assessing changing labor market conditions in low-income...
TRANSCRIPT
Assessing changing labor market conditions in
low-income settings:
A framework and application
Dhushyanth Raju
Labor Markets Core Course
World Bank, Washington, DC
March 30, 2010
1
Motivation
Standard labor market indicators (by themselves) have limited
diagnostic power in low-income settings.
Need to be combined with other indicators and particular cuts of
the relevant population to shed light on where potential labor
market problems lie and which groups are relatively
disadvantaged in the labor market.
Issue in low-income settings may have less to do with labor
market status (nonparticipation, employment, unemployment)
than the nature of employment (earnings and other job
attributes).
2
Motivation (cont.)
Lack of a systematic and coherent framework for characterizing
and assessing labor market conditions in developing countries.
The need for a technically-simple methodology that can be
carried out by the statistical and analytical arms of government
agencies of client countries.
3
Main aims
Overarching aim: Enhance labor market diagnostic capacity by
improving the information base on labor market conditions.
1. Enrich labor market profiles and accompanying labor market
assessments by taking advantage of micro data (e.g., individual,
household, and firm level).
2. To the extent possible, standardize labor market profiles in terms of
substance and structure across countries and time.
3. Identify important gaps in existing data collection instruments to fill in
the future.
Goal 1 is primary. Expectation: help identify (1) patterns or trends
that might be symptomatic of underlying labor market problems and
(2) relatively disadvantaged population groups.
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Data requirements
Best-case: Repeated nationally-representative multi-topic
household sample surveys with a good labor market module, as
well as earnings and consumption expenditure (or income)
data.
Use repeated nationally-representative labor force surveys if
the labor market module in the multi-topic household survey is
poor.
Key requirement: availability of earnings data with the widest
possible coverage.
Bonus: Panel data to study how the same individuals fared in
the labor market over time.
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Overarching research question
How do changing labor market conditions mediate the
relationship between economic growth and poverty reduction?
Relevant for determining what role LM institutions and policies should
play to take fuller advantage of the specific growth-poverty reduction
relationship in a given country and mitigate any significant adverse
effects that may arise.
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Key static research questions
1. What are the overall conditions in the labor market in terms of
employment and unemployment, earnings levels and
inequality, and other job characteristics?
2. Which groups of individuals are relatively disadvantaged as
measured across these various indicators?
3. What share of workers hold “bad” jobs as measured by labor
market earnings and other job attributes, and what share of
these workers reside in poor households?
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Key dynamic research questions
4. How have labor market conditions as measured by the
selected indicators changed over the recent past?
5. How has the incidence of “bad” jobs in the economy and the
extent of overlap of “bad” jobs and poverty evolved over time?
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Job quality
Quality of jobs is a multi-dimensional concept: contains both
pecuniary and non-pecuniary elements.
Job quality measured using earnings as well as non-pecunicary
attributes of jobs.
One proposed earnings-related measure for gauging job
quality: the low earnings rate
9
Issues with earnings data
Coverage: Earnings data are often only collected for wage and
salaried (WS) workers (if at all). WS segment is typically very thin in
low-income settings.
Converting to real terms: CPIs may be obtained from a non-
representative set of price centers.
Unpaid work: Even if earnings data are also collected for self-
employed workers, unpaid workers (contributing family
workers/apprentices) still left out. Large segment in developing
countries.
Content: What are self-employment earnings capturing? Firm
revenues? Firm profits? De Mel et al 2009 find large discrepancies between reported profits and reported revenues-
expenses, with reported profits being a more preferable measure, suggesting, in this case,
the detailed questioning may not yield greater accuracy. However, underreporting remains
an important issue with both measures. 10
Addressing lack of earnings coverage of unpaid
workers
Looking at individual earnings alone would mean the analysis of earnings is
confined to those with reported earnings (WS and perhaps SE workers)
Missing group: unpaid workers
Solution: Link unpaid workers to household enterprise earnings. Best case:
enabled by the survey data.
Second-best: Artificially construct a household enterprise (HE) worker.
How? If a SE worker or an unpaid worker lives with at least one (another)
SE worker, these workers are all HE workers. By definition, a given
household can have at most 1 HE.
