text:
Amreen Khan
Airbus Helicopters Delivers Six EC135T2+ Helicopters For Australian Defence Force
Airbus Helicopters Delivers Six EC135T2 Helicopters For Australian Defence Force
Airbus Helicopters has achieved Factory Acceptance of new EC135T2+ for the Helicopter Aircrew Training System (HATS) for the Australian Defence Force (ADF).
The last six helicopters' acceptance marks on-time completion of deliveries of the full fleet of 15 EC135T2+ helicopters.
Under the JP9000 Phase 7 HATS project, a new joint helicopter training program for Navy and Army aircrew will utilise 15 EC135T2+ helicopters, along with flight simulators and a new flight-deck equipped sea-going training vessel. Boeing Defence Australia is the prime contractor for the new training system, partnered by Thales Australia who supplies the flight simulators and synthetic training devices.
A member of the H135 family of light twin-engine helicopters, the EC135T2+ is an ideal training platform, and initial flying activities have commenced at their new home in Nowra, Australia.
"Airbus Helicopters is proud to know that Boeing has accepted now all 15 of their new EC135T2+, on time and on budget, for their world leading helicopter aircrew training system for the ADF", said Peter Harris, Head of Governmental Sales for Australia – Pacific. "Following contract signature in November of 2014, and in the space of only two years, we have trained the initial cadre of Boeing and Commonwealth aircrew and technicians and all 15 aircraft have now been accepted".
Boeing's HATS Director Terry Nichols said that the Boeing team is very happy with the performance thus far of the EC135 and commended Airbus Helicopters for their on-time delivery.
"Boeing Defence Australia chose the EC135 as our preferred helicopter solution for HATS; it's a proven and reliable platform. We have been delighted with the cooperation from Airbus Helicopters to deliver these platforms that will form an important part of a world class training system."
The EC135T2+ is a consummate military training helicopter, offering a glass cockpit with high visibility, a multi-axis auto-pilot, the performance and safety of a twin-engine helicopter replacing current single types, plus other advanced technologies to help instructors perform training missions safely and provide the ADF with the flexibility to undertake additional missions. The H135 family is part of successful training systems in Germany, Switzerland, Spain and Japan, and has recently been selected in the United Kingdom.
DCNS, Airbus Helicopters To Co-Design French Navy's Tactical VTOL Drone System
October 20, 2016 @ 11:44 AM
DCNS and Airbus Helicopters are joining forces to design French Navys tactical Vertical Take Off and Landing (VTOL) drone system. The teaming of DCNS and Airbus Helicopters will be equipped to address all technical challenges arising...
Airbus Helicopters Provides H175 To Mexico
August 24, 2016 @ 01:21 PM
Airbus helicopters has delivered the first H175 to Transportes Aereos Pegos air transport services for oil & gas operations in the Gulf of Mexico. This makes Transportes Aéreos Pegaso the first company to operate this new...
Airbus Helicopters Delivers First VIP Version of H175 Choppers
July 13, 2016 @ 07:43 AM
Airbus Helicopters has delivered the first VIP version of the H175, the companys new medium class twin-engine helicopter, which entered service in 2014 for offshore transportation. The VIP version of the H175 offers cabin interior provided...
Airbus Helicopters Completes First Firing Tests Of HForce Weapon System
Airbus Helicopters has recently completed a first round of firing tests with HForce, a generic weapon system in development for the companys commercial helicopter range. The innovative system, which includes a central core unit, Thales...
Airbus Helicopters Selected As Aircraft Service Provider For UK's Military Flying Training...
Airbus Helicopters has been selected by Ascent as the Aircraft Service Provider for the UKs Military Flying Training System (UKMFTS). The contract, worth £500 million over 17 years, will see Airbus Helicopters deliver aircraft and an...
Airbus Helicopters Launches X6 Concept Phase For Heavy-Lift Rotor craft
Airbus Helicopters announced today at the Paris Air Show, the launch of X6 concept phase, setting the standard for the next-generation heavy-lift rotorcraft that will be tailored for the civil market. X6 will initially target oil...
Sea Trials of Australia's First Air Warfare Destroyer From Next Year
The Australian Navy will commence sea trials of its Air Warfare Destroyer 'Hobart' by early next year. The trials of the first Hobart-class ship will undertake testing of combat and communications systems and further platform system...
US Approves Electronic Warfare Range System Sale To Australia For $115 Million
December 6, 2016 @ 09:11 AM
The US State Department has approved sale of AEA-18G Electronic Warfare Range System to Australia for an estimated cost of $115 million. The Defense Security Cooperation Agency delivered the required certification notifying Congress of this possible...
Australia Expects To Create 400 Direct Jobs From Offshore Patrol Vessel Project
The Australian multi-billion-dollar Offshore Patrol Vessel project is expected to create around 400 direct jobs, Christopher Pyne, Minister for Defence Industry said. The MoD has released the Request for Tender (RFT) for the OPV project to...
Thales To Upgrade Sonar System For Australian Collins-Class Submarine
Thales has won a $100 million contract from Australia to design a major sonar system upgrade for Collins class submarines. Thales Australia will engage other Australian-based companies including Sonartech Atlas and L3 Oceania, as well as...
Airbus Helicopters Hands Over First Three AS565 MBe Panther Helicopter to Indonesia
Airbus Helicopters has delivered first three of 11 AS565 MBe Panther helicopters to Indonesia at the companys headquarters in France. The delivery took place ahead of contract schedule, follows the contract signed in late 2014 with...


Nhs leadership essay Entrust your essay has an essay examples here get a social studies essay. Dec 02, leadership 500 word essay examples of nhs.
How To Write Essay About Leadership. and this is the best strategy to writing an interesting leadership essay. Use examples and samples to help you.


text:
Mag. 3.3 earthquake - South Pacific Ocean, 46 km north of Viña del Mar, Provincia de Valparaiso, Region de Valparaiso, Chile, on Thursday, 29 October 2015 at 17:04 (GMT)
This quake was likely an aftershock of the 7.8 quake Choapa, 36 km west of Illapel, Provincia de Choapa, Coquimbo Region, Chile, 16 September 2015 22:54 GMT, which had occurred 6 weeks earlier.
29 Oct 17:19 UTC: First to report: EMSC after 14 minutes.
Date & time: 29 Oct 2015 17:04:43 UTC
Epicenter latitude / longitude: 32.62°S / 71.65°W (South Pacific Ocean, Chile)
43 km (27 mi) WSW of La Ligua (Petorca) (pop: 24,900) --> See nearby quakes!
44 km (28 mi) WNW of Hacienda La Calera (pop: 49,100) --> See nearby quakes!
46 km (29 mi) N of Valparaiso (pop: 282,400) --> See nearby quakes!
48 km (30 mi) NW of Quillota (pop: 67,800) --> See nearby quakes!
51 km (32 mi) NNW of Quilpué (pop: 130,300) --> See nearby quakes!
54 km (33 mi) NNW of Villa Alemana (pop: 97,300) --> See nearby quakes!
Primary data source: EMSC (European-Mediterranean Seismological Centre)
Estimated released energy: 5.6 x 109 joules (1.56 megawatt hours, equivalent to 1.34 tons of TNT) More info
M 3.3 28 km 43 km al O de La Ligua GUG (U. Chile)

redpajama_set_name: RedPajamaCommonCrawl


My second order have cancelled by buyer. I did not request his dispute order. I need to know how can i send a dispute request?
Will it be problem with my gig in which gig i did get this order.Or it will get bad effect on my profile/multiple gigs?
Usually, the seller is given a chance to turn down the request unless the buyer went to CS and requested the cancellation, then there is not much you can do.
It will affect your completion on time stat.
Will i get any restriction to send buyer request for cancallation?
That depends on how low your stats fall.
What problem will i have to face for order canallation by buyer?
Your stats mus be 4.7 to see BR.


Praca w HITACHI DATA SYSTEMS POLSKA Sp. z o.o.
O HITACHI DATA SYSTEMS POLSKA Sp. z o.o.
Hitachi is Japan's largest electronics company, producing advanced technologies for millions of customers and businesses. A dedication to new ideas runs through everything we do as our worldwide employees constantly aim to 'Inspire the Next'.This enterprising spirit can be found in abundance in Hitachi Data Systems (HDS), an industry-leading provider of storage solutions to some of the biggest corporations in the world.
Hitachi Data Systems helps organizations transform raw data into valuable information by making it more accessible and simpler to manage. Our vision is that IT must be virtualized, automated, cloud-ready and sustainable. Hitachi Data Systems provides best-in-class information technologies, services and solutions that deliver compelling customer ROI, unmatched return on assets (ROA) and demonstrable business impact. As the leader in storage virtualization, only Hitachi Data Systems offers a common, virtualized platform for all data and information.
The Hitachi Newton Finance Centre in Krakow is a state-of-the-art organization, member of Hitachi Global SSC group, supporting Hitachi Data Systems in 15 European countries as well as the United States and Canada.
Newton Finance Centre enjoys a close-knit and supportive atmosphere for its 170+ strong team of finance professionals. This has been recently recognized, when Newton was ranked 6th in Poland by Great Place to Work Institute.
Newton also supports its employees in the professional education as an ACCA Approved Employer (Platinum).
This environment allows you to make progress in your career and become recognized as an expert in your field, as focused teams mean greater visibility.
The happiness and welfare of our staff is of huge importance to us at Hitachi Data Systems, so we have developed a generous benefits package for our Krakow FSSC team.
We encourage employees to learn continuously, and we support on-going professional development. Our development philosophy involves instructor-led training, self-learning, practice, on-the-job learning and stretch opportunities.
In addition to utilising language skills on a daily basis, some of our employees have the opportunity to travel to other countries to gain international exposure and learn how business operates in different cultures.
We have great opportunities for Partially Qualified Accountants and Fully Qualified Accountants. We have many career development opportunities across our Order to Cash team and Financial Accounting team.
For all these roles you should be fluent in English and have competent Polish language skills. Knowledge of another European language will also be highly desirable.
You will need to be educated to degree level and be working towards an accounting qualification. Technical financial knowledge is a requirement.


