WEBVTT 00:00:00.000 --> 00:00:03.720 Welcome to the deep dive. Today, we're tackling 00:00:03.720 --> 00:00:07.080 a bit of a puzzle. We've got a stack of sources 00:00:07.080 --> 00:00:10.160 here painting two, well, seemingly different 00:00:10.160 --> 00:00:13.279 pictures of the moment. One looks at the hard 00:00:13.279 --> 00:00:16.480 economic numbers versus how people actually feel 00:00:16.480 --> 00:00:19.780 about the economy. that disconnect. And the other 00:00:19.780 --> 00:00:23.859 dives into a really specific, very current political 00:00:23.859 --> 00:00:25.719 debate. It's happening right now in the Senate 00:00:25.719 --> 00:00:29.059 about government spending, social programs. Yeah, 00:00:29.239 --> 00:00:31.899 big one. So our mission, let's try and unpack 00:00:31.899 --> 00:00:34.039 this for you. Pull out the important nuggets 00:00:34.039 --> 00:00:37.320 and maybe see if there are some surprising links 00:00:37.320 --> 00:00:39.020 between these two things. Sounds good. Where 00:00:39.020 --> 00:00:41.049 should we start? OK, let's dive in. How about 00:00:41.049 --> 00:00:43.490 that economic picture first? The analysis you 00:00:43.490 --> 00:00:45.250 mentioned points to something pretty fascinating 00:00:45.250 --> 00:00:47.350 with the data, right? It really does. It makes 00:00:47.350 --> 00:00:49.729 this crucial distinction between what it calls 00:00:49.729 --> 00:00:53.609 soft data and hard data. OK, so soft data, that's 00:00:53.609 --> 00:00:56.950 like surveys, sentiment, confidence levels, expectations, 00:00:57.170 --> 00:00:59.789 kind of fuzzier stuff. Exactly. How people feel, 00:01:00.130 --> 00:01:02.149 what they think will happen. And then the hard 00:01:02.149 --> 00:01:05.530 data is the tangible stuff, measurable outputs. 00:01:05.870 --> 00:01:08.650 Like job numbers, production figures, sales. 00:01:08.909 --> 00:01:11.430 Things you can actually count. Precisely. And 00:01:11.430 --> 00:01:14.170 what this analysis is showing is, well, a pretty 00:01:14.170 --> 00:01:16.329 significant gap opening up between those two 00:01:16.329 --> 00:01:18.269 right now. It seems to be growing. It's a gap. 00:01:18.430 --> 00:01:20.409 How does that actually show up? What does it 00:01:20.409 --> 00:01:24.150 look like? Well, the analysis points to things 00:01:24.150 --> 00:01:27.709 like Bloomberg's soft data surprise index. OK. 00:01:28.079 --> 00:01:31.239 That index tracks how sentiment data comes in 00:01:31.239 --> 00:01:33.760 compared to forecasts. And lately, it's actually 00:01:33.760 --> 00:01:36.439 turned negative. Negative meaning. The mood is 00:01:36.439 --> 00:01:38.599 worse than analysts expected. That's it. The 00:01:38.599 --> 00:01:40.780 feeling, the sentiment, is below expectations. 00:01:41.700 --> 00:01:43.700 But at the same time, the hard data surprise 00:01:43.700 --> 00:01:46.739 index. Which tracks the concrete numbers. Right. 00:01:47.180 --> 00:01:49.159 That's actually stayed positive. Things like, 00:01:49.159 --> 00:01:51.239 say, the labor market readings, they've been 00:01:51.239 --> 00:01:53.900 coming in better than predicted. Huh. So you've 00:01:53.900 --> 00:01:56.799 got this odd situation. The official stats look 00:01:56.799 --> 00:01:59.299 relatively solid, maybe even better than expected. 00:01:59.920 --> 00:02:02.719 But the surveys, the mood on the ground, it feels 00:02:02.719 --> 00:02:06.319 weaker. Exactly that. And the analysis does offer 00:02:06.319 --> 00:02:08.879 some reasons why this might be happening. Like 00:02:08.879 --> 00:02:13.259 what? It puts some of it down to... ongoing factors 00:02:13.259 --> 00:02:15.439 like the trade policy uncertainty we've seen, 00:02:15.900 --> 00:02:18.039 you know, the tariffs and that whole situation. 00:02:18.139 --> 00:02:20.680 Yeah, that makes sense. It's, well, it's hard 00:02:20.680 --> 00:02:22.900 to feel confident about making big plans if you 00:02:22.900 --> 00:02:25.020 don't quite know the rules of the game. Right. 00:02:25.039 --> 00:02:27.539 And the analysis has some really good specific 00:02:27.539 --> 00:02:30.960 examples that bring this soft versus hard split 00:02:30.960 --> 00:02:33.259 to life. Take consumer confidence, for instance. 