Distribute earnings of all SE workers classified as HE workers over all HE
workers using hours of works in the reference week.
Alternative approach (Cichello and Giles): Predict earnings for non-wage
workers by applying parameter estimates to relevant covariates drawn from
wage regressions for wage workers.
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Low earnings concept
Earlier concept: Working poverty
Working poor (EU/ILO): all employed (+unemployed) workers
residing in poor households.
Drawback: Does not look directly at the earnings of the individual.
Thus, we cannot use this concept to discuss job quality.
Alternative: look directly at earnings relative to a low earnings line
adjusted for the extent of labor market participation in the
representative household.
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Low earnings line definition
Base low earnings line = individual poverty line (e.g., national
poverty line, $1 per day, $2 per day).
Scaling factor = median number of HH members to number of
working-age employed workers. Reflects the typical household
burden on an individual‟s earnings
Low earnings line = base low earnings line x scaling factor
Hypothetical question this line answers: does the worker earn
enough to lift him or herself as well as representative
dependents out of poverty?
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LM indicators
Selection criteria: (1) expected to shed light on labor market
conditions in low-income settings, and (2) allows a normative
interpretation: prima facie, can a change in LMI x be construed
as a positive or negative LM development?
Important to realize that further digging might reveal that a change
considered prima facie positive might turn out to be negative (e.g.,
increase in female participation rates during crises).
Prioritized: LMIs split into two groups based on general
importance – Level 1 and Level 2 LMIs.
Managed flexibility: The LMI set can be expanded or reduced
and Level 1 and Level 2 LMIs can be interchanged if justified by
country context. Researcher and policymaker preferences may
also affect this decisionmaking. 14
Level-1 LM indicators
Whether (working-age) people are working or not:
1. Nonparticipation rate
2. Standard unemployment rate
3. Employment-to-population rate
4. Child labor (work) rate (7-14 years)
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Level-1 LM indicators
If they are working, how much they are earning:
5. Median earnings
6. Incidence of workers with low earnings (low earnings rate)
7. Share of low earners who have low earnings due to short work hours
8. Share of low earners who work long hours
9. Share of non-earners who have non-low earnings due to long work hours
LMIs 5-9 are calculated separately for three distinct groups of working-age
employed workers: (1) wage and salaried (WS), (2) individual self-
employed (ISE), and (3) household enterprise workers (HE)
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Level-2 LM indicators
Unemployment:
1. Broad unemployment rate
2. Share of long-term unemployed
3. Poverty rate among unemployed workers
Earnings (for each worker group):
4. Earnings inequality (Gini coefficient)
5. Poverty rate among low earners
Employment and job attributes:
6. Distribution of employment by (1) sector, (2) skill, (3) schooling, (4) employment
status, and (5) employment contract.
7. Share of workers (1) registered, (2) with multiple jobs, (3) affiliated to statutory
Panel data-based LM indicators: earnings changes and transition matrices for
various LM states
17
Sample cuts
Applied to all labor market indicators.
Disaggregations (as relevant): (1) gender, (2) age, (3) ethnicity/race, (4)
urban/rural, (5) region, (6) schooling, and (7) poverty status of household
Disaggregated analysis comprised of two parts:
1. Groups that are significantly comparatively disadvantaged
Current incidence of labor market condition x in group y.
Change in the incidence of labor market condition x in group y.
2. Composition of labor market advantage/disadvantage
Current incidence of group y in labor market condition x.
Change in the incidence of group y in labor market condition x.
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Brazil case study
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Preliminaries: Low earnings line: Brazil-specific definition
Key characteristics: (1) time-invariant in real terms and (2) scaled up by the representative household dependency on the individual earnings of the worker.
Low earnings line 1 = individual monthly poverty line (state & capital/other urban/rural-specific) x the median HH member-worker dependency ratio.
Median HH member-worker dependency ratio = median number of household members to working-age employed household members = 2 in both 1996 and 2004.
Low earnings line 2 = individual poverty line.
