Evaluating the Impact of Trade Liberalization on Poverty with CGE/Micro-Simulation Models

Evaluating the impact of trade liberalization
on poverty with CGE/Micro-Simulation: a
review of literature and an illustration with
MIRAGE_HH (MIRAGE-Households)

Antoine Bouet
Carmen Estrades
David Laborde
Dakar, December 16th, 2011

Overview
1. Motivations
2. Data
3. Model
4. Illustrative results
5. Next steps

INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

1. MOTIVATIONS


Poverty: across-countries heterogeneity

Poverty headcount ratio at 1.24US$ a day (PPP) in % of pop. - 2007
Source: the World Bank
90

80

70

60

50

40

30

20

10

0

1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next steps

Within country heterogeneity: Rural vs. Urban

Estimates of poverty headcount in urban/rural areas
Source: the World Bank - 2005, 2006 and 2007
80

70

60

50

40 Rural poverty
Urban poverty
30

20

10

0
Cameroon Ecuador Bangladesh


Trade liberalization and Poverty

• Numerous evaluations of the impact of trade
liberalization on poverty
• World Bank GEP 2002 and 2004 (Dominique Van der
Mensbrugghe with the LINKAGE model)
• William Cline 2004 Institute for International Economics,
with the HRT model (Harrison Rutherford Tarr)
• Bernard Decaluwe et al., Laval University, PAUPER
system and the PEP network
• Poverty & the WTO, T.W.Hertel and L.A. Winters
• Ann Harrison, Globalization and Poverty, NBER
• UNECA, Regional Integration and Human Development,
Mohamed Chemingui


Trade liberalization and Poverty

• Analytical framework detailed by Winters et al.
(2003)
• Channels of trade on poverty:
1. Price and availability of goods
2. Factor prices, income and employment
3. Government transfers
4. Incentives for investment and innovation that affects
long term growth
5. External shocks and in particular changes in terms of
trade
6. Short run risks and adjustment costs


How to assess the poverty impact of trade scenarios?

• Hertel and Reimer (2002): distinction between
four methodologies:
• Cross country regression analysis
• Partial equilibrium and /or cost of living approaches
• General equilibrium analysis
• Micro-macro synthesis which links a model with
micro-level data.


Pros and Cons

• Cross country regression analysis
• Cannot offer a counterfactual analysis
• Cannot provide results on the impact of a policy
shock on numerous economic variables.
• Partial equilibrium and /or cost of living approaches
• Income and interdependence effects omitted
• The cost of living analysis focuses on consumption
effects
• General equilibrium analysis
• Micro-macro synthesis which links a model with
micro-level data.


CGE analysis

• CGE analysis are undertaken under
• Unique representative household hypothesis:
• The average income and total income are endogenous
• …while the moments of the distribution are exogenous
• Several representative households hypothesis
• How many representative agents?
• What are the criteria of selection?
• Use of Poverty Elasticities (GEP, 2002 and 2004;
Cline, 2004; UNECA, 2011)
• Mechanical effect of trade liberalization on poverty
• Do not identify who come out and come in poverty


CGE-MS Analysis
• Top-down approach (Decaluwe et al., 2001)
• Idea: combination of theoretical consistency of a CGE
model and the richness of information from a hhlds survey
• Implement few variables (Consumption prices, remuneration
of productive factors) into an household survey
• Advantages:
Modelling of household and labour market behaviour can be
done separately from the economy-wide analysis.
There is no need to reconcile household survey data with
national accounts data.
• Functional forms: Sadoulet/ de Janvry vs. Dervis/de Melo
/Robinson vs. Decaluwe
• No feedback effect: consistency between the micro-
simulation and CGE results.
• If unemployment/employment or informal/formal sectors,
selection problem


CGE-MS Analysis
• Non Parametric approach (Vos and Sanchez,
2010)
• Re-weighting techniques to get micro-macro consistency
• Random selection of individuals
• « Hand of God » criticism
• Evaluation of macroeconomic policy is somewhat arbitrary
• Cannot identify the losers and the winners, … and the accompanying
policies to be put in place
• This method is path dependant. “In other words, it could make a
difference, given the cumulative effects, whether in an assumed
sequence one would first simulate, say, changes in employment by
occupational category (O) rather than by sector of employment (S).”
Vos and Sanchez, 2010.


