Challenges of modelling regional impact of selected policies with use of CGE in Poland

Transcription

1 Challenges of modelling regional impact of selected policies with use of CGE in Poland Sejnäjoki Prof. Katarzyna Zawalińska Institute of Rural and Agricultural Development, Polish Academy of Sciences (IRWiR PAN)

2 Contents Challenges concerning the policies in question Common Agricultural Policy Research &Development increase Building nuclear power station (investment and operational stage) Challanges related to data for regional CGE National Make and Use and SIO tables Ragionalization Disagregation Dynamics POL-TERM model Features of the policies to include into the model Static version vs recursive dynamic version Sum up of the challenges Possible solutions to the problems

3 Challenges for modelling the 3 policies in general: - Common Agricultural Policy - R&D spending - Nuclear Power Station

4 Challenges of modelling the Common Agricultural Policy Large variety of measures and policy tools: investment subsidies, land subsidies, transfers. Many soft (non-investment) measures: trainings, project development, management of producers groups, etc. Complexity of financing: public money (from EU and national budget) and private money (from beneficiaries). Besides some contribution to generall EU budget annually. Many measures implemented at the farm level and many sectors (too detailed for CGE model) Beneficiaries are not only farmers and non-farmers but also public entities

5 Large variety of CAP measures and tools serving different purposes Competitiveness Semi-subsistence farms support 2.5 Total 100 Pillar I measures include: - Direct payments: 7 types among which some are per ha and some per unit of production - Market invetventions More than 40 Pillar II in the EU (in Poland 22) Priorities (Axis) Short name of measure % Axis Short name of measure % Environment LFA (Less favoured areas) 14.7 Competitiveness Young Farmers support 2.4 Environment Agri-Environmental programs Technical Assistance 2 Competitiveness Early Retirement 12.3 Competitiveness Advisory services 2 Competitiveness Modernization of farms 10 Quality of life Diversification 1.9 Quality of lfie Basic Services 8.3 Environment Restoring Forest 0.8 Competitiveness Added Value 6.2 Competitiveness Producer Groups 0.8 Competitiveness Micro-Enterprises 5.8 Competitiveness Food quality systems 0.6 Environment Afforestation 3.9 Competitiveness Training 0.2 Quality of life Development of Infrastructure 3.4 Competitiveness Information and Promotion 0.2 Quality of life Village Renewal LEADER (1+2+3) 4.7

6 Many soft measures Pillar 2 measures, sorted by size (annual spend) No. Short name of measure Millions EUR % No. Short name of measure Millions EUR % 1 LFA (Less favoured areas) Young Farmers support Agri-Environmental programs Technical Assistance Early Retirement Advisory Modernization of farms Diversification Basic Services Restoring Forest Added Value Producer Groups Micro-Enterprises Food quality systems Afforestation Training Information and 9 Development of Infrastructure Promotion Village Renewal LEADER (1+2+3) Semi-subsistence farms support Total Soft measures: LEADER (bottom-up initiatives), Information &promotion, Training, Advisory, technical assistance

7 Complexity of financing the Common Agricultural Policy Rural Development Program Direct payments are topped up from national budget RDP payments require 25% of public or private cofinancing

8 Farm level requirements of CAP policy example of greening payments 1. Minimum number of crops in rotation: farms below 10 ha of arable land - excluded from this requirement; farms 10 to 30 ha - at least 2 different crops and the main crop 75 % farms above 30 ha at least 3 different crops, the share of the main crop 75 % of arable land and the two main crops together 95 % arable land. 2. Maintaining the existing areas of permanent grassland, with the right to reduce the area by not more than 5% compared to base year; 3. Allocation of 5% of arable land to ecological focus area (EFA), including ecological land such as land left fallow, terraces, landscape features, buffer strips and afforested areas: farm s below 15 ha of arable land are excluded from EFA requirement Starting from 2018, the EFA may be increased to 7 %.

