1 00:00:00,280 --> 00:00:06,480 Speaker 1: Wow, she was like sugar red, qunchy like chips and 2 00:00:06,519 --> 00:00:16,159 Speaker 1: stuff like that. Actually, the days are crickets. Hello and 3 00:00:16,200 --> 00:00:19,000 Speaker 1: welcome to Stephanomics, the podcast that brings the global economy 4 00:00:19,040 --> 00:00:21,680 Speaker 1: to you. What you heard there might well be your 5 00:00:21,720 --> 00:00:24,759 Speaker 1: Christmas dinner in a few years time. Keep listening to 6 00:00:24,800 --> 00:00:27,639 Speaker 1: find out what those lucky people were tasting why it 7 00:00:27,760 --> 00:00:31,480 Speaker 1: might just save the world. We also tell you about 8 00:00:31,480 --> 00:00:34,760 Speaker 1: a new treasure trove of data showing just how much 9 00:00:34,840 --> 00:00:37,879 Speaker 1: federal stimulus checks have boosted the spending of black and 10 00:00:37,920 --> 00:00:42,160 Speaker 1: Hispanic families and how inflation and a slower economy could 11 00:00:42,159 --> 00:00:45,959 Speaker 1: take them back to square one. But first tune in, 12 00:00:46,240 --> 00:00:50,240 Speaker 1: drop out, Live flat. Their parents may have worked twelve 13 00:00:50,320 --> 00:00:54,720 Speaker 1: hour days for decades, supporting China's breakneck economic growth, but 14 00:00:54,800 --> 00:00:57,840 Speaker 1: a growing number of young people in this famously industrious 15 00:00:57,920 --> 00:01:01,160 Speaker 1: nation would rather not work at all. You could say 16 00:01:01,520 --> 00:01:05,040 Speaker 1: it was the Asian counterpart to the Great Resignation that 17 00:01:05,120 --> 00:01:08,480 Speaker 1: has business executives scratching their heads in the US and Europe. 18 00:01:09,000 --> 00:01:11,319 Speaker 1: But the life flat movement also tells us quite a 19 00:01:11,319 --> 00:01:15,280 Speaker 1: lot about how Chinese society has changed, and it could 20 00:01:15,280 --> 00:01:18,240 Speaker 1: even put the government's grand plans for the future at risk. 21 00:01:19,000 --> 00:01:21,640 Speaker 1: In a minute, I'll discuss all of that with Bloomberg 22 00:01:21,640 --> 00:01:24,759 Speaker 1: opinion columnist Truly Rehn. But first here is our Hong 23 00:01:24,840 --> 00:01:35,000 Speaker 1: Kong based China economy reporter Tom Hancock. In what has 24 00:01:35,040 --> 00:01:38,800 Speaker 1: been branded as the Great Resignation, US workers are quitting 25 00:01:38,840 --> 00:01:42,280 Speaker 1: their jobs in record numbers. More than twenty four million 26 00:01:42,360 --> 00:01:45,840 Speaker 1: did so from April to September this year. Millions have 27 00:01:45,920 --> 00:01:49,720 Speaker 1: dropped permanently out of the labor force, and similar trends 28 00:01:49,800 --> 00:01:53,559 Speaker 1: are being seen in parts of Europe. But even in China, 29 00:01:53,920 --> 00:01:57,200 Speaker 1: where coronavirus has been kept largely under control and the 30 00:01:57,240 --> 00:02:00,559 Speaker 1: economy has doubled in size in the last decade, people 31 00:02:00,680 --> 00:02:05,480 Speaker 1: are having similar feelings. The country's live flat movement, jump 32 00:02:05,560 --> 00:02:09,160 Speaker 1: started by a social media post, is also about opting out. 33 00:02:09,760 --> 00:02:12,320 Speaker 1: It's a reaction against the system in which are grueling 34 00:02:12,440 --> 00:02:16,480 Speaker 1: nine six work schedule nine am to nine pm, six 35 00:02:16,560 --> 00:02:20,600 Speaker 1: days a week is common in industries like technology, so 36 00:02:20,800 --> 00:02:24,760 Speaker 1: is unrelenting pressure from family, society, and even the government 37 00:02:24,960 --> 00:02:30,400 Speaker 1: to keep climbing the ladder. In October, thousands of employees 38 00:02:30,440 --> 00:02:33,800 Speaker 1: at Chinese tech giants, including Ali Baba Group and the 39 00:02:33,840 --> 00:02:36,960 Speaker 1: owner of TikTok groused about their long work hours in 40 00:02:37,000 --> 00:02:41,200 Speaker 1: an online campaign called workers Lives Matter and It's not 41 00:02:41,280 --> 00:02:44,520 Speaker 1: just a white color phenomenon. In the southern city of Shenjen, 42 00:02:44,880 --> 00:02:48,440 Speaker 1: migrant workers from the countryside, once celebrated for being willing 43 00:02:48,480 --> 00:02:52,280 Speaker 1: to work tough jobs in manufacturing, are rejecting manual jobs 44 00:02:52,400 --> 00:02:55,520 Speaker 1: and sometimes prefer to spend their time playing online games, 45 00:02:55,840 --> 00:02:59,880 Speaker 1: picking up day jobs as needed. Some see parallels between 46 00:03:00,000 --> 00:03:03,240 Speaker 1: lie flat sentiment in China and similar ideas that emerged 47 00:03:03,280 --> 00:03:06,560 Speaker 1: in Japan in the ninety nineties after its economic growth 48 00:03:06,639 --> 00:03:12,280 Speaker 1: slowed sharply, suggesting China might be facing impending Japan style stagnation, 49 00:03:14,560 --> 00:03:17,240 Speaker 1: but others argue it's more like the nineteen sixties style 50 00:03:17,320 --> 00:03:20,240 Speaker 1: counterculture movements that cropped up in the US and Europe 51 00:03:20,400 --> 00:03:23,600 Speaker 1: in a period of affluence, with ordinary people seeking a 52 00:03:23,639 --> 00:03:27,639 Speaker 1: lower pressure society that's more focused on personal development the 53 00:03:27,840 --> 00:03:31,720 Speaker 1: material success. That's the view of Chenza Young, a twenty 54 00:03:31,760 --> 00:03:34,240 Speaker 1: five year old who lives in shen Jen while studying 55 00:03:34,320 --> 00:03:39,000 Speaker 1: for a master's degree online. The society needs more definition 56 00:03:39,040 --> 00:03:42,040 Speaker 1: about sucsass at the people around you are doing so well, 57 00:03:42,160 --> 00:03:44,840 Speaker 1: or you are all competing for the same job or 58 00:03:44,960 --> 00:03:48,480 Speaker 1: for the same kind of success, so you kind of 59 00:03:48,560 --> 00:03:52,440 Speaker 1: like fear you are gonna laughed out, so some people 60 00:03:52,520 --> 00:03:55,880 Speaker 1: just give up. Even though China, the US and Europe 61 00:03:55,880 --> 00:03:59,280 Speaker 1: are very different economies, we can connect China's lying flat 62 00:03:59,280 --> 00:04:03,040 Speaker 1: discussion with the concept of the great resignation in developed countries, 63 00:04:03,320 --> 