text:
Q: react native navigation between screens I'm trying to use reactnavigation.org library and I have some issue with that. I have two types of navigation screens. Two screens are a registration and authorization. And others are regular app screens. Problem is that I can't render parent component App.js or other functionality to reload app from child component and thus change my screens block. Navigation between screens Run, History, Profile, OtherRunners works fine. Navigation between screens Main and Enter works fine. But if I need navigate from Run to Main, or from Enter to Run there is a problem.
App.js
const App = () => {
    const Stack = createStackNavigator()
    const Drawer = createDrawerNavigator()
    const [loggedIn, setLoggedIn] = useState(false)
    async function getUserEmail(){
        if(await AsyncStorage.getItem('@email')){
            setLoggedIn(true)
        }else{
            setLoggedIn(false)
        }
    }
    getUserEmail()
    function MenuDraw(){
        return(
            <Drawer.Navigator
                drawerContentOptions={}
                drawerType={}
                drawerStyle={{}}
            >
                <Drawer.Screen
                    name="Run"
                    component={Run}
                    options={}
                />
                <Drawer.Screen
                    name="History"
                    component={History}
                    options={}
                />
                <Drawer.Screen
                    name="Profile"
                    component={Profile}
                    options={}
                />
                <Drawer.Screen
                    name="OtherRunners"
                    component={OtherRunners}
                    options={}
                />
                <Drawer.Screen
                    name="LogOut"
                    component={Run}
                    initialParams={{action: 'logout'}}
                    options={}
                />
            </Drawer.Navigator>
        )
    }
    function MyDrawer(){
        if(loggedIn){
            return(
                <Stack.Navigator
                    screenOptions={{headerShown: false}}
                    initialRouteName={"MenuDraw"}
                >
                    <Stack.Screen name="MenuDraw" component={MenuDraw}/>
                    <Stack.Screen name="Details" component={Details}/>
                </Stack.Navigator>
            )
        }else{
            return (
                <Stack.Navigator
                    screenOptions={{headerShown: false}}
                    initialRouteName={"Main"}
                >
                    <Stack.Screen name="MenuDraw" component={MenuDraw} />
                    <Stack.Screen name="Details" component={Details} />

                    <Stack.Screen name="Main" component={Main} />
                    <Stack.Screen name="Enter" component={Enter} />
                </Stack.Navigator>
            )
        }
    }
  return(
      <NavigationContainer>
          <MyDrawer/>
      </NavigationContainer>
  )
}
export default App;

I tried using reload app tools, but those wasn't help me. They are wasn't working. In this section of code which is below. On the Run screen.
Run.js
const Run = ({navigation,route}) => {
    const clearStorage = async () => {
        await AsyncStorage.clear()
    }
    if(route.params){
        if(route.params.action === 'logout'){
            clearStorage()
            navigation.navigate("Main", {})
        }
    }
}
export default Run;

I'm trying to logout by cleaning the storage and navigate to Main screen. But it's not working on release mode. I've tried this, which is below.
App.js
function MyDrawer(){
        if(loggedIn){
            return(
                <Stack.Navigator
                    screenOptions={{headerShown: false}}
                    initialRouteName={"MenuDraw"}
                >
                    <Stack.Screen name="MenuDraw" component={MenuDraw}/>
                    <Stack.Screen name="Details" component={Details}/>

++++                <Stack.Screen name="Main" component={Main} />
++++                <Stack.Screen name="Enter" component={Enter} />
                </Stack.Navigator>
            )
        }else{
            return (
                <Stack.Navigator
                    screenOptions={{headerShown: false}}
                    initialRouteName={"Main"}
                >
                    <Stack.Screen name="MenuDraw" component={MenuDraw} />
                    <Stack.Screen name="Details" component={Details} />

                    <Stack.Screen name="Main" component={Main} />
                    <Stack.Screen name="Enter" component={Enter} />
                </Stack.Navigator>
            )
        }
    }

Not working either way. How can I fix it? Can I render App.js component from child screens? Or perhaps I should create two screen blocks line
<Stack.Screen name="MenuDraw" component={MenuDraw}/>
<Stack.Screen name="MenuRegister" component={MenuRegister}/>

and put all my screens by belongings to match section?
I need to logout from this section:
App.js
some code

               <Drawer.Screen
                    name="LogOut"
                    component={Run}
                    initialParams={{action: 'logout'}}
                    options={}
                />
            </Drawer.Navigator>

some code

A: Did you try using useEffect hook, to get on the road? :-)
  useEffect(() => {
    const getUserEmail = async () => {
        if(await AsyncStorage.getItem('@email')){
            setLoggedIn(true)
        }else{
            setLoggedIn(false)
        }
    };
    getUserEmail();
  }, []);

redpajama_set_name: RedPajamaStackExchange


text:
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 \centerline{\bf \Large The wage transition in developed countries and}
\vskip 2mm
 \centerline{\bf \Large its implications for China}
\vskip 20mm
\centerline{\large
Belal Baaquie$ ^1 $,
Bertrand M. Roehner$ ^2 $,
Qinghai Wang$ ^3 $
}

\vskip 8mm
\large

{\bf Abstract}\quad
The expression ``wage transition'' refers to the fact that
over the past two or three decades
in all developed economies wage increases have leveled off.
There has been a widening divergence and decoupling between
wages on the one hand and GDP per capita
on the other hand. Yet, in China
wages and GDP per capita climbed in sync (at least up to now).\hfill \break
In the first part of the paper we present comparative statistical
evidence which measures the extent of the wage
transition effect.\hfill \break
In a second part we consider the reasons of this
phenomenon, in particular we explain how
the transfers of labor from low productivity sectors
(such as agriculture) to high productivity sectors (such as
manufacturing) are the driver of productivity
growth, particularly through their synergetic effects.
Although rural flight represents only one of these
effects, it is certainly the most visible because of the
geographical relocation that it implies; it is also
the most well-defined statistically. Moreover,
it will be seen that it is
a good indicator of the overall productivity and attractivity of
the non-agricultural sector.\hfill \break
Because this model accounts fairly well
for the observed evolution in industrialized countries,
we use it to predict the rate of Chinese economic growth
in the coming decades. Our forecast for the average annual
growth of real wages ranges from 4\% to 6\% depending on
how well China will control the development of its healthcare
industry.

\vskip 10mm
\centerline{\it Provisional. Version of 29 April 2016.
Comments are welcome.}
\vskip 10mm

{\small Key-words: productivity, demographic transition,
economic growth,
wage, earnings, agriculture, industry, primary sector,
secondary sector, tertiary sector.
\vskip 4mm

{\normalsize
1: Physics Department, National University of Singapore.
Email: belalbaaquie@gmail.com\hfill \break
2: Institute for Theoretical and High Energy Physics (LPTHE),
University Pierre and Marie Curie, Paris, France.
Email: roehner@lpthe.jussieu.fr\hfill \break
3: Physics Department, National University of Singapore.
Email: qhwang@nus.edu.sg
}

\vfill\eject

\large

\qI{Introduction}

\qA{Post-industrial societies}

Nowadays (i.e. in early 2016)  the dominant conception is that the
post-industrial stage reached by most developed countries
and in which at least
75\% of employment is in the service sector is a superior
stage. It seems to realize the dream of clean, energy efficient
and highly productive economies. Clean and energy efficient they may be,
but are post-industrial societies also highly productive?
This is one of the questions that we try to answer in the present
paper.
\vskip 2mm plus 0.2mm minus 0.2mm

Perhaps it may be helpful to explain what lead us to question
the mainstream conception. Doubts were raised step-wise by
a number of observations among which one can highlight the following.
\hfill \par \hskip 4mm $ \bullet $ \hskip 2mm In the past two or three decades, following in the footsteps of
New York and other American cities,  home-delivery of pizzas
started in many west European cities. This marked a sharp
break with the period following World War II. In this period
labor had been considered as a rare and costly resource
which had to be economized as much as possible.
Home-delivery of pizzas was a low productivity
business which could be profitable only if served by poorly
paid employees. It was the harbinger of the creation of
a whole range of jobs similarly characterized by poor pay
and low productivity. As emphasized by economists such
as Jean Fourasti\'e, the fact that the
price of hair cuts or opera tickets remained unchanged
in the course of time strongly
suggests that the tertiary sector holds little promises
for productivity improvement. At the end of the paper
we will propose a broader explanation which relies on the
fact that most service goods have little or no
synergetic potential.
\hfill \par \hskip 4mm $ \bullet $ \hskip 2mm The second shock was the observation that
in the United States the average hourly wage had peaked around
1975 and stagnated ever since%
\qfoot{As a matter of fact there was a sudden change in the
trend of many other economic (e.g. income inequality)
and social indicators (e.g. proportion of inmate population).
These changes are documented more fully in Roehner (2009,
chapters 9-11).}
.
True, the Gross Domestic
Product (GDP) per capita had continued to increase but
this was due to concomitant changes, for instance the
fact that an increasing number of house wives took a full
time job or the fact that an increasing share of
income comes from non-salary earnings.
\hfill \par \hskip 4mm $ \bullet $ \hskip 2mm The third element which triggered our questioning
came from the observation of service-centered economies.
Switzerland and Singapore embody fairly well the dream
of an efficient, environment-friendly
post industrial economy%
\qfoot{Although very different in terms of territory and area,
the two countries
are in the same class in terms of population (8 and 5,4 million
respectively) and GDP per capita (US\$ 84,000 and 55,000); the data
are for 2013.}%
.
In addition both countries similarly benefited from
massive capital inflows and foreign direct investments.
Yet, in spite of such favorable conditions
they show the same syndrome of levelling off wages
as do other developed countries.
For Switzerland this can be seen in Fig. 3 and
for Singapore it can be observed that whereas wages
have been multiplied
by 5.5 in the 4 decades from 1965 to 2005, they have stagnated
over the last decade from 2005 to 2015 (Shanmugaratnam 2015).
In orther words, even under the
best circumstances the post-industrial, service-based economy
is not up to its promises.

