Analyzing the poverty and distributional impacts of macro shocks in developing

countries

A Microsimulation Approach

Poverty Reduction and Equity GroupPoverty Reduction and Economic Management Network

World BankApril 2011

2

Outline

Background

Approach

Model Basic structure and data requirements Methodology Standard outputs Limitations and assumptions

Illustrative results

Conclusions Applications to date Moving forward

3

Background

In wake of financial crisis: Urgency to design policy to mitigate impact of crisis but… … lack of real time micro data on employment and distributional impacts

High demand by client countries and regional teams for: Projections of short-term and medium-term impacts (Ex-ante) Identification of most vulnerable and affected groups

Microsimulation model (FY09-FY10) Development by PRMPR Application to Bangladesh, Mexico, Mongolia, Philippines and Poland (Partial application to Egypt)

Following crisis: Broader demand for microsimulation model to project employment and distributional

impact of macro shocks Collaboration with ADePT to design and develop simulation module

4

Approach

What is needed? To account for multiple transmission mechanisms To capture impacts:

▪ Over the entire income/consumption distribution▪ At the individual and household levels

Proposed approach Micro-simulation model

▪ Similar to methods outlined in Bourguignon et al. (2008)▪ Macroeconomic projections (not CGE)▪ Microeconomic data from household/LF surveys

Focus on▪ Labor markets (employment and earnings)▪ Non-labor income (remittances)▪ (Prices – food/non-food, other)

Main outputs▪ Individual level: Information on LF/employment status and labor earnings▪ Household level: Information on per capita (labor/non labor) income and consumption

Results▪ Employment impacts▪ Poverty and distributional impacts

5

Model

6

Basic structure

Micro data(Household/LF

survey)

Baseline(Pre-shock, t)

“Treatment”(Crisis)

Benchmark

Predictions(t+1…t+n)

Impa

ct (2

)

Macro projections(t+1…t+n)

Impact (1)

7

Data requirements

Control variables Macroeconomic projections (country teams)

▪ Total and sectoral GDP growth▪ International remittances growth▪ Inflation (general and food)▪ Exchange rate (local currency/USD)

Macroeconomic parameters (calculations from historical data)▪ Output elasticity of employment (total and sectoral)▪ Output elasticity of labor force participation (total and sectoral)

Population growth rate (by age group and gender)

Microeconomic data Survey with data on:

▪ Individual and household-level labor and non-labor income▪ Individual-level labor market outcomes

Poverty line(s)

8

Model overview

Baseline (Calibration)

Micro data

LF status modelEarnings equation

Migration/remittances

Rule: Best fit to micro data

estimate

Population growth

Simulation

Macro projections

∆ in LF status (ind)∆ real earnings (ind)∆ remittances (HH)

Populationpredict

Rule: Replicate macro proportional changes at

micro level

Assessment of impacts

Price data

Income and consumption

(individuals and HH)

adjust

Input

Output

Income/consumption distributions

Poverty and inequality measures

Results

15

Typical outputs

Datasets Original (baseline) dataset Simulated dataset(s)

Results Employment and earnings estimates Poverty and inequality aggregates Poverty profiling

▪ Poverty profiling of “new poor”▪ Poverty profiling for specific groups/areas

Distributional analysis▪ Growth incidence analysis▪ Transition matrices

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Limitations and assumptions

Limitations Level of disaggregation

Level of sectoral/regional disaggregation dependent on available level of disaggregation for macro projections

Labor market dynamics Structural relationships remain constant

▪ Validity of MNL and earnings (pre-crisis) estimates No explicit modeling of changes in composition of employment within sectors

▪ Changes in the proportion of formal/informal employment within each sector are due to individual transitions

Assumptions Immobility of factors of production (including labor) between regions and

between urban/rural areas No (additional) internal or external migration

Labor income and profits grow at same rate within each sector Constant marginal propensity to consume

Needed only if poverty is consumption-based

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Illustrative results

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Summary of main findings(on impact of financial crisis)

In Bangladesh, Mexico, Philippines and Poland, slower or negative GDP growth generally translated into:

Aggregate employment and poverty impacts Employment rates/levels fell due to the crisis…

▪ … with sectoral impacts varying by country Incomes were affected through both labor earnings and remittances…

▪ … although the mix varied across countries Poverty was expected to increase…

▪ … but there was little impact on aggregate measures of inequality Poverty profiling: Crisis vulnerable

New poor had specific characteristics that distinguished them from chronic poor and non-poor

Implications for design and targeting of safety net and crisis response packages Distributional impacts

Large impacts in middle of income distribution, associated with employment/earnings shocks

Implications for political economy of crisis response

Changes in total and sectoral employment

19

2010

Decline in employment levels (shown) and employment rates

Significant cross-country variation in sectoral impacts Manufacturing

affected in all countries but to different degrees

Services as fall-back sector in BD, in contrast with Philippines, Mexico, and especially Poland

Philippines Mexico Bangladesh Poland

(3.50)

(3.00)

(2.50)

(2.00)

(1.50)

(1.00)

(0.50)

-

0.50

1.00

% change in employment between benchmark and crisis

Agriculture Manufacturing/Industry Services

20

Changes in household income

2010

Decline in household income associated with reductions in both labor and non-labor incomes

