INFORGE MODULES - University Of Maryland · 2016 GWS mbH Page 9 Osnabrück, August 2016 Aim: get...
Transcript of INFORGE MODULES - University Of Maryland · 2016 GWS mbH Page 9 Osnabrück, August 2016 Aim: get...
Münster, Mai 2015WWW.GWS-OS.COM / © GWS 2016
INFORGE MODULESA selection of major model extensions
Anke Mönnig
2016 GWS mbH Page 2 Osnabrück, August 2016
Content
1. Overview2. Modules
a. TINFORGE Ib. TINFORGE IIc. QuBed. DEMOS
3. Outlook
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1.Overview
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Overview► Keeping the map-
perspective, INFORGE is a nice, tidy, smooth workingmodel
► Ready for anaylising manyresearch questions, related to e.g. industries economic actors regions taxes employment etc.
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► „Only dead fish swim with the stream“
► INFORGE is subject to constant changes –over a period of 20 (or 40?) years
► Often „forced from the outside“ due to classification revisions omission of data due to projects
► but also „forced from the inside“ due to new data new options new ideas improvement of „not so good“ approaches
Overview
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Overview► ... and it doesn‘t stop...
► Adding detail to the mapwith modules
► Modules partly with orwithout feedback to thecore model
► E.g. world trade migration qualification and
occupation household types
World trade
Qualificationand occupation
Householdtypes
Migration
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3. Modules
► Empirical observation / motive► Translated into INFORGE framework► Graphical overview of module
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► World trade important for Germany‘s economic growth► Especially for major sectors (cars, machineries, chemicals)► But yet, INFORGE depends on third party projections sequence of updates, economic perceptions etc. „not ours“.
TINFORGE I – Trade for INFORGE
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► Aim: get control over exogenous export vector in INFORGE► Solution: „build my own“ world trade model TINFORGE simple easily integrated easily updated full coverage of world trade
► How: combine bilateral trade matrices (OECD) with macromodels 154 bilateral trade matrices (by 32 products) 70 macro models (simple) export demand and import prices depend on trade
exports depend on other countries important demand PULL import prices depend on other countries export prices PUSH
TINFORGE I – Trade for INFORGE
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► Graphical overview
TINFORGE I – Trade for INFORGE
Country models
Wei
ghtin
gDomestic prices
Exogenousraw material
prices
Export prices[epi]
import prices [ipj]
Importnachfrage [im] Importnachfrage [im] import demand [mcj,q]
Bilateraler Welthandel
[BHM]
Bilateraler Welthandel
[BHM]
Bilateral World Trade[WBXTQi,j,q]
Export demand
[xci,q]
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TINFORGE II – Imigration to Germany► Population projections of
third parties normally haveno idea about migration
► The past has shown, thatpopulation projectioncontinously failed.
► Influence of net migrationunderestimated
► There is a need to learn moreabout who is (will be) comingin terms of nationality, age, sex, qualification, motives forcoming etc.
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► Aim: get control for net migration► Solution: „build my own“ imigration model simple easily integrated easily updated
► How: Migration by nation, sex, age integrated in TINFORGE Take UN population forecast for countries Determing emigration ratio for 154 countries (share of
emigration to Germany to total population in home country) Extrapolation of ratio according to emigration reasons
(demographic, political, socio-economic)
TINFORGE II – Imigration to Germany
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► Graphical overview
TINFORGE II – Imigration to GermanyU
N-P
opul
atio
n Pr
ospe
cts
Emig
ratio
n sh
are*
(CC
D
E) f
or20
07 -2
014
(AZR
)
Total emigration(∑ CC DE)
(CC_1 DE)
(CC_2 DE)
(CC_3 DE)
(CC_4 DE)
(CC_5 DE)
(CC_150 DE)
…
* share of people emigrating from country cc to Germany DE
Case differentiation for determiningfuture emigration share* Case 1: demographic
Case 2: socio-economic
Case 3: (geo-)politics
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► Increasing scarcity on labour market – especially in certainbranches
► Need to learn more which occupations and qualifications arerequired in the future
► Support forward looking politics (education system)
QINFORGE – Qualification and Occupation in INFORGE
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► Aim: building a labour market beyond industry level with theaim to match both sides of the labour market
► Solution: using micro data for more information► How: Labour demand and supply break-down to qualification
and occupational levels Not „on our own“: The qube-projekt.de:
Federal Institute for Vocational Education and Training (BIBB) Institute for Employment Research (IAB) Fraunhofer Institute for Applied Information Technology (FIT) Institute of Economic Structures Research (GWS)
Collaboration since 10 years Entering know the 4th version of QINFORGE model Over the years, approach got more and more sophisticated,
together with more and better data
QINFORGE – Qualification and Occupation in INFORGE
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► Graphical overview
QINFORGE – Qualification and Occupation in INFORGE
Labour demandLabour supply
Dem
ogra
phy
QualificationQualification
Matching
Balancingon
occupational level
Executedoccupation
Working population
Occupationspecificwages
Flexibility in occupational
choice
Wages &prices
Learnedoccupation
Labour participation
Choice ofoccupation
Education system
Economy
Executed occupationin sector 1
Executed occupationin sector 63
Executed occupationEmploymees
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DEMOS► Driving force: Project
„Reporting on socio-economic development in Germany“, 2013-2016
► Inequality has risen► Need to learn about who
contributes to economicgrowth, how theyconsume and how theyearn their income.
► Components of primaryincome of private households
Primary income
Labour compensation
Wealth incomeProportion between wealthincome / labour compensation
Prop
ortio
n
in b
n. E
uro
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► Aim: determination of household consumption byhouseholdtypes
► Solution: integration of sample census data► How: combining meso with macro data
(1) INFORGE results of estimated consumption purposes of private households
(2) Dynamic is transferred to consumption structure of different household types
(3) Feedback to INFORGE by extrapolation with growth rates ofnew consumption by purposes
DEMOS
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► Graphical overview
DEMOS
(1)
(2)
(3)
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3. Outlook
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► Research areas Digitization (4th industrial revolution) Globalisation / trade:
Sustainable Development Goals Criticism on globalisation: optimal „boarder opening“, etc. Social impact of trade: social footprint/labour footprint, etc.
Migration► Intensifying socio-economic modelling bridging to micro level social monitoring
► Model extensions: Population projection Regional Input-Output analysis Modelling on municipality level (LAU 2 (NUTS 5) level)
Outlook
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