WEBVTT
Kind: captions
Language: en
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the<00:00:06.990> well-being<00:00:07.200> of<00:00:07.620> people<00:00:07.680> varies<00:00:08.370> greatly
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the well-being of people varies greatly
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the well-being of people varies greatly
across<00:00:09.059> the<00:00:09.330> world<00:00:09.650> so<00:00:10.650> how<00:00:10.860> can<00:00:10.920> we<00:00:11.100> measure
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across the world so how can we measure
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across the world so how can we measure
well-being<00:00:11.969> in<00:00:12.719> a<00:00:13.230> way<00:00:13.500> that<00:00:13.740> reflects<00:00:14.160> the
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well-being in a way that reflects the
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well-being in a way that reflects the
quality<00:00:14.879> of<00:00:14.910> people's<00:00:15.209> lives<00:00:15.500> does<00:00:16.640> income
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quality of people's lives does income
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quality of people's lives does income
equate<00:00:18.660> to<00:00:18.689> well-being<00:00:19.970> gross<00:00:20.970> domestic
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equate to well-being gross domestic
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equate to well-being gross domestic
income<00:00:21.960> or<00:00:22.169> GDR<00:00:22.830> a<00:00:22.859> is<00:00:23.220> often<00:00:23.429> used<00:00:23.820> as<00:00:23.999> a
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income or GDR a is often used as a
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income or GDR a is often used as a
measure<00:00:24.329> of<00:00:24.480> well-being<00:00:25.249> sweden<00:00:26.249> in<00:00:26.400> the
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measure of well-being sweden in the
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measure of well-being sweden in the
united<00:00:26.999> states<00:00:27.029> have<00:00:27.720> similar<00:00:28.019> GDI<00:00:28.890> per<00:00:29.339> head
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united states have similar GDI per head
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united states have similar GDI per head
and<00:00:30.839> yet<00:00:30.989> the<00:00:31.140> experience<00:00:31.829> of<00:00:32.009> well-being
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and yet the experience of well-being
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and yet the experience of well-being
particularly<00:00:34.050> among<00:00:34.320> the<00:00:34.440> poor<00:00:34.890> in<00:00:35.190> these
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particularly among the poor in these
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particularly among the poor in these
societies<00:00:35.750> is<00:00:36.750> very<00:00:37.080> different<00:00:38.750> dr.<00:00:39.750> crystal
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societies is very different dr. crystal
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societies is very different dr. crystal
Lunt<00:00:40.500> works<00:00:41.160> in<00:00:41.370> a<00:00:41.460> special<00:00:41.880> ward<00:00:42.120> for
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Lunt works in a special ward for
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Lunt works in a special ward for
chronically<00:00:42.840> ill<00:00:43.170> homeless<00:00:43.530> people<00:00:44.070> in
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chronically ill homeless people in
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chronically ill homeless people in
Sweden<00:00:44.400> our<00:00:45.180> ward<00:00:45.510> was<00:00:45.750> created<00:00:46.290> for<00:00:46.440> those
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Sweden our ward was created for those
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Sweden our ward was created for those
too<00:00:46.950> ill<00:00:47.130> through<00:00:47.940> drug<00:00:48.390> abuse<00:00:48.810> or<00:00:49.050> mental
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too ill through drug abuse or mental
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too ill through drug abuse or mental
illness<00:00:49.890> to<00:00:50.550> partake<00:00:51.000> in<00:00:51.030> Sweden's<00:00:51.630> generous
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illness to partake in Sweden's generous
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illness to partake in Sweden's generous
welfare<00:00:52.410> and<00:00:52.860> health<00:00:53.190> care<00:00:53.340> systems<00:00:54.000> because
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welfare and health care systems because
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welfare and health care systems because
the<00:00:55.560> main<00:00:55.950> part<00:00:56.310> of<00:00:56.490> these<00:00:56.700> homeless<00:00:57.000> people
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the main part of these homeless people
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the main part of these homeless people
we<00:00:57.630> have<00:00:57.750> in<00:00:57.810> Sweden<00:00:58.350> they<00:00:59.010> are<00:00:59.190> M<00:00:59.430> on<00:01:00.420> drugs
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we have in Sweden they are M on drugs
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we have in Sweden they are M on drugs
and<00:01:01.080> have<00:01:01.320> psychiatric<00:01:01.860> diseases<00:01:02.490> or<00:01:02.910> one<00:01:03.570> of
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and have psychiatric diseases or one of
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and have psychiatric diseases or one of
these<00:01:04.890> or<00:01:05.189> both<00:01:05.489> and<00:01:06.469> they<00:01:07.469> are<00:01:07.649> really<00:01:08.009> sick
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these or both and they are really sick
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these or both and they are really sick
dr.<00:01:10.079> lund<00:01:10.259> works<00:01:10.679> with<00:01:10.950> the<00:01:11.009> most<00:01:11.189> difficult
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dr. lund works with the most difficult
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dr. lund works with the most difficult
patients<00:01:12.179> imaginable<00:01:12.929> with<00:01:13.380> psychosis
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patients imaginable with psychosis
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patients imaginable with psychosis
addiction<00:01:15.329> and<00:01:15.539> chronic<00:01:16.259> illness<00:01:17.119> they<00:01:18.119> can
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addiction and chronic illness they can
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addiction and chronic illness they can
come<00:01:18.509> in<00:01:18.689> however<00:01:19.530> they<00:01:20.429> are<00:01:20.460> because<00:01:21.179> they
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come in however they are because they
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come in however they are because they
are<00:01:21.600> very<00:01:21.689> severe<00:01:22.170> sickness<00:01:22.469> nuh<00:01:22.859> so<00:01:23.039> the
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are very severe sickness nuh so the
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are very severe sickness nuh so the
alternative<00:01:23.999> is<00:01:24.149> to<00:01:24.600> throw<00:01:24.840> them<00:01:25.079> out<00:01:25.229> in<00:01:25.409> the
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alternative is to throw them out in the
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alternative is to throw them out in the
street<00:01:25.740> again<00:01:25.890> and<00:01:26.310> that's<00:01:26.819> not<00:01:27.060> very
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street again and that's not very
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street again and that's not very
feministic<00:01:28.770> so
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feministic so
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feministic so
in<00:01:32.020> the<00:01:32.920> USA<00:01:33.689> around<00:01:34.689> fifteen<00:01:35.229> percent<00:01:35.710> live
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in the USA around fifteen percent live
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in the USA around fifteen percent live
in<00:01:36.130> poverty<00:01:36.490> and<00:01:36.820> there<00:01:37.299> are<00:01:37.420> estimates<00:01:37.600> that
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in poverty and there are estimates that
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in poverty and there are estimates that
three-quarters<00:01:39.009> of<00:01:39.250> a<00:01:39.429> million<00:01:39.700> are<00:01:40.030> homeless
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three-quarters of a million are homeless
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three-quarters of a million are homeless
anywhere<00:01:42.549> from<00:01:42.820> a<00:01:43.270> half<00:01:43.630> a<00:01:43.810> million<00:01:43.990> people
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anywhere from a half a million people
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anywhere from a half a million people
every<00:01:44.560> night<00:01:44.679> are<00:01:45.210> without<00:01:46.210> shelter<00:01:46.509> to<00:01:48.149> maybe
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every night are without shelter to maybe
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every night are without shelter to maybe
2<00:01:49.360> million<00:01:49.659> people<00:01:50.100> the<00:01:51.100> estimates<00:01:51.729> are
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2 million people the estimates are
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2 million people the estimates are
really<00:01:52.060> wide<00:01:52.240> because<00:01:52.569> again<00:01:53.170> the<00:01:53.319> data<00:01:53.530> is
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really wide because again the data is
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really wide because again the data is
not<00:01:54.729> that<00:01:54.939> great<00:01:55.560> but<00:01:58.259> somewhere<00:01:59.259> between<00:01:59.530> a
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not that great but somewhere between a
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not that great but somewhere between a
half<00:01:59.950> and<00:02:00.069> two<00:02:00.250> percent<00:02:00.640> probably<00:02:01.119> of<00:02:01.780> the
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half and two percent probably of the
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half and two percent probably of the
population<00:02:02.590> are<00:02:02.799> either<00:02:03.340> experiencing
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population are either experiencing
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population are either experiencing
homelessness<00:02:04.240> or<00:02:04.720> will<00:02:05.319> soon<00:02:05.770> or<00:02:06.039> have<00:02:06.250> in<00:02:06.610> the
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homelessness or will soon or have in the
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homelessness or will soon or have in the
very<00:02:07.330> recent<00:02:07.720> past<00:02:09.569> in<00:02:10.569> the<00:02:11.410> US<00:02:11.739> the
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very recent past in the US the
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very recent past in the US the
government<00:02:12.430> plays<00:02:12.670> a<00:02:12.700> relatively<00:02:13.329> small<00:02:13.450> role
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government plays a relatively small role
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government plays a relatively small role
in<00:02:14.049> dealing<00:02:14.590> with<00:02:14.620> homelessness<00:02:15.190> funding<00:02:16.180> for
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in dealing with homelessness funding for
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in dealing with homelessness funding for
the<00:02:16.480> homeless<00:02:16.930> comes<00:02:17.170> largely<00:02:17.440> from<00:02:17.739> charity
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the homeless comes largely from charity
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the homeless comes largely from charity
the<00:02:20.110> country<00:02:20.410> has<00:02:20.590> a<00:02:21.280> lot<00:02:22.180> of<00:02:22.209> wealth
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the country has a lot of wealth
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the country has a lot of wealth
relatively<00:02:23.560> speaking<00:02:23.769> when<00:02:24.280> you<00:02:24.370> think<00:02:24.549> about
