WEBVTT 00:00:01.875 --> 00:00:04.601 The world today has many problems. 00:00:04.625 --> 00:00:10.143 And they're all very complicated and interconnected and difficult. 00:00:10.167 --> 00:00:12.601 But there is something we can do. 00:00:12.625 --> 00:00:13.893 I believe 00:00:13.917 --> 00:00:18.684 that girls' education is the closest thing we have to a silver bullet 00:00:18.708 --> 00:00:23.101 to help solve some of the world's most difficult problems. 00:00:23.125 --> 00:00:25.226 But you don't have to take my word for it. NOTE Paragraph 00:00:25.250 --> 00:00:26.934 The World Bank says 00:00:26.958 --> 00:00:30.226 that girls' education is one of the best investments 00:00:30.250 --> 00:00:32.268 that a country can make. 00:00:32.292 --> 00:00:34.393 It helps to positively impact 00:00:34.417 --> 00:00:37.976 nine of the 17 Sustainable Development Goals. 00:00:38.000 --> 00:00:42.434 Everything from health, nutrition, employment -- 00:00:42.458 --> 00:00:46.417 all of these are positively impacted when girls are educated. 00:00:47.667 --> 00:00:52.809 Additionally, climate scientists have recently rated girls' education 00:00:52.833 --> 00:00:58.559 at number six out of 80 actions to reverse global warming. 00:00:58.583 --> 00:01:03.625 At number six, it's rated higher than solar panels and electric cars. NOTE Paragraph 00:01:05.542 --> 00:01:08.393 And that's because when girls are educated, 00:01:08.417 --> 00:01:10.226 they have smaller families, 00:01:10.250 --> 00:01:13.643 and the resulting reduction in population 00:01:13.667 --> 00:01:17.000 reduces carbon emissions significantly. 00:01:18.500 --> 00:01:22.726 But more than that, you know, it's a problem we have to solve once. 00:01:22.750 --> 00:01:27.059 Because an educated mother is more than twice as likely 00:01:27.083 --> 00:01:29.101 to educate her children. 00:01:29.125 --> 00:01:31.143 Which means that by doing it once, 00:01:31.167 --> 00:01:35.226 we can close the gender and literacy gap forever. NOTE Paragraph 00:01:35.250 --> 00:01:36.684 I work in India, 00:01:36.708 --> 00:01:39.559 which has made incredible progress 00:01:39.583 --> 00:01:42.809 in bringing elementary education for all. 00:01:42.833 --> 00:01:46.851 However, we still have four million out-of-school girls, 00:01:46.875 --> 00:01:48.750 one of the highest in the world. 00:01:49.917 --> 00:01:53.601 And girls are out of school because of, obviously poverty, 00:01:53.625 --> 00:01:55.893 social, cultural factors. 00:01:55.917 --> 00:01:59.042 But there's also this underlying factor of mindset. 00:02:00.250 --> 00:02:03.601 I have met a girl whose name was Naraaz Nath. 00:02:03.625 --> 00:02:05.809 Naaraaz means angry. 00:02:05.833 --> 00:02:08.309 And when I asked her, "Why is your name 'angry'?" 00:02:08.333 --> 00:02:12.750 she said, "Because everybody was so angry when a girl was born." 00:02:14.125 --> 00:02:16.601 Another girl called Antim Bala, 00:02:16.625 --> 00:02:18.893 which means the last girl. 00:02:18.917 --> 00:02:22.208 Because everybody hoped that would be the last girl to be born. 00:02:23.625 --> 00:02:25.333 A girl called Aachuki. 00:02:26.292 --> 00:02:28.351 It means somebody who has arrived. 00:02:28.375 --> 00:02:31.643 Not wanted, but arrived. 00:02:31.667 --> 00:02:33.809 And it is this mindset 00:02:33.833 --> 00:02:37.434 that keeps girls from school or completing their education. 