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Workshop on “Assessing the Potential Impact of the Belt and Road Initiative
on SDGs in Asian Countries”
Bangkok, Thailand
25-26 September 2019
Namsuk Kim
UN DESA
Session 2
Expanded World Economic Forecasting Model (WEFM-e) by DESA
Outline
1. DESA project on BRI and SDGs
2. Modelling tool methodology
Belt and Road Initiative
Infrastructure gap (1)
Availability of Transport Infrastructures
Infrastructure gap (2)
Quality of Transport InfrastructureQuality of Transport Infrastructures
Source: World Economic Forum Global Competitive Index 2017
Six major Economic Corridors (ECs)
• China-Indochina Peninsular ECo China-Lao EC (2017)o Lancang-Mekong Cooperation (2016)
• Bangladesh-China-India-Myanmar EC• China-Pakistan EC• New Eurasian Land Bridge• China-Mongolia-Russia EC• China-Central Asia-West Asia EC
Latest development of BRI
• 126 countries signed up (as of 20 April 2019)• Financial commitments – over one trillion
committed • Trade – over six trillion USD in 2013-2018 btw
China and other BRI countries• Investment – over 90 billion USD by China in
2013-2018; 82 overseas economic and trade zones, near 300,000 new jobs created
• Infrastructure - extensive development within and across borders
9
BRI projects are directly contributing to several UN SDGs and indirectly linked to many more
➢ Goal 1 – eliminating poverty: Thailand aims to become a high-income economy by 2036, under the 20-year National Strategy Framework
➢ Goal 9 – infrastructure: under the 20-year Transportation Development Strategy, 2017-36, the Thai government aims to provide effective, green and safe, inclusive and innovative transport for all, and increase connectivity of the 5 Special Economic Zones along the border
➢ Goal 10 – reduced inequalities: affordable public transport could help to reduce inequalities, particularly public service access inequalities
➢ Goal 8 – decent work and economic growth: increased transport connectivity could enable more efficient matching in Thai labourmarkets
Potential contributions to SDGs if managed well– synergies and trade-offs
• Infrastructure (energy, water, etc.)
• Income growth
• Job creation
• Poverty reduction
• Inequality
• Quality of education
• Health
• Environment
UN DESA project countries (2018-2020)
• Azerbaijan
• Bangladesh
• Cambodia
• Czechia
• Georgia
• Kazakhstan
• Kyrgyzstan
• Lao PDR
• Mongolia
• Myanmar
• Romania
• Serbia
• Sri Lanka
• Thailand
Lessons learnt so far (1) – limited data
• BRI was found to be not well understood in many participating countries.
• Lack of available information and evidence for its impact especially on SDGs.
• Policy formulation related to BRI and its activities are mainly driven by the Chinese counterparts given the weak capacity in many participating countries.
• Data collection, progress monitoring and evaluation on the impact of the BRI activities on SDGs would be crucial to inform and present possible options to policymakers to exploit the benefits while mitigating the risks to ensure the achievement of SDGs by 2030.
• Special attention should be paid to the spillover effects and trade-offs on the SDGs at the national level and on local communities.
Lessons learnt so far (2) – inclusive development
• Chinese investments in “connective infrastructure” produce positive economic spillovers that flatten the spatial distribution of economic activity and reduce regional inequality.
• Studies also demonstrate that Chinese development projects could produce a number of negative externalities, including possible displacement of local and marginalized communities, local corruption, environmental degradation, and low-level local community participation.
• BRI should both stimulate industries that channels more trade with China and allow for the diversification of local economy.
• Job creation should be accompanied by training and professional development to sustain the impacts.
• The role of environmental and social safeguards for infrastructure projects is pivotal for securing the overall vision of BRI.
Lessons learnt so far (3) – sustainable financing
Modelling tool and methodology
•Expanded World Economic Forecasting Model (WEFM-e)
•Asses impact of BRI on economic development of selected countries
•Simulate impact of investments in Infrastructure on income growth, labor market, fiscal sustainability, poverty reduction and so on
World Economic Forecasting Model
•Started linking country models in 1970s (LINK).
•Since 2005, integrated modelling tool covering 176 countries.