\count101=0  \ifnum\count101=1
If indeed  in terms of productivity
service-based economies lag behind, then this effect should
be most obvious in city-states which have little industry.
Is that the case?\hfill \break
At first sight it does not seem so. In Singapore, the
real (i.e. adjusted for inflation) average monthly wage
increased by 180\% from 1990 to 2015%
\qfoot{The data are from the ``Trading Economics'' website.}%
,
indeed a remarkable achievement;
This progression was not even slowed by the
 crisis of 2008-2009.
.
However, this picture must be corrected by two observations.
First of all, despite being a city-state, Singapore
is not purely a service economy. In 2014 its manufacturing sector
represented 18\% of its GDP (down from 25\% in 2000).
In addition, its semi-industrial port activity represented 33\% of its
GDP. Thus, altogether the industrial and semi-industrial
activities represented 51\% of GDP, i.e. much more than the share
of $ 15\% $ represented by
manufacturing, transportation and warehousing in the United States%
\qfoot{Manufacturing represented $ 11.5\% $, transportation and
  warehousing $ 3.5 \% $. The data are for 2007 and the source is:
Wikipedia: sectors of US economy as percent of
GDP (1947-2009).}%
.
\hfill \break
Secondly, it should be observed that
the Statistical office of Singapore publishes only
wage data for the whole labor force. This contrasts with
most western countries which
also publish wage data for workers, i.e. non-managerial
employees (see Appendix A in this respect).
However, through a declaration made by the Deputy Prime Minister
(Shanmugaratnam 2015) one learns that real wages for the lowest paid
workers have been multiplied by 5.5 between
1965 and 2005 but have stagnated in the last decade.\hfill \break
In  short, even in a successful economy like Singapore
the wages of workers leveled off in the last 10 years.
\fi

\qA{Parallel with the demographic transition}

Before describing the wage transition it may be useful
to present a transition of same kind, namely the
well-known ``demographic transition''. The two transitions are of
same kind in the sense that they occur over a similar time span
and in almost all countries.
In spite of the fact that the causes of the demographic transition
are not yet well understood, this paradigm gives us a predictive
perspective which, although rough, is quite useful nevertheless.
\vskip 2mm plus 0.2mm minus 0.2mm

There are many factors which may affect women fertility, that is
to say the average number of children that a woman will have
during her life-time. Just as illustrations on can
mention the following:
the mean age at marriage, the infant mortality rate,
the attitude of the prevailing religion
with respect to birth control, the laws ruling inheritance,
the place of residence (whether rural or urban%
\qfoot{The role of this factor is demonstrated in a fairly dramatic way
by the fact that the cities of Hong Kong, Macao and Singapore
have fertility rates as low as 1.17, 0.93 and 0.80 respectively
(the data are for 2014).}%
),
whether or not the family needs a second salary, the extent to which
girls have access to education. Many other factors could
be added to this list. As a matter of fact the list
is boundless in the sense
that for each ``primary'' factor  one can cite many ``secondary''
factors. For instance, the mean age at marriage can be
seen as a primary factor but it is affected
by countless social and economic circumstances.

\begin{figure}[htb]
\centerline{\psfig{width=17cm,figure=transdemo.eps}}
\qleg{Fig.\hskip 0.0mm 1\hskip 3mm Demographic transition in western Europe.}
{The thin lines are centered moving averages (over intervals of
5 years) of the differences between birth and death rates for
4 countries:
Germany (black),
Denmark (blue),
England and Wales (green).
Sweden (red).
The thick line
is the average of the 4 cases. The demographic transition
consisted in an almost simultaneous fall of death rates (mostly
due to a decrease in infant mortality) and birth rates; however,
because the second effect was predominant the net effect
was a decrease in the rate of natural increase.
The graph shows that this transition was
marked by several ups and
downs. In addition, for some countries (e.g. the Netherlands or France)
this rule did not apply at all.}
{Source: Flora et al. (1987, vol. 2, chapter 6)}
\end{figure}
\vskip 2mm plus 0.2mm minus 0.2mm
One may think that by focusing on a single country
the question will become more clearly defined
and hence easier to solve. This is an illusion, however.
As an illustration,
consider the case of France. Between 1850 and 1940, the average fertility
rate of French women and the resulting rate of natural increase of the
French population were
much lower than in other Western European countries%
\qfoot{Detailed comparative data can be found in Flora et al. (1987)
but a broad view can be obtained just by comparing the number of
immigrants to the United States or to Latin America. There is
a strong contrast  between the massive flows of Germans and Italians
and the very small numbers of French immigrants}%
.
The explanation most commonly accepted by historians relies on the
observation that according to French inheritance laws
all children were entitled to an equal part of their father's landholding.
Thus, it is said, to avoid splitting their land, farmers did not
wish several children. However, can such
an explanation be sufficient
when one realizes that in 1906 only 43\%
of the labor force was working
in agriculture and that only one half of
this percentage, that is to say some 20\% of the labor force,
were landowners (Flora et al. 1987, p. 500)?
\vskip 2mm plus 0.2mm minus 0.2mm

On the contrary, if, instead of focusing on a single case, the
scope of the analysis is broadened to a whole range of
similar countries, then a fairly well defined pattern
emerges which is called the {\it demographic transition}.

It consists in the fact that between 1900 and 1940,
in almost all west European countries the
annual rate of natural increase (per 10,000 population)
fell from about 120 to less than 50%
\qfoot{For instance, in Germany the fall was from  141 to 54,
in Britain from 116 to 30, in Sweden from 111 to 25, in
Switzerland from 100 to 46 (Bunle 1954, p. 16).
There were exceptions however, the most conspicuous of which
were Italy and the Netherlands. In both cases, the rate of natural
increase remained around 100. France should not be considered
as an exception because with its initial increase rate of only 11
per 1,000, no transition could happen.}%
.
\vskip 2mm plus 0.2mm minus 0.2mm
We do not know exactly what causes
triggered the fall in births per women
and the correlative fall in natural increase. Was it better education,
urbanization, higher income, or some other factors?
Yet, despite possible exceptions, the observation of the
West European demographic transition gave us a predictive rule.
This rule displayed its full usefulness when
between 1950 and 2000 a similar
demographic transition unfolded
in many developing countries, e.g. Brazil,
India, Indonesia, Mexico, South Korea, Turkey, Vietnam.
This transition was even of greater magnitude than the
first in the sense that within 50 years
fertility rates fell from a high level of about 5 to
a level of 2 which is the long-term replacement rate.
As in the first transition, in this second transition there were also
a few countries which did not follow the rule (e.g. the Philippines
or Nigeria). However, it can hardly be denied that
the demographic transition pattern gives us a better understanding
and allows us to predict future trends.

\qA{The wage transition rule}

In the present paper, we wish to describe what can be called
a {\it wage transition}. It may be summarized by the following
statement:
\qdec{\it {\color{blue} Wage transition rule.}\quad
In developed economies, after an initial phase during
which real wages increased exponentially (with a doubling-time of
about 30 years), a second phase set in around 1980 during which average
wages leveled off or even fell slightly.
Most often this second phase set in after the share of
agriculture in the labor force had fallen under 10\%.}
\vskip 2mm plus 0.2mm minus 0.2mm

Before stating this rule
we discussed briefly the demographic
transition because the two transitions share
several important aspects.
\hfill \par \hskip 4mm $ \bullet $ \hskip 2mm Although (as will be shown below)
the wage transition is confirmed by a large amount of statistical evidence,
there are a few exceptions (i.e. the UK).
\hfill \par \hskip 4mm $ \bullet $ \hskip 2mm The demographic transition could not
be attributed to a single factor but was rather correlated
with a whole bundle of variables; similarly the
wage transition comes along with a whole range of changes.
\hfill \par \hskip 4mm $ \bullet $ \hskip 2mm Despite these limitations, the wage transition rule
provides a framework that gives a better understanding
and allows testable predictions.
\vskip 2mm plus 0.2mm minus 0.2mm

As an example of a better understanding one can mention the
case of the United States.
When this example is considered alone it might appear
surprising that wages increased sharply until
1975 and slumped thereafter up to present day.
The observation
becomes less puzzling when seen in the light of
the wage transition rule because then it just appears
as an early instance of a transition that took place
in most countries. This tells us that instead of trying
to explain it by purely American causes (e.g. the
Vietnam War or the decline of the stock market)
we should rather consider
factors that are common to most industrialized countries.