CGE-MS Analysis

• Behavioral microsimulation (Bourguignon et al., 2003;
Lay, 2010)
• Combination of a CGE model and a behavioral model based
on econometric techniques
• Lay, 2010, CGE model with modeling of both formal and
informal sectors + econometric model with two stages:
probit/logit +OLS
• Very detailed results but
• 1) Theoretical consistency ?
• In the CGE change in behavior comes from changes in relative
prices
• In the behavioral model, it comes from individual
characteristics (education, gender…)
• 2) Validity of the estimation method? need for panel data’
• 3) This method overemphasizes labor supply factors and neglects
labor demand side factors.


CGE-MS Analysis
• Savard (2003): top-down/bottom-up approach
• Micro macro iteration to solve the aggregation error.
• 1 Resolution of the CGE model
• 2 Implementation of macro variables (prices and
employment) in a micro model
• 3 Calculation of new values for revenue variables by the
micro model
• 4 Re injected in the macro model.
• Until convergence…
• But convergence is not guaranteed
• And global procedure very demanding in terms of calculation
time.
• Not feasible in a multi-country CGE


CGE-MS Analysis
• Integrated approach
• Integration of a complete household survey in a CGE model
• Modeling of the behavior of each household in terms of
consumption and factor supply
• Cockburn (2006) 3,800 households in the case of Nepal
• Cockburn, Corong and Cororaton (2008): 24,000 households in
the case of Philippines
• Much more detailed view of how the impacts of trade
liberalization vary over the whole income distribution.
• Results are very sensitive to the choice of the poverty line.
• Very demanding in terms of calculation time
• Needs simplifying assumptions for the CGE
• Not feasible in a multi-country CGE


Developing an integrated framework in a dynamic global
CGE

• Hertel and Winters (2006) combines an evaluation of the
poverty impact of the DDA trade reform (multi country
CGE) with 12 country case studies based on single
country CGEs coupled with microsimulation.
• Results of the multi-country CGE (export and imports
prices/export and import volumes) are implemented in
national CGE/MS analysis
• Consistency issue: reaction of the country is already in
the multi-country CGE
• Different approaches to evaluate poverty impact: are
results comparable?


Developing an integrated framework in a dynamic global
CGE

• World model in order to understand differentiated impact in
various countries
• Integrated model to keep consistency
• Better method than linking heterogeneous CGE models
• Diversity of situations inside each country:
• Assuming a representative agent is very challenging for a micro-founded
approach
• Modeling the behavior of various households in terms of demand (e.g. non
homothetic) and supply (e.g. labor supply, savings)
• Dynamic issues and the role of adjustment costs:
• Inter sectoral mobility
• rural / urban mobility
• Domestic transfers and international remittance
• Savings / investments and liquidity constraint


2. DATA


Framework to build a systematic and flexible treatment
for a global model

• Raw household survey, all information
• Cleaning in STATA, Clustering analysis on relevant
Thousands of
HH dimensions

100-1000 HH
• All information formatted in an Excel workbook.
detailed
categories

• CGE model
• Aggregation should be changed easily (hierarchical
1-100 HH broad
category clustering analysis can guide the latter stage aggregation)


Clustering Analysis

• Tractability
• Household account broken down into a number of relatively homogeneous
household groups reflecting the socioeconomic characteristics
• Decaluwe et al, 1999:
• location (e.g. rural vs. urban);
• asset ownership (particularly land ownership in the rural areas and human capital in
urban areas);
• characteristics of the head or main earner,
• main employment status,
• main occupation,
• main branch of industry and educational attainment,
• gender
• Importance of capturing the household heterogeneity really modeled
(preferences, endowments)
• Clustering analysis taking into account:
• income per capita of the household (in logarithm),
• consumption structure (share of each GTAP product in total consumption)
• and income structure (share of capital, labor, self-employed labor and transfers in total
income of the household).