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relatively speaking when you think about
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relatively speaking when you think about
just<00:02:25.230> square<00:02:26.230> footage<00:02:26.650> of<00:02:27.000> housing<00:02:28.000> per
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just square footage of housing per
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just square footage of housing per
person<00:02:29.380> the<00:02:29.709> fact<00:02:29.920> that<00:02:30.010> there's<00:02:30.220> so<00:02:30.340> many
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person the fact that there's so many
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person the fact that there's so many
people<00:02:30.610> who<00:02:30.910> sleep<00:02:31.810> on<00:02:31.959> the<00:02:32.110> streets<00:02:32.410> because
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people who sleep on the streets because
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people who sleep on the streets because
they<00:02:32.890> can't<00:02:33.099> find<00:02:33.370> shelter<00:02:33.700> it<00:02:35.069> just<00:02:36.069> doesn't
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they can't find shelter it just doesn't
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they can't find shelter it just doesn't
seem<00:02:36.609> right<00:02:37.530> so<00:02:38.530> as<00:02:38.889> a<00:02:39.250> responsibility<00:02:40.180> as<00:02:40.480> a
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seem right so as a responsibility as a
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seem right so as a responsibility as a
community<00:02:41.139> to<00:02:41.230> take<00:02:41.500> care<00:02:41.709> of<00:02:41.769> that<00:02:42.069> the
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community to take care of that the
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community to take care of that the
people<00:02:42.790> who<00:02:42.850> can't<00:02:43.030> find<00:02:43.239> shelter<00:02:43.569> when
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people who can't find shelter when
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people who can't find shelter when
there's<00:02:43.989> so<00:02:44.139> much<00:02:44.260> shelter<00:02:44.650> that<00:02:44.799> could<00:02:45.280> be
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there's so much shelter that could be
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there's so much shelter that could be
available<00:02:45.940> I<00:02:46.120> think<00:02:46.870> it's<00:02:46.959> the<00:02:47.019> right<00:02:47.139> thing
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available I think it's the right thing
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available I think it's the right thing
for<00:02:47.530> a<00:02:47.560> community<00:02:47.739> to<00:02:47.980> do<00:02:48.750> the<00:02:49.750> shelter<00:02:50.230> for
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for a community to do the shelter for
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for a community to do the shelter for
the<00:02:50.500> homeless<00:02:50.639> receives<00:02:51.639> most<00:02:52.000> of<00:02:52.180> its
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the homeless receives most of its
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the homeless receives most of its
funding<00:02:52.780> from<00:02:53.079> private<00:02:53.170> donations<00:02:54.450> the
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funding from private donations the
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funding from private donations the
government's<00:02:55.989> aren't<00:02:56.260> providing<00:02:56.829> for<00:02:57.790> a
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government's aren't providing for a
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government's aren't providing for a
whole<00:02:58.000> lot<00:02:58.030> of<00:02:58.239> money<00:02:58.480> towards<00:02:58.989> homeless
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whole lot of money towards homeless
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whole lot of money towards homeless
shelters<00:03:00.400> we're<00:03:01.000> tapping<00:03:01.209> into<00:03:01.480> the
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shelters we're tapping into the
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shelters we're tapping into the
community<00:03:02.109> getting<00:03:03.100> community<00:03:03.639> dollars<00:03:04.060> you
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community getting community dollars you
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community getting community dollars you
know<00:03:04.480> individuals<00:03:05.019> giving<00:03:05.650> us<00:03:05.739> checks<00:03:06.069> for
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know individuals giving us checks for
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know individuals giving us checks for
fifty<00:03:06.850> hundred<00:03:07.030> dollars<00:03:07.420> a<00:03:07.450> month<00:03:07.600> and<00:03:08.109> we're
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fifty hundred dollars a month and we're
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fifty hundred dollars a month and we're
utilizing<00:03:08.799> that<00:03:09.400> to<00:03:09.880> provide<00:03:10.299> housing<00:03:10.870> for
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utilizing that to provide housing for
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utilizing that to provide housing for
people<00:03:11.290> in<00:03:11.560> weather<00:03:11.799> wise<00:03:12.190> wouldn't<00:03:12.609> have<00:03:12.730> it
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people in weather wise wouldn't have it
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people in weather wise wouldn't have it
we<00:03:13.840> rely<00:03:14.310> to<00:03:15.310> it<00:03:15.780> timaya<00:03:16.780> disgraceful<00:03:17.709> a
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we rely to it timaya disgraceful a
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we rely to it timaya disgraceful a
disgraceful<00:03:19.660> reliance<00:03:20.290> on<00:03:20.530> the<00:03:21.250> generosity
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disgraceful reliance on the generosity
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disgraceful reliance on the generosity
of<00:03:22.060> individuals<00:03:22.780> in<00:03:23.049> this<00:03:23.440> country<00:03:24.010> we<00:03:24.910> don't
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of individuals in this country we don't
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of individuals in this country we don't
take<00:03:25.900> care<00:03:26.169> of<00:03:26.230> our<00:03:26.799> poor<00:03:27.130> or<00:03:27.430> afford<00:03:28.000> them<00:03:28.349> the
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take care of our poor or afford them the
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take care of our poor or afford them the
opportunities<00:03:30.069> that<00:03:30.370> would<00:03:30.880> help<00:03:31.090> them<00:03:31.419> so
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opportunities that would help them so
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opportunities that would help them so
can<00:03:32.260> we<00:03:32.380> make<00:03:32.590> realistic<00:03:33.160> comparisons
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can we make realistic comparisons
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can we make realistic comparisons
between<00:03:34.269> countries<00:03:34.750> using<00:03:35.290> such<00:03:35.680> statistics
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between countries using such statistics
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between countries using such statistics
when<00:03:36.910> there's<00:03:37.120> so<00:03:37.299> much<00:03:37.450> variation<00:03:37.989> it<00:03:38.409> will
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when there's so much variation it will
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when there's so much variation it will
be
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be
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be
in<00:03:38.989> among<00:03:39.230> countries<00:03:39.470> and<00:03:39.920> within<00:03:40.640> countries
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in among countries and within countries
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in among countries and within countries
GDI<00:03:42.799> is<00:03:42.920> most<00:03:43.519> often<00:03:43.760> used<00:03:43.939> and<00:03:44.480> many<00:03:45.379> argue
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GDI is most often used and many argue
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GDI is most often used and many argue
that<00:03:46.010> GDI<00:03:46.370> is<00:03:46.489> a<00:03:46.670> good<00:03:47.090> measure<00:03:47.450> in<00:03:47.750> market
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that GDI is a good measure in market
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that GDI is a good measure in market
societies<00:03:48.890> because<00:03:49.670> the<00:03:49.849> prices<00:03:50.150> people<00:03:50.689> pay
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societies because the prices people pay
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societies because the prices people pay
for<00:03:51.230> goods<00:03:51.610> reflect<00:03:52.610> the<00:03:52.790> values<00:03:53.030> people
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for goods reflect the values people
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for goods reflect the values people
place<00:03:54.019> on<00:03:54.200> those<00:03:54.379> goods<00:03:55.150> when<00:03:56.150> we<00:03:56.269> add<00:03:56.390> up<00:03:56.510> all
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place on those goods when we add up all
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place on those goods when we add up all
the<00:03:56.870> output<00:03:57.290> of<00:03:57.319> the<00:03:57.470> society<00:03:58.069> and<00:03:58.220> divide<00:03:58.579> it
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the output of the society and divide it
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the output of the society and divide it
by<00:03:58.879> the<00:03:58.909> number<00:03:59.060> of<00:03:59.359> people<00:03:59.840> the<00:04:00.409> resulting
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by the number of people the resulting
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by the number of people the resulting
amount<00:04:01.220> has<00:04:01.640> a<00:04:01.670> good<00:04:02.030> claim<00:04:02.329> to<00:04:02.569> be<00:04:02.689> a<00:04:02.720> useful
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amount has a good claim to be a useful
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amount has a good claim to be a useful
measure<00:04:03.439> of<00:04:03.709> well-being
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measure of well-being
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measure of well-being
prices<00:04:06.420> reflect<00:04:06.600> the<00:04:06.930> value<00:04:07.260> we<00:04:07.410> place<00:04:07.560> on
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prices reflect the value we place on
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prices reflect the value we place on
output<00:04:08.450> for<00:04:09.450> instance<00:04:09.750> a<00:04:09.870> Mercedes<00:04:10.380> has<00:04:10.590> a
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output for instance a Mercedes has a
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output for instance a Mercedes has a
higher<00:04:10.920> price<00:04:11.340> than<00:04:11.370> a<00:04:11.610> Ford<00:04:11.940> because<00:04:12.630> we
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higher price than a Ford because we
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higher price than a Ford because we
value<00:04:13.020> the<00:04:13.380> engineering<00:04:14.100> technology
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value the engineering technology
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value the engineering technology
aesthetics<00:04:15.840> and<00:04:16.170> style<00:04:16.530> of<00:04:16.709> the<00:04:16.769> Mercedes
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aesthetics and style of the Mercedes
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aesthetics and style of the Mercedes
more<00:04:18.769> let's<00:04:19.769> look<00:04:20.010> at<00:04:20.190> how<00:04:20.370> building<00:04:21.060> and
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more let's look at how building and
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more let's look at how building and
consuming<00:04:21.750> a<00:04:21.840> car<00:04:22.110> adds<00:04:22.470> to<00:04:23.310> the<00:04:23.460> GDI<00:04:24.200> this<00:04:25.200> is
