assessing ex-ante poverty &...

ASSESSING EX-ANTE POVERTY & DISTRIBUTIONAL

IMPACT OF MACROECONOMIC SHOCKS:

A MICRO- SIMULATION APPROACH

Module 2: Welfare Impact of Macroeconomic Shocks: Overview of Selected Tools

Outline

1. Motivation

2. Methodological approach

3. Implementation

4. Typical results

5. Moving forward

Motivation

Main transmission mechanisms

Across countries

Trade

Financing (credit and FDI)

Migration and remittances

Within countries

Fiscal policy

Labor markets

Credit markets

These developments may lead to

Differentials impacts across groups and space

Increased vulnerability

Higher and deeper poverty

Motivation

What is needed?

To account for multiple transmission mechanisms

To capture impacts:

Over the entire income/consumption distribution

At the individual and household levels

Commonly used approaches

Pros Cons

Output Elasticity

of poverty

Easy to apply Only aggregate poverty impacts

PovStat Multiple channels – labor

market

Difficult to account for changes in Non-

labor income

Household level data Focus exclusively on household heads

Poverty & inequality changes No disaggregated distributional impact

Why a Micro-simulation model

Different methodologies:

1. Microeconomic techniques

2. Macro-microeconomic techniques

1. Problems:

Difficult to identify treatments and controls

Need to build a macro counterfactual

2. Macro-micro modeling framework

1. Macro models with RHG: lack of heterogeneity

2. Top-down modeling approach:

1. Accounting: envelope theorem not applicable

2. Behavioral

3. Feedback Loops from bottom to top

Methodological Approach


Micro data

(Household/LF

survey)

Baseline(Pre-crisis)

“Treatment”

(Crisis)

Benchmark

Predictions(2010)

Imp

act

Macro projections(Contemporaneous)

Micro-simulation model 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 Poverty impacts

Distributional impacts



Uses information and generates prediction for ALL individuals and

households

… compared to aggregate information or information for household heads

(PovStat)

Value added:

Closer to reality when LFP are relatively high

Allows for full distributional analysis

Works with income

… compared to consumption (PovStat and other simulation models)

Value added:

Allows for modeling of labor and non-labor income separately (especially important for

remittances and public transfers)

… but (i) concerns about income data quality and (ii) need to map income to

consumption

Methodological approach:

Model (I)

Baseline (Calibration)

Micro data

LF status model

Earnings equation

Migration/remittances

Rule: Best fit to micro data

estimate

Population growth

Simulation

Macro projections

∆ in LF status (ind)

∆ real earnings (ind)

∆ remittances (HH)

Populationp

redict

Rule: Replicate macro proportional changes at

micro level

Input

Output

Assessment of impacts

Price data

Income and

consumption

(individuals and HH)

adju

st

Income/consumption

distributions

Poverty and inequality

measures

Re

sults


Model (II) - Calibration

Household Income-Generation Model:

1. Labor income: occupational decision & earnings (individual)

2. Non-labor income: remittances, rents, interests, social income

(household level)

h = household,

i = members,

j = activities,

nh = total members of household h

J = total number of activities

hn

i

J

j

hj

hij

hi

h

h yyIn

y1 1

0

1


Model (III) - Calibration

Labor Income

Step 1: Modeling labor force status

LF status

Employed (agriculture, industry, services)

Non-employed

Number of “states” must match with availability of sectoral macro projections

Working age population (15-64)

Multinomial logit

Baseline year HH/LF survey data

High/low skill workers

JlallforUUifjI

JjwithvZU

lhi

jhi

jhi

Lji

Ljhi

jhi

,,0

,,0,


Model (IV) - Calibration

Step 2: Modeling earnings

Employed population (15-65)

Mincerian (OLS) equation

Non-Labor Income

Step 3: Modeling remittances

International

Information in household survey too limited to explicitly model decision to migrate and/or probability of receiving remittances

Instead design of assignment rule based on information from baseline household survey Regional distribution of additional remittances proportional to baseline distribution

Within regions all household remittance transfers in projection year equal in real terms to mean remittance transfer in baseline

Domestic

No assignment rule (i.e. no new recipient households), but some adjustments to account for changes in labor market conditions