\qA{Growth of the Chinese economy}

As an example of a testable prediction, one can mention
the issue of the growth of the Chinese
economy, a question much debated currently (November 2015).
The wage transition rule and the observation that the share
of Chinese agricultural employment will not fall under 10\% until
at least 2025 suggest the following prediction.
\qdec{After increasing exponentially (with a doubling time of 8.4 years)
from 1990 to 2015, the average wage in China should
follow the same trend for at least another decade
that is to say until 2025.}
\vskip 2mm plus 0.2mm minus 0.2mm
During the past three decades the increase of the average wage
paralleled the growth of the Gross Domestic Product (GDP).
From 1990 to 2015 the GDP per capita (at current prices)
grew from 1,866 RMB to 49,300 RMB. With an increase
of the GDP deflator from 12 to 42 this gives a multiplication
by 7.5 for the real GDP per capita. The average annual growth rate
was 8.2\%  which corresponds to a doubling time of 8.4 years.
In a country as large as China, domestic consumption plays
a crucial and increasing role which means that
a rising average wage is both the effect and engine
of economic growth.
\vskip 2mm plus 0.2mm minus 0.2mm

The following qualifications may apply as second order effects.
\hfill \par \hskip 4mm $ \bullet $ \hskip 2mm If productivity progress slows down
because of a rapid development of the healthcare sector
(there is a longer discussion of this point below)
then one would expect a somewhat slower growth, say around 6\%.
\hfill \par \hskip 4mm $ \bullet $ \hskip 2mm In addition, in
the coming decades it is likely that,
as in western countries, the share of non-salary earnings will
increase. As a result, salaries may grow slower
than the GDP/capita. This would lead to an annual growth forecast
for salaries in the range $ 4\%-5\% $.
\vskip 2mm plus 0.2mm minus 0.2mm

Actually, in the previous prediction
the key-point is not the exact value
of the rate but rather the statement that such a long-term
trend will not be derailed by one or two short-lived
recessions%
\qfoot{Just for the purpose of comparison, it can be of
interest to consider the case of Mexico.
Between 1980 and 2013 it experienced 3 major crises.
\hfill \par \hskip 4mm $ \bullet $ \hskip 2mm In December 1982 the country
was bailed out after defaulting
on its sovereign debt. It was not until 1994 that the GDP/capita
resumed its pre-crisis level.
\hfill \par \hskip 4mm $ \bullet $ \hskip 2mm Then,
in early 1995 a second bail out
(\$50 billion) was necessary to prevent a new default.
\hfill \par \hskip 4mm $ \bullet $ \hskip 2mm  Finally, the worldwide crisis of 2008 led to a 5\% fall of GDP.
\hfill \break
As nowadays for Greece, there were a succession of debt restructuring
plans: e.g. the Baker, Brady and Clinton plans.
Because of these recurrent crises, the GDP/capita expressed in
US dollars increased by only 31\% in the 33 years from 1980
to 2013. Expressed in constant dollars
the growth would be about zero. Thus, it
can be said that the ``North American Free Trade
Agreement'' (NAFTA) which started in 1994
has had but a dismal effect on the economic growth of
Mexico.}%
.
Nevertheless, it may not hold if there is an
upheaval similar to what happened in the Soviet Union in the
years following 1991.

\qA{Connected issues}

Before starting the discussion of the wage transition,
it may be helpful to keep in mind the following observation.
In economics all phenomena are inter-connected.
Therefore, it would be easy to list many effects
which may possibly play a role in the levelling off
of wages. For instance, the financialisation of the economy
contributes to the divergence between
GDP/capita and wages; growing inequality contributes
to the divergence between high and low earnings;
through expansion of the supply side, immigration
contributes to eroding wages; the gradual disappearance
of unions may have the same effect.
\vskip 2mm plus 0.2mm minus 0.2mm

Although all these effects may play a role, thanks to
our comparative perspective, we know that they do not play
the main role. Indeed, the great advantage of this approach
is that it allows us to filter out secondary factors.
In Japan
there is little immigration, in Sweden unions are still
important forces, in Germany, financialisation of the economy
is much less developed than in the US. Nevertheless, in
all these countries the wage transition effect can be observed.
In short, even if the previous effects exist, they are
only second-order effects in the sense given to this expression
in physics.

\qI{The wage transition in the United States}

\qA{Observation}

There are two good reasons for
starting this investigation with the case of the United States.
The first is that, as already said, it was the first country where
this transition occurred. Secondly,
it is probably the country where the wage transition was the sharpest.
This can be seen clearly by comparing Fig. 2 and Fig. 3.

\begin{figure}[htb]
\centerline{\psfig{width=17cm,figure=usa12.eps}}
\qleg{Fig.\hskip 0.0mm 2\hskip 3mm Wage transition in the United States.}
{In phase 1 the mean wage, $ w $, increased exponentially
at an average rate of $ 2.0\% $ per year
whereas in phase 2 it stagnated. Simultaneously
the proportion of the labor force employed in agriculture, $ f_a $
fell from $ 41\% $ in 1900 to $ 4.4\% $ in 1970. There
is a high correlation, namely 0.987, between the two changes.
The
relationship between the logarithms of the two variables
is: $ \log w =-a\log f_a+b,\quad a=0.67 \pm 0.09,\ b=1.87\pm 0.06 $.
However, because several other economic factors changed as well
during this time period,
this close connection does not necessarily mean that the wage effect
resulted from the shift in the labor force.}
{Sources: Historical Statistics of the United States 1975,
Liesner 1989.}
\end{figure}

Whereas in the US the transition from exponential increase to
stagnation occurred abruptly, elsewhere there was a smooth transition
involving a progressive reduction of the growth rate.

\qA{Possible ``explanations''}

If we limit our attention to the US case, many possible
``explanations'' may come to mind. For instance, one can mention
the following.
\hfill \par \hskip 4mm $ \bullet $ \hskip 2mm The decades after 1945 were marked by a progressive erosion
of the fighting capability and bargaining power
of American unions. This trend
already started with the Taft-Hartley Act of 1947 but
it really gained momentum only in the 1970s. One of its clearest
manifestations was the dramatic fall in the frequency of strikes.
More details can be found in Roehner (2009). For instance,
in the private sector the unionization rate dropped
from about 30\% in the 1950s to 8\% in 2007.
\hfill \par \hskip 4mm $ \bullet $ \hskip 2mm The suddenness of the transition may be ``explained'' by
a series of external shocks.
(i)
the confidence crisis which affected the dollar in the late 1960s
and which eventually led to President Nixon's
action in 1971 ending its convertibility to gold.
(ii) the slump of stock prices at the New York Stock Exchange,
(iii) the oil shock,
(iv) the aftershocks of the Vietnam War d\'eb\^acle.
\vskip 2mm plus 0.2mm minus 0.2mm

In order to narrow down the set of possible ``explanations''
we must consider a broader range of cases.
This is what is done in the next section. The fact that
the same wage transition can be observed in many countries will
ipso facto eliminate any specifically American explanation.

\qI{The wage transition in comparative perspective}

\qA{Average wage increases}
In February 2015, in his budget speech the Deputy Prime Minister
(and also Finance Minister) of Singapore, Tharman Shanmugaratnam,
warned that wage stagnation had set in for most
developed economies in the United States, Europe and Japan
(Shanmugaratnam 2015). As seen in the previous section
regarding the United States, and as will be seen in the present
section for the European countries and Japan, wage stagnation
is in fact a phenomenon which started over two decades ago.
However, as already mentioned,
in Singapore the phenomenon started only around 2005.
In other words, like the demographic
transition, the wage transition did not occur simultaneously
in all places but occurred gradually with
country-dependent time lags.
This is illustrated in Fig. 3.

\begin{figure}[htb]
\centerline{\psfig{width=17cm,figure=wcomp.eps}}
\qleg{Fig.\hskip 0.0mm 3\hskip 3mm Wage transition in several countries.}
{In phase 1 the average wage increased exponentially
whereas in phase 2 it stagnated or increased more slowly.
The names of the countries at the bottom of the graph
indicate the years when the transition occurred. To
the 5 countries shown one could add Singapore whose transition
occurred in 2005 as discussed in the text.}
{Sources:
USA: see Fig.1;
Japan: Historical Statistics of Japan, average monthly contractual
earnings;
Switzerland: The website of the ``Federal Office of Statistics''
gives an annual series of real wages starting in 1939;
UK: Until 1985: Liesner 1989, 1990-2015: Office of National Statistics,
average weekly earnings of manual workers;
Germany: Until 1985: Liesner 1989, 1990-2015: Federal
Statistical Office, gross hourly earnings for manual production
workers.}
\end{figure}

\qA{The case of Britain}

It can be observed that Britain stands apart both in Fig. 3 and
in Fig. 6. One may wonder why.
\vskip 2mm plus 0.2mm minus 0.2mm
In 1846, that is to say very early compared with other European
countries,
through the repeal of the Corn Laws which imposed heavy duties
on grain imports, Britain decided that grain supplies could
be obtained in a much cheaper way through overseas imports
particularly from the British Empire (e.g. from Canada or India%
\qfoot{To the point that India exported grains to Britain
even in years
when there were famines in some parts of the country.}%
).
This step was foreshadowed by recurrent land ``Enclosure Acts''
(e.g. those of 1801 and 1845)
which transformed into private property the
 ``common lands'' that hitherto had allowed
farming by landless farmers.
\vskip 2mm plus 0.2mm minus 0.2mm
Through these steps  people were compelled to move from the
countryside to cities. In other European countries a substantial
rural flight started only much later.