00:02:33.280 --> 00:02:36.099 Okay. The expectations part of that, from the 00:02:36.099 --> 00:02:38.300 conference board, it's actually a component of 00:02:38.300 --> 00:02:41.469 the index of leading economic indicators. So 00:02:41.469 --> 00:02:43.449 it's considered pretty important for forecasting. 00:02:43.629 --> 00:02:46.650 Very much so. And the analysis notes this expectations 00:02:46.650 --> 00:02:49.849 component saw a, well, a huge drop, 40 points 00:02:49.849 --> 00:02:52.409 from its peak after the last election. Wow, 40 00:02:52.409 --> 00:02:54.090 points just on expectations. That sounds like 00:02:54.090 --> 00:02:56.729 a big deal. It is. And, you know, the analysis 00:02:56.729 --> 00:02:59.569 points out that this plunge in sentiment kind 00:02:59.569 --> 00:03:03.090 of implied we'd see weaker real GDP growth. And 00:03:03.090 --> 00:03:06.189 did we? We did, yeah. First quarter. GDP came 00:03:06.189 --> 00:03:08.530 in slightly negative. So initially it looked 00:03:08.530 --> 00:03:10.990 like that soft data was, you know, a pretty decent 00:03:10.990 --> 00:03:14.310 warning signal. Okay, so soft data dips, signals 00:03:14.310 --> 00:03:17.449 a slowdown, hard data follows. That sounds logical. 00:03:18.129 --> 00:03:21.009 Where's the divergence then? Ah, here's where 00:03:21.009 --> 00:03:22.870 it gets really interesting according to the source. 00:03:23.490 --> 00:03:26.669 The latest reading for May for those consumer 00:03:26.669 --> 00:03:30.310 confidence expectations, it actually showed a 00:03:30.310 --> 00:03:33.150 pretty significant rebound from April. So sentiment 00:03:33.150 --> 00:03:36.229 bounced back? It did. It seems like that soft 00:03:36.229 --> 00:03:38.810 data is suddenly playing catch -up a bit, maybe 00:03:38.810 --> 00:03:40.969 aligning more with these stronger estimates that 00:03:40.969 --> 00:03:43.210 are now coming out for a second quarter GDP. 00:03:43.590 --> 00:03:46.610 So the soft data is maybe more volatile, less 00:03:46.610 --> 00:03:48.710 connected to the underlying trends sometimes? 00:03:48.909 --> 00:03:50.810 That seems to be the implication here, yeah. 00:03:50.810 --> 00:03:52.930 It highlights that volatility. What about inflation? 00:03:53.259 --> 00:03:55.180 That's something everyone feels, right? Definitely. 00:03:55.300 --> 00:03:57.800 How does that fit in? Well, inflation is another 00:03:57.800 --> 00:04:00.419 really fascinating example of this gap, according 00:04:00.419 --> 00:04:02.759 to the analysis. It looks at the University of 00:04:02.759 --> 00:04:05.419 Michigan's consumer sentiment survey. They ask 00:04:05.419 --> 00:04:08.340 about inflation expectations? They do, both short 00:04:08.340 --> 00:04:11.280 -term and longer -term. And the data shows those 00:04:11.280 --> 00:04:13.659 expected inflation numbers have spiked up recently. 00:04:13.939 --> 00:04:16.019 So people think inflation is going higher or 00:04:16.019 --> 00:04:18.360 staying higher. Exactly. They expect it to be 00:04:18.360 --> 00:04:21.259 higher, which is, well, it's in stark contrast 00:04:21.259 --> 00:04:23.600 to the actual core consumer price index. The 00:04:23.600 --> 00:04:27.220 official measure. Yeah, the actual core CPI has 00:04:27.220 --> 00:04:29.759 continued to trend lower year over year. Whoa. 00:04:30.360 --> 00:04:33.759 So expected inflation is up, but actual measured 00:04:33.759 --> 00:04:36.560 inflation is down. That's the disconnect. A pretty 00:04:36.560 --> 00:04:39.000 wide one. That's quite something. Are there other 00:04:39.000 --> 00:04:41.240 examples in the analysis? Yeah, it also looks 00:04:41.240 --> 00:04:44.629 at the misery index. Simple concept. Unemployment 00:04:44.629 --> 00:04:46.949 plus inflation. Right. The actual misery index 00:04:46.949 --> 00:04:49.370 has kept trending lower, mostly because unemployment's 00:04:49.370 --> 00:04:52.269 been low. OK, makes sense. But the expected misery 00:04:52.269 --> 00:04:55.290 index, using the UMICS surveys, expected unemployment 00:04:55.290 --> 00:04:58.009 and expected inflation, that's shot up dramatically. 00:04:58.129 --> 00:05:00.449 Ah, because of that spike in expected inflation. 00:05:00.730 --> 00:05:03.550 Precisely. It's just another way to see how different 00:05:03.550 --> 00:05:06.790 the felt reality or the expected one can be from 00:05:06.790 --> 00:05:08.870 the measured numbers. What about businesses? 