•Multi-country forecasting model
•Error-correction principle
•Supply, Demand, Monetary sides
UN World Economic Forecasting Model
Potential output
Labor supply
Exogenous total factor productivity
Export growth
Labor participation
PopulationOutput
Government consumption
Investment
Personal consumption
Exports and imports
Output gap
Inflation
Exchange rate against USD
US inflation
Shares on output
Interest rates
Output per capita
Production side
• Endogenous growth models extended for government services in the form of infrastructure investment postulate:• Infrastructure investment …
• Increase marginal product of private investment
• Raise level of education
• … and combined with higher government expenditures on education
• Raise growth of the total factor productivity
• Agénor (2011) uses following form of production function
𝑌 = 𝐾𝑃𝛼 𝜒𝐸 𝛽𝐾𝐺
1−𝛼−𝛽
Private capitalLevel of education Infrastructure
investment
Capital is presented in form of service flow
WEFM - production side
• Potential output growth depends on Trend growth of the Total Factor Productivity (TFP), change in the labor supply (labor force projection) and growth of exports
• Trend growth of TFP depends on TFP growth itself with an error term guarantying that labor productivity does not systematically deviates from the trend growth of TFP
• TFP growth is kept constant at 2%, 3% or 4% depending on the level of country development
Δ𝑇𝐹𝑃𝑡 = 𝜓Δ𝑇𝐹𝑃𝑡−1 + 1 − 𝜓 Δ𝑇𝐹𝑃𝑡 + (1 − 𝜓)1
3
𝑌𝐸𝑅𝑡−1𝐿𝑁𝑁𝑡−1
𝑌𝐸𝑅𝑡−4𝐿𝑁𝑁𝑡−4
− Δ𝑇𝐹𝑃𝑡−1
Δ𝑌𝐹𝑇𝑡 = 𝛼[Δ𝐿𝐹𝑁𝑡 + Δ𝑇𝐹𝑃𝑡] + 1 − 𝛼 Δ𝑋𝑇𝑅𝑡
WEFM-e: production side
• TFP growth depends on level of private investment, level of infrastructure investment and level of education
• Following Hong and Li (2017) a moving average is used in order to get smooth results, while splitting the investment in private and government
Δ𝑇𝐹𝑃𝑡 = 𝐴[1
5𝑋𝑡 + 𝑋𝑡−1 + 𝑋𝑡−2 + 𝑋𝑡−3 + 𝑋𝑡−4 ] − 𝐵
𝑋𝑡 = Τ𝑃𝑡𝐼𝑇𝑅 𝑌𝐸𝑅𝑡 Τ+ 1.04 ∗ 𝐺𝑡
𝐼𝑇𝑅 𝑌𝐸𝑅𝑡𝑋𝑡 = Τ𝐼𝑇𝑅𝑡 𝑌𝐸𝑅𝑡
Hong and Li (2017) Extended version with extra elasticity taken from Fedderke and Bogetic(2009)
Δ𝑇𝐹𝑃𝑡 = 𝑐1𝑅&𝐷𝑡
𝑌𝐸𝑅𝑡𝐿𝑁𝑁𝑡
, 3 + 𝑐2 𝑆𝐾𝑅𝐴𝑇𝑡,3 + 𝑐3 𝑁𝑋𝑡, 2 + 𝑐4 𝐺𝐼𝑁𝐼𝑡, 1 + 𝑐5 𝐼𝑇𝑅𝑡𝐼𝐹𝑅 , 2 + 𝐸𝐶𝑀𝑡−1
Total investment
GDP
Government investment
WEFM-e: production side
• TFP growth depends on level of private investment, level of infrastructure investment and level of education
Extra elasticity taken from Fedderkeand Bogetic (2009)
Private investmentGovernment investment
Δ𝑇𝐹𝑃𝑡 = 𝐺𝑡
𝐼𝑇𝑅
𝑌𝐸𝑅𝑡+
𝐺𝑡−1𝐼𝑇𝑅
𝑌𝐸𝑅𝑡−1+
𝐺𝑡−2𝐼𝑇𝑅
𝑌𝐸𝑅𝑡−2 ∗
1
3∗ 1.04 +
𝑃𝑡𝐼𝑇𝑅
𝑌𝐸𝑅𝑡+
𝑃𝑡−1𝐼𝑇𝑅
𝑌𝐸𝑅𝑡−1+
𝑃𝑡−2𝐼𝑇𝑅
𝑌𝐸𝑅𝑡−2 ∗
1
3 ∗ 0.14286
