01:35:50.000 --> 01:36:00.000
This is another work that is also possible and is also interesting. They think that the objective of this work was only to apply the J.

406
01:36:00.000 --> 01:36:09.000
To a superional level. Then to apply a methodology that us is already Develop it to R.

407
01:36:09.000 --> 01:36:20.000
. In order to see the disparity, the rational disparity because it's not only country, these national disparity because it's not only country because it's not only the regional disparity because it's not only countries, these countries is on the top but has rational area that is in the law.

408
01:36:20.000 --> 01:36:31.000
Then it's important to vocalize in these rations. This is the most important objective of this project.

409
01:36:31.000 --> 01:36:44.000
Of course we can Do a lot of interesting things. Then, but you lost that, yeah. And, to complement this, this answer.

410
01:36:44.000 --> 01:36:57.000
You see the map, calculating and computing both at the level of countries of the regional gender equality index.

411
01:36:57.000 --> 01:37:14.000
You can break the classical topology like a spinundersen elaborative for example because you have nerve the counties of the north at the continental countries and the South countries in the case of a thing, the particular cases are spent.

412
01:37:14.000 --> 01:37:26.000
Then you Break the classical classification. The gender inequality index and the policies developed in in some countries are in descent and with this measure very similar.

413
01:37:26.000 --> 01:37:37.000
In this sense, you have not a great correlation between the classical technology in time for Walter for state regimes and in terms of our resources.

414
01:37:37.000 --> 01:37:57.000
But as Sandra said, this is an initial work and we are going deep. In front of the steps.

415
01:37:57.000 --> 01:38:11.000
Yeah, thank you for your consideration about this point. I will be very fast because I think we don't have so much money but the point is exactly what the Sandra said.

416
01:38:11.000 --> 01:38:19.000
Our original idea said, let's see what we can do using only the indicators that are included by agent.

417
01:38:19.000 --> 01:38:31.000
This does not exclude the possibility of interpreting results according to different aspects. It is complex of course because causal relationships are not clear.

418
01:38:31.000 --> 01:38:52.000
However, for example, go back to southern Italy. The traditional culture is bringing reduced participation of a meal into a market labor and also because there is a lack of good welfare systems, especially for children.

419
01:38:52.000 --> 01:39:05.000
So the topics are very vast. The project was merely focused on the information comfortable from the indicators. Of course there is some interpretation of results.

420
01:39:05.000 --> 01:39:21.000
But, and I think this is important to say, there is another work not made by us, but by the Joint Research Centre about the regional monitoring of gender disparities in which they decided to use completely different indicators from gender equality index.

421
01:39:21.000 --> 01:39:43.000
It is a choice of course but then a you open a completely new chapter about which data you want to use and which are the theoretical foundations about why use that indicator rather than another one you have to decide which criteria the weights etcetera etc.

422
01:39:43.000 --> 01:39:53.000
Also the way things maybe change one country to another then we use the same awaiting that are used for the European level indicator.

423
01:39:53.000 --> 01:40:10.000
That indicator from a joint research centre that was published after that we won the project is very interesting because it is comparing capital cities not regions, regions and the capital cities in order to see the differences.

424
01:40:10.000 --> 01:40:13.000
And in some countries you see that the capital cities have much higher levels of gender equality rather than other regions.

425
01:40:13.000 --> 01:40:43.000
Also because people living in capital cities very often is self selected because you move to capital cities in some cases and so we have again that effect that I said in Italy but you live in some rural regions, people who are more traditional from a cultural point of view, most innovative ones are in active areas and that is connected also to what we were seeing

426
01:40:46.000 --> 01:40:54.000
before about the innovativeness index of these territories. Because they less innovative people is left behind.

427
01:40:54.000 --> 01:41:09.000
So I think this is important from policy because everyone should have equal opportunity in at least in one country. In the same country and regional analysis is always giving some interesting insights.

428
01:41:09.000 --> 01:41:15.000
So. I hope so. Thank you.

429
01:41:15.000 --> 01:41:45.000
Okay. And there's no question. Please have this. Yes, yes, so thank you very much for your work because you too that you had to you were missing 7 indicators from your analyzes which were those or do you think they any domain suffers because of those yeah and you can speak I show and you speak I have

430
01:41:51.000 --> 01:42:03.000
to update. Yes so of course there are some domains that are well covered for example because we have delivers for us.

431
01:42:03.000 --> 01:42:15.000
Of course there are some domains that are well covered for example because we have delivers for started, that is uniform, for example, because we have the liberals first started, it is uniform in our Europe, and it is a very good Then there are, okay, here some, some indicate goes that are.

432
01:42:15.000 --> 01:42:35.000
more complicated. They're not, we have a lot because it's It's totally, er, not important because I'm minister of the National.

433
01:42:35.000 --> 01:43:03.000
Some of them are not important, but others are important. For example, yeah, but in particular when we have high life expectancy at both people not smoking people not smoking people sorry maybe at home or people not smoking people sorry maybe at home people not smoking people sorry maybe at home people not smoking people sorry maybe at home people not smoking or people doing physical

434
01:43:03.000 --> 01:43:14.000
activities, people not smoking or people doing physical activities, healthy life or also about social activity. People not smoking or people doing physical activities, healthy life or also about the social activity.

435
01:43:14.000 --> 01:43:19.000
Many women involved in social institutions, etc. These data We can say all the data that are coming from the demographic point of view are not a problem.

436
01:43:19.000 --> 01:43:49.000
All the data that are coming from the work or income domains are not really problem. What are a problem are basically power, participation, participation to the society and in some aspects also something about health because the those relating to healthy behaviours.

437
01:43:51.000 --> 01:43:59.000
Of people during their their daily life.

438
01:43:59.000 --> 01:44:14.000
Skin. Yeah. 800 degrees. Thank you so much.