00:05:09.189 --> 00:05:11.230 Small businesses especially, you'd think they'd 00:05:11.230 --> 00:05:14.240 be sensitive to this stuff. Good question. The 00:05:14.240 --> 00:05:17.000 analysis looks at the NFIB survey that's small 00:05:17.000 --> 00:05:20.660 business optimism. And it shows, well, a similar 00:05:20.660 --> 00:05:23.779 kind of pattern. How so? Optimism jumped right 00:05:23.779 --> 00:05:27.060 after the election. But since then, about half 00:05:27.060 --> 00:05:29.740 of that gain has reversed this year. OK. And 00:05:29.740 --> 00:05:32.600 the analysis does note that NFIB data can sometimes 00:05:32.600 --> 00:05:35.360 have a bit of a political skew. And the recent 00:05:35.360 --> 00:05:38.339 big swings seem to be driven more by those soft 00:05:38.339 --> 00:05:42.199 components. The sentiment. the expectations right 00:05:42.199 --> 00:05:44.379 like that vibe session talk from a couple of 00:05:44.379 --> 00:05:46.639 years back but the feeling was recessionary but 00:05:46.639 --> 00:05:48.740 the data wasn't quite there exactly the analysis 00:05:48.740 --> 00:05:51.379 specifically mentions that it says back in 2022 00:05:51.379 --> 00:05:54.720 the nfid soft components plunged to all -time 00:05:54.720 --> 00:05:57.120 lows like worse than during the global financial 00:05:57.120 --> 00:05:59.720 crisis really worse than the gfc That's what 00:05:59.720 --> 00:06:01.600 the analysis says the soft component showed. 00:06:02.220 --> 00:06:04.139 But the hard components in that same survey, 00:06:04.339 --> 00:06:06.600 things like actual hiring plans or sales activity, 00:06:07.060 --> 00:06:09.579 they deteriorated, sure, but only fell to levels 00:06:09.579 --> 00:06:11.680 you'd see in prior slowdowns. They didn't match 00:06:11.680 --> 00:06:14.040 that massive plunge in sentiment. So sentiment 00:06:14.040 --> 00:06:16.980 way overshot the actual activity slowdown. It 00:06:16.980 --> 00:06:19.139 appears so. And the surge after the election 00:06:19.139 --> 00:06:22.579 showed the split too. Sentiment sword. But the 00:06:22.579 --> 00:06:25.000 hard activity measures just saw a bump nowhere 00:06:25.000 --> 00:06:27.560 near the same scale It really does seem to show 00:06:27.560 --> 00:06:30.160 how sentiment can you know get ahead of itself 00:06:30.160 --> 00:06:33.399 or overreact? So this pattern soft data being 00:06:33.399 --> 00:06:36.680 more jumpy more reactive and splitting off from 00:06:36.680 --> 00:06:39.519 the hard data It seems pretty consistent across 00:06:39.519 --> 00:06:41.699 different ways of measuring things based on the 00:06:41.699 --> 00:06:43.660 source That's really the main takeaway here. 00:06:43.660 --> 00:06:45.579 The analysis keeps coming back to persistent 00:06:45.579 --> 00:06:49.339 uncertainty around Trade policy the terrorists 00:06:49.339 --> 00:06:52.350 again. Yeah things like What are the goals? What 00:06:52.350 --> 00:06:54.750 will the rates be? Which sectors get hit next? 00:06:55.029 --> 00:06:57.230 That kind of ongoing question mark is likely 00:06:57.230 --> 00:06:59.990 a big reason why confidence isn't really roaring 00:06:59.990 --> 00:07:02.810 back, even if we've had some pauses lately. Does 00:07:02.810 --> 00:07:05.089 it look different regionally? Sometimes breaking 00:07:05.089 --> 00:07:07.449 it down gives you another angle. It does touch 00:07:07.449 --> 00:07:10.189 on that. It uses the Philadelphia Fed's regional 00:07:10.189 --> 00:07:14.930 surveys. And in late May, their manufacturing 00:07:14.930 --> 00:07:17.279 index showed a sharp bounce. Which is interesting 00:07:17.279 --> 00:07:19.259 since manufacturing is often right in the middle 00:07:19.259 --> 00:07:22.180 of tariff fights. Right. But the services index 00:07:22.180 --> 00:07:24.579 and services are a much bigger chunk of the economy. 00:07:24.839 --> 00:07:26.939 Remember that barely moved up at all. And both 00:07:26.939 --> 00:07:29.379 indexes were still actually in contraction territory. 00:07:29.720 --> 00:07:31.879 Meaning activity was still shrinking in the region 00:07:31.879 --> 00:07:34.339 according to the surveys. Correct. Still declining. 