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consuming a car adds to the GDI this is
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consuming a car adds to the GDI this is
about<00:04:25.530> 60,000<00:04:26.370> pounds<00:04:26.640> worth<00:04:26.790> of<00:04:26.940> motor<00:04:27.150> car
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about 60,000 pounds worth of motor car
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about 60,000 pounds worth of motor car
now<00:04:28.590> want<00:04:28.770> that<00:04:28.970> 60,000<00:04:29.970> about<00:04:31.160> 20,000<00:04:32.160> say
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now want that 60,000 about 20,000 say
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now want that 60,000 about 20,000 say
went<00:04:32.790> on<00:04:32.940> buying<00:04:33.330> inputs<00:04:33.840> from<00:04:33.990> other
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went on buying inputs from other
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went on buying inputs from other
industries<00:04:34.820> Bastille<00:04:35.820> the<00:04:36.300> rubber<00:04:36.540> the
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industries Bastille the rubber the
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industries Bastille the rubber the
leather<00:04:38.660> so<00:04:39.660> what<00:04:39.870> they<00:04:40.020> did<00:04:40.080> was<00:04:40.320> to<00:04:40.680> add
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leather so what they did was to add
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leather so what they did was to add
value<00:04:40.980> of<00:04:41.940> 40,000<00:04:42.630> pounds<00:04:44.420> what<00:04:45.420> do<00:04:45.480> they<00:04:45.600> do
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value of 40,000 pounds what do they do
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value of 40,000 pounds what do they do
with<00:04:46.110> that<00:04:46.260> 40,000<00:04:47.070> pounds<00:04:47.600> some<00:04:48.600> of<00:04:48.720> it<00:04:48.840> went
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with that 40,000 pounds some of it went
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with that 40,000 pounds some of it went
on<00:04:49.610> wages<00:04:50.750> some<00:04:51.750> of<00:04:51.870> it<00:04:51.990> went<00:04:52.200> on<00:04:52.350> interest
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on wages some of it went on interest
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on wages some of it went on interest
payments<00:04:53.660> some<00:04:54.660> of<00:04:54.780> it<00:04:54.870> was<00:04:55.050> profits<00:04:55.980> to<00:04:56.160> the
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payments some of it was profits to the
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payments some of it was profits to the
company<00:04:56.700> which<00:04:57.150> was<00:04:57.360> distributed<00:04:57.810> to<00:04:58.650> the
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company which was distributed to the
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company which was distributed to the
shareholders<00:04:59.010> in<00:04:59.550> the<00:04:59.640> form<00:04:59.850> of<00:04:59.940> dividends
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shareholders in the form of dividends
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shareholders in the form of dividends
and<00:05:00.540> so<00:05:00.720> on<00:05:00.920> in<00:05:01.920> other<00:05:02.070> words<00:05:02.340> all<00:05:03.180> of<00:05:03.810> the
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and so on in other words all of the
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and so on in other words all of the
value<00:05:04.440> added<00:05:05.300> represents<00:05:06.300> somebody's<00:05:07.050> income
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value added represents somebody's income
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value added represents somebody's income
the<00:05:08.730> national<00:05:09.210> product<00:05:09.600> is<00:05:10.010> identically
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the national product is identically
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the national product is identically
equal<00:05:11.040> to<00:05:12.000> the<00:05:12.120> national<00:05:12.510> income<00:05:13.280> there<00:05:14.280> are
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equal to the national income there are
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equal to the national income there are
many<00:05:14.610> problems<00:05:14.970> with<00:05:15.330> using<00:05:15.570> GDI<00:05:16.230> as<00:05:16.560> a
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many problems with using GDI as a
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many problems with using GDI as a
measure<00:05:16.950> of<00:05:17.070> well-being<00:05:17.960> but<00:05:18.960> some<00:05:19.290> of<00:05:19.410> the
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measure of well-being but some of the
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measure of well-being but some of the
main<00:05:19.650> ones<00:05:19.950> of<00:05:20.160> these<00:05:20.280> first<00:05:21.230> it<00:05:22.230> often<00:05:22.410> fails
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main ones of these first it often fails
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main ones of these first it often fails
to<00:05:23.010> pick<00:05:23.160> up<00:05:23.280> the<00:05:23.430> problems<00:05:23.850> created<00:05:24.060> by
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to pick up the problems created by
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to pick up the problems created by
pollution<00:05:24.990> and<00:05:25.350> damage<00:05:25.710> to<00:05:25.919> the<00:05:26.040> environment
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pollution and damage to the environment
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pollution and damage to the environment
these<00:05:27.630> costs<00:05:28.320> are<00:05:28.320> not<00:05:28.860> priced<00:05:29.310> into
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these costs are not priced into
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these costs are not priced into
conventional<00:05:30.050> GDR<00:05:31.050> estimates<00:05:32.840> second<00:05:33.840> GDI<00:05:34.260> /
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conventional GDR estimates second GDI /
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conventional GDR estimates second GDI /
head<00:05:34.919> gives<00:05:35.669> us<00:05:35.820> a<00:05:35.940> mean<00:05:36.300> average<00:05:36.720> standard<00:05:37.560> of
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head gives us a mean average standard of
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head gives us a mean average standard of
living<00:05:38.419> the<00:05:39.419> modal<00:05:39.930> living<00:05:40.350> standard<00:05:40.919> in<00:05:41.040> most
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living the modal living standard in most
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living the modal living standard in most
countries<00:05:41.550> is<00:05:41.940> way<00:05:42.210> below<00:05:42.450> this<00:05:42.840> with<00:05:43.590> many
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countries is way below this with many
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countries is way below this with many
living<00:05:44.310> in<00:05:44.430> poverty<00:05:44.820> what<00:05:45.660> others<00:05:46.020> are<00:05:46.110> very
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living in poverty what others are very
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living in poverty what others are very
comfortably<00:05:46.860> off<00:05:47.130> the<00:05:48.350> extent<00:05:49.350> to<00:05:49.410> this
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comfortably off the extent to this
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comfortably off the extent to this
problem<00:05:49.800> varies<00:05:50.280> considerably<00:05:50.490> between
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problem varies considerably between
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problem varies considerably between
countries<00:05:51.510> and<00:05:51.870> we'll<00:05:52.260> explore<00:05:52.770> the<00:05:53.010> issue
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countries and we'll explore the issue
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countries and we'll explore the issue
more<00:05:53.490> fully<00:05:53.760> in<00:05:53.940> a<00:05:54.030> later<00:05:54.240> film
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more fully in a later film
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more fully in a later film
a<00:05:57.409> third<00:05:58.409> problem<00:05:58.679> is<00:05:58.949> the<00:05:59.099> existence<00:05:59.399> in<00:05:59.819> some
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a third problem is the existence in some
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a third problem is the existence in some
societies<00:06:00.239> of<00:06:00.839> a<00:06:01.229> substantial<00:06:02.039> nan<00:06:02.399> market
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societies of a substantial nan market
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societies of a substantial nan market
economy<00:06:04.999> subsistence<00:06:05.999> agriculture<00:06:06.509> in<00:06:06.869> many
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economy subsistence agriculture in many
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economy subsistence agriculture in many
less<00:06:07.349> developed<00:06:07.649> economies<00:06:08.309> means<00:06:08.999> that
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less developed economies means that
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less developed economies means that
people<00:06:09.419> live<00:06:09.749> at<00:06:09.839> a<00:06:09.929> higher<00:06:10.199> standard<00:06:10.589> of
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people live at a higher standard of
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people live at a higher standard of
living<00:06:11.069> than<00:06:11.339> implied<00:06:12.029> by<00:06:12.269> the<00:06:12.329> GDI<00:06:12.809> figures
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living than implied by the GDI figures
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living than implied by the GDI figures
such<00:06:14.819> subsistence<00:06:15.629> output<00:06:16.139> doesn't<00:06:16.919> appear
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such subsistence output doesn't appear
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such subsistence output doesn't appear
in<00:06:17.519> the<00:06:17.789> national<00:06:18.209> accounts<00:06:19.459> the<00:06:20.459> simple<00:06:20.849> GDI
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in the national accounts the simple GDI
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in the national accounts the simple GDI
measure<00:06:21.599> doesn't<00:06:22.559> tell<00:06:22.769> you<00:06:22.889> the<00:06:23.069> kind<00:06:23.519> of
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measure doesn't tell you the kind of
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measure doesn't tell you the kind of
output<00:06:24.539> that's<00:06:24.779> being<00:06:25.019> produced<00:06:26.059> income<00:06:27.059> can
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output that's being produced income can
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output that's being produced income can
be<00:06:27.299> used<00:06:27.599> to<00:06:27.719> build<00:06:27.929> hospitals<00:06:28.349> schools
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be used to build hospitals schools
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be used to build hospitals schools
universities<00:06:30.619> parks<00:06:31.619> which<00:06:32.399> will<00:06:32.579> still
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universities parks which will still
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universities parks which will still
further<00:06:33.209> enhance<00:06:33.659> people's<00:06:33.959> lives<00:06:34.379> and
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further enhance people's lives and
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further enhance people's lives and
well-being<00:06:35.569> or<00:06:36.569> it<00:06:36.809> can<00:06:36.959> be<00:06:37.049> used<00:06:37.259> to<00:06:37.469> fight
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well-being or it can be used to fight
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well-being or it can be used to fight
wars<00:06:38.089> build<00:06:39.089> weapons<00:06:39.449> of<00:06:39.839> mass<00:06:40.049> destruction
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wars build weapons of mass destruction
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wars build weapons of mass destruction