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Preliminaries: Reclassification of workers from reported
to constructed employment statuses
Cross-tabulation of reported and constructed employment statuses Cell description: Level, 2004 (%) Change, 1996-2004 (% points)
Constructed employment status (primary job)
Reported employment status (primary job)
Wage and salaried worker
Individual self-
employed worker
Household enterprise
worker Unclassified
Wage and salaried worker 100.0
0.0 -- -- --
Own-account worker -- 56.4
2.0 43.6 -2.0
--
Employer -- 52.6 -1.3
47.4 1.3
--
Unpaid family worker -- -- 79.4 1.3
20.6 -1.3
Notes: -- indicates that, by construction, the cell should not have any observations.
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Preliminaries: Earnings data coverage
Earnings data coverage among workers, by reported employment status Share with positive monthly earnings data
Reported employment status (primary job)
1996 (in percent)
2004 (in percent)
Wage and salaried worker 99.0 98.7
Own-account worker 97.3 97.4
Employer 97.2 96.3
Unpaid family worker 0.0 0.0
Earnings data coverage among workers, by constructed employment status
Share with positive monthly earnings data Reported employment status
(primary job) 1996
(in percent) 2004
(in percent)
Wage and salaried worker 99.0 98.7
Individual self-employed worker 97.2 97.4
Household enterprise worker 90.1 91.3
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Preliminaries: How are WS, ISE, and HE workers
distributed by sector of employment?
Cross-tabulation of constructed employment status and sector of employment Cell description: Level, 2004 (%) Change, 1996-2004 (% points)
Sector (primary job)
Constructed employment status (primary job)
Agriculture Industry Services
Wage and salaried worker 9.1
-2.9 23.1 -4.4
67.9 7.2
Individual self-employed worker 13.2 -2.4
29.0 7.3
57.9 -4.9
Household enterprise worker 51.5 -2.4
12.8 3.1
35.8 -0.7
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LM indicators: Conceptual and practical issues
Missing indicators: The child labor rate, the broad unemployment rate, the share of long-term unemployed (6+ months), and the distribution of workers by employment contract could not be constructed due to lack of data.
Cross-time comparability: Change in occupational classification scheme between years; affected the construction of the distribution of workers by skill level.
HE workers: ~20% of unpaid workers were not classified as HE workers as they did not reside with self-employed workers. Unpaid workers represented ~8% of employed workers.
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Economic growth and poverty background
National income (real GDP per capita in I$)
Level, „04: $3,534.
∆ over t: +5.5%.
Poverty (PL: $1.08/day in PPP 1993 $)
Level, „04: 7.6%.
∆ over t: +0.7% pts (+10.6%)
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Level 1 LM indicators:
Whether people are working …
Unemployment rate
Level, „04: 9.1%.
∆ over t: +2.2% pts (+31.9%).
Employment-to-population ratio
Level, „04: 66.4%.
∆ over t: 1.3% pts (+2.0%).
Child work (or economic activity) rate
Level, „04: 10.1%.
∆ over t: -4.8% pts (-47.5%).
26
Level 1 LM indicators:
If working, how much are they earning?
Median earnings
Level, „04: med(EWS)> med(EISE)> med(EHE).
∆ over t: WS: -8.3%; ISE: -26.0%; HE: -15.1%.
Low earnings rate (low earnings line 1)
Level, „04: WS: 27.6%; ISE: 33.9%; HE: 51.2%.
∆ over t: WS: -8%; ISE: +38.4%; HE: +12.5%.
Low earnings rate (low earnings line 2)
Level, „04: WS: 6.8%; ISE: 15.3%; HE: 27.8%.
∆ over t: WS: +15.3%; ISE: +68.1%; HE: +18.3%.
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Level 1 LM indicators:
Low earnings and hours
% of low earners working long hours (i.e., full-time +)
∆ over t: WS: -15.1%; ISE: -9.5%.
% of low earners with low earnings due to short hours (< full-time)
∆ over t: WS: +13.4%; ISE: -13.5%.
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Level 1 LM indicators:
Summary of changes
While the Brazilian economy expanded slightly and the incidence of poverty increased …
On the negative side,
(1) the unemployment rate rose,
(2) median earnings fell for all three worker groups, but particularly for ISE and HE workers, &
(3) the low earnings rate increased for ISE and HE workers.