00:07:35.240 --> 00:07:37.660 And you know weakness in services is potentially 00:07:37.660 --> 00:07:40.529 more worrying. just given the size of that sector 00:07:40.529 --> 00:07:42.829 in the U .S. economy. Yeah, absolutely. The U 00:07:42.829 --> 00:07:44.930 .S. has a services surplus. It's the bulk of 00:07:44.930 --> 00:07:47.589 GDP. Exactly. So weakness there could have wider 00:07:47.589 --> 00:07:51.829 ripples. And the Philly Fed survey also had this 00:07:51.829 --> 00:07:54.449 special question this month that the analysis 00:07:54.449 --> 00:07:57.170 highlights. It's really relevant to that inflation 00:07:57.170 --> 00:07:59.029 perception thing we talked about. Yeah. What 00:07:59.029 --> 00:08:01.230 are they asked? They asked businesses if their 00:08:01.230 --> 00:08:03.629 customers had become less price sensitive. OK. 00:08:03.970 --> 00:08:06.730 And? Zero percent said yes. Zero. None. None. 00:08:06.889 --> 00:08:09.810 0 % said customers were less price sensitive. 00:08:10.230 --> 00:08:12.829 And on the flip side, half the businesses said 00:08:12.829 --> 00:08:14.850 their customers had become more price sensitive. 00:08:15.389 --> 00:08:19.519 Wow. That's pretty stark. It really is. And the 00:08:19.519 --> 00:08:21.560 analysis notes, you know, this is a reminder 00:08:21.560 --> 00:08:24.680 that for regular people, inflation isn't just 00:08:24.680 --> 00:08:27.600 some abstract number economists track month to 00:08:27.600 --> 00:08:30.379 month. No, it's how much did this cost last time? 00:08:30.660 --> 00:08:32.960 Exactly. It's psychological. It's about that 00:08:32.960 --> 00:08:35.740 price comparison, that volatility. And anxiety 00:08:35.740 --> 00:08:39.000 over that is probably putting real downward pressure 00:08:39.000 --> 00:08:42.240 on the soft data on how people feel, maybe more 00:08:42.240 --> 00:08:44.120 than the headline inflation rate itself would 00:08:44.120 --> 00:08:47.779 suggest. How are, say, financial markets digesting 00:08:47.779 --> 00:08:50.460 all this? Does the analysis touch on company 00:08:50.460 --> 00:08:53.279 earnings or analyst reviews? It does, yeah. It 00:08:53.279 --> 00:08:55.259 looks at analysts' expectations for corporate 00:08:55.259 --> 00:08:57.879 earnings. And it says, given all this policy 00:08:57.879 --> 00:09:00.379 uncertainty, especially tariffs, you've seen 00:09:00.379 --> 00:09:03.179 a rising number of S &P 500 companies either 00:09:03.179 --> 00:09:05.039 pull their future earnings guidance altogether 00:09:05.039 --> 00:09:07.600 or lower it. So companies are being cautious 00:09:07.600 --> 00:09:10.440 about predicting the future. Seems like it. And 00:09:10.440 --> 00:09:12.399 this has caused a decline in the earnings revision 00:09:12.399 --> 00:09:14.879 ratio that tracks how analysts are changing their 00:09:14.879 --> 00:09:17.220 forecasts. They're becoming more pessimistic 00:09:17.220 --> 00:09:19.820 generally. But haven't actual earnings been OK 00:09:19.820 --> 00:09:23.559 recently? Reasonably strong. Yes. According to 00:09:23.559 --> 00:09:26.460 the analysis, actual earnings growth has been 00:09:26.460 --> 00:09:28.700 trending higher looking through the first quarter 00:09:28.700 --> 00:09:33.120 of 2025 anyway. But the analysts, they haven't 00:09:33.120 --> 00:09:35.500 really extrapolated that strength forward. So. 00:09:35.799 --> 00:09:38.440 They see the recent good results, but maybe because 00:09:38.440 --> 00:09:40.460 of the company warnings and the uncertainty, 00:09:40.659 --> 00:09:43.240 they're hesitant to predict it'll continue. That 00:09:43.240 --> 00:09:45.960 seems to be the dynamic, yeah. So wrapping up 00:09:45.960 --> 00:09:48.440 this whole economic piece, the analysis really 00:09:48.440 --> 00:09:50.860 hammers home that policy uncertainty, particularly 00:09:50.860 --> 00:09:53.639 what it calls the mercurial nature of trade policy, 00:09:54.299 --> 00:09:57.179 is likely a key factor keeping expectations and 00:09:57.179 --> 00:09:59.799 sentiment kind of shaky. The source concludes 00:09:59.799 --> 00:10:02.490 that, you know, Businesses can adapt or nimble, 00:10:02.950 --> 00:10:04.649 but the real problem is not knowing the rules 00:10:04.649 --> 00:10:07.210 of the game. Right. Predictability matters. Immensely. 