The intermediate stage: the Excel workbook
• Feed the model/data procedure
• Systematic treatment
• Can be easily filled by external collaborators
(standardized platform)


Households survey in Excel workbook
• Household categories descriptions (from the
clustering analysis), frequency, model mapping
and flags

• Macro targets


Households survey in Excel workbook:
• HH resources

• HH expenditures


Data consistency

• Starting point:
• Household level: national household survey, information on
income by sources and detailed consumption of different goods
and services.
• SAM: GTAP 7
• This information is checked with information from other
sources:
• GDP, GDP per capita and GDP structure
• structure of population
• Aggregated saving rates
• poverty rates
• Automatic procedure, using iterative steps of cross
entropy, to build a dataset consistent with the GTAP
dataset


Data treatment: a standard and automatic procedure

Excel workbook read by GAMS (including sets and mappings). Checks if all
information is properly mapped
GTAP 7 imported (GDX)

Definition of minimal threshold (500 dollars for a household category)
Rules for household expansion coefficients

Treatment of final consumption by household
Correction for trade margins
Cross entropy to adjust expenditures structure to GTAP macro figures. Each
household keeps his share in overall expenditures.

Treatment of household income (production factor)
Retreatment for farm income and dwelling (virtual rental payments)
Cross entropy with different constraints depending on available information.
GTAP Value Added data may be modified.

Tax rate treatment
Factor specific tax rate from GTAP
Mapping of different taxes of the household survey (e.g. property tax)
Computation of overall taxes based on income factor structure
Homogenous Rescaling to maintain GTAP national tax level


Data treatment: a standard and automatic procedure

Transfert treatment and Savings
Received and Paid
Between Households (no bilateral matrix), Government and Rest of
the World
Cross entropy to ensure that domestically Sum paid = Sum received
under constraint of No negative savings (minimal rate of savings of
0.001 of disposable income). This constraint forces to replace
negative savings by intra household transfers.

For each country included in the treatment, a summary report of the changes
and results of cross entropy procedure is generated

Final output: a GDX file with different mappings, disaggregated
GTAP variables at the HH level: CVFM_HH, CFTRV_HH,
CVDPA_HH, CVIPA_HH, CVDPM_HH, CVIPM_HH… and other
indicators: transferts matrix between institutions (Household
categories, Government, Rest of the World)…


Data treatment: an illustration

• For the illustrative results, focus on two countries for
which recent HH survey are available:
• Uruguay: Income and Expenditure Survey (IES) 2005-2006,
carried out by the Statistics National Institute (INE)
• Pakistan: 2005-2006 Social & Living Standards Measurement
Survey, carried out by the Federal Bureau of Statistics of the
government
• Clustering analysis:
• 90 groups of households in Uruguay
• 142 in Pakistan
• Other countries have been processed (e.g. Brazil,
Tanzania, Vietnam).
• Challenges: findings household survey that detailed the
expenditures (preferences) and the income (factor
endowments) sides.



Categorization of households in Uruguay
Size = Percent of total households - Source : INE, 2005/06
5000

Montevideo urban capital income skilled male headed
4000
Mean monthly income (current USD)

Montevideo
urban labor
3000
income skilled
male headed

2000

Montevideo rural
transfers income
1000
medium skilled
female headed

0
-0.1 0 0.1 0.2 0.3 0.4 0.5 0.6

-1000
Mean share of food in total expenditure



Categorization of households in Pakistan
Size = Percent of total households - Source : FBS, 2005/06
700

600
Rural-Male-High educ.-Punjab-Farmers-Big land
Mean monthly income (current USD)

Urban-Male-High educ.-Punjab-Other empl-
500

400

300

200

Rural-Male-
100 No educ.-
Rest-Nofarmers-
0
Agric. and manuf
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