\qA{Interpretation}

Table 1 shows that with respect to productivity
the only clear and long-term distinction
is the one between agriculture and manufacturing.
The tertiary service sector is a mixed bag involving
activities of very different productivity levels.
As a consequence the composition of this sector will
be different from one country to another and in each
country it will change in the course of time.
For instance, the software industry which nowadays is
an important component in the United States was
non-nonexistent some 30 years ago and is still under-represented
in European countries. Another example is the health care
industry; according to table 1, in 2012
this sector represented 12\% of the
total employment and 7.1\% of the total GDP%
\qfoot{Another indicator is the weight of health
care expenditures; according to World Bank data in 2012
it represented 17\% of the US GDP.}%
; some 30 years ago the weight of this sector was much
smaller and nowadays it is still much lower
in many other developed countries.
\vskip 2mm plus 0.2mm minus 0.2mm
It is this changing shape of the tertiary sector which
makes it difficult to set up a model which would have
a permanent validity. There is a similar
difficulty with the demographic transition in the sense
that the transition described by Fig. 1 was in fact
followed by a second one in the early 21th century that
brought down the fertility rates of many developed countries
well below the replacement rate of 2.1.
Although the two transitions may share some characteristics
their mechanisms may not be identical.

\begin{table}[htb]

\small

\centerline{\bf Table 1 \quad GDP per employee  in various
economic sectors (USA, 2012)}

\vskip 5mm
\hrule
\vskip 0.7mm
\hrule
\vskip 2mm

$$ \matrix{
&\hbox{Sector}\hfill &  \hbox{Contribution} & \hbox{Employment}
\hfill&
\hbox{GDP per employee} \cr
&\hbox{}\hfill &  \hbox{to GDP} & \hbox{} \hfill &\cr
\vrule height 0pt depth 5pt width 0pt
&\hbox{}\hfill &   \hbox{[billion \$]}  & \hbox{[million]}
 & \hbox{[thousand \$]}\cr
\noalign{\hrule}
\vrule height 12pt depth 0pt width 0pt
1&\hbox{Educational services}\hfill & \hfill 183  & 3.34 & 54.8\cr
2&\hbox{Retail trade}\hfill & \hfill 932  & 14.9 & 62.5 \cr
3&\hbox{\color{blue} Health care, social assistance}\hfill & \hfill 1,153
&16.9 & \color{blue} 68.2 \cr
4&\hbox{Art, entertainment}\hfill & \hfill 157  & 2.09 & 75.1\cr
5&\hbox{\color{green}Agriculture, forestry, fishing }\hfill & \hfill 186 & 2.11
 & \color{green} 88.1\cr
6&\hbox{Government (federal and local)}\hfill & \hfill 2,197  &21.9 & 100\cr
7&\hbox{Transportation, warehousing}\hfill & \hfill 467  &4.41 & 106\cr
8&\hbox{Professional services}\hfill & \hfill 1,912  &17.9 & 106\cr
&\hbox{\color{magenta} All tertiary sectors}\hfill &
\hfill 11,929  & 97.6&  \color{magenta} 122\cr
&\hbox{\color{red} All sectors}\hfill & \hfill 14,509  & 112&
\color{red} 129\cr
&\hbox{\color{blue} All non-agricultural sectors}\hfill & \hfill 14,323  & 110&
\color{blue} 130\cr
9&\hbox{\color{blue} Manufacturing}\hfill & \hfill 1,983  &11.9 &
\color{blue}166\cr
10&\hbox{Wholesale trade}\hfill & \hfill 962  & 5.67 & 169\cr
11&\hbox{Information}\hfill & \hfill 737  & 2.74& 269\cr
12&\hbox{Financial activities}\hfill & \hfill 3,229  & 7.78& 415\cr
\vrule height 0pt depth 5pt width 0pt
13&\hbox{Mining}\hfill & \hfill 411  & 0.80 & 514\cr
\noalign{\hrule}
} $$
\vskip 1.5mm
\small
Notes: The GDP per employee given in the last column can be seen
as a measure of employee productivity.
Despite the modernization of US agricultural production
there is still a  1:2 productivity gap between
agriculture and manufacturing.
This table suggests that what is usually called the service
sector is in fact a very heterogeneous category in
which productivity covers a scale from 1 to 10.
The ratio between the productivities in agriculture and
non-agriculture sectors is equal to $ 130/88=1.47 $.
The present table is not an exhaustive list of all sectors
but it represents 90\% of the total GDP.\hfill \break
{\it Sources: GDP: Bureau of Economic Analysis (interactive table);
Employment: Bureau of Labor Statistics (``Table 2.1
Employment by major industry sector''.}
\vskip 5mm
\hrule
\vskip 0.7mm
\hrule
\end{table}

The global productivity of the tertiary sector is a weighted
average%
\qfoot{In fact in the present case the weighted average
is identical to the GDP per employee:
$ \sum (G_i/p_i)(p_i/P)=(1/P)\sum G_i $.}
which in the case of the sectors listed
in Table 1 (i.e. all 13 sectors except
agriculture, manufacturing and mining) is equal to \${$ 122,000 $}
per employee, a figure which is mid-way between
the productivities of agriculture and manufacturing.
\vskip 2mm plus 0.2mm minus 0.2mm
In the next section we examine the implications for
economic growth.

\qA{Implications for economic growth}

For China in 2013 the weights and productivity
of the agricultural sector on the one side and of
all non-agricultural sectors on the other side
are given in Table 2.

\begin{table}[htb]

\small

\centerline{\bf Table 2 \quad Contribution of different
sectors to the Chinese GDP, 1990-2013}

\vskip 5mm
\hrule
\vskip 0.7mm
\hrule
\vskip 2mm

$$ \matrix{
\hbox{Sector}\hfill &  \hbox{Share}  & \hbox{Share}&
\hbox{GDP per} & \hbox{Productivity}\cr
\hbox{}\hfill &  \hbox{of GDP}  & \hbox{of employment} \hfill &
\hbox{employee}& \hbox{multiplier}\cr
\hbox{}\hfill &  g_i & e_i & (g_i/e_i)g & k=(g_i/e_i)/(g_1/e_1)\cr
\vrule height 0pt depth 5pt width 0pt
\hbox{}\hfill &   \hbox{[\% of GDP]}  & \hbox{[\% of employment]}
 & \hbox{}&\cr
\noalign{\hrule}
\vrule height 12pt depth 0pt width 0pt
\hbox{\color{blue} 1990}\hfill &  &  & &\cr
\hbox{Agriculture}\hfill &  g_1=25  & e_1=60 & g_1/e_1=0.42g &1\cr
\hbox{Industry and tertiary sector}\hfill & g_2=75  & e_2=40 &
g_2/e_2=1.87g & 4.3 \cr
\hbox{\color{blue} 2013}\hfill &    &  & \cr
\hbox{Agriculture}\hfill &  g_1=10  & e_1=34 & g_1/e_1=0.29g & 1 \cr
\vrule height 0pt depth 5pt width 0pt
\hbox{Industry and tertiary sector}\hfill &  g_2=90  & e_2=66 & g_2/e_2=1.36g &
4.5 \cr
\noalign{\hrule}
} $$
\vskip 1.5mm
\small
Notes: $ g $ denotes the GDP per employee for the whole economy.
Between 1990 and 2013 the percentage of the total labor force
engaged in the agricultural sector fell from 60\% to 34\%.
In 2013 the productivity gap between
agriculture and the rest of the economy was 4.5; this was three times
the ratio of 1.47 found in Table 1 for the United States.
Needless to say, even in agriculture there was
a marked productivity increase due to the rapid growth of $ g $.
\hfill \break
{\it Sources: GDP data: Bajpai (2014); employment data: World Bank,
(Interactive table entitled ``Employment in agriculture (\% of total
employment)'').}
\vskip 5mm
\hrule
\vskip 0.7mm
\hrule
\end{table}

With respect to rural flight,
in the United States
the major transformation took place between 1900 and 1950.
In this time span the percentage of the labor force engaged
in agriculture fell from 41\% to 11\%,
that is to say at an annual rate of 0.6\%.
Based on the data of Table 1 and 2, Fig. 4 summarizes
the respective situations of the United States and China.
In the United States between 2000 and 2010 the percentage
of the labor force employed in agriculture fell
from 2.5\% to 1.5\%, that is to say at an annual rate of 0.1\%.
\begin{figure}[htb]
\centerline{\psfig{width=12cm,figure=uschine.eps}}
\qleg{Fig.\hskip 0.0mm 4\hskip 3mm Transfer of rural population
to the non-agricultural sector.}
{In China the transfer rate was over 10 times faster than
in the United States.The figure respects the order of magnitude
of the productivity gap within each country and also
the magnitude of the respective rates of transfer.
The scales of the two vertical productivity axes
are not the same; this allowed us to draw
two identical rectangles to represent the non-agricultural
sectors.}
{Sources: Tables 1 and 2.}
\end{figure}

In China during the same time interval, this percentage
fell from 50\% to 36\%, that is to say at an annual rate
of 1.4\%.
Whereas nowadays in the United States
this transfer of jobs from the agricultural sector to the
non-agricultural sector represents a very marginal
source of productivity progress, in the first half of the
20th century it was a major source of productivity growth.
However, the present transfer rate in China is twice
as fast as it was at that time in the US. It was between 1940 and
1950 that the US transfer rate reached its maximum
with an annual rate of 0.9\%, still lower than
seen in China in the past decades.

\qI{Model describing the transfer effect}

In order to test to what extent the mechanism described in
Fig. 4  can explain long-term wage increases we need to
set up a model along these lines.

\qA{Definition of key-variables}
In order to make it as simple as possible, we will
introduce only two sectors: agriculture (sector $ 1 $) on the one hand
and the rest of the economy (sector $ 2 $) on the other hand.
At first sight this may seem a rough approximation;
for instance, mining and health care will be together
in sector $ 2 $; however, mining is a fairly small
sector (it is 21 times smaller than health care).
The purpose of our two-sector model is to provide
a broad picture and to
explain (and predict) long-term trends.
\vskip 2mm plus 0.2mm minus 0.2mm

We introduce the following notations:\hfill \break
$ E $: total employment, $ E_i,\ i=1,2 $: employment in sectors 1,2
respectively. $ e_i=E_i/E $.\hfill \break
$ G $: total (real) GDP, $ G_i,\ i=1,2 $: GDP of sectors 1,2 respectively,
$ g_i=G_i/G $.\hfill \break
We denote by $ p=G/E $ the GDP per employee of the whole economy.
$ p_i=G_i/E_i=(g_i/e_i)(G/E)=(g_i/e_i)p $ denotes the GDP per employee
in each sector, that is to say their productivity.
\vskip 2mm plus 0.2mm minus 0.2mm

Now we introduce a variable that we call the {\it productivity
multiplier} that will characterize how much the productivity
of sector 2 is higher than the one of sector 1.
 $$ \hbox{Productivity multiplier:}\quad k={ p_2 \over p_1 }=
{ g_2/e_2 \over g_1/e_1 } = { (1-g_1)/(1-e_1)\over g_1/e_1 } $$

The interesting point is that $ k $ can be computed as soon as
one knows the two numbers $ g_1,e_1 $.