and<00:06:41.309> the<00:06:41.759> traffic<00:06:42.059> of<00:06:42.389> people<00:06:42.809> and<00:06:42.989> drugs<00:06:44.119> it's
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and the traffic of people and drugs it's
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and the traffic of people and drugs it's
a<00:06:45.179> very<00:06:45.419> imperfect<00:06:45.779> measure<00:06:46.259> I<00:06:46.679> mean<00:06:47.279> it's
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a very imperfect measure I mean it's
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a very imperfect measure I mean it's
been<00:06:47.609> born<00:06:47.999> recognized<00:06:48.689> but<00:06:49.049> some<00:06:49.289> of<00:06:49.379> the
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been born recognized but some of the
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been born recognized but some of the
imperfections<00:06:50.129> have<00:06:50.279> become<00:06:50.549> more<00:06:50.729> important
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imperfections have become more important
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imperfections have become more important
over<00:06:51.539> time<00:06:51.779> we<00:06:52.139> don't<00:06:52.259> you<00:06:52.529> measure<00:06:53.339> the<00:06:53.759> time
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over time we don't you measure the time
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over time we don't you measure the time
that<00:06:54.239> people<00:06:54.389> spend<00:06:54.569> outside<00:06:55.109> of<00:06:55.559> working<00:06:55.919> for
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that people spend outside of working for
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that people spend outside of working for
income<00:06:56.939> it's<00:06:57.929> always<00:06:58.169> been<00:06:58.289> true<00:06:58.679> that<00:06:58.949> a
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income it's always been true that a
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income it's always been true that a
large<00:06:59.309> part<00:06:59.759> of<00:06:59.879> our<00:06:59.999> time<00:07:00.419> majority<00:07:01.199> of<00:07:01.229> our
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large part of our time majority of our
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large part of our time majority of our
time<00:07:01.469> is<00:07:01.949> spent<00:07:02.039> not<00:07:02.610> working<00:07:02.969> for<00:07:03.149> income<00:07:03.509> but
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time is spent not working for income but
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time is spent not working for income but
spent<00:07:04.110> it<00:07:04.229> leisure<00:07:04.469> activities<00:07:05.129> taking<00:07:05.489> care
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spent it leisure activities taking care
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spent it leisure activities taking care
of<00:07:06.119> a<00:07:06.299> family<00:07:07.159> cleaning<00:07:08.159> a<00:07:08.249> house
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of a family cleaning a house
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of a family cleaning a house
conversations<00:07:09.299> even<00:07:10.199> taking<00:07:10.919> care<00:07:11.069> of
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conversations even taking care of
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conversations even taking care of
children<00:07:11.219> etc<00:07:11.809> another<00:07:12.809> thing<00:07:13.169> we<00:07:13.259> don't
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children etc another thing we don't
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children etc another thing we don't
measure<00:07:13.619> national<00:07:14.159> income<00:07:14.249> accounts
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measure national income accounts
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measure national income accounts
people's<00:07:15.509> increase<00:07:15.959> longevity<00:07:16.519> of<00:07:17.519> course<00:07:18.269> we
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people's increase longevity of course we
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people's increase longevity of course we
measure<00:07:18.659> the<00:07:18.929> income<00:07:19.319> if<00:07:19.439> they're<00:07:19.619> working
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measure the income if they're working
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measure the income if they're working
longer<00:07:20.249> and<00:07:20.429> they're<00:07:20.699> getting<00:07:20.939> more<00:07:21.059> income
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longer and they're getting more income
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longer and they're getting more income
that<00:07:21.509> comes<00:07:21.719> in<00:07:22.019> the<00:07:22.409> fact<00:07:22.649> that<00:07:22.769> people<00:07:23.099> put
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that comes in the fact that people put
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that comes in the fact that people put
an<00:07:23.459> enormous<00:07:23.729> value<00:07:24.209> on<00:07:24.689> living<00:07:25.589> longer<00:07:25.829> and
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an enormous value on living longer and
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an enormous value on living longer and
having<00:07:26.369> higher<00:07:26.549> quality<00:07:27.149> lives<00:07:27.629> and<00:07:27.989> that
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having higher quality lives and that
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having higher quality lives and that
this<00:07:28.379> has<00:07:28.559> changed<00:07:28.589> so<00:07:29.219> much<00:07:29.459> in<00:07:29.789> all
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this has changed so much in all
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this has changed so much in all
countries<00:07:30.329> of<00:07:30.629> the<00:07:30.659> world<00:07:30.869> during<00:07:31.439> the<00:07:31.769> last
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countries of the world during the last
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countries of the world during the last
century<00:07:32.899> and<00:07:33.899> still<00:07:34.079> changing<00:07:34.709> that<00:07:35.159> we<00:07:35.279> don't
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century and still changing that we don't
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century and still changing that we don't
measure<00:07:35.729> these<00:07:36.569> are<00:07:36.749> just<00:07:36.869> a<00:07:37.019> couple<00:07:37.319> of
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measure these are just a couple of
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measure these are just a couple of
examples<00:07:37.859> of<00:07:38.129> things<00:07:38.339> that<00:07:38.579> we<00:07:38.789> should
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examples of things that we should
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examples of things that we should
and<00:07:39.960> could<00:07:40.440> be<00:07:40.470> measuring<00:07:41.070> but<00:07:41.310> we're<00:07:41.430> not
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and could be measuring but we're not
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and could be measuring but we're not
doing<00:07:41.970> that<00:07:42.210> as<00:07:42.900> yet<00:07:43.340> largely<00:07:44.340> as<00:07:44.580> a<00:07:44.610> result<00:07:44.850> of
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doing that as yet largely as a result of
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doing that as yet largely as a result of
these<00:07:45.270> problems<00:07:45.630> with<00:07:45.990> conventional<00:07:46.770> GDI
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these problems with conventional GDI
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these problems with conventional GDI
estimates<00:07:47.940> a<00:07:48.090> relatively<00:07:48.750> new<00:07:49.080> measure<00:07:49.380> the
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estimates a relatively new measure the
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estimates a relatively new measure the
Human<00:07:50.430> Development<00:07:50.580> Index<00:07:51.150> or<00:07:51.660> HD<00:07:52.590> I<00:07:52.620> was
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Human Development Index or HD I was
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Human Development Index or HD I was
launched<00:07:53.520> in<00:07:53.669> 1990<00:07:54.500> it's<00:07:55.500> commissioned<00:07:56.010> by
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launched in 1990 it's commissioned by
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launched in 1990 it's commissioned by
the<00:07:56.160> United<00:07:56.730> Nations<00:07:56.790> and<00:07:57.330> is<00:07:57.870> launched<00:07:58.260> every
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the United Nations and is launched every
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the United Nations and is launched every
year<00:07:58.770> in<00:07:58.919> a<00:07:59.250> human<00:07:59.669> development<00:08:00.240> report<00:08:00.590> that
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year in a human development report that
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year in a human development report that
tracks<00:08:01.950> nations<00:08:02.460> developing<00:08:03.090> not<00:08:03.270> only<00:08:03.300> in
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tracks nations developing not only in
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tracks nations developing not only in
terms<00:08:03.660> of<00:08:04.169> production<00:08:04.919> and<00:08:05.250> commodities<00:08:05.910> but
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terms of production and commodities but
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terms of production and commodities but
in<00:08:06.810> terms<00:08:06.840> of<00:08:07.200> people's<00:08:07.530> lives<00:08:07.620> and
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in terms of people's lives and
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in terms of people's lives and
development<00:08:09.020> it<00:08:10.020> has<00:08:10.050> grand<00:08:10.530> claims<00:08:10.800> to
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development it has grand claims to
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development it has grand claims to
putting<00:08:11.760> people<00:08:12.300> back<00:08:12.570> at<00:08:12.780> the<00:08:12.930> center<00:08:13.350> of<00:08:13.380> the
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putting people back at the center of the
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putting people back at the center of the
development<00:08:14.190> process<00:08:14.340> rather<00:08:15.300> than<00:08:15.510> the
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development process rather than the
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development process rather than the
market<00:08:16.490> it's<00:08:17.490> great<00:08:17.820> claim<00:08:18.210> as<00:08:18.450> an
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market it's great claim as an
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market it's great claim as an
improvement<00:08:18.990> to<00:08:19.229> GDI<00:08:19.710> as<00:08:20.040> a<00:08:20.070> measure<00:08:20.430> of
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improvement to GDI as a measure of
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improvement to GDI as a measure of
welfare<00:08:21.150> is<00:08:21.389> that<00:08:22.110> it's<00:08:22.260> a<00:08:22.410> composite<00:08:23.010> measure
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welfare is that it's a composite measure
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welfare is that it's a composite measure
looking<00:08:24.150> at<00:08:24.300> health<00:08:24.600> and<00:08:25.070> education<00:08:26.070> as<00:08:26.220> well
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looking at health and education as well
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looking at health and education as well
as<00:08:26.850> income<00:08:27.210> so<00:08:27.870> it<00:08:28.020> measures<00:08:28.260> progress<00:08:28.979> by
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as income so it measures progress by
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as income so it measures progress by
using<00:08:29.520> a<00:08:29.760> concept<00:08:30.270> of<00:08:30.540> development<00:08:31.440> much
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using a concept of development much
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using a concept of development much
broader<00:08:32.370> than<00:08:32.880> that<00:08:33.180> allowed<00:08:33.630> by<00:08:33.870> the<00:08:33.930> GDP<00:08:34.969> in