00:10:07.409 --> 00:10:09.730 So the analysis suggests, if we get more clarity, 00:10:10.110 --> 00:10:12.610 maybe soft data catches up to the hard data. 00:10:12.649 --> 00:10:16.309 But if we don't, it's probably more likely the 00:10:16.309 --> 00:10:18.629 hard data eventually catches down to the weak 00:10:18.629 --> 00:10:21.389 sentiment. So view that soft data as a potential 00:10:21.389 --> 00:10:23.210 early warning. That's how this source frames 00:10:23.210 --> 00:10:25.899 it, yes. A crucial early warning system. OK, 00:10:25.899 --> 00:10:28.159 that gives us a really clear sense of that economic 00:10:28.159 --> 00:10:31.080 puzzle and the potential role policy uncertainty 00:10:31.080 --> 00:10:35.539 plays. Now, let's let's pivot. Let's look at 00:10:35.539 --> 00:10:38.080 our second source, which gives us a window into 00:10:38.080 --> 00:10:41.659 a very specific, very timely policy debate happening 00:10:41.659 --> 00:10:43.240 right now. Right. This one takes us straight 00:10:43.240 --> 00:10:45.799 to Washington, focusing on comments from Pennsylvania 00:10:45.799 --> 00:10:48.399 Senator Dave McCormick. OK, it's about a big 00:10:48.399 --> 00:10:50.860 budget bill. It just barely passed the House. 00:10:50.980 --> 00:10:53.919 And the source notes that everyone expects changes 00:10:53.919 --> 00:10:56.230 as it goes to the Senate. And the politics are 00:10:56.230 --> 00:10:58.009 pretty clear cut, at least on the House side 00:10:58.009 --> 00:11:01.169 for PA reps. Yes. The source mentions that Pennsylvania 00:11:01.169 --> 00:11:03.570 Democrats in the House all voted against it, 00:11:03.629 --> 00:11:05.669 while all the state's Republican lawmakers voted 00:11:05.669 --> 00:11:08.309 for it. OK. And what did Senator McCormick say? 00:11:08.590 --> 00:11:11.350 Well, speaking on TV, the source quotes him saying, 00:11:12.049 --> 00:11:13.669 it's pretty clear there's been out -of -control 00:11:13.669 --> 00:11:16.129 spending in a Medicaid program. And he feels 00:11:16.129 --> 00:11:18.710 the bill can be improved in the Senate by adding 00:11:18.710 --> 00:11:22.309 more fiscal restraint. That's his goal. How are 00:11:22.309 --> 00:11:25.629 Democrats framing this push for fiscal restraint? 00:11:25.830 --> 00:11:28.269 What's their take, according to the source? Well, 00:11:28.549 --> 00:11:30.850 according to the article, Democrats are warning 00:11:30.850 --> 00:11:34.490 that the Republican aim here is to make pre -substantial 00:11:34.490 --> 00:11:37.730 cuts to big social safety net programs. Like 00:11:37.730 --> 00:11:39.889 Medicaid, which McCormick mentioned. Exactly. 00:11:40.129 --> 00:11:43.590 Medicaid and also SNAP, the Supplemental Nutrition 00:11:43.590 --> 00:11:46.029 Assistance Program, often called food stamps. 00:11:47.220 --> 00:11:49.759 Democrats warn the goal is to slash these programs 00:11:49.759 --> 00:11:52.480 to pay for tax cuts elsewhere, potentially for 00:11:52.480 --> 00:11:55.360 wealthier individuals or corporations. Does the 00:11:55.360 --> 00:11:57.799 source mention the scale of cuts Democrats are 00:11:57.799 --> 00:11:59.700 warning about? It does. They're talking about 00:11:59.700 --> 00:12:02.500 potential cuts close to 900 billion dollars from 00:12:02.500 --> 00:12:05.480 Medicaid over 10 years and nearly 300 billion 00:12:05.480 --> 00:12:07.879 dollars from SNAP over the same period. Those 00:12:07.879 --> 00:12:10.320 are significant numbers. They are. And the source 00:12:10.320 --> 00:12:12.580 quotes Pennsylvania's other senator, John Fetterman, 00:12:12.620 --> 00:12:14.960 who's a Democrat, saying he flat out won't vote 00:12:14.960 --> 00:12:17.419 for the bill because of the impact on these assistance 00:12:17.419 --> 00:12:19.179 programs. OK, so you have the different political 00:12:19.179 --> 00:12:23.159 perspectives. Does the source bring in any nonpartisan 00:12:23.159 --> 00:12:25.830 context on the kind of cuts needed for major 00:12:25.830 --> 00:12:28.330 savings. It does, yeah. It references the Congressional 00:12:28.330 --> 00:12:30.809 Budget Office, the CBO. They're the nonpartisan 00:12:30.809 --> 00:12:33.289 scorekeepers. And a CBO report back in March 00:12:33.289 --> 00:12:35.789 apparently stated that if you wanted to cut federal 00:12:35.789 --> 00:12:39.429 budget costs by around $880 billion over 10 years, 00:12:40.149 --> 00:12:42.129 a number similar to the Medicaid figure being 00:12:42.129 --> 00:12:45.169 discussed, it would be basically impossible without 00:12:45.169 --> 00:12:47.750 making cuts to big programs like Medicaid, Medicare, 00:12:47.889 --> 00:12:49.919 or the Children's Health Insurance Program. So 00:12:49.919 --> 00:12:52.299 the CBO perspective seems to align with the idea 00:12:52.299 --> 00:12:54.879 that achieving savings of that magnitude would 00:12:54.879 --> 00:12:57.200 almost certainly involve these major programs. 