9 Challenges concering the modelling of building nuclear power station in Poland Virtual, hypothetical investment Uncertainty about the future of Polish energy market and energy mix Lack of data What should we model: construction or operation phase? Sensitive, political issue

10 Hypothetical investment At the moment only general idea but without any details such as location, technology, size Locations: Recommended Reserve Other possible Chosen by the investor

11 Uncertainty about the future of Polish energy market and energy mix Very dynamic situation, difficult to forecast: prices, sources (home production vs import), role of renewable energy sources etc. Scenario 1: expensive CCS (Carbon Capture and Storage) and no electric cars Scenario 2: cheap CCS (Carbon Capture and Storage) and electric cars Scenario 3: diversified and optimal energy mix nuclear power plants Forecasts on Polish energy mix (electric energy sources) in 2030 different scenarios, by the Ministry of Economy

12 Lack of data forecasts and analysis focused on safety, environmental and technological issues rather than economy, no forecasts usable for CGE modelling; previous analysis at national, not regional level

13 How to model: construction and operation phase Different resources, costs, benefits Different time horizon Different sectors

14 Sensitive, political issue

15 R&D policy challenges Low competitiveness of EU economy Failure of the Lisbon Strategy (both in the EU and Poland) New Europe 2020 strategy EU-level target of investing 3% of GDP in R&D For Poland the target is 1,7% of GDP in R&D : 0,9% of GDP in R&D If the goal is achievable, how and where more resources should be invested? Equal growth in all regions? Investments only in most developed regions? Investments only in underdeveloped regions? Invest in people or scientific infrastructure? How to finance the policy (taxes, redistribution)?

16 Low / very low innovativeness of Polish regions Regional Innovation Scoreboard 2014

17 R&D expenditures as % of GDP: Polnad is far below of the EU15 average ,5 2,0 1,5 1,0 0,5 PL EU15 EU10 0,0

18 Employment in R&D as % of population: Poland is far below of the EU15 average ,8 0,7 0,6 0,5 0,4 0,3 0,2 0,1 PL EU15 EU10 0,0

19 Data challenges: - National IO tables - Ragional data - Sectoral data for disagregation - Data for dynamics - Data for trade

20 Data challenges Only national IO tables (SIOT, Make and Use) available The latest release as of 2010 but in ESA 95 methodology Regional data is in ESA 2010 so regional accounts are not consistent with IO tables Data for modelling Common Agricultural Policy require regional accounts for agriculture: e.g. FADN, CAPRI model database Data for R&D Data for nuclear power station: many unknown for construction phase (where the uranium and knowhow will come from) and for operational phase (which share of the market it will take replacement of the closed coal-mines)?

21 CAPRI database for NUTS2 agriculture 61 Agricultural sectors at NUTS2 level 1 Cereals 21 VegPermcr 41 Heiffatlowe 2 Softwheat 22 Tomatoes 42 Macattlehiwe 3 RyeandMeslin 23 OthVegetab 43 Madulcatlowe 4 Barley 24 AppPearPea 44 Raisimcalves 5 Oats 25 OtherFruits 45 Raisifcalves 6 GrainMaize 26 Flowers 46 Fatmacalves 7 Othercereals 27 Fodderactivi 47 Fatfecalves 8 Oilseeds 28 Foddermaize 48 Beefmeat 9 Rape 29 Fodrootcro 49 OthCows 10 Sunflower 30 Foddotharab 50 Mcathiwe 11 Soya 31 Grassgrasing 51 Mcatlowwe 12 Otheroils 32 Grasgrazinte 52 Heiferfathi 13 Otherara 33 Setasidefall 53 Heiferfatlow 14 Pulses 34 Fallowland 54 Otheranimals 15 Potatoes 35 Allcattleact 55 Pork 16 SugarBeet 36 DCowhighy 56 PigBreeding 17 Flaxandhemp 37 DCowlowy 57 MilkEwGoat 18 Tobacco 38 OtherCows 58 SheepandGoat 19 Oindlcrop 39 Heiferbreed 59 Layinghens 20 Othercrops 40 Heiffathiwe 60 Poultryfat 61 Otheranimal