+ 0.07 ∗𝑆𝑀𝑆 + 𝑆𝐻𝑆
𝑆𝐿𝑆
Extra elasticity taken from Miller and Upadhyay (2002) Shares of Medium, High and Low
skilled labor from the data on labor force participation by education
WEFM: labor market
• Key variable is the labor participation rate, which changes in order to avoid long lasting discrepancy between labor productivity and trend growth of TFP
• This helps to keep the model from an explosive path, but means that growing trend TFP lowers labor participation
𝐿𝑅𝑋𝑡 = 𝐿𝑅𝑋𝑡−1 + 201
3
𝑌𝐸𝑅𝑡−1𝐿𝑁𝑁𝑡−1
𝑌𝐸𝑅𝑡−4𝐿𝑁𝑁𝑡−4
− Δ𝑇𝐹𝑃𝑡−1
3-year average change in the labor productivity
WEFM-e: labor market
• Labor participation rate is split in “male” and “female” components
• In line with empirical literature (ADB, 2016) the female participation rate is made dependent on level of education …
• … while the male participation rate is stabilized as in the existing model
• Total participation is defined as 𝐿𝑅𝑋𝑡 = 𝑆𝐹 ∗ 𝐿𝑅𝑋𝑡𝐹 + 𝑆𝑀𝐿𝑅𝑋𝑡
𝑀, where 𝑆𝐹 and 𝑆𝐹 are shares of female and male in the population;
• Growing female participation rate raises the overall labor force and through that the potential output growth
𝐿𝑅𝑋𝑡𝑀 = 𝐿𝑅𝑋𝑡−1
𝑀 + 201
3
𝑌𝐸𝑅𝑡−1𝐿𝑁𝑁𝑡−1
𝑌𝐸𝑅𝑡−4𝐿𝑁𝑁𝑡−4
− Δ𝑇𝐹𝑃𝑡−1
𝐿𝑅𝑋𝑡𝐹 = 𝐿𝑅𝑋𝑡−1
𝐹 + 𝜃 𝑆𝑘𝑖𝑙𝑙_𝑟𝑎𝑡𝑖𝑜𝑡
Female participation rate depends on education level
WEFM: demand side - Consumption
• Growth of personal consumption is determined by growth of real personal disposable income, change in population and shocks in inflation
• Interest rate is not explicitly present
• Expectation channel is not present
• Closing of the output gap, i.e. difference between output (YER) and potential output (YFT) is not guaranteed
]∆ log 𝑃𝐶𝑅𝑡
= 𝜑0 − 𝛽 log 𝑃𝐶𝑅𝑡−1 − log 𝑅𝑃𝐷𝐼𝑡−1 + 𝜑1 ∗ ∆ log 𝑅𝑃𝐷𝐼𝑡 + 1 − 𝜑1 ∆ log 𝑃𝑂𝑃𝑡 + 𝛿1[𝐼𝑁𝐹𝐿𝑡 − 𝐼𝑁𝐹𝐿𝑡−1𝑒
WEFM-e: demand side - Consumption
• Adding real interest rate and expectation channel trough difference between expected output and potential output• Expectation of stronger potential output growth leads to higher consumption growth today
• Standard interest rate channel – equilibrium real interest rate is approximated by a moving average
∆ log(𝑃𝐶𝑅𝑡) = 𝜑0 − 𝛽1[log(𝑃𝐶𝑅𝑡−1) − log(𝑅𝑃𝐷𝐼𝑡−1)] + 𝜑1 ∗ ∆ log(𝑅𝑃𝐷𝐼𝑡−1) + (1 − 𝜑1)∆ log(𝑃𝑂𝑃𝑡)
+ 𝛽[𝑌𝐹𝑇𝑡+1 − 𝑌𝐸𝑅𝑡+1] + 𝛿1[𝑅𝐼𝑅𝑡 − 𝑀𝐴(𝑅𝐼𝑅𝑡 , 5)]
Expectation about future growth
Real interest rate
WEFM: demand side - Investment
• Growth of investment is determined by discrepancy between level of investment and the gross domestic income (GDP adjusted by term-of-trade), and growth of personal consumption, government consumption and exports.