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broader than that allowed by the GDP in
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broader than that allowed by the GDP in
a<00:08:36.150> moment<00:08:36.570> we'll<00:08:37.169> use<00:08:37.409> our<00:08:37.589> statistical
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a moment we'll use our statistical
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a moment we'll use our statistical
knowledge<00:08:38.430> to<00:08:39.089> see<00:08:39.270> how<00:08:39.419> it's<00:08:39.599> calculated<00:08:41.690> the
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knowledge to see how it's calculated the
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knowledge to see how it's calculated the
nice<00:08:42.839> thing<00:08:43.140> about<00:08:43.169> the<00:08:43.529> Human<00:08:43.830> Development
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nice thing about the Human Development
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nice thing about the Human Development
Index<00:08:44.430> is<00:08:44.880> that<00:08:45.480> it's<00:08:45.630> simple<00:08:46.050> to<00:08:46.080> measure<00:08:46.410> and
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Index is that it's simple to measure and
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Index is that it's simple to measure and
very<00:08:47.160> easy<00:08:47.370> to<00:08:47.520> understand<00:08:48.260> it<00:08:49.260> takes<00:08:49.530> a<00:08:49.650> value
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very easy to understand it takes a value
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very easy to understand it takes a value
between<00:08:50.190> zero<00:08:50.820> and<00:08:50.910> one<00:08:51.120> where<00:08:51.900> zero<00:08:52.410> is<00:08:52.740> utter
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between zero and one where zero is utter
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between zero and one where zero is utter
misery<00:08:53.880> and<00:08:54.330> one<00:08:55.080> is<00:08:55.290> some<00:08:55.530> kind<00:08:55.830> of<00:08:55.950> utopia
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misery and one is some kind of utopia
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misery and one is some kind of utopia
not<00:08:57.570> only<00:08:57.720> does<00:08:57.990> the<00:08:58.170> index<00:08:58.320> vary<00:08:58.890> between
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not only does the index vary between
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not only does the index vary between
countries<00:08:59.760> it<00:09:00.720> varies<00:09:01.200> over<00:09:01.350> time
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countries it varies over time
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countries it varies over time
for<00:09:03.830> example<00:09:04.250> India<00:09:04.580> has<00:09:04.790> been<00:09:04.970> developing
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for example India has been developing
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for example India has been developing
rapidly<00:09:05.660> and<00:09:06.080> moving<00:09:06.920> up<00:09:07.010> the<00:09:07.130> rankings<00:09:08.350> China
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rapidly and moving up the rankings China
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rapidly and moving up the rankings China
too<00:09:09.620> has<00:09:09.860> developed<00:09:10.310> fast<00:09:10.580> it's<00:09:11.510> now<00:09:11.810> a
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too has developed fast it's now a
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too has developed fast it's now a
country<00:09:12.500> moving<00:09:13.190> up<00:09:13.460> towards<00:09:13.940> the<00:09:14.120> middle<00:09:14.390> of
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country moving up towards the middle of
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country moving up towards the middle of
the<00:09:14.720> HDI<00:09:15.350> rankings<00:09:16.450> so<00:09:17.450> let's<00:09:17.630> choose<00:09:17.960> China
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the HDI rankings so let's choose China
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the HDI rankings so let's choose China
to<00:09:19.040> see<00:09:19.250> how<00:09:19.550> the<00:09:19.970> HDI<00:09:20.510> is<00:09:20.960> arrived<00:09:21.320> at<00:09:21.670> before
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to see how the HDI is arrived at before
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to see how the HDI is arrived at before
we<00:09:22.760> do<00:09:22.910> so<00:09:23.180> we<00:09:23.390> have<00:09:23.660> to<00:09:23.780> be<00:09:23.810> clear<00:09:24.110> about<00:09:24.770> what
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we do so we have to be clear about what
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we do so we have to be clear about what
we<00:09:25.370> mean<00:09:25.670> by<00:09:25.700> the<00:09:26.230> geometric<00:09:27.230> mean<00:09:27.460> which<00:09:28.460> is<00:09:28.490> a
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we mean by the geometric mean which is a
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we mean by the geometric mean which is a
particular<00:09:29.120> kind<00:09:29.750> of<00:09:30.050> mean<00:09:30.440> average<00:09:30.940> which
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particular kind of mean average which
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particular kind of mean average which
indicates<00:09:32.450> the<00:09:32.660> central<00:09:33.200> tendency<00:09:33.950> or
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indicates the central tendency or
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indicates the central tendency or
typical<00:09:34.820> value<00:09:35.480> of<00:09:35.660> a<00:09:35.990> set<00:09:36.200> of<00:09:36.230> numbers<00:09:37.930> it's
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typical value of a set of numbers it's
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typical value of a set of numbers it's
similar<00:09:39.320> to<00:09:39.710> the<00:09:40.190> arithmetic<00:09:40.730> mean<00:09:41.890> which<00:09:42.890> is
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similar to the arithmetic mean which is
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similar to the arithmetic mean which is
what<00:09:43.250> most<00:09:43.520> people<00:09:43.910> think<00:09:43.940> of<00:09:44.300> with<00:09:44.510> the<00:09:44.600> word
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what most people think of with the word
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what most people think of with the word
average<00:09:45.700> except<00:09:46.700> that<00:09:46.850> here<00:09:47.300> the<00:09:47.750> numbers<00:09:48.140> are
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average except that here the numbers are
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average except that here the numbers are
multiplied<00:09:49.010> and<00:09:49.510> then<00:09:50.510> the<00:09:50.690> nth<00:09:50.930> root<00:09:51.590> where<00:09:52.580> n
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multiplied and then the nth root where n
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multiplied and then the nth root where n
is<00:09:52.910> the<00:09:53.390> count<00:09:53.660> of<00:09:53.870> numbers<00:09:54.230> in<00:09:54.350> the<00:09:54.440> set<00:09:54.710> of
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is the count of numbers in the set of
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is the count of numbers in the set of
the<00:09:55.520> resulting<00:09:56.000> product<00:09:56.390> is<00:09:56.540> taken<00:09:57.040> for
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the resulting product is taken for
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the resulting product is taken for
instance<00:09:58.460> the<00:09:59.140> geometric<00:10:00.140> mean<00:10:00.410> of<00:10:00.440> two
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instance the geometric mean of two
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instance the geometric mean of two
numbers<00:10:01.070> say<00:10:01.880> two<00:10:01.940> and<00:10:02.540> eight<00:10:02.770> it's<00:10:03.770> just<00:10:04.100> the
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numbers say two and eight it's just the
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numbers say two and eight it's just the
square<00:10:04.910> root<00:10:05.270> of<00:10:05.420> their<00:10:05.570> product<00:10:06.050> that<00:10:06.500> is<00:10:06.610> we
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square root of their product that is we
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square root of their product that is we
multiply<00:10:08.090> 2<00:10:08.150> times<00:10:08.450> 8<00:10:08.930> and<00:10:09.220> take<00:10:10.220> the<00:10:10.340> square
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multiply 2 times 8 and take the square
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multiply 2 times 8 and take the square
root<00:10:11.020> to<00:10:12.020> give<00:10:12.170> us<00:10:12.320> 4<00:10:14.320> that's<00:10:15.320> another<00:10:15.560> example
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root to give us 4 that's another example
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root to give us 4 that's another example
the<00:10:16.820> geometric<00:10:17.600> mean<00:10:17.930> of<00:10:17.980> the<00:10:18.980> three<00:10:19.370> numbers
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the geometric mean of the three numbers
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the geometric mean of the three numbers
for<00:10:20.830> one<00:10:21.830> and<00:10:22.450> one<00:10:23.450> over<00:10:24.260> 32<00:10:24.740> is<00:10:25.270> the<00:10:26.270> cubed
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for one and one over 32 is the cubed
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for one and one over 32 is the cubed
root of<00:10:27.260> their<00:10:27.440> product<00:10:29.110> which<00:10:30.110> would<00:10:30.320> give
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root of their product which would give
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root of their product which would give
us<00:10:30.530> a<00:10:31.510> half<00:10:33.970> the<00:10:34.970> cubed<00:10:35.330> root<00:10:35.330> of<00:10:35.660> 4<00:10:36.440> times<00:10:36.920> 1
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us a half the cubed root of 4 times 1
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us a half the cubed root of 4 times 1
times<00:10:38.920> 1<00:10:39.920> over<00:10:39.950> 32<00:10:40.820> equals<00:10:41.750> a<00:10:42.320> half<00:10:46.030> now<00:10:47.030> we<00:10:47.090> can
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times 1 over 32 equals a half now we can
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times 1 over 32 equals a half now we can
see<00:10:47.510> how<00:10:47.690> the<00:10:47.750> HDI<00:10:48.290> is<00:10:48.440> measured<00:10:49.450> the<00:10:50.450> first
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see how the HDI is measured the first
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see how the HDI is measured the first
step<00:10:50.930> is<00:10:51.110> to<00:10:51.380> create<00:10:51.940> sub-indices<00:10:52.940> of<00:10:53.270> each<00:10:54.050> of
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step is to create sub-indices of each of
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step is to create sub-indices of each of
the<00:10:54.380> dimensions<00:10:55.690> we<00:10:56.690> have<00:10:56.840> to<00:10:56.990> set<00:10:57.230> minimum
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the dimensions we have to set minimum
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the dimensions we have to set minimum
and<00:10:58.310> maximum<00:10:58.630> values<00:10:59.630> or<00:11:00.320> goal<00:11:00.680> posts<00:11:01.250> in
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and maximum values or goal posts in
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and maximum values or goal posts in
order<00:11:02.150> to<00:11:02.270> transform<00:11:02.870> the<00:11:03.110> indicators<00:11:03.800> in<00:11:03.980> two
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order to transform the indicators in two
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order to transform the indicators in two
indices<00:11:05.060> between<00:11:05.570> naught<00:11:06.260> and<00:11:06.590> one
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indices between naught and one
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indices between naught and one
once<00:11:08.990> we<00:11:09.170> have<00:11:09.350> the<00:11:09.500> minimum<00:11:09.770> and<00:11:10.160> the<00:11:10.580> maximum
