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Employability Analysis (Profile of the Economic Actors). World Bank Washington, DC March 24, 2009 Leonardo Garrido. Why do we need an Employability Analysis?. Inclusive Growth: Concerned about the pace and pattern of economic growth

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employability analysis profile of the economic actors

Employability Analysis(Profile of the Economic Actors)

World Bank

Washington, DC

March 24, 2009

Leonardo Garrido

why do we need an employability analysis
Why do we need an Employability Analysis?
  • Inclusive Growth: Concerned about the pace and pattern of economic growth
    • Rapid and sustained poverty reduction requires I.G., which allows previously non-included sectors to contribute and benefit from growth.
    • Growth should be broad based across sectors and inclusive of a large part of the country’s labor force
  • Growth Diagnostics fundamental equation addresses the issue of low returns to investments and entrepreneurship:
  • But being mainly directed to the analysis of businesses, it mostly overlooks a more fundamental question:
    • Are all economic actors properly endowed to benefit from and participate in the economic activity?
    • Non-Included groups may represent a significant share of population
growth diagnostics largely ignores employability issues
Growth Diagnostics largely ignores employability issues
  • Based on models that ignore dynamics of HK accumulation
  • Under Neo-Classical growth models, HK accumulation does not play a role in explaining long term growth rate or across country differences in per capita income
    • In fact, in Solow-Swan model, growth is explained by the exogenously defined growth rate of technology
      • See Ianchovichina’s presentation, slides # 5-6
    • Endogenous growth model help explain long term growth rate but leave still unanswered questions on cross country income differences
    • Growth model with HK partially help to bridge the gap
    • Vensim example
      • Solow model with HK and differential growth rates in population and labor force
      • “Students” and “Workers”
labor force employed mostly outside the modern sector in ldc
Labor Force Employed mostly outside the Modern Sector in LDC
  • Self-employment, non wage employment is higher in LDC.
  • Agricultural, informal self employment also absorbs a significant share of the employment in LDC.
  • Low employment rates in LDC masks issues of underemployment or employment at subsistence levels.
  • Substantial fraction of employed population receive earnings close to or below the poverty line.
existing body of research on hk helpful to guide employability analysis
Existing body of research on HK helpful to guide employability analysis
  • Nelson and Phelps (1966) : formulates hypothesis on the relationship of HK structure and technological progress.
    • HK helps speed the process of technology diffusion, not the advance of the technological frontier.
  • Lucas (1988) : Includes HK as an ordinary input in the production function.
    • Dynamics of both, HK and Physical Capital are explored in steady state analysis.
  • Benhabib and Spiegel (1994): Growth unrelated to Educational Attainment
    • HK significant if interacted with level of technology
  • Mankiw (1995) : HK has transitional but not perpetual effect on growth
    • Finite lifetimes determine a maximum limit to the amount of HK that can be accumulated
  • Jones (2002): long-run growth arises from the worldwide discovery of ideas, which depends on population growth
    • Growth can temporarily accelerate with education and research intensity
  • White and Anderson (2001): Consider distributional issues (patterns of growth) in analyzing determinants of growth.
  • Benhabib and Spiegel (2004): Find empirical support to Nelson and Phelps hypothesis.
  • Lutz, Crespo Cuaresma and Sanderson (2008): Succeed on linking changes in HK and growth
    • By means of improved data on demographics of educational attainment
questions to be answered by an employability analysis
Questions to be answered by an Employability Analysis:
  • Q1: Has output growth been accompanied by employment generation and poverty reduction?
  • - Which are the growing sectors?
  • - In which sector is growth being accompanied by employment generation and / or productivity increases?
  • - Are the poor benefitting from the observed employment and productivity increases?
  • - Is poverty more responsive to productivity increases or employment increases?
  • - Which sectors have a bigger effect on poverty?
  • - Is the incidence of unemployment, underemployment, or low returns among the poor higher than the average? How big are the gaps?