-100
Mean share of food in total expenditure


3. MODEL


Traditional modeling in
MIRAGE

One agent = public+private agent

It means that we suppose they get
same preferences

Savings of the representative agent
finance investment

New calibration of the CES – LES
modeled at the individual level


Traditional MIRAGE modeling: Main equations
• C(i,r,t,sim) =e=
Pop_ag("Totpop",r,t)*(cmin(i,r)+a_C(i,r)*AUX(r,t,sim)*(P(r,t,sim)/PC(i,r,t,sim))**sigma_C(r))

• P(r,t,sim)*AUX(r,t,sim) =e= sum(i$CO(i,r),PC(i,r,t,sim)*(C(i,r,t,sim)/Pop_ag("Totpop",r,t)-
cmin(i,r)));

• BUDC(r,t,sim) =e= sum(i$CO(i,r),PC(i,r,t,sim)* C(i,r,t,sim));

• DEMTOT(i,s,t,sim) =e= C(i,s,t,sim)$CO(i,s) + sum(j$ICO(i,j,s),IC(i,j,s,t,sim)) +
(KG(i,s,t,sim))$KGO(i,s);

• REV(r,t,sim)+(PIBMVAL(t,sim)*SOLD(r,t,sim)) =e= sum(i,PVA(i,r,t,sim)*VA(i,r,t,sim))+
RECTAX(r,t,sim);

• BUDC(r,t,sim) =e= (1-epa(r))*REV(r,t,sim);

• epa(r)*REV(r,t,sim) =e= PINVTOT(r,t,sim)*INVTOT(r,t,sim);


Modeling in MIRAGE
with a public agent

One private agent with CES LES calibrated
at the individual level

One public agent with Cobb Douglass
preferences

Savings of the private agent finance
investment and public deficit

Different closures are proposed concerning
the public agent:
- Deficit is constant and BUDG
adapts to changes in fiscal receipts (public
demand is reduced by lib’n)
- Deficit is constant thanks to
constant tax receipts through a lump sum
tax on the private agent
- …or another tax is changed
(consumption tax, income tax…)
-


MIRAGE modeling with public agent: Main equations

• CH(i,r,t,sim) =e=
Pop_ag("Totpop",r,t)*(cmin(i,r)+a_C(i,r)*AUX(r,t,sim)*(P(r,t,sim)/PC(i,r,t,sim))**sigma_C(r))

• PC(i,r,t,sim)*CG(i,r,t,sim) =e= alpha_G(i,r)*BUDG(r,t,sim)

• DEMTOT(i,s,t,sim) =e= CH(i,r,t,sim) + CG(i,r,t,sim) + sum(j$ICO(i,j,s),IC(i,j,s,t,sim)) +
(KG(i,s,t,sim))$KGO(i,s);

• REV(r,t,sim)+(PIBMVAL(t,sim)*SOLD(r,t,sim)) =e= sum(i,PVA(i,r,t,sim)*VA(i,r,t,sim))

• RECTAX(r,t,sim) =e= BUDG(r,t,sim) + PUBSOLD(r,t,sim)*sum(i,PVA(i,r,t,sim)*VA(i,r,t,sim))

• epa(r)*REV(r,t,sim) + PUBSOLDO(r)*sum(i,PVA(i,r,t,sim)*VA(i,r,t,sim)) =e=
PINVTOT(r,t,sim)*INVTOT(r,t,sim)
•


Modeling in MIRAGE with a
public agent, transfers and
income taxation and hhlds
disaggregation

Each private agent receives transfers from the public
agent

Each private agent’s income is taxed: new receipt for
the public agent

About transfers one option is that they are constant in
proportion of GDP (not neutral)
- other options ? Constant in real
terms ?... Different options may be proposed
- the distribution of transfers is
affected ??