00:12:57.539 --> 00:12:59.820 It provides that nonpartisan context for the 00:12:59.820 --> 00:13:02.679 scale, yes. It suggests that finding hundreds 00:13:02.679 --> 00:13:04.779 of billions in savings without touching those 00:13:04.779 --> 00:13:08.179 large entitlement programs is, well... Very difficult. 00:13:08.720 --> 00:13:10.840 Okay. Getting back to Senator McCormick's specific 00:13:10.840 --> 00:13:13.940 points on Medicaid, he did say the goal is protection 00:13:13.940 --> 00:13:16.419 for some groups. Yes, the source quotes him saying 00:13:16.419 --> 00:13:19.200 the goal is to ensure that the vulnerable people 00:13:19.200 --> 00:13:21.340 for whom this program was designed are protected 00:13:21.340 --> 00:13:24.039 and those benefits are protected. But he also 00:13:24.039 --> 00:13:26.259 specified groups he thinks should not be covered. 00:13:26.379 --> 00:13:36.519 He did. The source quotes him saying, So that 00:13:36.519 --> 00:13:39.480 defines a specific area where he sees potential 00:13:39.480 --> 00:13:42.419 savings or eligibility changes. From his perspective, 00:13:42.679 --> 00:13:46.200 yes. It targets those specific groups for potential 00:13:46.200 --> 00:13:48.690 exclusion or stricter requirements. Now, the 00:13:48.690 --> 00:13:52.470 source also brings in info from the state agency 00:13:52.470 --> 00:13:54.549 that actually runs these programs in Pennsylvania, 00:13:54.830 --> 00:13:56.889 right? The Department of Human Services. Yes, 00:13:57.230 --> 00:14:00.490 PADHS. And this gives some useful context on 00:14:00.490 --> 00:14:03.529 how things currently work, how they check eligibility 00:14:03.529 --> 00:14:05.929 and try to prevent fraud. What does it say they 00:14:05.929 --> 00:14:09.929 do? According to PADHS, they check income and 00:14:09.929 --> 00:14:12.450 household details against a whole bunch of databases, 00:14:12.549 --> 00:14:14.769 state and federal. Things like the State Department 00:14:14.769 --> 00:14:17.070 of Labor and Industry, Social Security Administration. 00:14:17.480 --> 00:14:19.879 And importantly, given the senator's comments, 00:14:20.379 --> 00:14:22.600 immigration database is to check citizenship 00:14:22.600 --> 00:14:25.179 and residency status. So they explicitly check 00:14:25.179 --> 00:14:27.320 immigration status currently. What else? They 00:14:27.320 --> 00:14:29.840 also cross check with the IRS, the state tax 00:14:29.840 --> 00:14:31.799 department, other public assistance agencies, 00:14:32.240 --> 00:14:34.019 even the state lottery in the Department of Health. 00:14:34.259 --> 00:14:35.919 Sounds like a pretty thorough vetting process. 00:14:36.100 --> 00:14:38.600 And what about catching fraud or improper payments? 00:14:38.740 --> 00:14:40.940 What does the source say about their efforts 00:14:40.940 --> 00:14:44.009 there? It mentions the PADHS Office of Inspector 00:14:44.009 --> 00:14:47.210 General investigated over 19 ,000 applications 00:14:47.210 --> 00:14:50.070 just in the last fiscal year. 19 ,000. Yeah, 00:14:50.190 --> 00:14:52.669 and it says the vast majority of those investigations 00:14:52.669 --> 00:14:55.470 came from referrals within the human services 00:14:55.470 --> 00:14:58.110 system itself, like caseworkers flagging issues. 