Back

hwjhi

wjhi

jhi niforXy ,,1log


Model (V) - Simulation

Step 1: Accounting for population growth

Changes in total and working age populations

Important step if (i) pre-crisis data is old and/or (ii) fertility rates are relatively high

Step 2: Accounting for aggregate changes in output and employment

Changes in employment

Use of predicted probabilities from LF status model to replicate aggregate changes in relative sectoral employment shares at micro level

Rescale changes in total and sectoral employment levels at micro level to match observed changes in employment at macro level

Changes in earnings

Use of Mincerian equation to predict wages for: (i) individuals that become employed between the base and final years, (ii) individuals that change jobs between the base and final years

Rescale changes in total and sectoral earnings at micro level to match observed changes in output at macro level


Model (VI) - Simulation

Step 3:

A. Accounting for aggregate changes in international remittances

Calculate remittance growth between baseline and projected years

Assign remittance growth across regions following baseline regional

distribution

E.g. In 2005 households in rural Dhaka received 23% of total remittances

Within regions select household randomly (i.e. independently of whether

they already receive remittances or not) and assign a per household

transfer that equal, in real terms, the average transfer in the region in

baseline

E.g. In 2005 selected households in rural Dhaka received, on average, an additional

2221/year in the form of international remittances

End result:

Overall regional distribution of remittances remains unchanged

Increase in number of recipient households (i.e. both household that did not receive

remittances in 2005 and household that did can receive new remittances in 2009-10)


Model (VII) - Simulation

Step 3 (cont’d):

B. Accounting for aggregate changes in domestic remittances

Domestic remittances are assumed to growth at the same rate as labor earnings

(i.e. different growth projections under each simulation scenario)

End result:

Real growth in domestic remittances, with slower growth under crisis scenario

No expansion/contraction in number of recipient households

Step 4: Adjusting additional non-labor income sources

Increase capital and financial income at same rate as real economy

Maintain real baseline value of public transfers

Back


Model (VIII) - Assessment of impact

Step 1: Accounting for changes in relative prices (i.e. food/non-food)

Poverty line is adjusted to ensure the same food basket is affordable

Step 2: Calculating per-capita HH income

Step 3: Calculating household per-capita consumption

Assumption: Expenditure to income ratio remains constant between base and final year (i.e. constant savings rate)

Step 4: Comparing outcomes across benchmark and treatment scenarios

baseh

baseh

hhy

PCExpyPCExp *ˆ*

hn

i

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j

hj

hij

hi

h

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ˆ


Caveats

Richness/accuracy of simulation is a function of nature and quality of data:

Capacity to disaggregate within sectors depends on macro data

Validity of simulation assumptions depends on distance between baseline and final year

Capacity to accurately predict employment and earning changes depends on information and assumptions (i.e. earnings and profits grow at same rate within a sector)

Capacity to model remittances depends on quality of data on migrants and remittances, particularly for countries with rapid and/or volatile growth of remittances

Working with income, not consumption

Income data tends to be of lower quality

Need assumptions to retrieve consumption from predicted income (i.e. constant savings rate)

Labor demand is not modeled

Assumes micro conditions mirror macro projections

Does not account for the possibility of structural shifts in labor demand

... although changes in relative demand for skills can be incorporated if additional analytical work exists

Back

Implementation of the method

Data requirements

1. Matrix inputs

2. Micro data

Estimation

1. Baseline (Calibration)

2. Simulation

Implementation of the method:

Data requirements

Matrix Inputs

1. Population growth projections

2. Macro Data

3. Prices

Micro data


Matrix inputs

1. Population Growth Projections

NSO Projections or UN – World Population Prospects http://esa.un.org/unpp/

Projections by five years in between (i.e. 2005-2010)

Population by five-years age group and sex

Interpolate years

Why do we need growth rate for total population?