00:04:06,520 Speaker 1: says Young Bio, director of the Max Planck Institute for 64 00:04:06,600 --> 00:04:10,960 Speaker 1: Social Anthropology in Germany. Both are about questioning the pursuit 65 00:04:11,040 --> 00:04:14,560 Speaker 1: of wealth at an individual and a social level. They 66 00:04:15,520 --> 00:04:18,400 Speaker 1: emerge at at the same time, and we can make 67 00:04:18,440 --> 00:04:22,599 Speaker 1: a connection and the even links to bigger idea such 68 00:04:22,720 --> 00:04:27,520 Speaker 1: as the growths such as a sustainability and this is 69 00:04:27,560 --> 00:04:31,640 Speaker 1: somehow all related because it is about the overheating about 70 00:04:31,720 --> 00:04:34,520 Speaker 1: US society. They become a excessively completed too, and just 71 00:04:34,680 --> 00:04:39,200 Speaker 1: not as sustainable, not only in terms of environmental extraction, 72 00:04:39,279 --> 00:04:44,560 Speaker 1: but also the mental the mental abendance is not as sustainable. 73 00:04:45,120 --> 00:04:48,440 Speaker 1: Among the new dropouts is Melana Cooler, a twenty six 74 00:04:48,520 --> 00:04:50,839 Speaker 1: year old from Germany who lost her job at the 75 00:04:50,880 --> 00:04:54,080 Speaker 1: height of the pandemic last year. She has chosen to 76 00:04:54,120 --> 00:04:58,320 Speaker 1: set up an environmentally sustainable commune in the countryside rather 77 00:04:58,360 --> 00:05:01,760 Speaker 1: than seeking full time work. Am I enough just the 78 00:05:01,800 --> 00:05:07,440 Speaker 1: way I am? Or do I constantly needs new equipment, 79 00:05:08,160 --> 00:05:12,840 Speaker 1: new material, new goods in my life, new experiences to 80 00:05:13,080 --> 00:05:18,120 Speaker 1: feel full in some form. So that's something I've really changed. 81 00:05:18,160 --> 00:05:23,560 Speaker 1: Here in developed countries, pressure has been building for decades. 82 00:05:24,120 --> 00:05:28,560 Speaker 1: Incomes of stagnated, job security has become precarious, and the 83 00:05:28,640 --> 00:05:32,080 Speaker 1: costs of housing and education of sword making it harder 84 00:05:32,120 --> 00:05:36,440 Speaker 1: to build a financially stable life. Melana story indicates how 85 00:05:36,440 --> 00:05:41,080 Speaker 1: the pandemic has sometimes converted those simmering concerns into action. 86 00:05:43,040 --> 00:05:45,720 Speaker 1: Will the Great Resignation and Life Let have a longer 87 00:05:45,839 --> 00:05:49,280 Speaker 1: term impact. While they are a challenge to conventional ideas 88 00:05:49,400 --> 00:05:53,000 Speaker 1: about work and consumption, Cimball points out that they seem 89 00:05:53,040 --> 00:05:57,279 Speaker 1: to lack specific demands about how to change society. Many 90 00:05:57,320 --> 00:06:00,480 Speaker 1: will be waiting to see if those ideas emerge. For 91 00:06:00,560 --> 00:06:10,240 Speaker 1: Bloomberg News, I'm Tom Hancock in Hong Kong, so I 92 00:06:10,279 --> 00:06:12,919 Speaker 1: wanted to talk a bit more about lying Flat and 93 00:06:13,200 --> 00:06:17,000 Speaker 1: what it tells us about China's future with Bloomberg Opinion 94 00:06:17,040 --> 00:06:21,040 Speaker 1: columnists Shuli Wren in Hong Kong. Shuli, welcome to Stephanomics. 95 00:06:21,560 --> 00:06:23,760 Speaker 1: I love the opening line of a columny wrote the 96 00:06:23,760 --> 00:06:27,640 Speaker 1: other day, China's young people are stressed out. I mean, 97 00:06:27,640 --> 00:06:31,520 Speaker 1: there are there are concrete forces making young people's lives 98 00:06:31,560 --> 00:06:35,480 Speaker 1: more stressful in China and the live flat movement, it's 99 00:06:35,520 --> 00:06:39,640 Speaker 1: just one reaction to that, so tell us more absolutely 100 00:06:39,800 --> 00:06:44,480 Speaker 1: definitely so so like after the Chinese governments a big 101 00:06:44,480 --> 00:06:48,960 Speaker 1: tack and big real estate developer crackdown like that actually 102 00:06:49,279 --> 00:06:53,960 Speaker 1: ruined a lot of the job prospects for China's young people, 103 00:06:54,080 --> 00:06:58,080 Speaker 1: especially those fresh university graduates. I mean, China has been 104 00:06:58,120 --> 00:07:03,479 Speaker 1: training about ten million university graduates every year, and in 105 00:07:03,520 --> 00:07:06,440 Speaker 1: the past they already struggled to find good jobs, and 106 00:07:06,480 --> 00:07:09,840 Speaker 1: now the jobs are even fewer than before. And then 107 00:07:09,880 --> 00:07:14,480 Speaker 1: like in the graduating class of only one third decided 108 00:07:14,520 --> 00:07:17,320 Speaker 1: to go into the job market, with another third saying, oh, 109 00:07:17,480 --> 00:07:20,320 Speaker 1: I'm just going to try to, you know, go out 110 00:07:20,360 --> 00:07:23,560 Speaker 1: to grad school because you know, like when there's a reception, 111 00:07:23,880 --> 00:07:27,200 Speaker 1: everyone applies for grass school, right, some young people they 112 00:07:27,240 --> 00:07:29,480 Speaker 1: just say, oh, I'm just going to take some time off, 113 00:07:29,720 --> 00:07:32,480 Speaker 1: lie on my parents cultures, and figure out what they 114 00:07:32,520 --> 00:07:34,880 Speaker 1: want to do. And I guess one of the one 115 00:07:34,880 --> 00:07:38,200 Speaker 1: of the things that's happening is that you know, China's 116 00:07:38,800 --> 00:07:41,320 Speaker 1: getting richer as a country and now it can have 117 00:07:41,560 --> 00:07:43,760 Speaker 1: dropouts just the way that you know, the US had 118 00:07:43,800 --> 00:07:48,520 Speaker 1: dropouts in the sixties. Absolutely, I mean being able to 119 00:07:48,600 --> 00:07:51,400 Speaker 1: life flat, to be honest, it's a luxury good, right. 120 00:07:52,320 --> 00:07:56,680 Speaker 1: And also like that the cost of of UH consumption 121 00:07:56,800 --> 00:07:59,160 Speaker 1: for a lot of young people is coming down, like 122 00:07:59,200 --> 00:08:03,480 Speaker 1: a China's a chancy just like elsewhere in US and 123 00:08:03,520 --> 00:08:06,360 Speaker 1: then the UK. They don't need very much money or 124 00:08:06,440 --> 00:08:09,000 Speaker 1: like they spend a lot of time on the internet 125 00:08:09,120 --> 00:08:13,160 Speaker 1: right surfing on the internet, chat rooms and playing video games. 