\qA{Order of magnitude and changes of key-variables}

In order to give some substance to these definitions, let us
give some numerical values for the United States and China.
The data for $ p $ are expressed in dollars of 1982 and renminbi of 2013
respectively.
\hfill \par \hskip 4mm $ \bullet $ \hskip 2mm {\color{blue} USA}: \hfill \break
{\small
$ 1900:\ p=\$9,593,\ e_1=41\%,\ g_1=23\%,\ g_1/e_1=0.56,\ k=2.32 $\hfill \break
$ 1950:\ p=\$20,424,\ e_1=11\%,\ g_1=6.9\%,\ g_1/e_1=0.62,\ k=1.66$\hfill \break
$ 1980:\ p=\$32,095,\ e_1=3.4\%,\ g_1=3.0\%,\ g_1/e_1=0.88,\ k=1.14$\hfill \break
}
\hfill \par \hskip 4mm $ \bullet $ \hskip 2mm {\color{blue} China}: \hfill \break
{\small
$ 1983:\ p=\hbox{RMB}\ 5,114,\ e_1=67\%,\ g_1=33\%,\ g_1/e_1=0.49,\ k=4.12$\hfill \break
$ 2000:\ p=\hbox{RMB}\ 24,184,\ e_1=50\%,\ g_1=50\%,\ g_1/e_1=0.30,\ k=5.67$\hfill \break
$ 2013:\ p=\hbox{RMB}\ 73,513,\ e_1=31\%,\ g_1=10\%,\ g_1/e_1=0.32,\ k=4.04$\hfill \break
}

A noteworthy point is the stability of $ g_1/e_1 $; it
changes much less than $ e_1 $ and $ g_1 $ separately and also much
less than $ p $. How should this be interpreted?
The fact that $ g_1/e_1=p_1/p $ is quasi-constant in spite of the
fact that $ p $ doubles, triples or quadruples
means that agricultural productivity
increases approximately at the same pace as global productivity.
Moreover, it can be observed that $ k $ changes fairly slowly
which for a structural variable is of course a welcome characteristic.
\vskip 2mm plus 0.2mm minus 0.2mm

Other data will be found in Table 3.
In addition, intermediate values of the GDP per employee
for 1995, 2000, 2005 and  2010 are as follows (expressed in 2015 RMB):\hfill \break
$ p_1: 7,434,\ 7,547,\ 10,150,\ 20,030 $ \hfill \break
$ p_2: 27,323,\ 42,189,\ 60,273,\ 75,394 $\hfill \break
During these 25 years the GDP per employee
has been increasing exponentially both in the agricultural sector
and in the rest of the economy. The coefficients of correlation
$ (t,\log p_i) $ are 0.97 and 0.99 respectively. The exponents
are given in the notes of Table 3.

\begin{table}[htb]

\small

\centerline{\bf Table 3:\quad China: GDP per employee in agriculture
and rest of economy, 1990-2015}

\vskip 5mm
\hrule
\vskip 0.7mm
\hrule
\vskip 2mm

$$ \matrix{
 & \hbox{} &  & 1990 & 2015 & \hbox{Ratio} \cr
\vrule height 0pt depth 5pt width 0pt
 & \hbox{} &  &  &  & 2015/1990 \cr
\noalign{\hrule}
\vrule height 12pt depth 0pt width 0pt
 & \hbox{\bf \color{blue} Whole economy} \hfill& &  &  &
\hbox{} \cr
1 & \hbox{GDP (billion of current RMB)} \hfill&  & 1,866  & 67,000 &
\hfill 36.00 \cr
2 & \hbox{GDP deflator} \hfill&  & 12  & 42 & \hfill 3.50 \cr
3 & \hbox{GDP (billion of 2015 RMB)} \hfill& G & 6,531  & 67,000 &
\hfill 10.30 \cr
4 & \hbox{Labor force (billion)} \hfill& E & 0.64  & 0.81 &  \hfill 1.26\cr
5 & \hbox{GDP per employee (2015 RMB)} \hfill& p & 10,204  & 82,716 &
\hfill 8.10\cr
 & \hbox{\bf \color{blue} Agriculture} \hfill&  & &  &  \cr
6 & \hbox{GDP (billion of 2015 RMB)} \hfill& G_1 & 1,763  & 6,030  &
\hfill 3.42\cr
7& \hbox{Labor force (billion)} \hfill& E_1 & 0.33  & 0.25 & \hfill 0.75 \cr
8& \hbox{GDP per employee (2015 RMB)} \hfill& p_1  & 5,342  & 24,210
&\hfill 4.51\cr
 & \hbox{\bf \color{blue} Rest of economy} \hfill&  & &  &  \cr
9& \hbox{GDP (billion of 2015 RMB)} \hfill& G_2 & 4,768& 60,970 &
\hfill 12.79 \cr
10& \hbox{Labor force (billion)} \hfill& E_2 & 0.31  & 0.56 & \hfill 1.81\cr
\vrule height 0pt depth 5pt width 0pt
11 &\hbox{GDP per employee (2015 RMB)} \hfill& p_2 &
15,380&108,875&\hfill 7.07 \cr
\noalign{\hrule}
} $$
\vskip 1.5mm
\small
Notes: It can be observed that
the rates of change of the labor force are an order of magnitude
smaller than the rates of change of the GDP. When intermediate
values are introduced one finds that both $ p_1 $ and $ p_2 $
increase as exponentials with exponents $ \alpha_1=0.062\pm 0.016 $
(doubling time of 11.1 years) and
$ \alpha_2=0.075\pm 0.011 $ (doubling time of 9.2 years).
As a result, in the long-term the productivity gap
increases as $ p_2-p_1 \sim \exp(\alpha_2t) $
\hfill \break
{\it Sources: World Bank, Trading Economics, http://www.investopedia.com.}
\vskip 5mm
\hrule
\vskip 0.7mm
\hrule
\end{table}

The exponential increase of $ p_1 $ and
$ p_2 $ has two interesting implications.
\hfill \par \hskip 4mm $ \bullet $ \hskip 2mm As is well known, in a limited world
an exponential growth cannot last for ever which means that
a saturation effect will set in sooner or later. The
data shown in Fig. 6 suggest that it should not occur before
2025.
\hfill \par \hskip 4mm $ \bullet $ \hskip 2mm Over past decades the
productivity gap between the agriculture sector and the rest
of the economy has increased exponentially:
$$ \hbox{gap}=p_2-p_1=p_2(1-p_1/p_2)=
p_2\left[1-(A_1/A_2)\exp(-d t)\right]\ d=\alpha_2-\alpha_1=0.013 $$
For large $ t $ the gap becomes equal to $ p_2 $
itself. Thus, any labor transfer from sector 1 to sector 2 will
trigger a productivity jump whose magnitude increases
exponentially in the course of time.
This effect may compensate for diminishing transfers as $ e_1 $
becomes smaller. In other words, the productivity effect of
rural flight will remain significant even when $ e_1 $ will
fall under 10\% as may happen around 2030 according to Fig. 6.

\qA{Describing the GDP change due to a transfer of labor force}
Now, in order to describe the mechanism of Fig. 4 we must assume that
$ e_1 $ decreases by a quantity $ \Delta e_1 $. In fact, in what follows
it will be more convenient to follow the variable $ e_2 $
because its variations $ \Delta e_2=-\Delta e_1 $ are positive
in the course of time.\hfill \break
The change
in $ G $ resulting from a transfer of population from sector 1 to
sector 2 will be:
 $$ G=G_1+G_2=p_1e_1E + p_2e_2E \rightarrow
\Delta G=\left( p_1\Delta e_1 + p_2\Delta e_2\right)E
=(p_2-p_1)E\Delta e_2 \qn{1} $$

In the above expression of $ \Delta G $ the productivities
$ p_1, p_2 $ have been kept constant because we wish to
focus on the effect of a labor force transfer%
\qfoot{The huge changes which occur in the course of time for
$ p_1 $ and $ p_2 $ are a different question which will
be discussed later.}%
.
By introducing the factor $ k $, expression (1) becomes:
$$ \Delta G=
(k-1)p_1 E\Delta e_2 =(k-1)\left({ g_1\over e_1 }\right)G\Delta e_2
\rightarrow
\left( { 1\over \Delta e_2 }\right){ \Delta G \over G }=
(k-1)\left({ g_1\over e_1 }\right) \qn{2} $$

We can take advantage of our previous observation regarding the stability
of $ g_1/e_1 $ to simplify expression (2) into:
$$ \left( { 1\over \Delta e_2 }\right){ \Delta G \over G }=
(k-1)<{ g_1\over e_1 }> \qn{3} $$

where $ <g_1/e_1> $ represents the average value
of $ g_1/e_1 $ over a period of several decades.
For the United states, one would have $ <g_1/e_1> =0.69 $
whereas for China: $ <g_1/e_1> = 0.35 $.
\vskip 2mm plus 0.2mm minus 0.2mm
In the left-hand side the numerator and denominator can be
multiplied by 100 which allows to express both $ \Delta e_2 $
and $ \Delta G/G $ in percent. Thus the left-hand side represents
the percentage variation of $ \Delta G/G $ for a 1\% variation
of $ e_2 $.
\vskip 2mm plus 0.2mm minus 0.2mm

Note that equation (3) applies to any transfer of labor force.
In particular, it can also describe the {\it decline} in  GDP
that results from the transfer of labor force
from manufacturing to healthcare, a key-feature
in industrialized countries over the past three decades%
\qfoot{In this case sector 1 would be the whole economy
except healthcare and sector 2 would designate the healthcare
sector.}%
.
This point may be developed in a subsequent paper.