• Interest rate channel is missing
Δ𝐼𝑇𝑅𝑡 = 𝜒0 − 𝛽2[𝐼𝑇𝑅𝑡−1 − 𝐺𝐷𝐼𝑡−1] + 𝜒1Δ[𝑃𝐶𝑅𝑡 + 𝐺𝐶𝑅𝑡 +𝑋𝑇𝑅𝑡
𝑁
𝑌𝐸𝐷𝑡] + 𝜒2Δ𝐼𝑇𝑅𝑡−1
WEFM-e: demand side - Private investment
• Investment is split in private and government investment• Private investment follow the same patter as total investment in existing version of the
model
• Impact of real interest rate is added to simulate eventual crowding-out effect of unsustainable fiscal policy
Δ𝑃𝑡𝐼𝑇𝑅 = 𝜒0 − 𝛽[𝐼𝑇𝑅𝑡−1 − 𝛼𝐺𝐷𝐼𝑡−1] + 𝜒1Δ 𝑃𝐶𝑅𝑡 + 𝐺𝐶𝑅𝑡 +
𝑋𝑇𝑅𝑡𝑁
𝑌𝐸𝐷𝑡 + 𝜒2Δ𝑃𝑡−1
𝐼𝑇𝑅 + 𝛿2[𝑅𝐼𝑅𝑡
− 𝑀𝐴(𝑅𝐼𝑅𝑡 , 5)] Real interest rate
WEFM-e: demand side - Government investment
• Government investment is critical – represents most of infrastructure investment• Modeled in proportion to government revenues to ensure sustainability
• For practical simulations the data on government investment will be probably taken as exogenous
Growth of government investment converges to the growth of government revenues
Δ𝐺𝑡𝐼𝑇𝑅 =∋1 Δ𝐺𝑡−1
𝐼𝑇𝑅 + (1 −∋1)(Δ𝐺𝐺𝑅𝑡 − Δ𝑌𝐸𝐷𝑡) +∋2 [𝐺𝑡−1𝐼𝑇𝑅 − 𝜎(𝐺𝐺𝑅𝑡−1/𝑌𝐸𝐷𝑡−1)]
Level of government investment converges to chosen share on government revenues
WEFM: government budget deficit and debt
• Government consumption is related to weighted average of potential output and gross domestic income …
• … and government revenues to nominal GDP and nominal exports
• Budget deficit is difference between revenues on one side and government consumption and other expenditures on the other
Δ𝐺𝐶𝑅𝑡 = 𝜙1Δ𝑌𝐹𝑇𝑡 + (1 − 𝜙1)Δ𝐺𝐷𝐼𝑡
Δ𝐺𝐺𝑅𝑡 = 𝜙2Δ𝑌𝐸𝑁𝑡 + (1 − 𝜙2)Δ𝑋𝑇𝑁𝑡
𝐺𝐿𝑁𝑡 = 𝐺𝐺𝑅𝑡 − 𝐺𝐶𝑅𝑡 ∗ 𝑌𝐸𝐷𝑡 − 𝐺𝑡𝑂𝑇𝐻
Δ𝐺𝑡𝑂𝑇𝐻 = Δ𝑌𝐸𝑁𝑡
Budget deficit
Other government expenditures grow at the rate of nominal GDP
WEFM: government budget deficit and debt
• Government consumption is related to weighted average of potential output and gross domestic income …
• … and government revenues to nominal GDP and nominal exports
• Budget deficit is difference between revenues on one side and government consumption and other expenditures on the other
Δ𝐺𝐶𝑅𝑡 = 𝜙1Δ𝑌𝐹𝑇𝑡 + (1 − 𝜙1)Δ𝐺𝐷𝐼𝑡
Δ𝐺𝐺𝑅𝑡 = 𝜙2Δ𝑌𝐸𝑁𝑡 + (1 − 𝜙2)Δ𝑋𝑇𝑁𝑡
𝐺𝐿𝑁𝑡 = 𝐺𝐺𝑅𝑡 − 𝐺𝐶𝑅𝑡 ∗ 𝑌𝐸𝐷𝑡 − 𝐺𝑡𝑂𝑇𝐻
Δ𝐺𝑡𝑂𝑇𝐻 = Δ𝑌𝐸𝑁𝑡
Budget deficit
Other government expenditures grow at the rate of nominal GDP
WEFM-e: government budget deficit and debt• Other government expenditures are redefined as a combination of government
investment and interest payments on existing debt
• Interest payments depend on previous period government debt and interest rates
𝐺𝑡𝐼𝑅𝑃 = 𝐺𝐷𝑁𝑡−1 ∗ 𝑆𝑇𝐼𝑡−1
• Budget deficit suddenly depends on interest rates and same applies for the debt
𝐺𝑡𝑂𝑇𝐻 = (𝐺𝑡
𝐼𝑇𝑅 ∗ 𝑌𝐸𝐷𝑡) + 𝐺𝑡𝐼𝑅𝑃 Other
expenditures in nominal terms
Interest payments in nominal terms
Government investment in real terms
Deflator
𝐺𝐷𝑁𝑡 = 𝐺𝐷𝑁𝑡−1 − 𝐺𝐿𝑁𝑡 Accumulation of government debt
Budget deficit is function of interest rates (among others)
WEFM: monetary policy
