IDS Impact Innovation and Learning Workshop March 2013: Day 1, Paper Session 2 Peter Loewe

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IDS Impact Innovation and Learning Workshop March 2013: Day 1, Paper Session 2 Peter Loewe

  1. 1. Exploring the Potential ofSystems Dynamics ModellingImpact Innovation and Learning: Towards a Research andPractice Agenda for the FutureBrighton 26/27 March 2013Peter Loewe
  2. 2. Content• Impact paths of enterprise development projects• Linear causal chains vs closed causal loops• Causal loop diagrams: 3 investment strategies• System Dynamics past and present• The UNIDO demonstration tool• Two simulations• Status, lessons, way forward
  3. 3. Causal chain model of PSD poverty impactSource: OECD Donor Committee for Enterprise DevelopmentHow Private Sector Development leads to Pro-Poor Impacts: A Framework for Evidence
  4. 4. Poverty impact paths under scrutinyEmploymentreductionIUInterventionCompetitivenessimpactIndustrialDevelopment/Economic GrowthImpactEmploymentimpactSkill content impact Additional EffectsPoverty impactCrowding out of lesscompetitive SMEsBusiness declineJob-less growth(productivity path)High skill jobsLow skill jobsNon-poor growthIncrease in povertyReduction in localpurchasing power,consumption,production, SMEs,and employment increase in povertyMarketaccess &partnershipCluster &networkingPro-poor skilldevelopment& trainingPro-poorsocialprotectionPro-poortargeting(regions, sectors,firms, services)Pro-poorentrepreneur &SMEdevelopmentBusinessmaintenanceHigh skill jobsCompetitive SMEs(high road/costs &I U)Business expansionof local suppliers &subcontractorsBusiness expansionin export marketsBusiness expansionin local marketEmployment creation/ job-rich growth(expansion path) Low skill jobsPro-poorgrowthIncrease in localpurchasing power,consumption,production, SMEs,and employment poverty reductionEmploymentmaintenanceBusiness decline ofless competitive localsuppliers &subcontractorsCompetitive SMEs(low road/costs)Possible impact drivers are shown in red.
  5. 5. Causal Chains vs. Systems Dynamics• Linear causal chain modeling (logframe)– state-of-the-art in development cooperation– too simplistic for complex cases• System Dynamics Modelling (SDM)– Causal loops instead of causal chains– Negative feed-back loops (“goal seeking”)– Positive feed-back loops (“exponential growth”)– Interconnected loops– Strong / weak coupling– Variables can be “stocks” or “flows”– Short term / long term behavior– Non-linear behavior(small causes – big effects)
  6. 6. 6Causal loop example (1)ProfitsInvestmentExpansionCompetitiveneson price++-+Negative feed backloop ("goal seeking")
  7. 7. 7Causal loop example (2)ProfitsInvestmentRationa-lizationExpansionCompetitivenesson price++-+++Exponentialgrowth(buttoothebottom)Positive feed back loop(but jobless "low road"))
  8. 8. 8Causal loop example (3)ProfitsInvestmentRationa-lizationExpansionCompetitivenesson price++-+++Exponential growth(buttoo the bottom) Positive feed back loop(but jobless "low road")Competitiveness onquality & new productsInnovation+-+Positive feed backloop ("high road")Negative feed backloop (equilibrium)
  9. 9. 9Entry points for interventionsProfitsInvestmentRationa-lizationExpansionCompetitivenesson price++-+++Competitiveness onquality & new productsInnovation+-+BusinessclimateAccess tofinanceTradeliberalizationImprove nationalquality and innovationsystem
  10. 10. System Dynamics: Past & present• Early applications– Engineering: Technological feed-back systems– Biology: Ecological systems (predator – prey systems)• 1961: Jay Forrester (MIT) application to management :“Industrial Dynamics”• 1972: Forrester/Meadows: “World Model” – Limits of Growth• 1980s: System Dynamics tools for PC (Ithink; Vensim)• Current applications: management; traffic/city/regionalplanning; energy and environment; economic development• World System Dynamics Society: www.systemdynamics.org• 2005: Millennium Institute “Threshold 21” simulation tool
  11. 11. 11I believe we are proposing the “Process” of modeling ratherthan particular frozen and final models. … It seems to me thatthe average person will be greatly concerned if he feels thatthe future and alternatives are being frozen once and for allinto a particular model, instead we are suggesting thatmodels will help to clarify our processes of thought; they willhelp to make explicit the assumptions we are alreadymaking; and they will show the consequences of theassumptions. But as our understanding, our assumptions,and our goals change, so can the models.John Forrester (1985), “The” model versus a modeling process, in: SystemDynamics Review, 1, S. 133-134.Importance of the modeling process