Savings of all private agents finance investment and
public deficit

Different closures will be proposed
- Deficit is constant and BUDG
adapts to changes in fiscal receipts (public demand is
reduced by lib’n)
- Tax receipts are constant through a
lump sum tax on private agents
- or lump sum tax on each household
(lst(r)) such that public sold is constant in terms of
GDP
- Another tax is changed (income tax
!!)
-- Redistribution policies


Modeling in MIRAGE with a public agent, transfers and
income taxation and hhlds disaggregation: main equations


Channels of income redistribution
• Public transfers to households
• Constant in nominal terms
• Constant in real terms
• Constant in % of households’ income
• Incomes taxes/consumption taxes/other taxes
• Inter-households transfers
• Lucas and Stark’ model (1985) of tempered altruism/
enlightened self interest
• Share of paid transfers in total income of the payer
convex, then concave function of disposable income


5. ILLUSTRATIVE RESULTS


First study, first results
• Design of the study
• Perfect competition in all sectors
• Dynamics : 2007 -2025
• Liberalization shock: progressive elimination of all
import duties throughout the world
• Implemented in 2011, linearly in ten years.
• 19 sectors, 23 countries/zones
• 5 countries with household breakdown
• Brazil, Pakistan, Tanzania, Uruguay, Vietnam


Geographic disaggregation


Sectoral disaggregation


Household disaggregation
• Current disaggregation
• Brazil: 13 representative households
• Pakistan: 25 representative households
• Tanzania: 35 representative households
• Uruguay: 39 representative households
• Vietnam: 33 representative households

• More disaggregation soon
• 80-100 households by country


Alternative closures
• Design of the central scenario:
• Public transfers to households are constant in real terms
• Public expenditures are constant in real terms
• Public deficit is constant in terms of GDP (no crowding-out effect
on private investment)
• A lump-sum tax is perceived in order to compensate for the loss
of public revenues and maintain the public deficit constant
• Sensitivity Analysis on:
• How do public transfers to hhlds adjust ? Either constant in real
terms or in % of GDP
• How do public expenditures adjust ? Either constant in real terms or
in % of GDP
• Compensation fiscal revenue


Using traditional MIRAGE: impact of FTL on
real income (%) – 2025 – Scenario/Baseline
10

9

8

7

6

5

4

3

2

1

0

-1


Using traditional MIRAGE: impact of FTL on
macroeconomic variables (%) – 2025 – Scenario/Baseline


Macroeconomic results
• From a sectoral point of view:
• Uruguay and Brazil: main force = large gains in access to
foreign markets = terms of trade improvement
• Pakistan, Tanzania and Vietnam: main force = removal of
domestic distortions = allocative efficiency : gains for
consumers (final and intermediate)
• Uruguay: expansion of animal products; but also textile resulting in a
large augmentation of the remuneration of land and unskilled labor
• Brazil: expansion of seeds and oilseeds, cattle and meat sectors (in
general all agricultural sectors)
• Pakistan: expansion of textile and leather industries
• Vietnam: expansion of rice/textile/wearing/apparel/leather sectors
• Tanzania: cattle and meat sectors + textile

INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next steps

Heterogeneous effects across households
• Using MIRAGE HH with households
disaggregation
• Main results
• 1) At the hhlds level, in terms of real income: large
heterogeneity of impacts
• 2) Divergences in gains and losses come mainly from
the channel of factor prices (less from the channel of
consumption structure)
• 3) If transfers are indexed on GDP or another way, it
may significantly change the picture.
• 4) Impact on poverty is significant


Impact of full trade liberalization on households’ real income
Brazil 2025: ln income in the baseline on x-axis; variation of real income
Baseline/Reference on y-axis; bubbles are proportional to population

8

6

4

2

0
-2 0 2 4 6 8 10 12

-2

-4

-6

-8


Pakistan 2025: ln income in the baseline on x-axis; variation of real income
Baseline/Reference on y-azis; bubbles are proportional to population