• Monetary policy block is for majority of countries reduced to a relative version of purchasing power parity (PPP) equation that determines the exchange rate depending on inflation differential
• That works for all countries with fixed or managed exchange rate• Majority of countries in the world despite of official rhetoric
𝐸𝑋𝑅𝑡 = 𝐸𝑋𝑅𝑡−1 ∗ (𝜋𝑡 − 𝜋𝑡
𝑈𝑆𝐴)
100+ 1 Real exchange rate is
assumed to be constant implicitly
WEFM-e: monetary policy
• It allows to introduce a reverse version of the uncovered interest rate parity (UIP) equation and determine domestic nominal interest rate suing US rates and expected change in the exchange rate
• Risk premium depends on government debt𝑆𝑇𝐼𝑡 = 𝑆𝑇𝐼𝑇
𝑈𝑆𝐴 + 𝐸𝑋𝑅𝑡+1
𝐸𝑋𝑅𝑡− 1 ∗ 100 + 𝑃𝑅𝐸𝑀𝑡
Risk premium
Nominal interest rate
𝑃𝑅𝐸𝑀𝑡 = 𝑃𝑅𝐸𝑀𝑡−1 ∗ 𝜔[(𝐺𝐷𝑁𝑡
𝑌𝐸𝐷𝑡− 𝜖) + 1]
Threshold value for the debt to GDP ratio
Government debt to GDP ratio
WEFM-e: monetary policy
• Real interest rate that enters equations for personal consumption and private investment is calculated using Fisher equation
• And the risk premium enters also the PPP equation to capture the impact of mounting government debt on the exchange rate
Expected inflation
Real interest rate𝑅𝑇𝐼𝑡 = 𝑆𝑇𝐼𝑡 − 𝜋𝑡+1
𝑌𝐸𝐷
𝐸𝑋𝑅𝑡 = 𝐸𝑋𝑅𝑡−1 ∗ (𝜋𝑡 − 𝜋𝑡
𝑈𝑆𝐴)
100+ 1 ∗
𝑃𝑅𝐸𝑀𝑡
100+ 1
WEFM-e: monetary policy block
• In countries with floating exchange rate and well established monetary policy framework and monetary policy operations the nominal interest rate can be determined by an interest rate rule
𝑆𝑇𝐼𝑡 = 𝑐1𝑆𝑇𝐼𝑡−1 + 1 − c1 [𝑀𝐴 𝑅𝐼𝑅𝑡, 5 + 𝜋𝑡+1𝐸 + 𝑐2 𝜋𝑡+1
𝐸 − 𝜋𝑇 + 𝑐3 𝑌𝑡]
Output gap
Approximation of equilibrium real interest rate
Inflation target
WEFM-e: poverty reduction
• We follow the WB’s Long Term Growth Model (LTGM v4.1) developed by Pennings (2018)
• Assumptions:• Gini coefficient – 0.364 (for Laos, according to the latest data from the WB);
• Poverty line, L – 0.8 USD/day (for Laos, according to the latest data from ADB);
• ln(𝑦𝑃𝐶)~𝑁 𝜇, 𝜎2 , where yPC is the income per capita in USD, μ is the mean of income per capita (in log) and σ2 is the variance;
• The poverty headcount rate 𝑃, which is the proportion of people with incomes below the poverty line 𝐿• 𝑃𝑡 = ɸ(
𝑙𝑛𝐿−𝜇𝑡
𝜎𝑡), where ɸ(.) is the standard normal CDF;
• The aim is to calculate 𝑃𝑡+1;
WEFM-e: poverty reduction
• Step 1: standard deviation 𝜎 is calculated using the Gini coefficient
from 𝐺𝑡 = 2ɸ𝜎𝑡
2− 1
• 𝜎𝑡 = 2 ∗ ɸ−1(𝐺𝑡+1
2)
• Based on assumptions about Gini coefficient and the equation above, 𝜎𝑡 equals 0.669 (and is constant over time, as Gini is constant);
• Steep 2: calculate the initial value of 𝜇𝑡: • Based on the assumption of poverty line, L and the initial value of headcount
rate, P (to be 0.234 for Laos, according to the latest WB data), we can calculate initial value of 𝜇𝑡 from:
• 𝜇𝑡 = 𝑙𝑛𝐿 − 𝜎𝑡 ∗ ɸ−1(𝑃𝑡);
• For the initial period of 2010, 𝜇𝑡 = 0.2626.