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once we have the minimum and the maximum
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once we have the minimum and the maximum
values<00:11:11.680> we<00:11:12.680> can<00:11:12.710> calculate<00:11:13.130> the<00:11:13.670> sub-indexes
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values we can calculate the sub-indexes
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values we can calculate the sub-indexes
using<00:11:15.770> the<00:11:15.860> following<00:11:16.280> formula
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using the following formula
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using the following formula
the<00:11:18.890> dimension<00:11:19.370> index<00:11:19.760> is<00:11:20.060> given<00:11:20.480> us<00:11:20.630> the
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the dimension index is given us the
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the dimension index is given us the
actual<00:11:21.740> value<00:11:22.480> minus<00:11:23.480> the<00:11:23.840> minimum<00:11:24.050> value
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actual value minus the minimum value
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actual value minus the minimum value
over<00:11:26.330> the<00:11:26.420> maximum<00:11:27.050> value<00:11:27.080> minus<00:11:27.830> the<00:11:28.160> minimum
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over the maximum value minus the minimum
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over the maximum value minus the minimum
value
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and<00:11:31.160> then<00:11:31.820> the<00:11:31.940> actual<00:11:32.480> HDI<00:11:33.170> score<00:11:33.550> is<00:11:34.550> a
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and then the actual HDI score is a
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and then the actual HDI score is a
geometric<00:11:35.570> mean<00:11:36.020> of<00:11:36.050> these<00:11:36.920> three<00:11:37.480> dimension
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geometric mean of these three dimension
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geometric mean of these three dimension
indices<00:11:38.990> and<00:11:40.090> the<00:11:41.090> three<00:11:41.450> indices<00:11:42.410> are<00:11:43.690> life
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indices and the three indices are life
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indices and the three indices are life
education<00:11:46.250> and<00:11:47.180> income<00:11:48.140> and<00:11:48.760> they<00:11:49.760> are
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education and income and they are
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education and income and they are
equally<00:11:50.570> weighted<00:11:50.870> each<00:11:51.380> having<00:11:51.890> a<00:11:52.070> weight<00:11:52.310> of
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equally weighted each having a weight of
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equally weighted each having a weight of
a<00:11:52.610> third
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the<00:11:56.919> minimum<00:11:57.489> values<00:11:57.970> are<00:11:58.179> set<00:11:58.359> at<00:11:58.509> 20<00:11:59.079> years
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the minimum values are set at 20 years
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the minimum values are set at 20 years
for<00:11:59.649> life<00:11:59.889> expectancy<00:12:01.379> nought<00:12:02.379> for<00:12:02.410> both
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for life expectancy nought for both
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for life expectancy nought for both
education<00:12:03.759> variables<00:12:04.329> and<00:12:05.220> a<00:12:06.220> hundred<00:12:07.149> and
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education variables and a hundred and
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education variables and a hundred and
sixty<00:12:07.689> three<00:12:07.899> dollars<00:12:08.439> for<00:12:09.279> per<00:12:09.579> capita<00:12:10.410> gni
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the<00:12:14.559> maximum<00:12:15.220> values<00:12:15.669> are<00:12:16.359> set<00:12:16.749> to<00:12:16.959> the<00:12:17.079> actual
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the maximum values are set to the actual
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the maximum values are set to the actual
observed<00:12:17.850> maximum<00:12:18.850> values<00:12:19.299> of<00:12:19.449> the<00:12:19.539> indicator
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observed maximum values of the indicator
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observed maximum values of the indicator
in<00:12:20.319> whatever<00:12:20.769> country<00:12:21.129> they<00:12:21.339> happen<00:12:21.819> to<00:12:21.850> be
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in whatever country they happen to be
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in whatever country they happen to be
let's<00:12:23.410> see<00:12:23.589> how<00:12:23.709> that<00:12:23.769> works<00:12:23.919> out<00:12:24.429> for<00:12:24.669> China
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let's see how that works out for China
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let's see how that works out for China
the<00:12:27.170> table<00:12:27.620> shows<00:12:27.830> the<00:12:28.430> Chinese<00:12:29.740> indicators
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the table shows the Chinese indicators
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the table shows the Chinese indicators
now<00:12:33.310> the<00:12:33.370> first<00:12:33.730> step<00:12:33.940> is<00:12:34.270> to<00:12:34.450> calculate<00:12:34.630> the
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now the first step is to calculate the
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now the first step is to calculate the
life<00:12:36.130> dimension<00:12:36.910> index<00:12:37.560> and<00:12:38.560> the<00:12:38.590> life
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life dimension index and the life
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life dimension index and the life
expectancy<00:12:40.000> index<00:12:40.810> is<00:12:42.810> 73.5<00:12:43.810> as<00:12:44.170> you<00:12:44.800> can<00:12:44.980> see
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expectancy index is 73.5 as you can see
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expectancy index is 73.5 as you can see
in<00:12:45.280> the<00:12:45.370> table<00:12:45.760> for<00:12:45.910> China<00:12:46.800> minus<00:12:47.800> 20<00:12:48.280> the
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in the table for China minus 20 the
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in the table for China minus 20 the
minimum<00:12:49.630> value<00:12:51.120> over<00:12:53.640> 83.2<00:12:55.380> which<00:12:56.380> is<00:12:56.590> the
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minimum value over 83.2 which is the
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minimum value over 83.2 which is the
observed<00:12:57.330> maximum<00:12:58.330> value<00:12:58.840> which<00:12:59.770> in<00:12:59.950> this
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observed maximum value which in this
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observed maximum value which in this
case<00:13:00.100> happens<00:13:00.730> to<00:13:00.820> be<00:13:01.060> for<00:13:01.330> Japan<00:13:02.760> minus
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case happens to be for Japan minus
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case happens to be for Japan minus
twenty<00:13:04.600> our<00:13:05.130> minimum<00:13:06.130> value
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twenty our minimum value
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twenty our minimum value
and<00:13:08.360> if<00:13:09.080> we<00:13:09.200> work<00:13:09.410> that<00:13:09.590> out<00:13:09.650> it<00:13:10.100> gives<00:13:10.400> us
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and if we work that out it gives us
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and if we work that out it gives us
naught<00:13:11.570> point<00:13:12.250> 847<00:13:13.250> for<00:13:14.030> China
00:13:16.519 --> 00:13:16.529 align:start position:0%
00:13:16.529 --> 00:13:19.900 align:start position:0%
now<00:13:16.829> step<00:13:17.100> two<00:13:17.129> is<00:13:17.639> to<00:13:17.699> calculate<00:13:18.300> the
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now step two is to calculate the
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now step two is to calculate the
education<00:13:20.910> dimension<00:13:21.540> index<00:13:22.550> let's<00:13:23.550> see<00:13:23.730> how
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education dimension index let's see how
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education dimension index let's see how
we<00:13:23.910> do<00:13:24.180> that<00:13:24.889> we<00:13:25.889> take<00:13:26.100> first<00:13:26.370> of<00:13:26.519> all<00:13:26.670> the<00:13:26.850> mean
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we do that we take first of all the mean
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we do that we take first of all the mean
years<00:13:27.810> of<00:13:27.870> schooling<00:13:28.410> index<00:13:29.310> which<00:13:30.240> is<00:13:31.699> 7.5
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years of schooling index which is 7.5
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years of schooling index which is 7.5
the<00:13:34.259> Chinese<00:13:34.709> value<00:13:35.129> showed<00:13:35.370> in<00:13:35.519> the<00:13:35.610> table
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the Chinese value showed in the table
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the Chinese value showed in the table
minus<00:13:37.559> 0<00:13:38.629> /<00:13:40.100> 13.2<00:13:41.180> which<00:13:42.180> is<00:13:42.360> the<00:13:42.540> observed
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minus 0 / 13.2 which is the observed
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minus 0 / 13.2 which is the observed
maximum<00:13:44.040> value<00:13:44.490> which<00:13:44.850> in<00:13:44.999> this<00:13:45.089> case<00:13:45.149> happens
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maximum value which in this case happens
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maximum value which in this case happens
to<00:13:45.990> be<00:13:46.259> for<00:13:46.470> the<00:13:46.529> USA<00:13:47.540> minus<00:13:48.540> 0
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to be for the USA minus 0
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to be for the USA minus 0
which<00:13:50.920> gives<00:13:51.330> no<00:13:52.330> point<00:13:52.660> five<00:13:52.900> six<00:13:53.230> eight<00:13:55.530> now
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which gives no point five six eight now
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which gives no point five six eight now
we<00:13:56.590> need<00:13:56.710> the<00:13:57.000> expected<00:13:58.000> years<00:13:58.330> of<00:13:58.570> schooling
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we need the expected years of schooling
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we need the expected years of schooling
index<00:13:59.640> which<00:14:00.640> is<00:14:02.340> 11.4<00:14:03.340> for<00:14:03.640> China<00:14:04.110> minus<00:14:05.110> zero
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index which is 11.4 for China minus zero
00:14:06.060 --> 00:14:11.310 align:start position:0%
index which is 11.4 for China minus zero
over<00:14:07.230> 20.6<00:14:08.310> the<00:14:09.310> observed<00:14:09.790> maximum<00:14:10.600> which<00:14:11.140> in
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over 20.6 the observed maximum which in
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over 20.6 the observed maximum which in
this<00:14:11.410> case<00:14:11.470> is<00:14:12.240> Australia<00:14:13.800> minus<00:14:14.800> zero<00:14:16.560> which
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this case is Australia minus zero which
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this case is Australia minus zero which
gives<00:14:17.830> us<00:14:17.980> nor<00:14:18.550> point<00:14:18.820> 553<00:14:21.180> now<00:14:22.180> we<00:14:22.240> work<00:14:22.570> out
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gives us nor point 553 now we work out
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gives us nor point 553 now we work out
the<00:14:22.840> education<00:14:23.740> dimension<00:14:24.340> index<00:14:25.080> which<00:14:26.080> we
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the education dimension index which we
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the education dimension index which we