Assumption: composition within households remains constant

0

2

4

6

8

10

12

14

BAN 10 vs 05 PHI 10 vs 06 MEX 10 vs 08

15-64Total

http://esa.un.org/unpp/

Implementation:

Matrix inputs


2. Macro Data

Total and sectoral changes in output

Total and sectoral changes in employment

Changes in remittances from abroad

3. Prices


Matrix inputs

2. Macro data

I. Total and sectoral changes in output

Output in real terms

Calculate variations between base year and projections both

scenarios

2. Total and sectoral changes in employment

1. Total and sectoral projections if available

2. How do we link GDP with Labor Market?

1. Employment elasticity by sector and Activity rate elasticity

2. What is the data requirement?

3. Which are the residual labor status that close the model?

4. Estimate variations in shares for both scenarios


Matrix inputs Back

2. Macro data

2. Total and sectoral changes in employment

4. Estimate variations in shares for both scenarios

Total Inactive LF Unemployed Employed Manuf. Other Ind.

2006 51.13 16.12 34.59 3.16 31.43 10.63 2.98 1.92 15.90

share 0.32 0.68 0.09 0.91 0.34 0.09 0.06 0.51

Benchmark

2009 55.13 17.87 37.26 3.22 34.01 11.30 2.90 2.13 17.71

share 0.32 0.67 0.09 0.91 0.33 0.09 0.06 0.52

D % share 0.02837 -0.01593 -0.05354 0.00449 -0.0176 -0.1001 0.0231 0.0293

2010 56.54 18.64 37.89 3.18 34.67 11.47 2.90 2.11 18.23

share 0.33 0.66 0.08 0.91 0.33 0.08 0.06 0.53

D % share 0.0460 -0.0258 -0.0815 0.0068 -0.0216 -0.1182 -0.0051 0.0391

Crisis

2009 55.13 17.66 37.46 3.86 33.67 11.19 2.92 2.11 17.49

share 0.32 0.67 0.10 0.90 0.33 0.09 0.06 0.52

D % share 0.01640 -0.00921 0.13006 -0.01090 -0.01747 -0.08674 0.02351 0.02659

2010 56.54 18.31 38.23 4.31 34.05 11.31 2.92 2.08 17.77

share 0.32 0.67 0.11 0.89 0.33 0.09 0.06 0.52

D % share 0.0275 -0.0154 0.2347 -0.0197 -0.0178 -0.0967 -0.0001 0.0316

AgriIndustry

SsPeople in million

3 matrices:

i. Activity

ii.Unemployment

iii.Sectors


Matrix inputs

2. Macro data

3. Changes in remittances from abroad

-

10

20

30

40

50

60

70

80

BANGLADESH PHILIPPINES

Labor income Non-labor income Implicit rent

0

10

20

30

40

50

60

70

BANGLADESH PHILIPPINES

Capital Remittances Social Other non-labor

73% Abroad 77 % Abroad

hn

i

J

j

hj

hij

hi

h

h yyIn

y1 1

0

1


Matrix inputs

Changes in remittances from abroad

Remittances in real terms vary depending on changes in the

foreign exchange rate, remittances in nominal terms and

internal inflation

CPI

RR

NR

Δ𝑅𝑅 =

> 0 Δ𝑅𝑁𝜀 > 0

Δ𝐶𝑃𝐼 ≤ 0

= 0 Δ𝑅𝑁𝜀 = Δ𝐶𝑃𝐼

< 0 Δ𝑅𝑁𝜀 ≤ 0Δ𝐶𝑃𝐼 > 0


Matrix inputs


2. Macro Data

3. Prices

Why do we need to adjust by prices?

What are the data requirements?


Matrix inputs Back

3. Prices Why do we need to adjust by prices?

Projected incomes or expenditures in real terms adjusted for

spatial differences

Two factors:

1. Poverty line anchored to a fixed basket of food items

2. Food prices move at a different rate than CPI

What are the data requirements?

1. CPI: historical data and projections

2. Weight structure of CPI: food & non-food

3. Weight structure of Poverty line: food & non-food


Micro-data

Why income and not consumption?