00:02:37.458 --> 00:02:40.143 It's this belief that a goat is an asset 00:02:40.167 --> 00:02:41.875 and a girl is a liability. NOTE Paragraph 00:02:44.667 --> 00:02:48.143 My organization Educate Girls works to change this. 00:02:48.167 --> 00:02:51.059 And we work in some of the most difficult, rural, 00:02:51.083 --> 00:02:52.917 remote and tribal villages. 00:02:54.000 --> 00:02:55.518 And how do we do it? 00:02:55.542 --> 00:02:57.851 We first and foremost find 00:02:57.875 --> 00:03:02.059 young, passionate, educated youth from the same villages. 00:03:02.083 --> 00:03:04.434 Both men and women. 00:03:04.458 --> 00:03:05.851 And we call them Team Balika, 00:03:05.875 --> 00:03:07.476 balika just means the girl child, 00:03:07.500 --> 00:03:10.417 so this is a team that we are creating for the girl child. 00:03:11.417 --> 00:03:14.309 And so once we recruit our community volunteers, 00:03:14.333 --> 00:03:17.625 we train them, we mentor them, we hand-hold them. 00:03:18.458 --> 00:03:20.018 That's when our work starts. 00:03:20.042 --> 00:03:24.393 And the first piece we do is about identifying every single girl 00:03:24.417 --> 00:03:26.684 who's not going to school. NOTE Paragraph 00:03:26.708 --> 00:03:29.934 But the way we do it is a little different and high-tech, 00:03:29.958 --> 00:03:32.268 at least in my view. 00:03:32.292 --> 00:03:35.018 Each of our frontline staff have a smartphone. 00:03:35.042 --> 00:03:37.726 It has its own Educate Girls app. 00:03:37.750 --> 00:03:40.934 And this app has everything that our team needs. 00:03:40.958 --> 00:03:46.434 It has digital maps of where they're going to be conducting the survey, 00:03:46.458 --> 00:03:48.768 it has the survey in it, all the questions, 00:03:48.792 --> 00:03:51.643 little guides on how best to conduct the survey, 00:03:51.667 --> 00:03:55.792 so that the data that comes to us is in real time and is of good quality. 00:03:56.833 --> 00:03:58.101 So armed with this, 00:03:58.125 --> 00:04:01.518 our teams and our volunteers go door-to-door 00:04:01.542 --> 00:04:05.309 to every single household to find every single girl 00:04:05.333 --> 00:04:08.226 who may either we never enrolled or dropped out of school. NOTE Paragraph 00:04:08.250 --> 00:04:11.518 And because we have this data and technology piece, 00:04:11.542 --> 00:04:15.476 very quickly we can figure out who the girls are and where they are. 00:04:15.500 --> 00:04:17.934 Because each of our villages are geotagged, 00:04:17.958 --> 00:04:20.351 and we can actually build that information out 00:04:20.375 --> 00:04:21.875 very, very quickly. 00:04:22.542 --> 00:04:25.143 And so once we know where the girls are, 00:04:25.167 --> 00:04:28.893 we actually start the process of bringing them back into school. 00:04:28.917 --> 00:04:31.809 And that actually is just our community mobilization process, 00:04:31.833 --> 00:04:35.268 it starts with village meetings, neighborhood meetings, 00:04:35.292 --> 00:04:39.226 and as you see, individual counseling of parents and families, 00:04:39.250 --> 00:04:41.893 to be able to bring the girls back into school. 00:04:41.917 --> 00:04:46.542 And this can take anything from a few weeks to a few months. NOTE Paragraph 00:04:47.625 --> 00:04:50.059 And once we bring the girls into the school system, 00:04:50.083 --> 00:04:51.518 we also work with the schools 00:04:51.542 --> 00:04:55.434 to make sure that schools have all the basic infrastructure 00:04:55.458 --> 00:04:57.363 so that the girls will be able to stay. 00:04:57.387 --> 00:05:00.184 And this would include a separate toilet for girls, 00:05:00.208 --> 00:05:01.809 drinking water, 00:05:01.833 --> 00:05:04.851 things that will help them to be retained. 