126 00:08:13,360 --> 00:08:16,280 Speaker 1: It doesn't cost very much money, so they don't need 127 00:08:16,320 --> 00:08:19,520 Speaker 1: to really earn that much, so they can afford it 128 00:08:19,560 --> 00:08:22,480 Speaker 1: in a way. And also the parents. China has this 129 00:08:22,680 --> 00:08:25,320 Speaker 1: major like only child policy, right, so the parents will 130 00:08:25,360 --> 00:08:27,920 Speaker 1: be like, oh, that's my baby, and what can I do? 131 00:08:28,000 --> 00:08:30,280 Speaker 1: I have to let them life that I want to 132 00:08:30,280 --> 00:08:34,120 Speaker 1: get on with two what how the government's responded. But 133 00:08:34,240 --> 00:08:36,880 Speaker 1: before I get to that, I wanted to ask you 134 00:08:36,920 --> 00:08:42,319 Speaker 1: about young women facing particular challenges in this labor force. 135 00:08:42,520 --> 00:08:45,480 Speaker 1: Tell us a bit more about that. So the best 136 00:08:45,559 --> 00:08:49,200 Speaker 1: jobs for young women are in the services industries such 137 00:08:49,240 --> 00:08:54,120 Speaker 1: as financial industry like UH and the media industry. And 138 00:08:54,160 --> 00:08:57,800 Speaker 1: then like in China as in South Korea, like when 139 00:08:57,920 --> 00:09:00,839 Speaker 1: when the young people apply for jobs they they need, 140 00:09:01,000 --> 00:09:04,600 Speaker 1: they oftentimes are asked to attach a photo of themselves, right, 141 00:09:04,840 --> 00:09:07,720 Speaker 1: So so young women start to feel that, oh, you know, 142 00:09:07,840 --> 00:09:10,880 Speaker 1: if I I look better that that that makes me 143 00:09:10,960 --> 00:09:13,440 Speaker 1: look a little bit more presentable. So a lot of 144 00:09:13,480 --> 00:09:16,920 Speaker 1: them start to do a lot of clusters surgery like facelifts, 145 00:09:16,920 --> 00:09:19,480 Speaker 1: you know, those jobs, so on, so forth, which is 146 00:09:19,559 --> 00:09:22,520 Speaker 1: quite awful. H A while back, I also wrote about 147 00:09:22,559 --> 00:09:25,160 Speaker 1: like aesthetics medicine in China, and then most of the 148 00:09:25,280 --> 00:09:28,960 Speaker 1: demand don't come from older women but from eighteen to 149 00:09:29,040 --> 00:09:32,080 Speaker 1: twenty four year olds, and it has become such a problem. 150 00:09:32,240 --> 00:09:34,760 Speaker 1: And then like basically a facelift costs a lot of 151 00:09:34,800 --> 00:09:37,480 Speaker 1: money and they don't have money for that, right, So 152 00:09:37,480 --> 00:09:41,000 Speaker 1: so there are e commerce websites such as Ali baba 153 00:09:41,160 --> 00:09:46,160 Speaker 1: um that uh, that give young women so called consumer 154 00:09:46,240 --> 00:09:51,839 Speaker 1: loans to do those um a statics medicine procedures um. 155 00:09:52,000 --> 00:09:54,160 Speaker 1: And then the government is calling a stop to that. 156 00:09:54,200 --> 00:09:58,240 Speaker 1: They said that, you know, companies can no longer sell 157 00:09:58,320 --> 00:10:05,839 Speaker 1: asset back securities which are backed by plastic facelift loans. Unbelievable. 158 00:10:06,400 --> 00:10:11,520 Speaker 1: It's clearly alarming, I would think for China's government that 159 00:10:11,559 --> 00:10:14,720 Speaker 1: has such big plans to to lead the world in 160 00:10:15,360 --> 00:10:20,720 Speaker 1: several critical industries and become bigger and better and more 161 00:10:20,800 --> 00:10:24,520 Speaker 1: skilled as a nation. To have young people dropping out 162 00:10:24,559 --> 00:10:28,800 Speaker 1: like this, and how's the government responded, I mean present 163 00:10:28,880 --> 00:10:32,920 Speaker 1: Shinping is very very worried that he he openly talked 164 00:10:32,960 --> 00:10:37,640 Speaker 1: about like the lying flat problem phenomenon in China. And 165 00:10:37,679 --> 00:10:41,000 Speaker 1: then like in October, so so like China that the 166 00:10:41,040 --> 00:10:45,280 Speaker 1: State Council proposed this so called deal training program. It's 167 00:10:45,320 --> 00:10:49,040 Speaker 1: it's essentially the German model. Like basically, China thinks that 168 00:10:49,000 --> 00:10:52,840 Speaker 1: that the society is turning out too many fresh university 169 00:10:52,840 --> 00:10:55,400 Speaker 1: graduates who just don't want to go to the factory 170 00:10:55,440 --> 00:10:57,959 Speaker 1: flaws because they feel like, oh I I did four 171 00:10:58,000 --> 00:11:00,200 Speaker 1: years of undergraduate that why do I want to go 172 00:11:00,280 --> 00:11:03,679 Speaker 1: to the manufacturing sector? Right, So as a result, they 173 00:11:03,720 --> 00:11:07,040 Speaker 1: decide to life flat and China things that society has 174 00:11:07,080 --> 00:11:09,600 Speaker 1: too many of these people, and they want to have 175 00:11:09,800 --> 00:11:13,480 Speaker 1: more young people go into um like so called the 176 00:11:13,559 --> 00:11:17,160 Speaker 1: vocational training, like you know, you spend a few years 177 00:11:17,480 --> 00:11:20,080 Speaker 1: learning skills in the school, but also a few more 178 00:11:20,160 --> 00:11:23,200 Speaker 1: years you know, doing on the job training at the 179 00:11:23,280 --> 00:11:27,040 Speaker 1: biggest blue chip companies such as Huawei or the China's 180 00:11:27,240 --> 00:11:31,280 Speaker 1: power grid and that seems to be their their their solutions. 181 00:11:31,320 --> 00:11:34,600 Speaker 1: They want more young people to go into high end 182 00:11:34,600 --> 00:11:39,520 Speaker 1: manufacturing that does you know, electric evy supply chains or 183 00:11:39,600 --> 00:11:42,599 Speaker 1: like chip manufacturing, and the China doesn't have enough of 184 00:11:42,679 --> 00:11:47,520 Speaker 1: that right now. Is any of that working? Well, this 185 00:11:47,640 --> 00:11:51,120 Speaker 1: is just a start. They just proposed this so called 186 00:11:51,120 --> 00:11:54,160 Speaker 1: the German model in October. One problem is like, um, 187 00:11:54,880 --> 00:11:57,520 Speaker 1: there is a little bit of a stigma when it 188 00:11:57,559 --> 00:12:01,360 Speaker 1: comes to working on the factory floors, and the China's 189 00:12:01,559 --> 00:12:04,200 Speaker 1: middle class families they don't really want their children to 190 00:12:04,240 --> 00:12:08,160 Speaker 1: go into vocational school training, right, I mean the US 191 00:12:08,160 --> 00:12:12,400 Speaker 1: that that hasn't been very successful either. So so what 192 00:12:12,600 --> 00:12:16,160 Speaker 1: the state Council proposes that you know, some high, very 193 00:12:16,240 --> 00:12:19,679 Speaker 1: high end like vocational schools. They should be able to 194 00:12:19,760 --> 00:12:23,000 Speaker 1: offer bachelor's degrees as well. So so even though you 195 00:12:23,000 --> 00:12:26,679 Speaker 1: are technically going to you know, um, the German model, 196 00:12:26,720 --> 00:12:30,479 Speaker 1: you're still have a degree. So perhaps that will persuade 197 00:12:30,480 --> 00:12:33,840 Speaker 1: the China's middle class families to allow their children to 198 00:12:33,920 --> 00:12:38,119 Speaker 1: go into this kind of German uh deal track training programs. 