  12. 12. The UNIDO experimental simulation tool• Part of an evaluation of “Industrial Upgrading” projects• Concrete case: Leather project in Ethiopia• Programmer: Sebastian Derwisch (University of Bergen)• Consultant: Cornelia Staritz (Austrian DevelopmentFoundation)• Group model building workshop in December 2011• Computer model using VENSIM software:– About 200 internal variables / equations– Three external factors– Eight input variables (development interventions)– Eleven output variables• Presentations to project managers and management• Recommendation to pursue
  13. 13. Structure of the UNIDO model
  14. 14. 3 external factors• Import tariffs (increased imports)• Cost of raw materials• Increased competition on export markets
  15. 15. 8 interventions (input variables)• Investment in equipment• Labor intensity• Investment in skills• Access to credit• Strengthening of National Quality Infrastructure• Logistics and customs infrastructure• “Buy local” campaign• Promotion of labor standards
  16. 16. 11 effects (output variables)• Equipment• Skills• Productivity• Costs• Quality• Price• Local demand• International demand• Production• Jobs• Wages
  17. 17. 17"Imports increasing (Tariff reduction)"0 1Increase of imports0.60.450.30.1502010 2015 2020 2025 2030 2035Time (Year)dmnl"Imports increasing (Tariff reduction)" : Baseline with trade liberalizationRepresents an increase in competition on the nationalmarket. The parameter can be varied between 0 (nocompetition - everything produced for the domesticmarket can be sold) and 1 (full competition, nothingproduced for the domestic market can be sold)Simulation 1External change: trade liberalizationin 2011 - 2014Slider to change the parameter between 0 and 1Simulation period: 2010 to 2035
  18. 18. 18Simulation 1:Effects of the external changeCosts21.40.82010 2015 2020 2025 2030 2035Time (Year)dmnl".expected costs." : Baseline with trade liberalizationProduction10.750.50.2502010 2015 2020 2025 2030 2035Time (Year)dmnl".production." : Baseline with trade liberalizationPrice10.80.62010 2015 2020 2025 2030 2035Time (Year)dmnl".expected price." : Baseline with trade liberalizationInternational Demand0.20.102010 2015 2020 2025 2030 2035Time (Year)dmnl".international demand." : Baseline with trade liberalizationLocal Demand10.502010 2015 2020 2025 2030 2035Time (Year)dmnl".local demand." : Baseline with trade liberalizationEquipment10.70.42010 2015 2020 2025 2030 2035Time (Year)dmnl".equipment." : Baseline with trade liberalizationProductivity21.71.41.10.82010 2015 2020 2025 2030 2035Time (Year)dmnlrelative productivity : Baseline with trade liberalizationQuality10.80.62010 2015 2020 2025 2030 2035Time (Year)dmnlscore quality : Baseline with trade liberalizationJobs10.70.42010 2015 2020 2025 2030 2035Time (Year)dmnl".jobs." : Baseline with trade liberalizationWages10.90.82010 2015 2020 2025 2030 2035Time (Year)dmnlwages : Baseline with trade liberalizationSkill per worker21.71.41.10.82010 2015 2020 2025 2030 2035Time (Year)dmnlrelative average skills per worker : Baseline with trade liberalization
  19. 19. Promotion of labour standards0 5logistics and customs upgrading program0 5Equipment upgrading program0 5Skills upgrading program0 5buy local campaign0 5access to credit0 5NQS upgrading program0 5Investment in Equipment2102010 2015 2020 2025 2030 2035Time (Year)dmnlEquipment upgrading program : 8 Liberalisation responseLogistics and customs upgrading21.40.82010 2015 2020 2025 2030 2035Time (Year)dmnllogistics and customs upgrading program : 8 Liberalisation responseInvestment in skills4322010 2015 2020 2025 2030 2035Time (Year)dmnlSkills upgrading program : 8 Liberalisation responseNQS upgrading21.40.82010 2015 2020 2025 2030 2035Time (Year)dmnlNQS upgrading program : 8 Liberalisation responseBuy local campaign21.71.41.10.82010 2015 2020 2025 2030 2035Time (Year)dmnlbuy local campaign : 8 Liberalisation responseLabour standards21.510.502010 2015 2020 2025 2030 2035Time (Year)dmnlPromotion of labour standards : 8 Liberalisation responseAccess to credit21.751.51.2512010 2015 2020 2025 2030 2035Time (Year)dmnlaccess to credit : 8 Liberalisation responseInterventions reduces labor requirements - Saveson workforce and maintains production. Theparameter can be varied between -1 (fullautomatization, no workers needed) and 1(labour intensity increased by 100%)Interventions increases labor productivity byinvestments into skill building - increasesproductivity of workers. The parameter can bevaried between 0 (no additional investment inskills) and 5 (investment in skills increased by500%)Interventions affecting productivitylabor intensity intervention-1 1Intervention increases investment into equipment -Maintains woker and increases stock of equipment.The parameter can be varied between 0 (noadditional investment in equipment) and 5(investment in equipment increased by 500%)Labor intensity-0.4-0.7-12010 2015 2020 2025 2030 2035Time (Year)dmnllabor intensity intervention : 8 Liberalisation responseOther InterventionsRepresents a campaign that stimulates localdemand - whats inserted is the assumed increase oflocal demand by the campaign. The parameter canbe varied between 0 (additional increase indemand) and 5 (local demand increased by 500%)Represents an upgrading of wages - thevalue inserted represents the increase of thewages by promoting better pabor standards,higher wages have an effect on the skill perworkerAccess to credit lifts the overall investmentby the value inserted. The parameter can bevaried between 0 (no additional investment)and 5 (investment increased by 500%)NQS upgrading represents investment in NQSfacilities. The parameter canbe varied between0 (no additional investment into NQSupgrading) and 5 (investment into NQSupgrading increased by 500%)Represents investments to improve logisticswhich reduces fluctuations in the delivery delay.The parameter can be varied between 0 (noadditional investment into logistics) and 5(investment into logistics increased by 500%)Simulation 2:Interventions of a possible development programSet parametersfor year nObserve effectsfor year n+1Adapt parametersfor year n+1Observe effectsfor year n+2Adapt parametersfor year n+3
  20. 20. 20Simulation 2: How the interventions of the responseprogram overcome the effects of the external changeCosts2102010 2015 2020 2025 2030 2035Time (Year)dmnl".expected costs." : 8 Liberalisation responseProduction201510502010 2015 2020 2025 2030 2035Time (Year)dmnl".production." : 8 Liberalisation responsePrice21.40.82010 2015 2020 2025 2030 2035Time (Year)dmnl".expected price." : 8 Liberalisation responseInternational Demand201002010 2015 2020 2025 2030 2035Time (Year)dmnl".international demand." : 8 Liberalisation responseLocal Demand10502010 2015 2020 2025 2030 2035Time (Year)dmnl".local demand." : 8 Liberalisation responseEquipment6302010 2015 2020 2025 2030 2035Time (Year)dmnl".equipment." : 8 Liberalisation responseProductivity432102010 2015 2020 2025 2030 2035Time (Year)dmnlrelative productivity : 8 Liberalisation responseQuality8402010 2015 2020 2025 2030 2035Time (Year)dmnlscore quality : 8 Liberalisation responseJobs21.40.82010 2015 2020 2025 2030 2035Time (Year)dmnl".jobs." : 8 Liberalisation responseWages21.40.82010 2015 2020 2025 2030 2035Time (Year)dmnlwages : 8 Liberalisation responseSkill per worker864202010 2015 2020 2025 2030 2035Time (Year)dmnlrelative average skills per worker : 8 Liberalisation response
  21. 21. Work in progress - some preliminary conclusions• SD: an appropriate approach to cope with complexity• A “meta language” - alternative to linear causal chains• “Qualitative modeling” through “Group Model Workshops”:– Consensus building on parameters & dynamics of complex settings– Making implicit assumptions explicit– Feeding “lessons learned” from evaluation into model structure• Computer simulation (“quantitative modeling”)– Not a must - “qualitative modeling” is useful exercise in itself– Requires experienced programmer– Rather time consuming• Useful for generic types of interventions (project families)• Towards an “Artificial Intelligence” tool for program design?
  22. 22. 22Omitting structures or variablesknown to be important becausenumerical data are unavailable isactually less scientific and lessaccurate than using your bestjudgment to estimate theirvalues.To omit such variables isequivalent to saying they havezero effect - probably the onlyvalue that is known to be wrong!John Sterman (2002), All models are wrong:reflections on becoming a systems scientist, SystemDynamics Review, Vol. 18, p. 523The simple is false- but the complexis unusablePaul ValéryAlso known as“Bonini’s paradox”The complexity of our mental modelsvastly exceeds our capacity tounderstand their implications. …Formalizing qualitative models andtesting them via simulation often leadsto radical changes in the way weunderstand reality.John Sterman (2000), Business Dynamics, p. 29„Allmodelsarewrong“

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