  • - What has been the impact of growth and distributional changes over observed poverty? What has been the role of each component of labor income?
  • Q2: What is the employment and labor income profile of the population?
  • - Which are the sectors in which the poor are working? Which are the Non Included sectors?
  • - What are the characteristics of the Labor Force? Education, Health Status, Endowments, Employment Status…
  • Q3: What is the role of segmentation, labor supply, skill mismatch and labor demand?
  • - Returns to Education
  • - Composition of the labor force and skills mismatch
  • - Decision to participate and probability of getting a “good” or “bad” job
  • - Estimating labor demand
elements of an employability analysis
Elements of an Employability Analysis
  • Study of demographic trends (Answer Q2)
    • To analyze dynamics of the population and labor force
    • To ascertain expected changes in participation rates
      • Jesus Crespo Cuaresma presentation
  • Dynamics of Output, Poverty and Employment (Answer Q1)
    • Growth decomposition in employment and productivity changes
      • Paul Cichello’s presentation
    • Poverty – Growth links
      • Kenneth Simler and Roy Katayama’s presentation
  • Profile of the Labor Force (Answer Q2)
    • Analysis of selected labor groups:
      • Employed vs unemployed (and underemployed)
      • Agricultural, Informal, Self employed vs Modern Employees
      • Rural vs Urban + Poor vs non poor (i.e. Poor Rural vs Poor Urban)
      • Selected Economic Activities
  • Labor Market Analysis (Answer Q3)
    • Returns to Education, segmentation and skills mismatchs
    • Estimating labor demand, probability of participation and probability of getting “good” jobs
demographic analysis
Demographic Analysis
  • Relevant for Inclusive Growth analysis if at least one of the following is expected to occur during the relevant period:
    • A demographic transition
      • Changes in fertility and / or mortality rates
    • Changes in migration patterns
      • Internal and / or across the border
    • Changes in participation rates
      • Mainly linked to changes in schooling and / or increased participation of female in the labor market
    • Demographic shock
      • Fragile or post-conflict states.
      • Natural disasters
    • HIV / AIDS or any other epidemics affecting population stock and / or leading to changes in morbidity rates
demographic analysis1
Demographic Analysis
  • Most growth models do not distinguish between output per capita (Ypc) and output per unit of worker (Ype).
  • In a demographic transition this is not necessarily true:
    • Changes in fertility, mortality and migration patterns yields different dynamics in population and labor force growth rate (for given participation rates)
  • emp = employment rate
  • par = participation rate
  • wapr = Working age population ratio to total population
demographic analysis fertility and mortality rates
Demographic analysis: Fertility and Mortality Rates
  • Countries have a window of opportunity to cash in a “demographic dividend” if they take advantage of improvements in the age dependency ratio (adr)
demographic analysis case example kenya
Demographic analysis. Case example: Kenya
  • After modeling population dynamics, labor force estimation can be calculated for assumptions on the participation rate
    • Links to education and gender goals
profile of the labor force
Profile of the labor force
  • Knowledge of the distribution of working age population and labor force is essential to identify productive and non-included groups
profile of the labor force country specific and data intensive
Profile of the labor force: Country specific and data intensive
  • Every Inclusive Growth analysis reveals particular issues of interest regarding the Labor Force:
    • Tajikistan: Migration, cotton workers
    • Zambia: Poor agricultural farmers
    • Mongolia: Skills mismatch and poor agricultural farmers
    • Benin: Informal economy
    • Kenya: informal economy and youth employment
  • Macro data alone is insufficient to generate a profile of economic actors
    • LSMS data
    • Labor Force Surveys
    • DHS data
using adept labor for micro survey data analysis
Using Adept Labor for (micro) Survey Data analysis
  • ADePT Labor is an integrated set of programs that allows users to produce tables for analysis of labor market conditions in low- and middle-income countries. It includes indicators:
      • For assessing labor market conditions and how they evolve in developing countries:
        • “A Guide for Assessing Labor Market Conditions in Developing Countries”
      • For understanding how growth is affecting earnings and employment of the different income segments of the population