find<00:14:26.680> by<00:14:27.040> taking<00:14:27.610> nor<00:14:27.970> point<00:14:28.240> 5<00:14:28.450> 6<00:14:28.810> 8<00:14:29.520> x<00:14:30.540> naught
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find by taking nor point 5 6 8 x naught
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find by taking nor point 5 6 8 x naught
point<00:14:31.750> 5<00:14:32.020> 53<00:14:32.860> and<00:14:34.200> now<00:14:35.200> we<00:14:35.260> take<00:14:35.530> the<00:14:35.800> square
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point 5 53 and now we take the square
00:14:36.070 --> 00:14:39.180 align:start position:0%
point 5 53 and now we take the square
root<00:14:36.190> of<00:14:36.610> that
00:14:39.180 --> 00:14:39.190 align:start position:0%
00:14:39.190 --> 00:14:43.980 align:start position:0%
so<00:14:39.640> we<00:14:39.730> then<00:14:39.850> take<00:14:40.030> that<00:14:40.210> value<00:14:40.420> minus<00:14:40.990> 0<00:14:41.940> /<00:14:42.990> nor
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so we then take that value minus 0 / nor
00:14:43.990 --> 00:14:48.300 align:start position:0%
so we then take that value minus 0 / nor
point<00:14:44.350> 9<00:14:44.920> 51<00:14:45.690> which<00:14:46.690> is<00:14:46.900> the<00:14:47.050> observed<00:14:47.530> maximum
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point 9 51 which is the observed maximum
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point 9 51 which is the observed maximum
value<00:14:48.730> which<00:14:48.940> in<00:14:49.120> that<00:14:49.270> case<00:14:49.330> happens<00:14:49.990> to<00:14:50.080> be
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value which in that case happens to be
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value which in that case happens to be
New<00:14:50.500> Zealand<00:14:51.960> minus<00:14:52.960> zero
00:14:55.090 --> 00:14:55.100 align:start position:0%
New Zealand minus zero
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New Zealand minus zero
which<00:14:55.760> gives<00:14:56.090> us<00:14:56.270> an<00:14:56.450> education<00:14:57.110> dimension
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which gives us an education dimension
00:14:57.620 --> 00:15:02.519 align:start position:0%
which gives us an education dimension
index<00:14:57.950> of<00:14:58.190> 9.5<00:14:59.030> 89
00:15:02.519 --> 00:15:02.529 align:start position:0%
00:15:02.529 --> 00:15:06.540 align:start position:0%
now<00:15:03.129> we<00:15:03.189> calculate<00:15:03.910> the<00:15:04.769> income<00:15:05.769> dimension
00:15:06.540 --> 00:15:06.550 align:start position:0%
now we calculate the income dimension
00:15:06.550 --> 00:15:10.920 align:start position:0%
now we calculate the income dimension
index<00:15:06.999> and<00:15:07.529> here<00:15:08.529> we<00:15:08.649> use<00:15:08.879> logs<00:15:09.879> of<00:15:10.329> the<00:15:10.540> income
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index and here we use logs of the income
00:15:10.930 --> 00:15:13.049 align:start position:0%
index and here we use logs of the income
data
00:15:13.049 --> 00:15:13.059 align:start position:0%
data
00:15:13.059 --> 00:15:17.699 align:start position:0%
data
so<00:15:13.809> the<00:15:13.959> income<00:15:14.559> index<00:15:15.159> gives<00:15:15.879> us<00:15:16.439> the<00:15:17.439> log<00:15:17.679> of
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so the income index gives us the log of
00:15:17.709 --> 00:15:23.429 align:start position:0%
so the income index gives us the log of
7<00:15:18.309> to<00:15:18.789> 63<00:15:20.789> that's<00:15:21.789> the<00:15:22.089> from<00:15:22.419> the<00:15:22.539> Chinese<00:15:23.079> data
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7 to 63 that's the from the Chinese data
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7 to 63 that's the from the Chinese data
in<00:15:23.799> the<00:15:23.919> table<00:15:25.079> minus<00:15:26.079> the<00:15:26.259> log<00:15:26.529> of<00:15:26.909> 163<00:15:28.109> which
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in the table minus the log of 163 which
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in the table minus the log of 163 which
is<00:15:29.319> the<00:15:29.529> observed<00:15:29.969> minimum<00:15:30.969> value<00:15:31.829> which
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is the observed minimum value which
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is the observed minimum value which
happens<00:15:33.189> to<00:15:33.249> be<00:15:33.369> in<00:15:33.699> Zimbabwe
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happens to be in Zimbabwe
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happens to be in Zimbabwe
over<00:15:37.160> the<00:15:37.279> log<00:15:37.519> of<00:15:37.759> 10<00:15:38.269> 8<00:15:38.829> to<00:15:39.829> 11<00:15:41.350> that's<00:15:42.350> the
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over the log of 10 8 to 11 that's the
00:15:42.619 --> 00:15:45.939 align:start position:0%
over the log of 10 8 to 11 that's the
maximum<00:15:43.519> observed<00:15:44.179> value<00:15:44.809> for<00:15:45.379> the<00:15:45.439> United
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maximum observed value for the United
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maximum observed value for the United
Arab<00:15:46.249> Emirates<00:15:47.649> minus<00:15:48.649> the<00:15:48.799> log<00:15:49.009> of<00:15:49.040> 163
00:15:53.470 --> 00:15:53.480 align:start position:0%
00:15:53.480 --> 00:15:55.840 align:start position:0%
and<00:15:53.990> that<00:15:54.110> gives<00:15:54.350> us<00:15:54.560> a<00:15:54.680> value<00:15:54.829> of<00:15:55.070> naught
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and that gives us a value of naught
00:15:55.850 --> 00:16:02.280 align:start position:0%
and that gives us a value of naught
point<00:15:56.380> 584<00:15:57.380> for<00:15:58.130> the<00:15:58.190> Chinese<00:15:58.699> income<00:15:59.690> index
00:16:02.280 --> 00:16:02.290 align:start position:0%
00:16:02.290 --> 00:16:05.110 align:start position:0%
so<00:16:03.290> our<00:16:03.410> final<00:16:03.589> step<00:16:03.949> is<00:16:04.190> now<00:16:04.730> to<00:16:04.790> calculate
00:16:05.110 --> 00:16:05.120 align:start position:0%
so our final step is now to calculate
00:16:05.120 --> 00:16:08.439 align:start position:0%
so our final step is now to calculate
the<00:16:05.449> Human<00:16:05.930> Development<00:16:06.050> Index<00:16:06.940> by<00:16:07.940> combining
00:16:08.439 --> 00:16:08.449 align:start position:0%
the Human Development Index by combining
00:16:08.449 --> 00:16:12.910 align:start position:0%
the Human Development Index by combining
these<00:16:09.110> three<00:16:09.589> sub-indices<00:16:11.230> so<00:16:12.230> the<00:16:12.350> HDI<00:16:12.769> is
00:16:12.910 --> 00:16:12.920 align:start position:0%
these three sub-indices so the HDI is
00:16:12.920 --> 00:16:15.990 align:start position:0%
these three sub-indices so the HDI is
given<00:16:13.550> in<00:16:13.730> the<00:16:14.149> following<00:16:14.269> way<00:16:14.690> we<00:16:14.930> multiply
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given in the following way we multiply
00:16:16.000 --> 00:16:21.250 align:start position:0%
given in the following way we multiply
nor<00:16:17.000> point<00:16:17.269> 847<00:16:17.930> by<00:16:18.860> northpoint<00:16:19.430> 589<00:16:20.260> by
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nor point 847 by northpoint 589 by
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nor point 847 by northpoint 589 by
northpoint<00:16:21.800> 584<00:16:22.610> and<00:16:23.440> then<00:16:24.440> take<00:16:24.680> the<00:16:24.829> cubed
00:16:25.240 --> 00:16:25.250 align:start position:0%
northpoint 584 and then take the cubed
00:16:25.250 --> 00:16:28.060 align:start position:0%
northpoint 584 and then take the cubed
root of<00:16:25.790> their<00:16:25.970> product<00:16:26.389> and<00:16:26.570> it<00:16:27.560> gives<00:16:27.829> us
00:16:28.060 --> 00:16:28.070 align:start position:0%
root of their product and it gives us
00:16:28.070 --> 00:16:32.840 align:start position:0%
root of their product and it gives us
not<00:16:28.940> point<00:16:29.800> 663
00:16:32.840 --> 00:16:32.850 align:start position:0%
00:16:32.850 --> 00:16:36.769 align:start position:0%
so<00:16:33.630> this<00:16:33.839> HDI<00:16:34.589> value<00:16:35.190> for<00:16:35.370> China<00:16:35.639> puts<00:16:36.269> it<00:16:36.480> well
00:16:36.769 --> 00:16:36.779 align:start position:0%
so this HDI value for China puts it well
00:16:36.779 --> 00:16:38.809 align:start position:0%
so this HDI value for China puts it well
below<00:16:37.139> the<00:16:37.350> Scandinavian<00:16:38.130> countries<00:16:38.160> in<00:16:38.730> the
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below the Scandinavian countries in the
00:16:38.819 --> 00:16:42.050 align:start position:0%
below the Scandinavian countries in the
rankings<00:16:39.240> but<00:16:40.230> well<00:16:40.470> above<00:16:40.670> many<00:16:41.670> less
00:16:42.050 --> 00:16:42.060 align:start position:0%
rankings but well above many less
00:16:42.060 --> 00:16:45.230 align:start position:0%
rankings but well above many less
developed<00:16:42.360> nations<00:16:43.670> even<00:16:44.670> if<00:16:44.730> you<00:16:44.850> think<00:16:44.880> that
00:16:45.230 --> 00:16:45.240 align:start position:0%
developed nations even if you think that
00:16:45.240 --> 00:16:47.300 align:start position:0%
developed nations even if you think that
HDI<00:16:45.630> is<00:16:45.779> an<00:16:46.050> improvement<00:16:46.470> over<00:16:46.860> using
00:16:47.300 --> 00:16:47.310 align:start position:0%
HDI is an improvement over using
00:16:47.310 --> 00:16:50.210 align:start position:0%
HDI is an improvement over using
national<00:16:48.089> income<00:16:48.449> statistics<00:16:48.949> this<00:16:49.949> is<00:16:50.130> a
00:16:50.210 --> 00:16:50.220 align:start position:0%
national income statistics this is a
00:16:50.220 --> 00:16:53.090 align:start position:0%
national income statistics this is a
problem<00:16:50.459> and<00:16:50.819> the<00:16:50.850> problem<00:16:51.630> is<00:16:52.199> that<00:16:52.440> it's
00:16:53.090 --> 00:16:53.100 align:start position:0%
problem and the problem is that it's
00:16:53.100 --> 00:16:56.660 align:start position:0%
problem and the problem is that it's
still<00:16:53.399> essentially<00:16:54.029> arbitrary<00:16:55.370> why<00:16:56.370> are<00:16:56.430> we
00:16:56.660 --> 00:16:56.670 align:start position:0%
still essentially arbitrary why are we
00:16:56.670 --> 00:16:58.400 align:start position:0%
still essentially arbitrary why are we
choosing<00:16:56.940> this<00:16:57.300> particular<00:16:57.690> set<00:16:58.110> of<00:16:58.139> weights
00:16:58.400 --> 00:16:58.410 align:start position:0%
choosing this particular set of weights
00:16:58.410 --> 00:17:01.309 align:start position:0%
choosing this particular set of weights
rather<00:16:58.829> than<00:16:59.069> another<00:16:59.870> there's<00:17:00.870> also<00:17:01.079> the
00:17:01.309 --> 00:17:01.319 align:start position:0%
rather than another there's also the
00:17:01.319 --> 00:17:04.480 align:start position:0%
rather than another there's also the
problem<00:17:01.470> that<00:17:02.069> it's<00:17:02.220> still<00:17:02.519> a<00:17:02.699> mean<00:17:03.420> average
00:17:04.480 --> 00:17:04.490 align:start position:0%
problem that it's still a mean average
00:17:04.490 --> 00:17:07.579 align:start position:0%
problem that it's still a mean average
within<00:17:05.490> a<00:17:05.579> society<00:17:05.959> there<00:17:06.959> can<00:17:07.079> be<00:17:07.110> huge
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within a society there can be huge
00:17:07.589 --> 00:17:09.860 align:start position:0%
within a society there can be huge
variations<00:17:08.490> in<00:17:08.699> experience<00:17:09.390> of<00:17:09.600> health
00:17:09.860 --> 00:17:09.870 align:start position:0%
variations in experience of health
00:17:09.870 --> 00:17:13.490 align:start position:0%
variations in experience of health
education<00:17:10.530> and<00:17:11.370> living<00:17:11.970> standards<00:17:12.480> in<00:17:13.319> much
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education and living standards in much
00:17:13.500 --> 00:17:16.039 align:start position:0%
education and living standards in much
the<00:17:13.530> same<00:17:13.709> way<00:17:13.949> that<00:17:14.429> mean<00:17:15.120> average<00:17:15.480> wealth
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the same way that mean average wealth
00:17:16.049 --> 00:17:19.549 align:start position:0%
the same way that mean average wealth
data<00:17:16.289> tends<00:17:17.189> to<00:17:17.400> hide<00:17:17.669> a<00:17:17.939> very<00:17:18.689> uneven
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data tends to hide a very uneven
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data tends to hide a very uneven
distribution<00:17:19.770> of<00:17:20.459> wealth<00:17:21.409> do<00:17:22.409> you<00:17:22.500> see<00:17:22.799> the
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distribution of wealth do you see the
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distribution of wealth do you see the