12

10

8

6

4

2

0
-1 0 1 2 3 4 5 6 7 8

-2

-4

-6

-8


Tanzania 2025: ln income in the baseline on x-axis; variation of real income

10

8

6

4

2

0
-2 0 2 4 6 8 10

-2

-4

-6

-8


Uruguay 2025: ln income in the baseline on x-axis; variation of real income
20

15

10

5

0
2 3 4 5 6 7 8

-5


Vietnam 2025: ln income in the baseline on x-axis; variation of real income

30

20

10

0
-1 0 1 2 3 4 5 6 7

-10

-20

-30


Poverty analysis
• Side product of this approach: poverty analysis
• Micro accounting approach for poverty analysis
• Approach initially developed by Lofgren et al., 2002, and
Agenor et al., 2003.
• CGE results (consumption prices/factor remunerations/public
transfers/private transfers) implemented in the hhld survey
with the strict correspondence CGE Representative Hhld /
hhlds in the survey
• This method accounts for intra group real income variation
• Calculation of FGT indexes FGT0, FGT1
• Calculation of Gini and Theil indexes


Impact of full trade liberalization on
households

Uruguay Brazil Vietnam Tanzania Pakistan
Poverty indicators
Base value 20.52 22.39 18.45 38.10 21.92
Poverty headcount
Percentage change -10.7 -1.6 -28.3 -3.8 -13.5
Base value 7.4 9.3 4.9 16.5 6.5
Poverty gap
Extreme poverty Base value 2.6 8.5 12.1 26.5 4.0
headcount
Base value 0.7 3.3 2.9 10.6 1.5
Extreme poverty gap


households


Income distribution indicators

Base value 0.456 0.596 0.426 0.591 0.645
Gini index

Percentage change -0.424 -0.588 -0.588 -0.043 0.103

Base value 0.386 0.751 0.367 0.901 1.150
Theil index

Percentage change -0.435 -0.423 -0.423 -0.070 0.273


households


Poverty headcount by household head sex

Base value 20.9 22.4 19.7 37.9 22.5
Male headed


Base value 19.9 22.5 14.8 38.7 16.3
Female headed



households


Poverty headcount by household head education level

Base value 28.3 31.8 27.6 40.2 30.3
Low education

Base value 16.4 17.4 17.9 28.9 18.2
Medium education

Base value 1.4 3.5 5.4 24.5 6.9
High educated
Percentage change -28.7 0.1 -43.1 -1.6 -15.9


Brazil – dynamics of welfare variation at the household level – Stock
graph with “open/low/high/close” 2011/2025 – percent- Simulation /
Baseline

6

4

2

0
HH1 HH2 HH3 HH4 HH5 HH6 HH7 HH8 HH9 HH10 HH11 HH12 HH13

-2

-4

-6

-8


Pakistan – dynamics of welfare variation at the household level – Stock
Baseline
12

10

8

6

4

2

0

-2

-4

-6

-8


Tanzania – dynamics of welfare variation at the household level – Stock
Baseline

8

6

4

2

0

-2

-4

-6


Uruguay– dynamics of welfare variation at the household level – Stock
Baseline

18

16

14

12

10

8

6

4

2

0

-2


Vietnam – dynamics of welfare variation at the household level – Stock
Baseline
20

15

10

5

0

-5

-10

-15

-20

-25


Brazil – decomposition of the rate of variation in households’ welfare
into consumption price effect and factor remuneration effect – 2025 -
Scenario/baseline
10

8

6

4

2 welfare
price effect
0 income effect
HH1 HH2 HH3 HH4 HH5 HH6 HH7 HH8 HH9 HH10 HH11 HH12 HH13
-2

-4

-6

-8


Pakistan– decomposition of the rate of variation in households’ welfare
Scenario/baseline
15

10

5
welfare
price effect
income effect
0

-5

-10


Tanzania – decomposition of the rate of variation in households’ welfare
Scenario/baseline