Several transmission mechanisms: labor and non-labor market

Poverty on consumption → Map Income into Consumption

Household Income-Generation Model:

1. Labor income: occupational decision & earnings (individual)

2. Non-labor income: remittances, rents, interests, social income

(household level)

hn

i

J

j

hj

hij

hi

h

h yyIn

y1 1

0

1


Micro-data requirements

Main socio-economic variables

1. Household id

2. Demographics: gender, age, relation with household-head

3. Housing & land

4. Education variables

5. Regional variables

6. Labor variables: labor status, labor relation, sector (main and

secondary occupation)

7. Income variables: Labor and Non-labor income

8. Poverty: poverty lines

All income and consumption variables must be adjusted for

spatial/regional price differences

Back


Baseline (Calibration)

Occupational decision model

1. Linear projection

2. Residuals

Earning Equations

1. Linear projection

2. Residuals

Check if MNLM and OLS results have sense

Yes estimate linear projections and residuals

No solve problems and estimate again both models

Back

Results:

Overview of Typical Results

Household income, poverty and inequality

1. Earnings and income impacts

2. Overall poverty and inequality impact

3. Impacts across regions and areas

4. Other analysis: by gender

Distribution analysis

1. A profile of the “crisis-vulnerable”

2. Growth incidence curves

3. Transition matrices

Household income, poverty & inequality: Earnings and income impacts

FY09: almost no impact – average household income falls by 0.7%

Labor income falls by 1% and non-labor income by 0.25%

Actual remittances were higher than projected and matched benchmark projections

FY10: average household income projected to fall by 2.7%

Due to declines in both labor (1.4%) and non-labor (6%) income

Fall in non-labor income driven mainly by remittances (10% fall)

Mean earnings per worker 1.8% lower

Household income, poverty & inequality: Overall poverty and inequality impacts

Comparing hypothetical benchmark (no crisis) and crisis scenarios for FY09 and FY10

FY09: just a slight increase in extreme poverty rate and poverty gap

FY10: 2.3 pct pt increase in poverty rate and 1.4 pct pt increase in extreme poverty rate (~4 million additional people in poverty)

Around 0.5 point increase in poverty gap and 0.1 pt increase in severity

Slight reduction in inequality measures (Gini, Theil indices)

2005Benchmark Crisis

2009 2010 2009 2010

Poverty

-Headcount rate 40.0 32.9 27.6 32.9 29.9

-Poverty gap 9.0 7.4 6.0 7.5 6.5

-Severity of poverty 2.9 2.4 1.9 2.5 2.0

Extreme poverty

-Headcount rate 25.1 20.3 16.8 20.5 18.2

-Poverty gap 4.7 3.9 3.0 3.9 3.3

-Severity of poverty 1.3 1.1 0.9 1.1 0.9

Inequality

-Gini 0.33 0.35 0.35 0.34 0.34

-Theil 0.22 0.23 0.24 0.22 0.22

Household income, poverty & inequality: Poverty impact by region

Impact higher in the East (especially Sylhet and Chittagong) than in the West

~4% drop in total income in the East, compared to the average impact of 2.7%

The east has greater concentration of industry and are higher recipients of remittances

However, poverty in the East, even with crisis, much lower than in the West

Distribution analysis:A profile of “crisis-vulnerable”

Question: Households that would not have been poor in 2010 if

had there been NO crisis

How we define our population of analysis:1. Define the poor in benchmark scenario

2. Define the poor in crisis scenario

3. “Vulnerable”: poor in crisis & non-poor in benchmark

4. “Structurally”: poor in crisis & poor in crisis

Poor Non-poor

Poor Structural Vulnerable

Non-poor New rich Never poor

Benchmark

Crisis

Distribution analysis:

A profile of “crisis-vulnerable”

“Crisis vulnerable”: households who are poor with crisis in 2010, but would not be so if there were no crisis

Their characteristics

82% are rural compared to 75% of the general population

Dependency ratio 22% higher compared to the overall population

94% with education 0-9 yrs compared to 75% for general population

Suffer large income losses with a 33% drop in average hhold income

Mostly due to fall in remittances – both no. of receivers and amounts

Even though the direct impact is low in agricultural sector, those vulnerable to becoming poor are mainly in rural areas

Lower incomes in the first place – fall in remittance has large impact

Distribution analysis:Growth incidence curves

Question: Did the crisis impact uniformly the entire distribution?