\qI{Labor force transfers as an engine of economic growth}

\qA{Rural flight seen in the broader view of all labor force transfers}

In the present paper we focus on the effect of rural flight
on economic growth for two main reasons.
\hfill \par \hskip 4mm $ \bullet $ \hskip 2mm This labor force transfer has a clear statistical
definition because of the fact that agriculture is a well defined
economic sector. In contrast, a sector such as healthcare
has a fairly fuzzy
statistical definition. For instance, do nursing homes belong
to healthcare or to the hospitality industry?
\hfill \par \hskip 4mm $ \bullet $ \hskip 2mm Historically rural flight has been of great importance
in all developed countries and nowadays it is still a major
factor in countries such as China and India.
\vskip 2mm plus 0.2mm minus 0.2mm

However, this focus should not hide the fact that rural flight
is only one case (albeit the most significant)
of a broad variety of labor force transfers which take place
continuously. For instance in developed countries two sectors
have had a rapid growth over the past 50 years: (i) the healthcare
and (ii) the information technology sector.
According to Table 1, around 2012
in the United States healthcare employed
about 15\% of the total work force whereas the share of the
IT industry was of the order of one or two percent.
What impact on global productivity has had the development
of these sectors? Table 1 shows that healthcare's
and IT's productivities were one half and twice the national
average respectively.

\qA{A case in point: healthcare versus infrastructure development}

Across nations there are great disparities in the extension
of the healthcare sector. For countries with an aging populations
(as is the case of all developed countries) it is of course
natural to have an expanding healthcare sector but
it must be recognized that its actual extent
is determined by a political decision.
Thus, in the United Kingdom which has a nationalized
medical system the weight of healthcare in the economy
is only one half of what it is in the US%
\qfoot{In spite of that, around 2009 life expectancy in the UK was
2.5 years longer than in the US.}%
.
\vskip 2mm plus 0.2mm minus 0.2mm

The fact that through its own poor productivity level,
the healthcare sector drags down overall
productivity is not its only liability.  Because
most of its patients are elderly persons beyond
retirement age, health care has
little synergy with other activities.
\vskip 2mm plus 0.2mm minus 0.2mm

Completely different is the case of infrastructure
expenses. Many other sectors will benefit from
better transportation infrastructure. Clearly,
healthcare based development and infrastructure
oriented development constitute two different
models. As one knows, the infrastructure model
is promoted by the Chinese government not only
domestically but also for the
rest of the Asian continent. Regarding healthcare,
at this point it is too early to say
which system China will choose.
Will it be the US or the UK model%
\qfoot{One can be sure that this kind of decision will
be subject to lobbying efforts from many sides.}
?
Clearly this choice will affect the productivity
of the whole Chinese economy. Too much
resources and investments sunk in healthcare
will be a drag on the economy.
At the beginning of the paper we said that one
would expect a 8\% growth trend until 2025 but an
over extended healthcare industry
may well slow down this expectation to 6\% or 5\%.

\qI{What fraction of growth does rural flight explain?}

In this section we compare the changes in GDP per employee
actually observed to those predicted by the
rural flight effect in three different ways.
\qA{Correlation of relative variations}
Firstly, we wish to see if there is a connection
between the ups and downs in $ e_2 $ on the one hand and
the wage changes on the other hand. Obviously,
cases (such as China)
in which there were little ups and downs will not be appropriate
for this test. In contrast, the test can be done
in good conditions for the case of the US because its
wage changes display several ups and downs.
\vskip 2mm plus 0.2mm minus 0.2mm

The comparison between observed wage changes and those
predicted by the transfer model shows a significant
correlation (Fig. 5).
\begin{figure}[htb]
\centerline{\psfig{width=12cm,figure=model.eps}}
\qleg{Fig.\hskip 0.0mm 5\hskip 3mm Observed US wage changes versus
changes resulting from the transfer effect.}
{The correlation between the two series is
0.64 (95\% confidence interval is 0.08 to 0.90).
It can be observed that real wages did not fall
during the Great Depression. This is due to the fact that
the price consumer index fell faster than nominal wages.}
{Sources: Same sources as for Fig. 3.}
\end{figure}
Although one would like to
perform additional tests, it is
not easy to find appropriate cases. For instance,
the income of
countries such as France, Germany or Japan which have
experienced wars on their territory will be affected
by exogenous shocks which have nothing to do
with the transfer effect.

\qA{Percentage of growth directly due to rural flight in 1990 and
  2015}

The correlation test performed in the previous subsection
was largely independent of the magnitude of the respective
changes. Now
we wish to determine what proportion of the increase in
GDP per employee
can be accounted for by the rural flight effect.
For this test one needs a case in which the change rate
is as large as possible. China is well suited for this
purpose. Before making a formal calculation we will
compute the expected changes at the two ends of the time
interval 1990-2015. For this purpose we will use
the data given above (Table 3 and additional data).
\vskip 2mm plus 0.2mm minus 0.2mm

As already explained the rural flight effect is only one
of a number of transfer effects which contribute to
GDP growth. One of the clearest manifestations of the
effects of these transfers are the productivity
increases {\it within} the two sectors. This effect cannot be
explained by rural flight. Indeed, rural flight can only
explain the global growth of $ G $ due to the interaction
between agriculture and the rest of the economy.
The productivity
increases within the two sectors are due to interactions
between their various sub-sectors.
\hfill \par \hskip 4mm $ \bullet $ \hskip 2mm Tractors, trucks instead of horse driven carriages,
engines, pumps, motorcycles,
mobile phones and many other items that are produced in
sector 2 progressively
came into use in the country side, thereby lifting
$ p_1 $. These changes would happen independently of
any transfer of labor force.
\hfill \par \hskip 4mm $ \bullet $ \hskip 2mm At the same time, in sector 2 outdated textile
factories were equipped with modern spinning and
weaving machines manufactured in other factories of sector 2,
newspaper printing machines were replaced by computerized
systems, supermarkets replaced small stores. As above,
these changes would happen independently of
any transfer of labor force.
Because sector 2
is more diversified than sector 1 it experiences
more synergy and that is probably
why the endogenous growth of sector 2 is faster than
the endogenous growth of sector 1.
\vskip 2mm plus 0.2mm minus 0.2mm

According to the comments following Table 3, we expect
a greater effect of rural flight in 2015 than in 1990.
Let us see if this is confirmed by observation.
\vskip 2mm plus 0.2mm minus 0.2mm

Over the whole period 1990-2015 the labor force in sector 1
decreased by 170 millions which gives an average
change of 34 millions over every 5-year interval.
\vskip 2mm plus 0.2mm minus 0.2mm

{\bf \color{blue} 1990-1995} \quad Over this time interval,
one gets (everywhere RMB means ``RMB of 2015'')
\hfill \par \hskip 4mm $ \bullet $ \hskip 2mm The average of $ p_1 $ is
$ \overline{p_1}=(5,231+7,434)/2=6,332 $ RMB
\hfill \par \hskip 4mm $ \bullet $ \hskip 2mm  The average of $ p_2 $ is
$ \overline{p_2}=(15,380+27,323)/2=21,351 $ RMB
\hfill \break
Thus, rural flight from 1 to 2 will increase $ G $
by:
$ \Delta G' = 34\times 10^6(\overline{p_2}-\overline{p_1})=511\times
10^9 $ RMB.
\vskip 2mm plus 0.2mm minus 0.2mm
The actual increase in $ G $ was
$ \Delta G=G(1995)-G(1990)= 5,627\times 10^9 $ RMB. Thus, the variation
due to rural flight represents: $ 511/5627=9.1\% $.
\vskip 2mm plus 0.2mm minus 0.2mm

{\bf \color{blue} 2010-2015} \quad A similar computation
leads to: $ \Delta G' =2,383\times 10^9 $ RMB, and
$ \Delta G=G(2015)-G(2010)= 5,627\times 10^9 $ RMB. In this case
the variation due to rural flight represents: $ 11.4\% $.
\vskip 2mm plus 0.2mm minus 0.2mm

As expected we see that in 2015 the change due to rural flight is a
larger proportion of the total increase than in 1990. We also see
that the largest part of GDP growth must be attributed
to endogenous increases of $ G_1 $ and $ G_2 $ which in turn are
due to transfers occurring {\it within} these sectors.