199 00:12:39,000 --> 00:12:40,680 Speaker 1: There's an awful lot of countries that have tried to 200 00:12:40,720 --> 00:12:44,600 Speaker 1: be more German in particularly this respect. For many decades. 201 00:12:44,640 --> 00:12:47,160 Speaker 1: Anybody who ever looks at the UK economy always concludes 202 00:12:47,160 --> 00:12:49,200 Speaker 1: that we should be more German in our approach to 203 00:12:49,360 --> 00:12:54,880 Speaker 1: education and have exactly this emphasis on technical skills and 204 00:12:54,960 --> 00:12:59,839 Speaker 1: giving the same amount of esteem and regard to people 205 00:13:00,000 --> 00:13:03,600 Speaker 1: doing engineering and working on the shop floors to people 206 00:13:03,640 --> 00:13:05,680 Speaker 1: in universities. And it hasn't worked. They've been trying to 207 00:13:05,679 --> 00:13:09,640 Speaker 1: do it for decades. Yeah, China will trial as well, 208 00:13:09,679 --> 00:13:13,480 Speaker 1: I guess, but it certainly seems like the consequence of 209 00:13:13,480 --> 00:13:15,880 Speaker 1: a new kind of China. When you say that, people 210 00:13:16,000 --> 00:13:18,680 Speaker 1: say that it is demeaning to work on the shop 211 00:13:18,800 --> 00:13:21,200 Speaker 1: on the shop floor in factories. Presumibly its people's parents 212 00:13:21,240 --> 00:13:23,600 Speaker 1: originally did that. I mean, all of this development has 213 00:13:23,640 --> 00:13:29,280 Speaker 1: happened in very few generations. I mean absolutely. My my 214 00:13:29,320 --> 00:13:32,640 Speaker 1: father was a chemical engineer and that he did pretty well, 215 00:13:32,679 --> 00:13:35,920 Speaker 1: like you know, like in the last two decades and 216 00:13:36,040 --> 00:13:42,640 Speaker 1: basically exporting pollution, like you know, polluting the exporting products 217 00:13:42,640 --> 00:13:45,160 Speaker 1: and stuff, and he did well. But he absolutely does 218 00:13:45,280 --> 00:13:48,120 Speaker 1: not want me to go into engineering. He said it's 219 00:13:48,160 --> 00:13:51,080 Speaker 1: tough work. And then he rather me you know, writing instead. 220 00:13:51,280 --> 00:13:54,400 Speaker 1: Like the parents, they just don't want their children to 221 00:13:54,440 --> 00:13:56,760 Speaker 1: go out to factory for and I think it's a 222 00:13:56,800 --> 00:14:01,319 Speaker 1: sign that China is no longer really the typical virgin market. 223 00:14:01,679 --> 00:14:04,760 Speaker 1: It kind of has emerged in a way, you know, 224 00:14:04,840 --> 00:14:08,840 Speaker 1: like and you're and you're part of the problem, it seems, yeah, exactly, 225 00:14:08,960 --> 00:14:10,840 Speaker 1: And the whole of time, I'm afraid cannot all work 226 00:14:10,880 --> 00:14:17,120 Speaker 1: at Bloomberg. I'm afraid that Uliman, thank you so much 227 00:14:17,120 --> 00:14:28,600 Speaker 1: for joining us, Thanks for having me so. Bloomberg has 228 00:14:28,640 --> 00:14:31,760 Speaker 1: recently published a series of articles under the tagline of 229 00:14:31,880 --> 00:14:35,520 Speaker 1: Race and Recovery, looking at how America's economic recovery is 230 00:14:35,560 --> 00:14:39,200 Speaker 1: playing out in minority communities. There's some really eye opening 231 00:14:39,280 --> 00:14:41,920 Speaker 1: data and stories about life in different parts of the 232 00:14:41,960 --> 00:14:45,640 Speaker 1: economy in this series, and the final article came out 233 00:14:45,680 --> 00:14:48,920 Speaker 1: this week and seemed to me particularly timely as the 234 00:14:49,000 --> 00:14:51,640 Speaker 1: US Central Bank starts to talk about raising interest rates 235 00:14:52,080 --> 00:14:54,680 Speaker 1: and we enter what feels like a new phase of 236 00:14:54,720 --> 00:14:59,360 Speaker 1: the recovery. Andre Tartar is a data journalist for Bloomberg 237 00:14:59,480 --> 00:15:02,120 Speaker 1: based in New York, and he's with me now. And 238 00:15:03,000 --> 00:15:06,200 Speaker 1: the data you pulled together for this piece with Christopher 239 00:15:06,280 --> 00:15:11,280 Speaker 1: Cannon this week paid a fascinating picture of US household 240 00:15:11,280 --> 00:15:16,080 Speaker 1: spending through this pandemic period and particularly impact that government 241 00:15:16,160 --> 00:15:20,080 Speaker 1: stimulus money has had on particular parts of the population. 242 00:15:20,280 --> 00:15:22,360 Speaker 1: So just give us some of the big headlines from 243 00:15:22,400 --> 00:15:25,400 Speaker 1: those numbers, sure thing, and thank you so much for 244 00:15:25,440 --> 00:15:29,000 Speaker 1: having me. So we got access to spending data for 245 00:15:29,200 --> 00:15:33,840 Speaker 1: just over a ten million people, a constant sample over 246 00:15:33,920 --> 00:15:36,560 Speaker 1: time that allowed us to really kind of look for 247 00:15:36,640 --> 00:15:39,880 Speaker 1: those trends and changes in spending behaviors. And the thing 248 00:15:39,960 --> 00:15:42,280 Speaker 1: that we really kind of struck us was just how 249 00:15:42,360 --> 00:15:46,920 Speaker 1: much spending had surged, in particular among black and lower 250 00:15:46,960 --> 00:15:51,360 Speaker 1: income families, um And we could see the very clear 251 00:15:51,960 --> 00:15:57,280 Speaker 1: uh impact of those federal stimulus checks, in part because 252 00:15:57,360 --> 00:16:00,600 Speaker 1: both of those groups, black and lower income groups are 253 00:16:00,680 --> 00:16:03,480 Speaker 1: much more reliant on debit card spending. And we could 254 00:16:03,480 --> 00:16:07,000 Speaker 1: see in this data just how much the debit card 255 00:16:07,120 --> 00:16:10,440 Speaker 1: usage kind of rows around the time of the stimulus 256 00:16:10,520 --> 00:16:12,640 Speaker 1: checks because you're already spending what you have. You can 257 00:16:12,680 --> 00:16:14,400 Speaker 1: only spend what you have in your account because you 258 00:16:14,440 --> 00:16:18,080 Speaker 1: don't necessarily get credit exactly right. Whereas whereas, for instance, 259 00:16:18,120 --> 00:16:20,680 Speaker 1: if you look at higher income spenders or you know, 260 00:16:20,760 --> 00:16:23,440 Speaker 1: which you know also will capture a large portion of 261 00:16:23,520 --> 00:16:26,640 Speaker 1: white and agent spenders, their use of credit is much 262 00:16:26,720 --> 00:16:29,760 Speaker 1: much higher. They were clearly in this data and and 263 00:16:29,920 --> 00:16:33,160 Speaker 1: other research has shown that they that those groups were 264 00:16:33,280 --> 00:16:36,320 Speaker 1: much more able to hold back spending right in those 265 00:16:36,600 --> 00:16:39,400 Speaker 1: first few months waiting this out, which is much harder 266 00:16:39,480 --> 00:16:42,480 Speaker 1: for those groups which live a bit tighter, you know, 267 00:16:42,720 --> 00:16:46,240 Speaker 1: month to month. What kind of numbers are we talking 268 00:16:46,240 --> 00:16:48,360 Speaker 1: about in terms of just the impact of the of 269 00:16:48,440 --> 00:16:51,960 Speaker 1: the stimulus packages relative to what they would have spent 270 00:16:52,120 --> 00:16:55,200 Speaker 1: would have been spending in say, in the case of 271 00:16:55,280 --> 00:16:59,720 Speaker 1: say spending by the black people in our sample, it 272 00:16:59,840 --> 00:17:03,760 Speaker 1: was up almost um this past kind of spring and 273 00:17:03,800 --> 00:17:08,280 Speaker 1: summer compared to twenty m minting levels, which is, you know, 274 00:17:08,359 --> 00:17:10,760 Speaker 1: a trend that would not be seen normally, you know, 275 00:17:10,960 --> 00:17:14,199 Speaker 1: even factoring in just normal economic growth, normal inflation, that 276 00:17:14,280 --> 00:17:17,879 Speaker 1: is a really significant bump up um, you know, and 277 00:17:17,920 --> 00:17:20,280 Speaker 1: there certainly are other factors at play, but you know, 278 00:17:20,359 --> 00:17:24,080 Speaker 1: given kind of this, given the scope of that of 279 00:17:24,080 --> 00:17:28,320 Speaker 1: that injection of cash really into into parts of economy, 280 00:17:28,320 --> 00:17:31,359 Speaker 1: it was significant, and across the sample, I think it 281 00:17:31,400 --> 00:17:35,360 Speaker 1: was even it was increase in spending, which only which 282 00:17:35,400 --> 00:17:39,080 Speaker 1: only came back a certain amount when things like the 283 00:17:39,160 --> 00:17:44,879 Speaker 1: unemployment benefits ran out in in the full absolutely, I 284 00:17:44,920 --> 00:17:48,200 Speaker 1: mean as of October, which was the last month that 285 00:17:48,240 --> 00:17:52,439 Speaker 1: we had data for overall spending across this group of 286 00:17:52,440 --> 00:17:56,560 Speaker 1: about ten million Americans was still up about fifteen compared 287 00:17:56,600 --> 00:17:59,959 Speaker 1: to twenty miniting levels. And we should just say briefly 288 00:18:00,080 --> 00:18:02,040 Speaker 1: what this what this data was? Where did you get 289 00:18:02,080 --> 00:18:04,760 Speaker 1: hold of these numbers? So this DADA comes from from 290 00:18:04,760 --> 00:18:08,560 Speaker 1: a company called Affinity Solutions, Inc. Which have been incredibly 291 00:18:08,560 --> 00:18:11,399 Speaker 1: generous in working with us and often partner with the 292 00:18:11,440 --> 00:18:16,760 Speaker 1: FED academics to provide access to their data, which nationally 293 00:18:17,119 --> 00:18:20,520 Speaker 1: covers more than a hundred million debit and credit cards 294 00:18:20,680 --> 00:18:23,720 Speaker 1: UM and has an incredible amount of depth to it. 295 00:18:23,840 --> 00:18:27,040 Speaker 1: So you know, it was really great Chricket access to 296 00:18:27,160 --> 00:18:30,320 Speaker 1: this UM and being able to also use the data 297 00:18:30,400 --> 00:18:34,600 Speaker 1: that they have linking demographics and income into this data, 298 00:18:34,600 --> 00:18:37,760 Speaker 1: which is a very kind of complicated process that they 299 00:18:38,000 --> 00:18:41,040 Speaker 1: undertake UM on their end, so you know, it really 300 00:18:41,040 --> 00:18:43,439 Speaker 1: gives a lot of insights that otherwise are hard to 301 00:18:43,440 --> 00:18:47,959 Speaker 1: get at that at that level of detail. It's so 302 00:18:48,000 --> 00:18:50,280 Speaker 1: exciting when you get hold of a big, meaty data 303 00:18:50,320 --> 00:18:52,720 Speaker 1: set like this and to have it been relatively timely 304 00:18:52,720 --> 00:18:55,480 Speaker 1: as well as going through to October. And I have 305 00:18:55,520 --> 00:18:57,800 Speaker 1: to say you do a fantastic job of making it 306 00:18:57,840 --> 00:19:00,720 Speaker 1: come alive with some really lovely colorful graphics, which we're 307 00:19:00,760 --> 00:19:04,359 Speaker 1: not doing justice too on the podcast. Right a shout 308 00:19:04,400 --> 00:19:06,919 Speaker 1: out to Christopher Cannon on the graphics who made all 309 00:19:06,920 --> 00:19:11,000 Speaker 1: those beautiful graphics and definitely recommend folks to go and 310 00:19:11,080 --> 00:19:15,560 Speaker 1: a metical look. I mean, inflation poses a risk to 311 00:19:15,640 --> 00:19:18,200 Speaker 1: this big increase in spending power. Right when we look 312 00:19:18,240 --> 00:19:22,840 Speaker 1: forward to next year, are we potentially seeing this big 313 00:19:22,880 --> 00:19:26,800 Speaker 1: improvement in in particularly black and Hispanic households spending power 314 00:19:26,920 --> 00:19:32,480 Speaker 1: being being really hit by both inflation and potentially a 315 00:19:32,480 --> 00:19:37,120 Speaker 1: slowing economy. I mean absolutely right. So this really kind 316 00:19:37,119 --> 00:19:41,960 Speaker 1: of that injection of that federals federal simulus really closed 317 00:19:42,080 --> 00:19:45,560 Speaker 1: a huge consumption gap and also showed us the kind 318 00:19:45,560 --> 00:19:49,640 Speaker 1: of untapped potential right that exists in those communities who 319 00:19:49,840 --> 00:19:52,720 Speaker 1: who just tend to have lower incomes and lower access 320 00:19:53,160 --> 00:19:56,280 Speaker 1: to credit. So both so both kind of the fact 321 00:19:56,440 --> 00:19:59,800 Speaker 1: that now most of that you know, additional cash has 322 00:20:00,000 --> 00:20:02,239 Speaker 1: have been spent, and as you mentioned, inflation is going 323 00:20:02,280 --> 00:20:05,040 Speaker 1: to begin to eat in to some of those core 324 00:20:05,119 --> 00:20:09,520 Speaker 1: spending staple groups that just can't be avoided. Certainly foods, 325 00:20:09,520 --> 00:20:13,040 Speaker 1: certainly gas are ones where you know, groups who have 326 00:20:13,119 --> 00:20:15,760 Speaker 1: to go physically into work, who don't have a lot 327 00:20:15,920 --> 00:20:19,879 Speaker 1: of ability to hold off spending or access credit. I 328 00:20:19,920 --> 00:20:22,840 Speaker 1: think we will certainly see a very you know, a 329 00:20:22,960 --> 00:20:26,320 Speaker 1: very likely a kind of a tempering or even a 330 00:20:26,400 --> 00:20:29,040 Speaker 1: full on slow down of the spending recovery, which as 331 00:20:29,080 --> 00:20:32,240 Speaker 1: we know, powers the vast majority of the U. S economy. 332 00:20:32,520 --> 00:20:34,560 Speaker 1: So you know, it both has implications for the kind 333 00:20:34,560 --> 00:20:37,000 Speaker 1: of equity side of the picture, but also just for 334 00:20:37,119 --> 00:20:41,119 Speaker 1: the recovery more broadly. Yeah, and there's certainly a lesson 335 00:20:41,119 --> 00:20:42,919 Speaker 1: if you if you want to stimulate the economy and 336 00:20:42,920 --> 00:20:44,920 Speaker 1: get people to spend, give it to pull people who 337 00:20:44,960 --> 00:20:47,000 Speaker 1: don't have enough money to buy the things they want 338 00:20:47,040 --> 00:20:49,920 Speaker 1: to buy. There actually are some fascinating studies out there 339 00:20:49,920 --> 00:20:53,320 Speaker 1: which have really shown though that that stimulus money, if 340 00:20:53,359 --> 00:20:55,960 Speaker 1: you were a wealth your household, you basically saved it 341 00:20:56,000 --> 00:20:58,040 Speaker 1: all right, So I mean, really it wasn't doing what 342 00:20:58,160 --> 00:21:00,480 Speaker 1: it was meant to do. Of course, for reasons of 343 00:21:00,600 --> 00:21:03,960 Speaker 1: you know, of efficiency, the idea was to just give 344 00:21:04,359 --> 00:21:06,840 Speaker 1: checks all around. But as you mentioned, I mean, this 345 00:21:06,920 --> 00:21:09,639 Speaker 1: data really shows that you can get the biggest bang 346 00:21:09,720 --> 00:21:12,880 Speaker 1: for your stimulus buck by really sending it to those 347 00:21:12,960 --> 00:21:15,760 Speaker 1: lower income households and communities of color, you know who 348 00:21:15,800 --> 00:21:18,080 Speaker 1: kind of aren't being able to tap into their full 349 00:21:18,119 --> 00:21:21,959 Speaker 1: consumption potential. It's a difficult challenge for the Federal reserver, 350 00:21:22,200 --> 00:21:24,119 Speaker 1: right because they're supposed to be they're supposed to be 351 00:21:24,200 --> 00:21:29,440 Speaker 1: focused on both stable inflation and full employment. They've said 352 00:21:29,440 --> 00:21:33,679 Speaker 1: they're going to focus more on everybody's unemployment rate, not 353 00:21:33,800 --> 00:21:36,480 Speaker 1: just the headline rate, and of course on black unemployment 354 00:21:36,520 --> 00:21:39,520 Speaker 1: is still quite a lot higher, um than the than 355 00:21:39,560 --> 00:21:42,760 Speaker 1: the average for the rest of the country, but they're 356 00:21:42,840 --> 00:21:46,040 Speaker 1: kind of weighing up. Inflation hurts poor people, digs into 357 00:21:46,040 --> 00:21:50,960 Speaker 1: their spending power, but not getting those jobs created, not 358 00:21:51,040 --> 00:21:55,960 Speaker 1: bringing down unemployment hurts them too. It's a difficult challenge absolutely, um, 359 00:21:55,960 --> 00:21:58,320 Speaker 1: I mean, and on top of that, there's also you know, 360 00:21:58,400 --> 00:22:01,560 Speaker 1: there's a machine momentum that um, we are in where 361 00:22:01,600 --> 00:22:04,639 Speaker 1: we have I think it's something like a ten million 362 00:22:04,720 --> 00:22:08,520 Speaker 1: unful jobs, and yet we're still not seeing really that 363 00:22:08,600 --> 00:22:12,560 Speaker 1: broad based, you know, decline in unemployment. So it really 364 00:22:12,600 --> 00:22:17,080 Speaker 1: does present a very difficult obstacle course for the Fed policymakers, 365 00:22:18,760 --> 00:22:22,440 Speaker 1: but also fantastic time to be doing data journalism. Thanks 366 00:22:22,480 --> 00:22:33,760 Speaker 1: so much, Thank you, definitely, I really appreciate it. M H. Finally, 367 00:22:34,200 --> 00:22:36,320 Speaker 1: at the start of the program. I think I promised 368 00:22:36,320 --> 00:22:39,360 Speaker 1: to you the future of food is Hong Kong reporter 369 00:22:39,840 --> 00:22:45,480 Speaker 1: Juan ha. It has the rain natty taste. That's Malaysian 370 00:22:45,560 --> 00:22:49,560 Speaker 1: entrepreneur Kevin Wou describing the perfect burger that he wants 371 00:22:49,600 --> 00:22:52,639 Speaker 1: to put into restaurants and grocery stores. So we added 372 00:22:52,680 --> 00:22:55,679 Speaker 1: some spices to give it that kind of familiar beefy taste, 373 00:22:55,840 --> 00:22:59,720 Speaker 1: the grounded beef taste. And we've added certain kind of 374 00:23:00,040 --> 00:23:04,040 Speaker 1: hats like mushroom seasoning to give you that umami flavor. 375 00:23:04,440 --> 00:23:09,600 Speaker 1: And the secret ingredient. We've perized our crickets, you heard right, 376 00:23:10,080 --> 00:23:14,560 Speaker 1: crickets roasted and processed into a fine brown powder, and 377 00:23:14,600 --> 00:23:17,399 Speaker 1: then we mix it with other plant based protein source. 378 00:23:17,920 --> 00:23:20,919 Speaker 1: I think it's pretty tasty, especially when you when you 379 00:23:20,960 --> 00:23:23,000 Speaker 1: look at the burglar, it looks at normal burger, small, 380 00:23:23,240 --> 00:23:29,280 Speaker 1: normal burger, even sizzles like a normal burger. Who's the 381 00:23:29,320 --> 00:23:33,080 Speaker 1: founder and CEO of Ento based in Kuala Lumpur. He's 382 00:23:33,080 --> 00:23:36,719 Speaker 1: an evangelist for protein made from insects, and he's not alone. 