Growth incidence curve plots the growth rate at each quantile of per

capita income (or expenditure)

The graph can allow us to compare the “impact” of the crisis in poorer

segments of the population with that of richer segments

How you can construct them:

1. Rank the observations by per capita income (or expenditure) from poorest to

richest for benchmark and crisis distributions

2. Calculate quantiles of per capita income (or expenditure) in both scenarios

3. Use the mean income (or expenditure) measure for a given quantile p at two

different scenarios, crisis and benchmark, to calculate the growth rate for

quantile p:

1)(

)()(

2010

2010

py

pypg

b

cc


Negative growth in consumption highest from 50th to 90th pctile

Leads to relatively low impact on poverty rate

Raises concerns about distribution and the middle class –beyond absolute poverty

Impact highest among 20th-60th pctile in urban areas, skewed towards

the better off in rural areas

-5.0

0-4

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-3.0

0-2

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-1.0

00

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% C

han

ge

0 10 20 30 40 50 60 70 80 90 100

Percentile

-5.0

0-4

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-3.0

0-2

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-1.0

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% C

han

ge

0 10 20 30 40 50 60 70 80 90 100

Percentile

Urban Rural

Per capita Expenditure PCEXP by urban/rural


Loss of labor income highest in industry, followed by services

Distributed uniformly in industry, skewed towards the lower earners

in services

-5.0

0-4

.00

-3.0

0-2

.00

-1.0

00.

00

% C

hang

e

0 10 20 30 40 50 60 70 80 90 100

Percentile

Agriculture Industry Services

Labor income by sector


Consumption loss higher and more

unevenly distributed in the East

Impact highest for 20th-50th pctile of pcexp

Impact smaller and more evenly distributed

in the West

East-west differences mainly due to

remittances

Not much difference in loss of labor income

But those above 60th percentile lose more

labor income in East than West

-8.0

0-7

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-6.0

0-5

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-4.0

0-3

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-2.0

0-1

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0.0

01

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2.0

0

% C

han

ge

0 10 20 30 40 50 60 70 80 90 100

Percentile

West: Barisal, Khulna & Rajshahi East: Chittagong, Dhaka & Sylhet

-8.0

0-7

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-6.0

0-5

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0-3

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% C

han

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0 10 20 30 40 50 60 70 80 90 100

Percentile

West: Barisal, Khulna & Rajshahi East: Chittagong, Dhaka & Sylhet

GIC of per capita labor income

GIC of per capita expenditure

Distribution analysis:Transition matrices

Question: How were the upward and downward household movements’ in

deciles terms?

Transition matrices allow us to look movement of households across the

distribution as a result of a crisis compared to benchmark

How you can build them:


richest for benchmark scenario

2. Calculate deciles of per capita income (or expenditure) in benchmark

3. Take each deciles limits


richest for crisis scenario

5. Calculate deciles of per capita income (or expenditure) in crisis with deciles

limits benchmark

Distribution analysis:Transition matrices

Deciles defined by benchmark levels household move relative to the “no-crisis” distribution

Most of the “transitions” driven by the fall in non-labor income, mainly remittances

95% of households remain in the same decile for per capita labor income, 70% for per capita total income

Most of the transitions occurs in the middle of the distribution (4th to 8th deciles)

Shift in terms of the distribution of labor income highest for the 7th and 8th deciles

Movements down are larger than movements up for the 4th decile and above

Transition matrices can also be constructed by consumption and for sub-groups

Implications of Results

Identification of main transmission mechanisms to households

In the case of Bangladesh, remittances and food prices were a key determinant of

poverty impact

Identification of possible “leading indicators” to monitor the likely

poverty impact of an economic crisis

E.g. wages, remittance flows

Poverty measures do not capture the full distributional impact

Has implications for targeting of policies and safety nets

Back

Moving forward

Possible extensions, depending on the country context

Commodity price changes

More disaggregated treatment of sectors

More sophisticated treatment of remittances and internal migration

Explicit modeling of demand for skills

Where it can be applied – data requirements

Up-to-date macro projections by sector, with and without crisis; recent

household survey with incomes classified by sector

Even better: surveys (e.g. labor force, rapid monitoring) from 2009, migration info

in household survey

Robustness of results: path dependence analysis and confidence

intervals

assessing ex-ante poverty &...

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