00:14:58.269 --> 00:15:00.690 Okay. And they have a specific unit. the Bureau 00:15:00.690 --> 00:15:03.669 of Program Integrity, focused just on Medicaid 00:15:03.669 --> 00:15:06.909 fraud. They even use, apparently, an AI system 00:15:06.909 --> 00:15:09.990 for fraud detection, looking for outliers. And 00:15:09.990 --> 00:15:11.710 they work with the state attorney general on 00:15:11.710 --> 00:15:14.230 cases. Did they mention any results from these 00:15:14.230 --> 00:15:16.870 efforts, any actual savings found? They did. 00:15:16.929 --> 00:15:20.389 For the 2023 -24 fiscal year, PADHS reported 00:15:20.389 --> 00:15:23.750 removing 325 providers from the Medicaid program 00:15:23.750 --> 00:15:26.409 for cause, things like fraud or abuse. And they 00:15:26.409 --> 00:15:29.529 estimate that saved nearly $34 million. OK. So 00:15:29.529 --> 00:15:32.009 the source provides that context. There's an 00:15:32.009 --> 00:15:34.070 ongoing effort within the existing system to 00:15:34.070 --> 00:15:37.250 ensure program integrity and fine savings alongside 00:15:37.250 --> 00:15:39.590 this political debate about broader changes to 00:15:39.590 --> 00:15:41.809 spending and eligibility. Exactly. It paints 00:15:41.809 --> 00:15:43.730 a picture of the current operational checks and 00:15:43.730 --> 00:15:47.370 balances. Right. So let's step back. We've looked 00:15:47.370 --> 00:15:50.509 at these two distinct areas from our sources. 00:15:51.110 --> 00:15:54.549 We have this confusing economic picture, weak 00:15:54.549 --> 00:15:58.070 feelings, okayish, hard data, with policy uncertainty 00:15:58.070 --> 00:16:01.570 looming large. And then we have this very real, 00:16:01.769 --> 00:16:04.110 very current political fight over government 00:16:04.110 --> 00:16:07.009 spending, fiscal restraint, and who qualifies 00:16:07.009 --> 00:16:09.009 for major safety net programs like Medicaid. 00:16:09.450 --> 00:16:11.419 Yeah. So now let's try to connect them. What 00:16:11.419 --> 00:16:13.259 happens when you put these side by side? Well, 00:16:13.379 --> 00:16:15.799 one source tells us policy uncertainty dampens 00:16:15.799 --> 00:16:18.019 confidence, right? Right. Key theme. And the 00:16:18.019 --> 00:16:19.899 other source gives us a perfect live example 00:16:19.899 --> 00:16:23.159 of exactly that kind of high stakes policy uncertainty 00:16:23.159 --> 00:16:26.100 playing out right now. And it's about fundamental 00:16:26.100 --> 00:16:29.419 things, spending, safety nets. It really is uncertainty 00:16:29.419 --> 00:16:31.600 in action, isn't it? And could there be a more 00:16:31.600 --> 00:16:34.169 direct link? You mentioned that Philly Fed survey 00:16:34.169 --> 00:16:36.669 finding people are highly sensitive to prices 00:16:36.669 --> 00:16:39.169 right now. Yeah, 0 % feeling customers are less 00:16:39.169 --> 00:16:41.710 price sensitive, half feeling they're more sensitive. 00:16:42.029 --> 00:16:44.710 So if people are already feeling squeezed by 00:16:44.710 --> 00:16:48.149 costs, could the debate about potentially cutting 00:16:48.149 --> 00:16:50.990 assistance programs designed to help with those 00:16:50.990 --> 00:16:53.669 very costs make them feel even more anxious? 00:16:54.250 --> 00:16:57.029 Could that feed directly into the negative sentiment 00:16:57.029 --> 00:16:59.990 the soft data is picking up? That seems plausible, 00:16:59.990 --> 00:17:01.809 doesn't it? If you're worried about making ends 00:17:01.809 --> 00:17:04.690 meet, hearing about potential cuts to food assistance 00:17:04.690 --> 00:17:08.490 or health care assistance, well, that's likely 00:17:08.490 --> 00:17:11.730 to increase anxiety, not reduce it. It could 00:17:11.730 --> 00:17:14.549 definitely contribute to that weak feeling about 00:17:14.549 --> 00:17:17.109 the economy. It connects the psychological impact 00:17:17.109 --> 00:17:19.710 of perceived inflation with the psychological 00:17:19.710 --> 00:17:22.569 impact of policy uncertainty around support systems. 00:17:22.890 --> 00:17:25.289 And what about the uncertainty itself, just the 00:17:25.289 --> 00:17:27.710 political fighting back and forth? Regardless 00:17:27.710 --> 00:17:30.690 of the final outcome of this Senate bill, how 00:17:30.690 --> 00:17:32.930 much does the process, the prolonged debate, 00:17:33.329 --> 00:17:35.509 the headlines, the arguments contribute to the 00:17:35.509 --> 00:17:37.569 kind of uncertainty that the economic analysis 00:17:37.569 --> 00:17:39.910 said weighs down confidence? That's a really 00:17:39.910 --> 00:17:42.170 important point. The economic analysis stressed 00:17:42.170 --> 00:17:44.490 that a lack of clarity on the rules of the game 00:17:44.490 --> 00:17:47.890 is the drag. Well, a public often quite heated 00:17:47.890 --> 00:17:50.710 debate about the future rules for major government 00:17:50.710 --> 00:17:53.609 programs creates a period of intense uncertainty 00:17:53.609 --> 00:17:56.549 about those rules. It seems very likely that 00:17:56.549 --> 00:17:58.769 the stress and unpredictability of the debate 00:17:58.769 --> 00:18:02.029 itself could be hitting sentiment, hitting confidence, 00:18:02.289 --> 00:18:04.349 even before any changes are actually made. So 00:18:04.349 --> 00:18:06.490 the debate itself becomes an economic factor. 