WEFM-e: poverty reduction
• Step 3: next step is to calculate the future values of 𝜇𝑡:• The economic growth will shift the whole income distribution to the right by
increasing the mean, μ;
• Therefore, growth of the GDP per capita can be written as:
1 + 𝑔𝑦,𝑡+1𝑃𝐶 = Τത𝑦𝑡+1
𝑃𝐶 ത𝑦𝑡𝑃𝐶 =
exp 𝜇𝑡+1 +𝜎𝑡+1
2
2
exp 𝜇𝑡 +𝜎𝑡
2
2
= exp(𝜇𝑡+1 − 𝜇𝑡 +1
2(𝜎𝑡+1
2 − 𝜎𝑡2))
• From this equation we can get the equation for 𝜇𝑡+1:
𝜇𝑡+1 = ln 1 + 𝑔𝑦,𝑡+1𝑃𝐶 + 𝜇𝑡 −
1
2𝜎𝑡+1
2 − 𝜎𝑡2
• Using the approximation rule (1 + 𝑔) ≈ 𝑔 (for small enough 𝑔), the equation above becomes:
𝜇𝑡+1 = 𝑔𝑦𝑃𝐶 + 𝜇𝑡 −
1
2(𝜎𝑡+1
2 − 𝜎𝑡2)
Development – poverty reduction
• Step 4: having the values of 𝜇𝑡+1, we can now calculate the future
values of poverty headcount rate, 𝑃𝑡+1;
• We can use the following equation:
𝑃𝑡+1 = ɸ(𝑙𝑛𝐿 − 𝜇𝑡+1
𝜎)
WEFM-e: Data Issues (1)
• Different measure have been proposed in the literature to measure the
infrastructure investment;
• Commonly used measure – GFCF;
• Not all components of GFCF are related to infrastructure;
• To overcome this problem, the ADB (2017) report proposes three alternative
measure, which (potentially) more accurately estimate infrastructure
investment;
• The description of the ADB (2017) methodology was sent to National
Consultants in November 2018;
WEFM-e: Data Issues (2)
• Examination of the effect of R&D and Human Capital on TFP requires collection
of more data:
• Time series data on R&D expenditure (as a % of GDP) by country can be found in the
following sources:
• The World Bank data;
• The UNESCO database (Science, Technology and Innovation section);
• Note: significant portion of the data might be missing for some countries, therefore, the
National Consultants are encouraged to look for the data in other sources;
WEFM-e: Data Issues (2’)
• The data on Human Capital can be extracted from the International LabourOrganization’s (ILO) database;
• The data on following variables can be collected:• Labour force participation rate by sex, age and education;
• Labour force participation rate by sex and age – ILO modelled estimates, July 2017;
• Employment distribution by education (by sex and age);
• Employment-to-population ratio by sex and age;
• Unemployment rate by sex, age and education;
• Unemployment rate by sex and age – ILO modelled estimates, May 2018;
• Unemployment distribution by education (by sex and age);
• The ILO database give an option to choose these specific variables with a desired time range.
Changes in WEFM-e
•Create simplified (reduced-form) version of complex non-linear relationships for• infrastructure investment and education level in
production• labor participation based on GDP per capita and
education level• Poverty reduction• Consumption and investment behavior in relation to
expected growth and the real interest rate• Government debt accumulation and its impact on the
country risk premium, real interest rate and the exchange rate with the back loop in private consumption and investment
Future work on WEFM-e
•Data
•Calibrations
•Region-integrated simulations
+ Qualitative studies
Namsuk [email protected]
Session 3Thailand
Simulations for Thailand scenario
•Official BRI related investment data is currently unavailable
•Hypothetical $10 billion over five years (2015 –2019)
•Additional investment accompanied by fast growing private investment leads to a faster investment growth in the modified model
-3%
0%
3%
6%
9%
12%
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
Investment growth
Original Modified
2%
3%
4%
5%2
01
2
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
Trend productivity growth
Original Modified
0%
2%
4%
6%
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
Potential output growth
Original Modified
0%
3%
6%
9%
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
GDP growth
Original Modified
-2
0
2
4
62
01
22
01
32
01
42
01
52
01
62
01
72
01
82
01
92
02
02
02
12
02
22
02
3
Output gap
Original Modified
10
25
40
2012 2014 2016 2018 2020 2022
Deb-to-GDP ratio
Original Modified
-2
0
2
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
Budget Balance
Original Modified
-2
0
2
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
RIR
5%
8%
11%
14%2
01
2
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
Poverty headcount rate
30
45
60
752
01
2
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
Labor force participation
Modified_Female Modified_Male
Original Modified
250000
400000
550000
700000
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
CO2 emission (kt)
Simulation for Thailand summary
• Faster TFP growth, trend productivity growth (SDG 4,8,17)
• Higher level of government expenditures
• debt-to-GDP ratio higher than original (SDG 17)
• poverty headcount rate drops from 15% in 2015 to 7% in 2023 (SDG 1, 10)
• Labor market participation was steadily declining in Thailand. overall labor force participation increases in the modified model (SDG 8)
• Gender gap in labor force decreases (SDG 5, 8)
• High risk of environmental side effects from growth (SDG 13)
Session 4
Cambodia, Lao PDR, Myanmar
Simulations for Lao PDR scenario
•Historical trend of Gov investment – 8% of GDP (2000-2015)
•BRI investment – after MOU with China, committed investment plan (2017-2021), above 8%
•After 2021, back to historical trend
•Medium period projection until 2023
-15%
0%
15%
30%2
01
2
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
Investment growth
Original Modified
5%
7%
9%2
01
2
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
Potential output growth
Original Modified
2%
5%
8%
11%2
01
2
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
GDP growth
Original Modified
3%
5%
7%2
01
2
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
Trend productivity growth
Original Modified
-2
2
6
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
Output gap
Original Modified
-10
-5
02
01
2
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
Budget Balance
Original Modified
50
60
702
01
2