22 POL-TERM model challenges: - Modelling soft measures: investments in human capital (training, advisory, etc.) - Modelling endogenous growth linked to R&D. - Differences by sectors? - Nuclear power station: modelling of the sector which does not exist (nuclear energy sector) and is very specific (knowhow and uranium will be imported) - Modelling investments phase (which will last 20 years) and the operational phase

23 Possible solutions in order to meet the challengs In case of CAP: Grouping the measures or model one measure at a time Using systems of models - more detailed (e.g. partial equilibrium) models in order to formulate simulations as an input to CGE Need for detailed modelling of land and production links in order to take into account (de)couppling Example how in practice model the CAP

24 Example of modelling the CAP in POL-TERM The research was carried out for the Polish Ministry of Agriculture The main goal was to find out which distriburtion of funds within Rural Development Program is potentially the most beneficial for development of Poland and its individual regions. The RDP measures were classified into 4 priorities : competitiveness (11 measures), environment (4 measures), quality of life (4 measures), LEADER bottom-up programme (3 measures) Regional CGE model POLTERM was used for the scenario simulations and analysis of the results.

25 1. Competitiveness 2. Environment 3. Quality of life 4. LEADER Initial distrbution of Rural Development Measures Approximately 53% of the budget is allocated to (1) competitiveness ( 13, million); 22% to (2) environment ( 5, million), 20% to (3) quality of life ( 4, million) and 5% to (4) LEADER ( million), with 1% available to fund technical assistance ( million) From EU National contributions Private Prevailing character of the measures: 1. Competitiveness: direct income payments, investments in human and physical capital 2. Environment: land subsidies 3. Quality of life: large scale investments in physical capital 4. LEADER: mainly soft measures

26 Regional distribution of funds by NUTS2 regions Zach.pomorskie 57% 43% Lubuskie 52% 48% PB DP PROW RDP Dolnośląskie Pomorskie mln PLN 56% % 58% Kuj.-Pomorskie 60% 57% 43% Wielkopolskie 58% Opolskie 42% 64% 36% 40% Łódzkie 52% Śląskie 47% 48% Świętokr 46% zyskie 54% 53% 42% Warm.-Mazurskie 58% Małopolskie 42% Mazowieckie 52% 48% 56% 44% Podkarpackie 44% 56% Podlaskie Lubelskie 53% 47% DP = direct payments to farmers (Pillar 1) RDP = rural development programme (Pillar 2)

27 Policy scenarios for Rural Development Policy (RDP) Limits of funds based on Council Regulation (EC) No 1698/2005 Scenario 1: allocated 75% of RDP funds into environment,10% for competitiveness, 10% for quality of life in rural areas and 5% for LEADER. Scenario 2: allocated 60% of RDP budget to support competitiveness, 25% environment, 10% quality of life and 5% LEADER. Scenario 3: allocated 60% of RDP funds into quality of life, 10% for competitiveness, 25% for environment, 5% into LEADER.

28 POLTERM a bottom-up multi-regional model of Poland POLTERM is an implementation of the TERM model (Horridge et al. 2005) to the Polish economy. It is described in details in the recently published paper: A bottom-up multi-regional comparative static CGE model that explicitly captures the behaviour of industries, households, investors, government and exporters at the regional level. Producers in each region are assumed to minimize production costs subject to industry-specific production technologies. A representative household in each region purchases goods in order to obtain the optimal bundle in accordance with its preferences and disposable income.