use<00:17:24.390> of<00:17:24.419> the<00:17:24.689> Human<00:17:25.020> Development<00:17:25.169> Index<00:17:25.709> as
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use of the Human Development Index as
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use of the Human Development Index as
being<00:17:27.179> a<00:17:27.449> better<00:17:28.110> way<00:17:28.620> than<00:17:29.039> national<00:17:29.490> income
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being a better way than national income
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being a better way than national income
accounts<00:17:30.299> of<00:17:30.450> trying<00:17:31.110> to<00:17:31.200> assess<00:17:31.530> the<00:17:31.890> level
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accounts of trying to assess the level
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accounts of trying to assess the level
of<00:17:32.490> income<00:17:32.669> that<00:17:32.909> people<00:17:33.450> have<00:17:34.460> now<00:17:35.460> really<00:17:36.059> I
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of income that people have now really I
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of income that people have now really I
mean<00:17:36.750> it's<00:17:37.650> it's<00:17:38.460> hard<00:17:38.730> as<00:17:38.880> in<00:17:39.030> the<00:17:39.120> right
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mean it's it's hard as in the right
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mean it's it's hard as in the right
place<00:17:39.630> that<00:17:40.200> is<00:17:40.350> it's<00:17:40.530> trying<00:17:40.770> to<00:17:40.950> get<00:17:41.100> at
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place that is it's trying to get at
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place that is it's trying to get at
things<00:17:41.460> like<00:17:41.580> health<00:17:42.030> and<00:17:42.830> education<00:17:43.830> any
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things like health and education any
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things like health and education any
income<00:17:44.580> but<00:17:44.730> it<00:17:44.970> doesn't<00:17:45.360> have<00:17:45.510> a<00:17:46.789> method<00:17:47.789> or
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income but it doesn't have a method or
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income but it doesn't have a method or
has<00:17:48.059> an<00:17:48.240> arbitrary<00:17:48.809> method<00:17:49.260> of<00:17:49.380> combining
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has an arbitrary method of combining
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has an arbitrary method of combining
them<00:17:50.130> into<00:17:50.159> a<00:17:50.429> single<00:17:50.700> index<00:17:51.240> it<00:17:51.419> usually
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them into a single index it usually
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them into a single index it usually
waits<00:17:51.929> them<00:17:52.140> equally<00:17:52.530> the<00:17:52.770> changes<00:17:53.220> equally
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waits them equally the changes equally
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waits them equally the changes equally
and<00:17:53.820> then<00:17:53.970> adds<00:17:54.299> them<00:17:54.539> together<00:17:54.690> and<00:17:55.200> that
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and then adds them together and that
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and then adds them together and that
doesn't<00:17:55.980> make<00:17:56.039> any<00:17:56.190> any<00:17:56.610> any<00:17:57.740> economic<00:17:58.740> sense
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doesn't make any any any economic sense
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doesn't make any any any economic sense
doesn't<00:17:59.220> make<00:17:59.370> any<00:17:59.780> happiness<00:18:00.780> sense<00:18:01.230> you
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doesn't make any happiness sense you
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doesn't make any happiness sense you
have<00:18:01.950> to<00:18:02.130> think<00:18:02.460> of<00:18:02.580> a<00:18:02.700> way<00:18:02.909> how<00:18:03.150> can<00:18:03.360> we<00:18:03.450> do
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have to think of a way how can we do
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have to think of a way how can we do
that<00:18:03.690> in<00:18:04.140> a<00:18:04.260> way<00:18:04.289> that's<00:18:05.070> grounded<00:18:05.730> in<00:18:05.909> some
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that in a way that's grounded in some
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that in a way that's grounded in some
kind<00:18:06.539> of<00:18:06.659> basic<00:18:07.409> analysis<00:18:08.070> economic<00:18:08.880> or
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kind of basic analysis economic or
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kind of basic analysis economic or
statistical<00:18:09.570> analysis<00:18:09.990> and<00:18:10.080> there<00:18:10.169> are<00:18:10.289> ways
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statistical analysis and there are ways
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statistical analysis and there are ways
to<00:18:10.500> do<00:18:10.860> it<00:18:11.010> what<00:18:11.760> I<00:18:11.789> don't<00:18:12.059> like<00:18:12.120> about<00:18:12.240> the
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to do it what I don't like about the
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to do it what I don't like about the
human<00:18:13.140> development<00:18:13.620> in<00:18:13.799> diseases<00:18:14.159> they've
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human development in diseases they've
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human development in diseases they've
been<00:18:14.760> developed<00:18:15.240> is<00:18:15.450> that<00:18:16.020> they<00:18:16.169> do<00:18:16.230> it<00:18:16.440> in
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been developed is that they do it in
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been developed is that they do it in
arbitrary<00:18:17.039> way<00:18:17.340> it's<00:18:17.549> not<00:18:17.730> based<00:18:17.970> on<00:18:18.150> anything
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arbitrary way it's not based on anything
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arbitrary way it's not based on anything
the<00:18:19.440> United<00:18:19.799> Nations<00:18:20.000> recognizes<00:18:21.000> the
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the United Nations recognizes the
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the United Nations recognizes the
limitations<00:18:21.840> of<00:18:21.960> HDI<00:18:22.440> and<00:18:22.740> is<00:18:22.830> trying<00:18:23.100> to<00:18:23.250> do
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limitations of HDI and is trying to do
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limitations of HDI and is trying to do
something<00:18:23.669> about<00:18:23.909> it<00:18:24.230> one<00:18:25.230> particular<00:18:25.650> recent
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something about it one particular recent
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something about it one particular recent
change<00:18:26.760> has<00:18:27.270> been<00:18:27.510> with<00:18:27.690> regard<00:18:28.080> to<00:18:28.169> the
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change has been with regard to the
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change has been with regard to the
distribution<00:18:29.100> of<00:18:29.159> income<00:18:29.630> so<00:18:30.630> they've<00:18:30.780> tended
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distribution of income so they've tended
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distribution of income so they've tended
to<00:18:31.320> move<00:18:31.620> HDI<00:18:32.429> down<00:18:33.030> in<00:18:33.360> countries<00:18:34.200> where
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to move HDI down in countries where
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to move HDI down in countries where
income<00:18:34.830> disparity<00:18:35.429> is<00:18:35.549> considerable<00:18:36.590> it's
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income disparity is considerable it's
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income disparity is considerable it's
often<00:18:37.919> the<00:18:37.950> case<00:18:38.039> that<00:18:38.280> his<00:18:38.580> economies<00:18:39.150> grow
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often the case that his economies grow
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often the case that his economies grow
from<00:18:39.720> low<00:18:39.929> levels<00:18:40.320> there's<00:18:40.860> a<00:18:40.980> widening
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from low levels there's a widening
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from low levels there's a widening
income<00:18:41.970> disparity<00:18:43.070> this<00:18:44.070> happened<00:18:44.429> in
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income disparity this happened in
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income disparity this happened in
Central<00:18:44.970> and<00:18:45.120> Eastern<00:18:45.419> Europe<00:18:45.809> after<00:18:46.020> the
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Central and Eastern Europe after the
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Central and Eastern Europe after the
apps<00:18:46.590> of<00:18:46.770> communism<00:18:47.220> there<00:18:48.110> something
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apps of communism there something
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apps of communism there something
similar<00:18:49.559> may<00:18:50.130> well<00:18:50.400> now<00:18:50.640> take<00:18:50.700> place<00:18:50.970> in<00:18:51.510> China
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similar may well now take place in China
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similar may well now take place in China
we<00:18:53.610> started<00:18:54.030> by<00:18:54.210> saying<00:18:54.480> that<00:18:54.750> GDI<00:18:55.169> has<00:18:55.530> real
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we started by saying that GDI has real
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we started by saying that GDI has real
problems<00:18:56.070> as<00:18:56.520> an<00:18:57.000> indicator<00:18:57.450> of<00:18:57.630> well-being
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problems as an indicator of well-being
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problems as an indicator of well-being
but<00:18:59.460> so<00:18:59.730> does<00:18:59.970> HDI<00:19:02.030> unless<00:19:03.030> we<00:19:03.270> know<00:19:03.690> what
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but so does HDI unless we know what
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but so does HDI unless we know what
we're<00:19:04.260> measuring<00:19:04.559> we<00:19:05.549> won't<00:19:05.789> know<00:19:05.970> what
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we're measuring we won't know what
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we're measuring we won't know what
significance<00:19:06.620> to<00:19:07.620> attach<00:19:07.890> to<00:19:08.130> any<00:19:08.400> measure<00:19:08.880> of
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significance to attach to any measure of
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significance to attach to any measure of
well-being<00:19:09.530> but<00:19:10.530> at<00:19:10.620> least<00:19:10.740> if<00:19:10.980> we<00:19:11.100> understand
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well-being but at least if we understand
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well-being but at least if we understand
the<00:19:11.789> statistics<00:19:12.419> we<00:19:13.110> know<00:19:13.380> exactly<00:19:13.980> what
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the statistics we know exactly what
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the statistics we know exactly what
those<00:19:14.309> limitations<00:19:14.880> are<00:19:15.799> you<00:19:16.799> might<00:19:16.950> like<00:19:17.190> now
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those limitations are you might like now
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those limitations are you might like now
to<00:19:18.210> do<00:19:18.360> your<00:19:18.600> own<00:19:18.630> HDI<00:19:19.530> calculations<00:19:20.520> for<00:19:21.030> your
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to do your own HDI calculations for your
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to do your own HDI calculations for your
own<00:19:21.299> particular<00:19:21.690> country
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you
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you