WEBVTT X-TIMESTAMP-MAP=LOCAL:00:00:00.000,MPEGTS:270000 00:00:03.169 --> 00:00:06.706 Newscaster: The US unemployment rate reached 14.7 percent. 00:00:06.706 --> 00:00:09.409 That is the highest it’s been since the Great Depression. 00:00:10.043 --> 00:00:14.114 People are depressed. People are angry. They’re in a panic. 00:00:15.081 --> 00:00:19.352 They call here with such desperation that it hits you right away. 00:00:19.819 --> 00:00:22.522 During this crisis they are frankly terrified 00:00:22.522 --> 00:00:24.257 about how they’re going to make ends meet. 00:00:29.129 --> 00:00:32.132 Every year, 60 billion dollars worth of benefits 00:00:32.132 --> 00:00:36.403 that can help people access health care, food or pay for other essentials, 00:00:36.403 --> 00:00:37.737 such as home heating, 00:00:37.737 --> 00:00:40.707 goes unclaimed by people who are otherwise eligible for it. 00:00:41.441 --> 00:00:44.210 The practical thing that Benefits Data Trust can do 00:00:44.210 --> 00:00:49.049 is make sure that we can pull down as much of those 60 billion dollars 00:00:49.049 --> 00:00:52.752 in unclaimed benefits that people are eligible for as possible. 00:00:57.257 --> 00:01:00.427 I’ve become a little bit of a therapist and a troubleshooter. 00:01:00.693 --> 00:01:03.029 I’m hearing from a lot of first time callers. 00:01:03.029 --> 00:01:05.732 So people who had maybe their own business 00:01:05.732 --> 00:01:07.534 or who had been working, 00:01:07.534 --> 00:01:10.236 never felt like they needed public benefits. 00:01:10.236 --> 00:01:12.305 And so they’re calling in saying, 00:01:12.305 --> 00:01:15.508 ‘This is embarrassing, I’ve never had to do this before.’ 00:01:15.508 --> 00:01:18.044 So I feel like there’s some shame and some guilt. 00:01:18.445 --> 00:01:22.148 You also have people who frankly were living on the economic cusp 00:01:22.148 --> 00:01:23.683 before the pandemic, 00:01:23.683 --> 00:01:26.686 and whose earnings have been cut or the hours have been cut. 00:01:26.886 --> 00:01:28.721 And we’re also there to help them. 00:01:29.189 --> 00:01:30.390 Ok, how many people live in your home? 00:01:30.890 --> 00:01:32.358 Because that is going to mean a difference 00:01:32.358 --> 00:01:34.427 between making ends meet or not, 00:01:34.427 --> 00:01:37.931 and keeping house and home and family and health together, or not. 00:01:40.133 --> 00:01:42.368 Thanks to The Rockefeller Foundation’s investment 00:01:42.368 --> 00:01:44.537 and joining a collaborative effort, 00:01:44.537 --> 00:01:48.174 we are doubling down on our use of technology and data, 00:01:48.174 --> 00:01:49.576 on our capabilities. 00:01:53.012 --> 00:01:55.548 Data science can be smarter about 00:01:55.548 --> 00:02:00.120 how we identify who should be better served and how. 00:02:00.120 --> 00:02:02.822 To scale the solutions to reach even more, 00:02:02.822 --> 00:02:04.757 especially in this time of crisis. 00:02:05.125 --> 00:02:07.760 So with data science innovation supporting them, 00:02:07.760 --> 00:02:11.097 Yvonne and Trooper, and all of their colleagues, 00:02:11.097 --> 00:02:14.200 can more simply and efficiently reach and serve 00:02:14.200 --> 00:02:17.137 the millions of people that need help today. 00:02:17.137 --> 00:02:20.773 It has given us the capability to pivot our strategy 00:02:20.773 --> 00:02:25.211 and our operations to meet this moment of extraordinary demand. 00:02:26.079 --> 00:02:29.849 During this crisis we also need to be asking the hard questions 00:02:29.849 --> 00:02:31.784 and having the hard conversations 00:02:31.784 --> 00:02:34.387 about how do we fundamentally improve 00:02:34.387 --> 00:02:36.422 the benefits system in this country. 00:02:37.123 --> 00:02:40.560 We need to use this moment to expand the conversation, 00:02:40.560 --> 00:02:43.296 to make sure that people understand that public benefits 00:02:43.296 --> 00:02:45.498 aren’t just there for “those” people, 00:02:45.498 --> 00:02:47.700 public benefits are there for all of us. 00:02:48.134 --> 00:02:50.170 I mean it always makes me feel good. 00:02:50.170 --> 00:02:54.741 Some people just thank you and bless you and cry. 00:02:54.741 --> 00:02:56.543 Some people are just, 00:02:56.543 --> 00:03:01.347 there’s so much joy that I feel like I know this family. 00:03:01.347 --> 00:03:05.518 At the end of a call I’m like ‘Wow, that was pretty incredible.’