00:18:06.829 --> 00:18:09.289 Quite possibly, yeah. It contributes to that 00:18:09.289 --> 00:18:11.450 atmosphere of unpredictability that businesses 00:18:11.450 --> 00:18:14.240 and consumers seem to dislike so much. according 00:18:14.240 --> 00:18:16.539 to the first source. Okay, so that first source 00:18:16.539 --> 00:18:19.140 suggested soft data is like an early warning 00:18:19.140 --> 00:18:22.319 system. Are these weak sentiment signals? We're 00:18:22.319 --> 00:18:24.740 seeing warnings about the potential future impact 00:18:24.740 --> 00:18:27.440 if these debated policy changes actually happen. 00:18:27.519 --> 00:18:29.640 That's one possibility. Or are they more just 00:18:29.640 --> 00:18:31.940 reflecting the current stress and anxiety caused 00:18:31.940 --> 00:18:34.160 by the uncertainty of the debate itself? It's 00:18:34.160 --> 00:18:37.420 probably... you know, some complex mix of both, 00:18:37.680 --> 00:18:39.880 realistically. The sources don't give us a definitive 00:18:39.880 --> 00:18:41.500 answer there, but they definitely make you think 00:18:41.500 --> 00:18:44.559 about it. Is the sentiment predicting a future 00:18:44.559 --> 00:18:48.920 hit from policy changes? Or is it reflecting 00:18:48.920 --> 00:18:52.420 a current hit from policy uncertainty? It's a 00:18:52.420 --> 00:18:54.779 really critical distinction to consider. Absolutely. 00:18:55.559 --> 00:18:58.880 So we've taken a deep dive today into, well, 00:18:59.000 --> 00:19:01.799 two really fascinating areas, this puzzling gap 00:19:01.799 --> 00:19:04.579 between the economic numbers and the economic 00:19:04.579 --> 00:19:08.460 mood. The hard versus soft data story. And this 00:19:08.460 --> 00:19:11.119 very pointed, very current political debate over 00:19:11.119 --> 00:19:13.140 government spending and the future of crucial 00:19:13.140 --> 00:19:15.849 assistance programs. And understanding both the 00:19:15.849 --> 00:19:17.809 data points and the different viewpoints in that 00:19:17.809 --> 00:19:19.950 policy debate feels really key right now, doesn't 00:19:19.950 --> 00:19:22.150 it? To just being well informed. Definitely. 00:19:22.230 --> 00:19:23.970 These aren't just headlines. They really touch 00:19:23.970 --> 00:19:26.269 on how we measure economic health and the big 00:19:26.269 --> 00:19:28.230 choices being made about government's role. And 00:19:28.230 --> 00:19:30.809 looking at them together, like we did, reveals 00:19:30.809 --> 00:19:33.349 these potential connections. How policy debates 00:19:33.349 --> 00:19:35.730 might directly impact economic sentiment, for 00:19:35.730 --> 00:19:38.109 example, that you might miss otherwise. OK, so 00:19:38.109 --> 00:19:40.609 here's a final, maybe provocative thought for 00:19:40.609 --> 00:19:44.450 you to chew on as you go about your day. If consumer 00:19:44.450 --> 00:19:47.150 confidence, if expectations are as sensitive 00:19:47.150 --> 00:19:49.970 as that first analysis suggested, sensitive to 00:19:49.970 --> 00:19:51.990 policy uncertainty, sensitive to feeling squeezed 00:19:51.990 --> 00:19:56.289 by prices, does the very act of having a prolonged, 00:19:56.309 --> 00:19:58.670 very public political fight over these fundamental 00:19:58.670 --> 00:20:01.430 economic programs become a significant economic 00:20:01.430 --> 00:20:04.349 headwind in itself? Could the disagreement itself 00:20:04.349 --> 00:20:07.210 impact sentiment, impact behavior, even before 00:20:07.210 --> 00:20:09.509 a single law is changed? What does that tell 00:20:09.509 --> 00:20:11.849 us about the interplay between politics, perception, 00:20:11.869 --> 00:20:12.650 and the economy?