00:05:04.875 --> 00:05:08.643 But all of this would be useless if our children weren't learning. 00:05:08.667 --> 00:05:11.559 So we actually run a learning program. 00:05:11.583 --> 00:05:13.684 And this is a supplementary learning program, 00:05:13.708 --> 00:05:16.018 and it's very, very important, 00:05:16.042 --> 00:05:19.893 because most of our children are first-generation learners. 00:05:19.917 --> 00:05:22.809 That means there's nobody at home to help them with homework, 00:05:22.833 --> 00:05:25.059 there's nobody who can support their education. 00:05:25.083 --> 00:05:26.768 Their parents can't read and write. 00:05:26.792 --> 00:05:28.434 So it's really, really key 00:05:28.458 --> 00:05:32.601 that we do the support of the learning in the classrooms. NOTE Paragraph 00:05:32.625 --> 00:05:34.518 So this is essentially our model, 00:05:34.542 --> 00:05:36.851 in terms of finding, bringing the girls in, 00:05:36.875 --> 00:05:39.476 making sure that they're staying and learning. 00:05:39.500 --> 00:05:42.101 And we know that our model works. 00:05:42.125 --> 00:05:44.018 And we know this because 00:05:44.042 --> 00:05:47.184 a most recent randomized control evaluation 00:05:47.208 --> 00:05:48.917 confirms its efficacy. 00:05:50.792 --> 00:05:54.518 Our evaluator found that over a three-year period 00:05:54.542 --> 00:05:59.684 Educate Girls was able to bring back 92 percent of all out-of-school girls 00:05:59.708 --> 00:06:01.434 back into school. NOTE Paragraph 00:06:01.458 --> 00:06:07.976 (Applause) NOTE Paragraph 00:06:08.000 --> 00:06:09.434 And in terms of learning, 00:06:09.458 --> 00:06:12.101 our children's learning went up significantly 00:06:12.125 --> 00:06:14.226 as compared to control schools. 00:06:14.250 --> 00:06:18.143 So much so, that it was like an additional year of schooling 00:06:18.167 --> 00:06:19.976 for the average student. 00:06:20.000 --> 00:06:21.393 And that's enormous, 00:06:21.417 --> 00:06:24.768 when you think about a tribal child who's entering the school system 00:06:24.792 --> 00:06:26.167 for the first time. NOTE Paragraph 00:06:27.042 --> 00:06:29.434 So here we have a model that works; 00:06:29.458 --> 00:06:31.434 we know it's scalable, 00:06:31.458 --> 00:06:35.101 because we are already functioning at 13,000 villages. 00:06:35.125 --> 00:06:36.434 We know it's smart, 00:06:36.458 --> 00:06:39.393 because of the use of data and technology. 00:06:39.417 --> 00:06:42.184 We know that it's sustainable and systemic, 00:06:42.208 --> 00:06:45.184 because we work in partnership with the community, 00:06:45.208 --> 00:06:47.393 it's actually led by the community. 00:06:47.417 --> 00:06:49.643 And we work in partnership with the government, 00:06:49.667 --> 00:06:52.375 so there's no creation of a parallel delivery system. 00:06:53.417 --> 00:06:56.643 And so because we have this innovative partnership 00:06:56.667 --> 00:07:00.559 with the community, the government, this smart model, 00:07:00.583 --> 00:07:03.875 we have this big, audacious dream today. 00:07:05.042 --> 00:07:08.851 And that is to solve a full 40 percent of the problem 00:07:08.875 --> 00:07:12.125 of out-of-school girls in India in the next five years. NOTE Paragraph 00:07:12.958 --> 00:07:19.351 (Applause) NOTE Paragraph 00:07:19.375 --> 00:07:21.976 And you're thinking, that's a little ... 