29 POLTERM application to CAP In the short-run, investors allocate new units of capital to regional industries on the basis of expected rates of return. Long-run capital supply to each regional industry is elastic at given rates of return. Commodity-specific export demands for each region are modelled via constant elasticity demand functions. POLTERM differs from the standard TERM template in terms of extended land supply function X 1,r LAND X N,r LAND CES CES X 1,r LFA X 1,r non-lfa X N,r LFA X N,r non-lfa CET CET X r LFA X r non-lfa The simulations are based on standard long run closure

30 DATA sources Make and use tables of 2005 national versions from the Polish Statistical Office and own regionalisation, based on regional accounts from the regional Polish Statistical Offices 16 regions (NUTS2) and 88 sectors Regional distribution of funding for the measures based on data from the Polish Ministry of Agriculture for Interregional trade based on a gravity rule Elasticities calculated and compared with other models (LEITAP, CAPRI, etc.)

31 Results: which budget allocations is the most benefitial for the developoment of Poland? NatMacro Max Environment Max Competitiveness Max Quality of life 1 RealHou 1,68 1,81 1,83 2 RealInv 1,28 1,29 1,40 3 RealGov 1,96 2,1 2,09 4 ExpVol -1,54-1,66-1,54 5 ImpVolUsed 0,66 0,73 0,72 6 ImpsLanded 0,67 0,73 0,73 7 RealGDP 0,88 0,92 1,00 8 AggEmploy 0,23 0,23 0,25 9 realwage_io 2,28 2,29 2,52 10 plab_io 2,12 2,17 2,26 11 AggCapStock 1,38 1,45 1,58 12 GDPPI 0,2 0,22 0,13 13 CPI -0,16-0,12-0,25 14 ExportPI 0,39 0,42 0,39 15 ImpsLandedPI 0,00 0,00 0,00 16 Population 0,00 0,00 0,00 17 NomHou 1,52 1,7 1,58 18 NomGDP 1,08 1,14 1,12

32 Dolnośląskie Kujawsko-pomorskie Lubelskie Lubuskie Łódzkie Małopolskie Mazowieckie Opolskie Podkarpackie Podlaskie Pomorskie Śląskie Świętokrzyskie Warmińsko-mazurskie Wielkopolskie Zachodniopomorskie Regional Results Which budget allocations are the most benefitial for particular regions? 7,00 6,00 5,00 4,00 3,00 2,00 1,00 0,00-1,00 max environment max competitiveness max quality of life

33 Regional Results Which budget allocations are the most benefitial for particular regions? Regional GDP (% change) due to 3 scenarios max environment Zachodniopomorskie Wielkopolskie Warmińsko-mazurskie Świętokrzyskie Dolnośląskie 7,00 6,00 5,00 4,00 3,00 2,00 1,00 0,00-1,00 Kujawsko-pomorskie Lubelskie Lubuskie Łódzkie max competitiveness max quality of life Śląskie Małopolskie Pomorskie Mazowieckie Podlaskie Podkarpackie Opolskie

34 Conclusions concering the example The more effective support for rural areas is in form of large scale investments, which prevail in priority called quality of life in rural areas However, the regional results differ by scenarios. The highest growth due to max of environment is in regions abundant in land with high nature values - Podlaskie, Warmińsko- Mazurskie and Zachodniopomorskie; The highest growth due to max of competitiveness is in regions which are either predominantly agricultural (Lubelskie, Łódzkie) or in regions with large and effective farms (Wielkopolskie and Kujawsko-Pomorskie) The highest growth due to max of quality of life is in regions lacking basic infrastructure in rural areas - Podkarpackie, Małopolskie, Świętokrzyskie. For the future allocation of the RDP funds it is crucial to have measures which represent a proper mix of economic

35 Questions on modelling in POL-TERM: Modelling nuclear power station: How to prepare the database: include new sector (nuclear energy) into initial database by desagregation of e.g. electricity sector? Investment phase: how to tackle the pecificity of this sector: highly qualified staff, foreign know-how and imports of uranium, etc. Operationa phase: how to deal with so many unknown in 20 years from now? Modelling CAP: How to model soft measures of CAP: investments in human capital, technical assistance, trainings, etc. in POL-TERM? Modelling R&D spending: How to tackle spill-over effects of R&D sector?

36 Thank you and welcome to IRWiR Polish Academy of Sciences