8

6

4

2
welfare
0 price effect
HH11
HH9
HH1
HH2
HH3
HH4
HH5
HH6
HH7
HH8

HH10

HH12
HH13
HH14
HH15
HH16
HH17
HH18
HH19
HH20
HH21
HH22
HH23
HH24
HH25
HH26
HH27
HH28
HH29
HH30
HH31
HH32
HH33
HH34
HH35
income effect
-2

-4

-6

-8


Uruguay– decomposition of the rate of variation in households’ welfare
Scenario/baseline
30

25

20

15

welfare
10 price effect
income effect

5

0
HH10

HH12
HH13
HH14
HH15
HH16
HH17
HH18
HH19
HH20
HH21
HH22
HH23
HH24
HH25
HH26
HH27
HH28
HH29
HH30
HH31
HH32
HH33
HH34
HH35
HH36
HH37
HH38
HH39
HH1
HH2
HH3
HH4
HH5
HH6
HH7
HH8
HH9

HH11

-5

-10


Vietnam – decomposition of the rate of variation in households’ welfare
Scenario/baseline
20

15

10

5

0
HH4
HH1
HH2
HH3

HH5
HH6
HH7
HH8
HH9
HH10

HH12
HH13
HH14
HH15
HH16
HH17
HH18
HH19
HH20
HH21
HH22
HH23
HH24
HH25
HH26
HH27
HH28
HH29
HH30
HH31
HH32
HH33
HH11

welfare
-5 price effect
income effect
-10

-15

-20

-25

-30


Households’ real income – Brazil– 2025 – Scenario/Baseline - %
(Households are ranked in increasing 2025 income)
Rule of indexation of public transfers matters

6

4

2

0
HH10 HH13 HH9 HH12 HH8 HH11 HH1 HH5 HH6 HH2 HH7 HH3 HH4 main
sa1
-2

-4

-6

-8


Households’ real income – Pakistan– 2025 – Scenario/Baseline - %

12

10

8

6

4

main
2
sa1

0

-2

-4

-6

-8


Households’ real income – Tanzania– 2025 – Scenario/Baseline - %

8

6

4

2
main
sa1
0

HH2
HH7

HH9
HH4
HH6

HH1
HH5
HH3

HH8
HH35
HH32
HH29
HH33
HH31
HH30
HH34

HH13
HH14
HH10
HH12

HH11

HH17

HH15
HH18
HH16
HH20
HH26
HH21
HH19
HH22
HH23
HH24
HH25
HH27
HH28
-2

-4

-6


Households’ real income – Uruguay– 2025 – Scenario/Baseline - %

18

16

14

12

10

main
8
sa1

6

4

2

0
HH2
HH1
HH3
HH4
HH6
HH5
HH8
HH7
HH9

HH15

HH18
HH10
HH11
HH13
HH12

HH14

HH19
HH16
HH17
HH20
HH29
HH22
HH21
HH23
HH24
HH27
HH25
HH30
HH26
HH28
HH31
HH33
HH32
HH35
HH36
HH34
HH37
HH39
HH38

Households’ real income – Vietnam– 2025 – Scenario/Baseline - %

25

20

15

10

5

0
main
sa1
-5

-10

-15

-20

-25

-30


Concluding remarks
• Four main conclusions
• - Diversity of impact of trade liberalization at the
households’ level within a country.
• - Positive impact on poverty ; ambiguous impact on
inequality
• - Factor remuneration channel is much more
important than commodities price channel.
• - Accompanying policies (transfers, indirect or direct
taxes…) are important and can amplify gains and
losses or (totally) compensate for losses at the
households’ level.


6. NEXT STEPS


Further developments

• Increasing the number of countries in our library
• Run more scenarios with larger number of
households in each country (80-120)
• More sensitivity analysis, in particular concerning
accompanying policies
•


Main challenges: dynamic issues
• Inter households transfers behavior
• Rural / Urban migration
• Dynamic evolution of endowments at the
household level:
• Skilled vs Unskilled Labour supply
• Capital accumulation, investment decisions
• Modeling of households’ saving decisions


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