\qA{General formulation}

In this subsection the previous computation is presented
in a broader and more formal way.
\vskip 2mm plus 0.2mm minus 0.2mm
Let us denote by $ p $ the GDP adjusted for
inflation and per employee. From 1985 to 2014, $ p $
has increased regularly at an average annual rate of
9.2\%; for the whole interval
this rate resulted in a multiplication by 10.3
\vskip 2mm plus 0.2mm minus 0.2mm

The productivity multiplier $ k =p_2/p_1 $ fluctuated between
4 and 6.
In order to get an upper bound of the growth
accounted for by rural flight, we will
give $ k $ its maximum value of 6. As a first step,
consider the two extreme situations: $ e_1=1 $
and $ e_1=0 $. Moving from the first to the second would
result in a multiplication of $ G $ by 6. In other words,
in this thought experiment
the transfer effect explains only $ 6/10.3=58\% $ of the growth.
\vskip 2mm plus 0.2mm minus 0.2mm

In order to get a more realistic estimate we write
the values of $ G $ in 1985 and 2014 in the following way
($ G' $ denotes the values of $ G $ predicted by rural flight):
 $$ G'(i)=p_1(i)E_1+6p_1(i)E_2=\left[e_1(i)+6e_2(i)\right]p_1(i)E $$

With $ e_1(1)=67\% $ and $ e_1(2)=31\% $ one gets:
 $ G'(2)/G'(1)=1.67\left( p_1(2)/p_1(1) \right) $.
In the rural flight model the productivity $ p_1 $ is supposed fixed,
that is $ p_1(1)=p_1(2) $. Under this assumption
the transfer of labor accounts for only
$ 1.67/11=15\% $ of the actual productivity growth.
\vskip 2mm plus 0.2mm minus 0.2mm

Equation (3) would lead to a similar conclusion.
For average annual changes it gives:\hfill \break
$ \Delta G'/G'=(k-1)<g_1/e_1>\Delta e_2=5\times 0.4\times 1.2=2.4\% $\hfill \break
whereas the actual annual GDP growth is $ \Delta G/G=9.2\% $.

\qA{Implications}

The fact that rural flight accounts for only a fraction of the
total growth
has two important implications.
\hfill \par \hskip 4mm $ \bullet $ \hskip 2mm A recession may temporarily stop employment shifts
from sector 1 to sector 2. If this transfer would account for
a high percentage (say some 80\% or more)
of economic growth, then its discontinuation
would freeze further growth altogether
and transform the recession into a depression.
However, if employment transfer actually is
only a secondary factor
the synergy engine will continue to work and will pull
the economy out of the recession%
\qfoot{Needless to say, economic growth can be thwarted
for many other reasons, e.g. deflation, capital flight,
disruption of the banking sector and so on.}%
.
\hfill \break
This is indeed what was
observed in developed countries in the decades following World
War II. There were only mild recessions.
In other words, unless there is a political
upheaval no major interruption of
growth should be expected in China for
at least 15 years, that is to say until it has become a service
economy too.
\hfill \par \hskip 4mm $ \bullet $ \hskip 2mm As seen earlier, in developed countries
the growth of wages stopped or slowed down in the 1980s.
This was due to the shift of their economies toward
a service sector economy which is dominated by low productivity
sectors of which healthcare is an important component.
The fact that rural flight almost came to an end in the 1980s
was due to two circumstances: (i) The labor force of sector 1
was already largely depleted.
(ii) Because productivity growth in sector 2 was slowed down,
this sector lost its attractivity in terms of higher
salaries. Indeed, the growth of sector 2 was largely due to
the introduction of so-called ``odd jobs'',
``second class jobs'' or ``deskilled casual work''.

\qI{Conclusion}

\qA{Chinese economic growth over 2015-2035 in comparative perspective}

Several interesting features are highlighted in Fig. 6.
\hfill \par \hskip 4mm $ \bullet $ \hskip 2mm  The
transfer of labor force from agriculture to the industrial
and service sectors has progressed unabated in all
developed countries. Economic crises or even the two world wars
may have slightly slowed down the evolution (as in the
case of Austria for instance) but they did not have
any lasting impact.
\begin{figure}[htb]
\centerline{\psfig{width=12cm,figure=agri.eps}}
\qleg{Fig.\hskip 0.0mm 6\hskip 3mm Labor force in agriculture.}
{Apart from the UK which stands apart, all developed
countries experienced more or less the same evolution.
The fact that the curves for Sweden and Germany are
almost the same shows that wars had almost no
impact. The curve for China was extrapolated based on the
rates seen in other countries and particularly in
South Korea. Regarding the special case of Britain see the
explanation given above in relation with Fig. 3.}
{Sources: Until 1970: Flora et al, (1987) and Historical
Statistics of the United States (1975).
After 1970: Trading Economics website and Saint Louis
Federal Reserve.}
\end{figure}
\hfill \par \hskip 4mm $ \bullet $ \hskip 2mm We have already suggested
that South Korea may be a good predictor of the future
of the Chinese economy.
The development of South Korea followed the pattern
set by developed countries.
We see that in terms of proportion of the labor force working
in the agricultural sector
Finland was about 20 years ahead of South Korea
which itself was 30 years ahead of China%
\qfoot{In this respect one should not forget that, as in Taiwan,
the Japanese occupation had resulted in a substantial
industrialization not only in the north of Korea but also
in the south; more evidence can be found in Roehner (2015).}%
.
If one uses the rate of South Korea to extrapolate the curve
for China one arrives to the conclusion that in 2035
the percentage of the labor force in agriculture will be
about 10\%.
\hfill \par \hskip 4mm $ \bullet $ \hskip 2mm Among developed countries the UK is a case apart.
Its agricultural sector
fell to a level of 10\% some 60 years before the United
States.
In this sense, Britain was the first
post-industrial economy; this may explain the fact
(observed in Fig. 3) that its rate of wage increase was
substantially lower than in other countries.
\vskip 2mm plus 0.2mm minus 0.2mm

What was the evolution of agricultural employment in the USSR
and later on in the Russian Federation? By collecting
data from various sources (Gaidar 2012, W\"adekin 1982,
Lerman et al. 2003, Trading Economics website)
we got the following picture:

\begin{table}[htb]
\small
\centerline{\bf Table  4\quad Russia: Employment in agriculture
in percentage of total labor force}

\vskip 3mm
\hrule
\vskip 0.7mm
\hrule
\vskip 2mm
 $$ \matrix{
\vrule height 0pt depth 5pt width 0pt
 1930 & 1960 & 1978 & 1985 & 1990 & 2000 & 2010 & 2014 \cr
\noalign{\hrule}
\vrule height 12pt depth 0pt width 0pt \vrule height 0pt depth 5pt width 0pt
87\% & 39\% & 21\% & 15\% & 14\% & 15\% & 9\% & 7\% \cr
\noalign{\hrule}
} $$
\vskip 2mm
\hrule
\vskip 0.7mm
\hrule
\end{table}

The table shows that the interruption of the fall
of the share of agricultural employment coincided with the
crisis of 1988-2000. So, once again, we see that
this variable
is a good indicator of long-term economic growth.
We see also that the time interval 1930-1960 saw a massive
reduction in agricultural employment and we know that
it was accompanied by rapid economic development.

\qA{Rural flight seen as a good indicator of productivity growth}

In the first part of the paper
it was shown that a wage transition occurred in most
developed countries in the 1980s. After decades of rapid growth,
wages started to slump.  Rural flight appeared as
a good candidate for
explaining not only this wage transition but also the rapid
growth observed in China in past decades.
Incidentally,
one should not think that there was no or little
growth before 1983. According to Chinese GDP statistics,
the real average annual GDP growth rate was $ 9.8\% $ from 1952 to
1982 and $ 9.9\% $ from 1983 to 2014. Even though
national accounting methods may have been different
before 1983,
the idea of a stagnant economy before 1983
seems inappropriate.
Actually, the most conspicuous difference
between the two periods is that in the first one growth was
much more irregular than in the second. The coefficients
of variation of the annual growth rates were 194\%
and 27\% respectively.
\vskip 2mm plus 0.2mm minus 0.2mm
In order to test the  idea of a connection between rural flight
and economic growth
we set up a model which allowed us to predict the
change of income resulting from rural flight.
It was seen that this model is in
good qualitative agreement with
observation. As expected, rural flight accounts for only
a fraction of total growth but it is also a good indicator
of the dynamism of sector 2. If tomorrow the
Chinese economic growth starts to create a lot of ``second class
jobs'' (for instance by an over-extension of the healthcare
sector) then, those young people who presently still remain in
Chinese villages (see Appendix C) will certainly think
about it twice
before moving to cities. This, in turn, will make economic growth
more sluggish. So, the main issue is whether
in the coming decades China will be able to
avoid the ``odd job trap''.

\qA{Wage progression and labor inflow}

It may seem surprising that in this paper we did not
consider the question of immigration.
As for any other good, the cost of labor is affected by supply and
demand changes. Supply is controlled by labor force expansion
either endogenously or exogenously (i.e. through immigration)
whereas demand is conditioned by GDP growth. This
question would deserve a separate study but here we will limit
ourselves to a few observations.
\hfill \par \hskip 4mm $ \bullet $ \hskip 2mm Due to the sheer size
of the Chinese population foreign  immigration
will have little incidence in China.
The only country whose population
could match the population of China is India. However, whereas
there are important Indian
communities in the UK and US, immigrants to China
would have to learn Chinese, quite a formidable
challenge for them.
\hfill \par \hskip 4mm $ \bullet $ \hskip 2mm The case of Japan suggests
that at the level of a whole country (as
opposed to a specific sector) labor inflow through immigration
is rather a second-order effect. Indeed,
despite a much lower immigration rate
Japanese wages leveled off in the same way as in European countries
(see Fig. 3).
\hfill \par \hskip 4mm $ \bullet $ \hskip 2mm In contrast, at sectorial level, immigration
may play a role. In this respect, as a case study,
it would be interesting to
investigate the impact on labor cost
of the inflow of Filipino nurses into the US%
\qfoot{Their immigration
is facilitated by the fact that their curriculum
integrates the requirements and specifications of the US healthcare
industry which includes practising the English
language.}%
.

\vskip 4mm
{\bf Acknowledgments}\quad We wish to express our gratitude
to Ms. Corina Neuerer of the ``Statistisches Bundesamt''
(German Federal Statistical Office) and to Ms. Alyson Williams
of the British ``Office of National Statistics'' for
helping us to locate and retrieve some of the statistical series
used in this paper.

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