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
Debt-to-GDP ratio
Original Modified
-4
0
4
8
122
01
2
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
RIR
0%
4%
8%
12%
16%2
01
2
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
Poverty Headcount Rate
70
75
802
01
2
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
Labor force participation
Modified_Female Modified_Male
Original Modified
1000
2000
3000
40002
01
2
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
CO2 emission (kt)
Simulations for Lao PDR summary
• Increases in labor force participation and jobs (SDG 8)
• Decreases the gender gap (SDG 5, 8)
• Boosts the productivity growth (SDG 4, 8, 17)
• Leads to a faster output growth (SDG 8)
• Reduces poverty level (SDG 1, 10)
• Has measured impacts of partnerships and debt sustainability (SDG 17)
Simulations for Cambodia scenario
• Official BRI data not available
• Hypothetical $10 billion investment in 5 years (2015-2019)
• Investment growth jumps in 2015 and drops after the end of the projects. Beyond these years, investment growth closely follows the original path
• Government and private investments grows, on average, 11% and 9%, respectively. High growth of government investment feeds infrastructure investments, which grows steadily at about 8% after 2020
-20%
0%
20%
40%
60%2
01
2
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
Investment growth
Original Modified
2%
4%
6%
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
Trend productivity growth
Original Modified
5%
7%
9%2
01
2
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
Potential output growth
Original Modified
0%
7%
14%
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
GDP growth
Original Modified
-5
0
5
10
15
202
01
2
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
Output gap
Original Modified
0
20
40
602
01
2
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
Debt-to-GDP ratio
Original Modified
-9
-6
-3
02
01
2
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
Budget Balance
Original Modified
0
3
6
9
122
01
2
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
RIR
0%
4%
8%
12%2
01
2
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
Poverty headcount rate
30
50
70
902
01
2
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
Labor force participation
Modified_Female Modified_Male
Original Modified
5000
6000
7000
80002
01
2
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
CO2 emission (kt)
Simulations for Cambodia summary
• higher TFP growth, faster growth in trend productivity. By the end of the forecasted horizon, trend productivity also decelerates. Consequently, potential output growth is also faster initially and decelerates at the end of the forecasted horizon
• Output gap opens up and stays positive until the end of the BRI investment inflows in 2019.
• Acceleration of the GDP growth lowers the budget deficit and debt to GDP ratio declines initially. Later, budget deficit widens and debt to GDP ratio raises again and in the long run. On the other hand, budget deficit widens further and reaches about 9% of GDP in the end of the forecasted horizon.
• As the premium declines because of declining debt to GDP ratio, RIR also declines initially. However, increased borrowing raises the premium again and as a result RIR also rises.
• Poverty headcount rate drops from 7% in 2015 to just above 1% in 2023
• Very minor change in the path of the labor force participation
• CO2 emission also declines until 2021. Afterwards, as the economy recovers CO2 emission again starts to increase, though it does not reaches 2017 level even in the long run. Highly correlated with the economic activity
Simulations for Myanmar scenario
•Official BRI data not available
•Hypothetical $10 billion investment in 5 years (2015-2019)
• fast growth of private and government investment with both growing at around 10% until the end of the forecasted horizon
0%
5%
10%
15%2
01
2
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
Investment growth
Original Modified
3%
5%
7%
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
Trend productivity growth
Original Modified
5%
7%
9%2
01
2
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
Potential output growth
Original Modified
5%
8%
11%
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
GDP growth
Original Modified
-4
0
4
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
Output gap
Original Modified
20
55
90
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
Debt-to-GDP ratio
Original Modified
-20
-10
0
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
Budget Balance
Original Modified
0
4
8
122
01
2
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
RIR
20%
30%
40%2
01
2
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
Poverty Headcount rate
203040506070
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
Labor force participation
Modified_Female Modified_Male
Original Modified
10000
15000
20000
250002
01
2
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
CO2 emission (kt)
Simulations for Myanmar summary
• faster TFP growth, faster growth of trend productivity, faster potential output growth
• GDP grows faster during the investment, and recover to higher rate after the investment ends.
• Output gaps hikes, and approaches to the original forecasting path.
• Debt is explosive, due to the assumption of no remedial policy by the Government.
• country risk premium also raises sharply, which leads real interest rate to grow
• Poverty headcount rate increases initially, however, once the BRI investment comes in force and drives up the GDP, headcount rate falls steadily in subsequent periods. From 2015 poverty headcount rate falls by approximately 8 pp to 24%
Session 5Bangladesh, Sri Lanka
Simulations for Bangladesh scenario
•No official BRI data available
• “proposed” 23 USD billion worth of projects throughout 5 years (2016-2020)
•Government and private investment follows fairly stable growth path.