383 00:23:37,119 --> 00:23:40,240 Speaker 1: Startups from Europe, Asia and North America are joining the 384 00:23:40,320 --> 00:23:43,840 Speaker 1: race for alternative proteins and say bugs are making their 385 00:23:43,840 --> 00:23:47,520 Speaker 1: way to our dinner plates. It's the next big frontier 386 00:23:47,640 --> 00:23:51,639 Speaker 1: for planet friendly sustainable protein after fake meat. If you 387 00:23:51,680 --> 00:23:54,840 Speaker 1: were to compare one kilogram of beef versus one kilogram 388 00:23:55,000 --> 00:23:58,560 Speaker 1: of cricket powder um, it will require tolf times last feet, 389 00:23:58,960 --> 00:24:02,480 Speaker 1: twenty times less land, two thousand times less water, and 390 00:24:02,520 --> 00:24:06,040 Speaker 1: amid two thousand times less greenhouse gas emissions. So, in 391 00:24:06,119 --> 00:24:09,440 Speaker 1: its most natural form, insect based protein has already high 392 00:24:09,760 --> 00:24:13,199 Speaker 1: enriched levels of proteins and other macro nutrients. And in 393 00:24:13,240 --> 00:24:16,560 Speaker 1: an into promotional video, taste testers who tried the company's 394 00:24:16,600 --> 00:24:21,119 Speaker 1: cricket snacks gave it a thumbs up. There's like sugars, 395 00:24:22,760 --> 00:24:26,560 Speaker 1: red clunchy like chips and stuff like that. Actually thottle 396 00:24:26,560 --> 00:24:32,119 Speaker 1: taste are crickets. There's also a sustainability reason why edible 397 00:24:32,160 --> 00:24:35,639 Speaker 1: bugs are on the menu. Constant Tetter is CEO of 398 00:24:35,720 --> 00:24:39,320 Speaker 1: Fly Farm. You know protein is needed, It's going to 399 00:24:39,320 --> 00:24:42,639 Speaker 1: be an increasing need as the human population expands. His 400 00:24:42,800 --> 00:24:46,040 Speaker 1: company is setting up industrial farms to harvest black Soldier 401 00:24:46,040 --> 00:24:49,920 Speaker 1: fly larvae. You know that those needs cannot be met sustainable, 402 00:24:50,119 --> 00:24:53,200 Speaker 1: and so insect farming has a giant potential to be 403 00:24:53,200 --> 00:24:57,199 Speaker 1: able to alter that entire protein landscape, whether that use 404 00:24:57,320 --> 00:24:59,399 Speaker 1: is going directly to humans, or whether it's going to 405 00:24:59,600 --> 00:25:03,000 Speaker 1: feed that then go to animals that then go to humans. 406 00:25:04,359 --> 00:25:08,760 Speaker 1: Tedder and other industry insiders say insect protein is gaining momentum. 407 00:25:09,119 --> 00:25:11,399 Speaker 1: The industry was worth just a little under a billion 408 00:25:11,400 --> 00:25:14,919 Speaker 1: dollars in nine and Barclay's estimates it will be an 409 00:25:14,920 --> 00:25:19,360 Speaker 1: eight billion dollar market. Still, there's a lot of ketchup 410 00:25:19,440 --> 00:25:21,960 Speaker 1: that bug protein needs to do to challenge the plant 411 00:25:21,960 --> 00:25:26,159 Speaker 1: based protein industry that's now thirty billion dollars strong. The 412 00:25:26,200 --> 00:25:30,960 Speaker 1: market has matured massively in the last couple of couple 413 00:25:30,960 --> 00:25:35,119 Speaker 1: of years. Katharina Unger's company Live in Farms is building 414 00:25:35,160 --> 00:25:39,000 Speaker 1: industrial scale automated factories that grow and harvest the larvae 415 00:25:39,000 --> 00:25:42,480 Speaker 1: of black soldier flies. The CEO says, one of the 416 00:25:42,520 --> 00:25:45,560 Speaker 1: biggest obstacles standing in the way of insect protein being 417 00:25:45,600 --> 00:25:50,200 Speaker 1: accepted as food is the yuck factor. I didn't grow 418 00:25:50,320 --> 00:25:53,320 Speaker 1: up loving insects or loving the idea of eating insects. 419 00:25:53,320 --> 00:25:55,679 Speaker 1: On the contrary, my hands were shaking when I was 420 00:25:55,760 --> 00:25:58,600 Speaker 1: first doing doing it, you know, and I really had 421 00:25:58,640 --> 00:26:02,800 Speaker 1: to also myself get over this. This concept of eating 422 00:26:02,880 --> 00:26:07,280 Speaker 1: a larva. Larvae is the industry's preferred term for what 423 00:26:07,320 --> 00:26:11,320 Speaker 1: most people would describe our living writhing maggots. So far, 424 00:26:11,440 --> 00:26:13,920 Speaker 1: many of those willing to try larvae and other critters 425 00:26:13,960 --> 00:26:18,200 Speaker 1: are young consumers looking to make planet friendly choices. Here's 426 00:26:18,200 --> 00:26:25,680 Speaker 1: wu from into our current segments, mostly urban, affluent millennials, 427 00:26:25,760 --> 00:26:30,560 Speaker 1: who are aware about the negative impact that traditional agriculture 428 00:26:30,640 --> 00:26:34,560 Speaker 1: and meat based production has on our planet. Some of 429 00:26:34,600 --> 00:26:38,800 Speaker 1: the world's major producers are betting on that sustainability value proposition. 430 00:26:39,200 --> 00:26:42,600 Speaker 1: There are now investing millions of dollars to help bring crickets, beetles, 431 00:26:42,600 --> 00:26:45,480 Speaker 1: meal worms, and fly larvae to mouths around the world, 432 00:26:46,080 --> 00:26:49,320 Speaker 1: and the market may not need that much prodding. Globally, 433 00:26:49,359 --> 00:26:52,159 Speaker 1: more than two billion people already eat insects. According to 434 00:26:52,200 --> 00:26:55,720 Speaker 1: the United Nations, nearly two thousand types of bugs are edible, 435 00:26:56,359 --> 00:26:59,760 Speaker 1: and that's catching the attention of food producers. The company 436 00:27:00,240 --> 00:27:03,440 Speaker 1: Chicken of the Sea Tuna Thai Union launched a thirty 437 00:27:03,480 --> 00:27:07,480 Speaker 1: million venture fund to invest in alternative proteins. It's invested 438 00:27:07,520 --> 00:27:11,440 Speaker 1: in three insect protein startups so far, including an Israeli 439 00:27:11,480 --> 00:27:15,760 Speaker 1: company that's working on tuna from fruit fly larvae. Antoine 440 00:27:15,840 --> 00:27:19,280 Speaker 1: Hubert is CEO of insect in France. So the two 441 00:27:19,280 --> 00:27:22,160 Speaker 1: main trends are execually meat replacements and false in efficient 442 00:27:22,600 --> 00:27:26,560 Speaker 1: serial or an eergy drinks, which was the first market 443 00:27:26,640 --> 00:27:29,920 Speaker 1: before it would be the first grow where it's more 444 00:27:30,000 --> 00:27:35,679 Speaker 1: about performance and not about tastes. The company is building 445 00:27:35,760 --> 00:27:39,560 Speaker 1: some of the biggest industrial insect farms after attracting millions 446 00:27:39,560 --> 00:27:43,120 Speaker 1: of dollars in investments from venture capitalists and Hollywood star 447 00:27:43,280 --> 00:27:46,520 Speaker 1: Robert Downey Jr. Who Bear says the industry got a 448 00:27:46,560 --> 00:27:50,320 Speaker 1: big boost when European Union lawmakers passed legislation this year 449 00:27:50,560 --> 00:27:54,320 Speaker 1: to allow insects to be fed to farm animals. Last week, 450 00:27:54,440 --> 00:27:58,040 Speaker 1: crickets were approved as food for humans. They joined meal 451 00:27:58,080 --> 00:28:00,359 Speaker 1: worms and locusts to get the green light to be 452 00:28:00,400 --> 00:28:04,320 Speaker 1: marketed in Europe. Lawmakers are also considering nearly a dozen 453 00:28:04,359 --> 00:28:08,119 Speaker 1: other insects seeking approval to be sold as food. Clearlier, 454 00:28:08,200 --> 00:28:12,240 Speaker 1: the Europe is a more advance framework there, which is 455 00:28:12,320 --> 00:28:17,000 Speaker 1: then helping companies to innovate Row, which invests with the 456 00:28:17,080 --> 00:28:22,080 Speaker 1: visibility and investors that there are more stronger insurance of 457 00:28:22,160 --> 00:28:26,679 Speaker 1: success because the markets are there. The company is selling 458 00:28:26,720 --> 00:28:30,160 Speaker 1: burger patties made from buffalo meal worm protein that's being 459 00:28:30,280 --> 00:28:33,720 Speaker 1: offered in restaurants and grocery stores in Austria and Denmark. 460 00:28:34,240 --> 00:28:37,600 Speaker 1: Meal worm protein has a nutty mild taste and it's 461 00:28:37,600 --> 00:28:40,880 Speaker 1: being used in baked goods and pastas. But the company's 462 00:28:40,960 --> 00:28:43,480 Speaker 1: bigger business now and for the next few years is 463 00:28:43,480 --> 00:28:48,239 Speaker 1: selling bug protein as feed for animals. Food giants from 464 00:28:48,280 --> 00:28:52,320 Speaker 1: Cargil to Archer Daniels Midland are putting money into such initiatives. 465 00:28:52,960 --> 00:28:55,880 Speaker 1: Katharina Unger of Living Farms sees that as the low 466 00:28:55,960 --> 00:28:59,480 Speaker 1: hanging fruit, turning insects into pet food or as feed 467 00:28:59,560 --> 00:29:02,440 Speaker 1: for farm animals and fish, our customers in the food 468 00:29:02,440 --> 00:29:06,400 Speaker 1: and feet industry. They typically make six digit losses on 469 00:29:06,480 --> 00:29:10,800 Speaker 1: their food waste, disposing it at a cost to biogas digestors. 470 00:29:11,960 --> 00:29:15,280 Speaker 1: A new planet friendly regulations to cut waste and emissions 471 00:29:15,280 --> 00:29:18,160 Speaker 1: in Europe and elsewhere put insects front and center to 472 00:29:18,240 --> 00:29:22,080 Speaker 1: upscale organic food waste. Anger's company has contracts to build 473 00:29:22,080 --> 00:29:24,880 Speaker 1: three plants next year in Europe and Asia for food 474 00:29:24,880 --> 00:29:27,920 Speaker 1: and beverage companies. Live in Farms plans to work with 475 00:29:27,960 --> 00:29:31,320 Speaker 1: the likes of breweries, bakeries and juice makers that produce 476 00:29:31,440 --> 00:29:36,040 Speaker 1: tons of organic waste. With our solution, they can produce 477 00:29:36,400 --> 00:29:39,120 Speaker 1: higher value products that they can sell so they turn 478 00:29:39,480 --> 00:29:44,680 Speaker 1: their losses into revenues and into an emission saving process. 479 00:29:44,840 --> 00:29:49,120 Speaker 1: We save about sevent of emissions turning the waste into 480 00:29:49,160 --> 00:29:52,400 Speaker 1: insect proteins. The bug protein will be sold to pet 481 00:29:52,400 --> 00:29:56,280 Speaker 1: food producers, while insect oil and excrement called frast is 482 00:29:56,320 --> 00:30:00,000 Speaker 1: sold as fertilizer. Who bear of insect also sees more 483 00:30:00,000 --> 00:30:02,240 Speaker 1: our money and investment over the next few years for 484 00:30:02,360 --> 00:30:07,600 Speaker 1: bug protein and more acceptance by consumers. So we will 485 00:30:07,680 --> 00:30:10,480 Speaker 1: have thousands of in six months in the world. That's 486 00:30:10,560 --> 00:30:14,240 Speaker 1: that's supporting this more fest nipple food system. You will 487 00:30:14,320 --> 00:30:17,480 Speaker 1: see insect on the menu everywhere. It would be a 488 00:30:17,560 --> 00:30:20,280 Speaker 1: normal You won't eat it every day, of course, but 489 00:30:20,320 --> 00:30:21,960 Speaker 1: it will be no more to sit on the menu 490 00:30:22,080 --> 00:30:24,480 Speaker 1: in the restaurants, no more to sit in the supermarkets. 491 00:30:24,840 --> 00:30:28,000 Speaker 1: The company's third factory comes online next year near Paris. 492 00:30:28,480 --> 00:30:31,240 Speaker 1: It says that will be the world's largest insect farm 493 00:30:31,360 --> 00:30:54,480 Speaker 1: in the world. For Bloomberg News, I'm one hard. That's 494 00:30:54,480 --> 00:30:56,480 Speaker 1: it for this episode of Stephonomics. Will be back next 495 00:30:56,520 --> 00:31:00,240 Speaker 1: week with a special Christmas episode of Stephonomics featuring the one, 496 00:31:00,360 --> 00:31:03,600 Speaker 1: the Only Larry Summers. In the meantime, if you like 497 00:31:03,720 --> 00:31:06,800 Speaker 1: the program, please rate it and follow at economics on 498 00:31:06,840 --> 00:31:11,200 Speaker 1: Twitter for more news analysis from Bloomberg Economics. This episode 499 00:31:11,200 --> 00:31:14,760 Speaker 1: was produced by Magnus Hendrickson. As we mentioned earlier, Christopher 500 00:31:14,760 --> 00:31:17,880 Speaker 1: Cannon worked with Andrew Tarter on that report on US 501 00:31:17,960 --> 00:31:22,280 Speaker 1: household spending. Special thanks also to Shulei Wren, Tom Hancock, 502 00:31:22,640 --> 00:31:26,640 Speaker 1: and Juan Haa. Mike Sasso is executive producer of Stephonomics 503 00:31:26,680 --> 00:31:29,480 Speaker 1: and the head of Bloomberg Podcast is Francesca Levy.