On the positive side,
(1) the low earnings rate fell for WS workers,
(2) the child work rate fell dramatically, &
(3) the share of WS and ISE low earners working full-time+ fell.
Other changes include an increase in the employment-to-population ratio (female-driven).
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Level 2 LM indicators: Distribution of workers
along selected characteristics
Sector of activity
∆ over t: Agriculture: -4.5% pts; industry: -0.6% pts; services: +5.1% pts.
Formal schooling attainment
∆ over t: None: -4.3% pts; fundamental: -9.3% pts; intermediate: +10.4% pts, and higher: +3.2% pts.
Reported employment status (primary job)
∆ over t: WS: +2.2% pts; OA: -0.8% pts: ER: +0.4% pts; UP: -1.8% pts.
Constructed employment status (primary job)
∆ over t: WS: +1.9% pts; ISE: +0.1% pts: HE: -2.0% pts.
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Level 2 LM indicators:
Unemployment-related
Broad unemployment rate
Data on discouragement unavailable.
Share of long-term unemployed
Data on # of months without work unavailable.
Poverty rate among unemployed workers
Level, „04: 44.1%.
∆ over t: +24.9%.
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Level 2 LM indicators: Earnings-related
Earnings inequality (Coefficient of variation)
Level, „04: CV(EWS)<CV(EISE)≈CV(EHE).
∆ over t: WS: +3.1%; ISE: +20.4%; HE: +1.5%.
Poverty rate among low earners
Level, „04: WS: 42.4%; ISE: 48.8%; HE: 50.8%.
∆ over t: WS: +11.0%; ISE: +2.7%; HE:-10.9%.
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Level 2 LM indicators:
Non-wage employment characteristics
% holding 2+ jobs in reference week
Level‟ 04: 4.7%.
∆ over t: +2.2%.
% affiliated to social security
Level‟ 04: 48.7%.
∆ over t: +5.0%.
% WS registered (i.e., holding a signed worker card)
Level, „04: 62.6%
∆ over t: -0.6%.
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Level 2 LM indicators: Summary of changes
While the Brazilian economy grew and the incidence of poverty increased ...
On the positive side,
(1) the distribution of schooling among workers shifted to the right,
(2) the poverty rate among HE workers fell, &
(3) the share participating in social security increased.
On the negative side,
(1) earnings inequality increased for all groups, particularly for ISEworkers,
(2) the poverty rate among the unemployed increased, &
(3) the poverty rate among WS and ISE workers increased.34
Level 2 LM indicators:
Summary of changes
Other changes include:
(1) share holding multiple jobs fell,
(2) share in wage employment increased, &
(3) employment share in services increased, mainly at
the expense of the share in agriculture.
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Conclusion
Brazil experienced little economic growth and
rising poverty. The labor market indicators show
both important improvements and important
deteriorations.
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A rich statistical profile is the very first step.
Deeper analysis of the data is required to firm up any
normative assessments of changing LM conditions.
Some of the types of deeper analysis required to go
from profile to policy are discussed during the LM core
course.
Next step: From LM profile to LM policy
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ADePT: From data to report
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Accepts individual- and household-level data in Stata and
SPSS format. Uses Stata for computations.
Minimal data preparation required from the users
Extensive diagnostics of possible problems with the data
ADePT is a tool for simulations and sensitivity analysis
Intuitive user-friendly interface
Tested on the datesets from more than 50 countries: LSMS,
HBS, DHS
Several thousand users in the WB, international research
institutions, universities, government agencies.
Expected increase in the number of users with the release
of the stand-alone ADePT
ADePT V4.0
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ADePT ROI:
Costs:
ADePT is a FREE software – zero monetary cost
ADePT requires minimal training – little cost in terms
of time and HR.
The tasks simplified by ADePT are present in any
process of production of analytical data – nothing new
needs to be introduced.
Benefits:
All the benefits +
Reduce the distance between the policymakers and
the data.
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Website: www.worldbank.org/adept
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