Significant cross-country variation in relative importance of shocks to different income sources, reflecting variation in relative importance of different transmission mechanisms

Distributional implications

Total HH Income HH Labor Income HH Remittances

-10.0

-9.0

-8.0

-7.0

-6.0

-5.0

-4.0

-3.0

-2.0

-1.0

0.0

% change in household income between benchmark and crisis

Philippines Mexico Bangladesh Poland

21

Poverty Headcount Poverty Gap Gini

-5.0

0.0

5.0

10.0

15.0

20.0

% change in poverty/inequality indicators between crisis and benchmark

(numbers in parentheses represent pct. point change)

Philippines Mexico Bangladesh Poland

Changes in poverty and inequality

2010

Increase in level and depth of poverty, especially in MX…

… but little impact on aggregate inequality

Caution: Impact in relative (%) versus absolute (PP) terms

(1.5)

(3.9)

(1.2)

(0.7)

(3.4)

(0.3)

(0.001)

(0.012)

(-0.004)

(0.4)

(0.2)

(-0.002)

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2010

HH of new poor households relatively more skilled than those chronic poor households…

… but less skilled than the average HH

Similar pattern across countries

Characteristics of the crisis-vulnerable (I)

Philippines Mexico Bangladesh Poland0

20

40

60

80

100

120

% of crisis-vulnerable household heads who are low-skilled(0-9 yrs of education)

Crisis-vulnerable Structurally poor Entire population

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2010

New poor relatively more likely to reside in urban areas than chronic poor…

… but less likely to reside in urban areas than the average household

Similar pattern across countries, but differences more acute in Philippines and MX (higher level of urbanization? higher rate of poverty reduction in urban areas in recent years?) and no differences in Poland (higher level of penetration of off-farm activities in rural areas)

Characteristics of the crisis-vulnerable (II)

Philippines Mexico Bangladesh Poland0

10

20

30

40

50

60

70

80

90

% of crisis-vulnerable living in rural areas

Crisis-vulnerable Structurally poor Entire population

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Distributional impacts:Growth Incidence Curves

-8.0

0-7

.00

-6.0

0-5

.00

-4.0

0-3

.00

-2.0

0-1

.00

0.00

% C

hang

e

0 10 20 30 40 50 60 70 80 90 100

Percentile

Urban Rural-6

.00

-5.0

0-4

.00

-3.0

0-2

.00

-1.0

00.

00

% C

hang

e0 10 20 30 40 50 60 70 80 90 100

Percentile

Urban Rural

Philippines Bangladesh

-16.00

-14.00

-12.00

-10.00

-8.00

-6.00

-4.00

-2.00

0.00

% C

hang

e

0 10 20 30 40 50 60 70 80 90 100

Percentile

Urbano Rural

Mexico

On average larger losses in urban areas, where employment impacts are highest

But important distributional impacts within rural and urban areas as well (except for Philippines)

BD: Remittances MX: Most severe

employment losses

PO: Farm versus non-farm employment (rural areas)

Poland1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97

-3.00

-2.00

-1.00

0.00

1.00

2.00

3.00

UrbanRural

25

1 2 3 4 5 6 7 8 9 10 -

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

Distributional impacts:Transition Matrices

Philippines Bangladesh

1 2 3 4 5 6 7 8 9 100.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

Mexico

1 2 3 4 5 6 7 8 9 10 -

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

1 2 3 4 5 6 7 8 9 10 -

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

Poland

Most people remain within decile but

Important movements up and esp. down – particularly in middle of distribution

MX: Largest impact on poor

PO: Largest movements “up” at bottom due to UI

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Conclusions (I):Main applications to date

Crisis monitoring: Identification of main transmission mechanisms Identification of possible “leading indicators” to monitor the likely poverty impact

of an economic crisis▪ E.g. manufacturing employment/job losses, wages, aggregate remittance flows, change in

relative price of food Design of policy response:

Identification of affected/vulnerable individuals/households/groups SP for the new poor?

▪ Effectiveness of traditional/existing SN programs and automatic stabilizers Political economy implications of distributional impacts (esp. impacts on urban

middle class) Poverty/Distributional impact of policy response:

Safety nets versus automatic stabilizers (e.g. Mexico and Poland)

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Conclusions (II):Main applications to date (publications)

(Stand alone) Country notes Bangladesh (PRWP 5238) Philippines (PRWP 5286) Mongolia (Policy Note, 2010) Poland (Policy Brief, 2010)

Contributions to country reports Mexico

Philippines Overview and summary of findings

Economic Premise (March 2010) Summary piece in EUVox.org (April 2010) Gender impacts Distributional Impact of the Financial Crisis (forthcoming Fall 2011)

Webpage Poverty Reduction and Equity Group (PRMPR)

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Conclusions (III):Moving forward

Possible extensions, depending on the country context Commodity/food price changes More disaggregated treatment of sectors

▪ Export/non-export▪ Formal/informal

More sophisticated treatment of remittances and internal migration

Timeline Launch of ADePT SIM 1.0 over summer 2011

▪ Basic features▪ Manual

Dissemination and training▪ BBLs▪ Training at HQ + customized country/regional training events on demand (e.g. Egypt, Mexico,

Tunisia) (Possible customization on demand)