0%
4%
8%
12%2
01
2
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
Investment growth
Original Modified
3%
5%
7%
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
Trend productivity growth
Original Modified
4%
6%
8%
10%2
01
2
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
Potential output growth
Original Modified
3%
6%
9%2
01
2
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
GDP growth
Original Modified
-9
-6
-3
0
3
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
Output gap
Original Modified
30
35
40
452
01
2
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
Debt-to-GDP ratio
Original Modified
-8
-4
02
01
2
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
Budget Balance
Original Modified
-3
0
3
6
9
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
RIR
0%
15%
30%
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
Poverty Headcount rate
0
20
40
60
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
Labor force participation
Modified_Female Modified_Male Modified Original
60000
80000
100000
CO2 emission (kt)
Simulations for Bangladesh summary
• Productivity growth and potential output growth until 2020, then approaches the original path.
• BRI investment inflows causes GDP to grow faster. In 2020, GDP growth decelerates and even drops below the original growth level. The economy recovers afterwards and GDP growth exceeds the original path in the long run.
• Fiscal deficit and debt rises. country risk premium also raises, which leads real interest rate to grow
• poverty headcount rate falls steadily throughout forecasted horizon and reaches about 8% in 2023 (down from 23.5% in 2015)
• in the absence of the environmental policies, harm to the environment from improved economic activity can be substantial
Session 6
Mongolia, Central Asia, Georgia
Simulations for Kyrgyzstan scenario
• total BRI related investment amounts to 1.3 USD billion and is spread across 7 years, from 2012 to 2018
• Investment growth is historically very volatile in Kyrgyzstan
•Medium period projection until 2023
0%
15%
30%
Investment growth
Original Modified
-5%
15%
35%
Investment growth (Modified Model)
Infrastructure Inv. Private Gvt
2%
3%
4%
5%
Trend productivity growth
Original Modified
2%
6%
10%Potential output growth
Original Modified
0%
5%
10%
GDP growth
Original Modified
30
40
50
60
70
2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
Labor force participation
Modified_Female Modified_Male
Original Modified
15%17%19%21%23%25%27%29%31%33%
Poverty Headcount Rate
-9
-6
-3
0
Budget deficit
Original Modified
30
40
50
60
70
2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
Debt-to-GDP ratio
Original Modified
8000
9000
10000
11000
12000
CO2 emission (kt)
Simulations for Kyrgyzstan summary
• Increases in labor force participation and jobs (SDG 8)
• Decreases the gender gap (SDG 5, 8)
• Boosts the productivity growth (SDG 4, 8, 17)
• Leads to a faster output growth (SDG 8)
• Reduces poverty level (SDG 1, 10)
• Gov deficit and debt managable (SDG 17)
• Overall, the magnitude of impacts is small, due to limited BRI investment.
Simulations for Kazakhstan scenario
•BRI related investments amounted $4.4 billion during 2012 – 2020.
•Medium period projection until 2023
0%
3%
6%
9%2
01
2
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
Investment growth
Original Modified
-5%
0%
5%
10%
15%2
01
2
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
Investment growth (Modified Model)
Infrastructure Inv Private Gvt
1%
3%
5%
2012 2014 2016 2018 2020 2022
Trend productivity growth
Original Modified
1%
4%
7%2
01
2
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
Potential output growth
Original Modified
0%
3%
6%
9%2
01
2
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
GDP growth
Original Modified
40
50
60
702
01
2
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
Labor force participation
Modified_Female Modified_Male
Modified Original
-8
-4
0
4
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
Fiscal balance
Original Modified
0
10
20
302
01
2
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
Deb-to-GDP ratio
Original Modified
220000
240000
260000
280000
300000
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
CO2 emission (kt)
Simulations for Kazakhstan summary
• Limited impact in labor force participation and jobs (SDG 8)
• Limited impact on decreasing the gender gap (SDG 5, 8)
• Boosts the productivity growth (SDG 4, 8, 17)
• Leads to a faster output growth (SDG 8)
• Gov deficit and debt manageable (SDG 17)
• Overall, the positibe impacts on economic growth, cautions on environment and social indicators.
Session 7DESA project implementation plan
DESA project implementation plan
• Online access to simulation scenarios at www.brisdgs.org
• Interim report (mid 2019), and final report (mid 2020)
• Regional workshops – Tbilisi (Oct 2019), China (early 2020)
• National workshops in countries – Myanmar, Cambodia, Bangladesh, Sri Lanka … (2019-20).
• In-depth training course on modelling tools including WEFM-e if requested