        • “The Role of Employment and Labor Income in Shared Growth: What to Look For and How”
        • http://siteresources.worldbank.org/INTEMPSHAGRO/Resources/RoleOfJobsForSharedGrowth.pdf
  • Employment and shared growth website
    • http://go.worldbank.org/M33YHN6CS0
a quick demo using adept labor case example kenya
A Quick Demo Using ADePT Labor. Case Example: Kenya
  • Main labor market indicators
    • 1.1.- Main indicators of the labor market
    • 1.2.- Hierarchical decomposition of the labor force (levels, rates)
    • 1.3.- Employment categories, shares in total employment
    • 1.4.- Earnings, poverty and inequality by employment category
    • 1.5.- Distribution of employment by selected characteristics (Economic sector and education)
    • 1.6.- Earnings inequalities by level of education. (Gini coefficient and Theil Index)
    • 1.7.- Earnings inequalities by sector of economic activity
  • Linking poverty and labor markets
    • 2.1.- Poverty headcount rate of the working age population, by rural/urban and employment status (individual and HH head)
    • 2.2.- Poverty headcount rate of the working age population, by employment category and urban/rural (individual and HH head)
    • 2.3.- Poverty headcount rates of working age population by sector of employment (individual and HH head)
    • 2.4.- Distribution of working age population by poverty and employment status (individual and HH head)
    • 2.5.- Distribution of working age population by poverty and individual sector of employment (levels, shares)
    • 2.6.- Distribution of employment by poverty and employment category (individual and HH head)
  • Disaggregation on main indicators
    • A.1.- Unemployment rates among selected groups
    • A.2.- Employment among selected groups
    • A.3.- Child labor rates, by groups
    • A.4.- Earnings by selected groups
    • A.5.- Low earnings rates
    • A.6.- share of low earners with low earnings due to short hours
    • A.7.- share of low earners who work full time hours or more
    • A.8.- Broad unemployment rate
    • A.9. , A10- Poverty rate among unemployed and low earners
labor market analysis
Labor market analysis
  • Dynamics of human capital
    • Returns to education
      • Mincer specification (See Jesus Crespo Cuaresma presentation)
  • Segmentation:
    • Estimating differences in return to individual characteristics
      • Oaxaca-Blinder method
  • Labor supply and mismatch of skills
    • Composition of labor force
    • Mismatch tests
      • Katz & Murphy (1992) and Murphy & Welch (1993)
      • Layard, Nickel & Jackman (1991)
  • Decision to participate and probability of getting a “good” job
    • Probit specifications
  • Estimating labor demand
    • Static and dynamic specifications
segmentation oaxaca blinder method
Segmentation: Oaxaca-Blinder Method
  • j,s = labor market segments (say, public sector and private modern sector)
  • wj , ws = earnings in segments j and s
  • Xj, Xs = average observed characteristics between segments (say, average education and experience)
  • bj, bs= Returns to specific characteristics
  • The model says that earning differential across sectors reflect, differences in observed characteristics and also differences in returns to individual characteristics.
skills mismatch
Skills mismatch
  • Test of change of skill premium times change in relative supply
    • If the relative demand of 2 groups (say, educational categories, or sectors of economic activity) is stable, then the increase in relative supply of a group must lead to a reduction of a relative wage in that group
  • Measure of skills mismatch in non-competitive labor markets
    • From calculations of unemployment rates among different educational groups (ui) compared to overall unemployment rate (u). Also, if unemployment is considered a not adequate measure of excess labor supply, one could add to the ratio figures of “bad jobs” per educational group (bi) and overall bad jobs in the economy (b).
decision to participate and probability of getting a good job
Decision to participate and probability of getting a good job
  • Based on econometric specifications for the number of hours an individual ‘i’ works (Hi) as a function of a vector of determinants (Xi) of the wage rate (wi) and other individual characteristics that influence participation in the labor market (Zi)

If one defines H as: H=1 if Hi*>=0

H=0 otherwise

labor demand equations
Labor Demand Equations
  • Static approach: Estimating long run labor demand
    • Output elasticity of labor demand
    • Elasticity of substitution among production inputs
    • Own wage demand elasticity
  • Dynamic approach: Estimating short term adjustments in labor demand