00:07:22.000 --> 00:07:25.018 You know, how am I even thinking about doing that, 00:07:25.042 --> 00:07:28.875 because India is not a small place, it's a huge country. 00:07:30.042 --> 00:07:32.559 It's a country of over a billion people. 00:07:32.583 --> 00:07:36.518 We have 650,000 villages. 00:07:36.542 --> 00:07:38.184 How is it that I'm standing here, 00:07:38.208 --> 00:07:40.184 saying that one small organization 00:07:40.208 --> 00:07:43.417 is going to solve a full 40 percent of the problem? 00:07:44.125 --> 00:07:46.726 And that's because we have a key insight. 00:07:46.750 --> 00:07:48.018 And that is, 00:07:48.042 --> 00:07:52.518 because of our entire approach, with data and with technology, 00:07:52.542 --> 00:07:55.226 that five percent of villages in India 00:07:55.250 --> 00:07:58.268 have 40 percent of the out-of-school girls. 00:07:58.292 --> 00:08:01.059 And this is a big, big piece of the puzzle. 00:08:01.083 --> 00:08:04.268 Which means, I don't have to work across the entire country. 00:08:04.292 --> 00:08:07.434 I have to work in those five percent of the villages, 00:08:07.458 --> 00:08:09.518 about 35,000 villages, 00:08:09.542 --> 00:08:12.750 to actually be able to solve a large piece of the problem. 00:08:13.875 --> 00:08:15.143 And that's really key, 00:08:15.167 --> 00:08:17.559 because these villages 00:08:17.583 --> 00:08:20.726 not only have high burden of out-of-school girls, 00:08:20.750 --> 00:08:23.601 but also a lot of related indicators, right, 00:08:23.625 --> 00:08:28.976 like malnutrition, stunting, poverty, infant mortality, 00:08:29.000 --> 00:08:30.684 child marriage. 00:08:30.708 --> 00:08:32.643 So by working and focusing here, 00:08:32.667 --> 00:08:35.143 you can actually create a large multiplier effect 00:08:35.167 --> 00:08:37.768 across all of these indicators. 00:08:37.792 --> 00:08:39.058 And it would mean 00:08:39.082 --> 00:08:43.457 that we would be able to bring back 1.6 million girls back into school. NOTE Paragraph 00:08:44.792 --> 00:08:50.875 (Applause) NOTE Paragraph 00:08:51.750 --> 00:08:55.184 I have to say, I have been doing this for over a decade, 00:08:55.208 --> 00:09:00.018 and I have never met a girl who said to me, 00:09:00.042 --> 00:09:01.768 you know, "I want to stay at home," 00:09:01.792 --> 00:09:03.184 "I want to graze the cattle," 00:09:03.208 --> 00:09:05.476 "I want to look after the siblings," 00:09:05.500 --> 00:09:07.518 "I want to be a child bride." 00:09:07.542 --> 00:09:11.750 Every single girl I meet wants to go to school. 00:09:12.917 --> 00:09:14.893 And that's what we really want to do. 00:09:14.917 --> 00:09:19.042 We want to be able to fulfill those 1.6 million dreams. NOTE Paragraph 00:09:20.625 --> 00:09:21.934 And it doesn't take much. 00:09:21.958 --> 00:09:25.893 To find and enroll a girl with our model is about 20 dollars. 00:09:25.917 --> 00:09:29.101 To make sure that she is learning and providing a learning program, 00:09:29.125 --> 00:09:31.393 it's another 40 dollars. 00:09:31.417 --> 00:09:33.559 But today is the time to do it. 00:09:33.583 --> 00:09:37.726 Because she is truly the biggest asset we have. 00:09:37.750 --> 00:09:41.101 I am Safeena Husain, and I educate girls. NOTE Paragraph 00:09:41.125 --> 00:09:42.393 Thank you. NOTE Paragraph 00:09:42.417 --> 00:09:45.958 (Applause)