Employability AnalysisEmployability Analysis(Profile of the Economic Actors)(Profile of the Economic Actors)

World Bank

Washington, DC

March 24, 2009Leonardo Garrido

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:

2

1rk

k

y

y

c

cg

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 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”

3

Labor Force Employed mostly outside the Modern Sector in LDC

4

y = -8.5203x + 101.63

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6 7 8 9 10 11 12

Se

lf E

mp

loye

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of

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yed

LN(GDPpc PPP $ of 2005)

Ratio of Self Employed to total Employment vs Per Capita Income. Cross Country Data, 2001.

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6 7 8 9 10 11 12Wag

e an

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LN(GDPpc PPP $ of 2005)

Ratio of Waged and Salaried Workers to total Employment vs Per Capita Income. Cross Country Data, 2001.

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 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

5

Questions to be answered by an Employability Analysis:

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Q1: Has output growth been accompanied by employment generation and Q1: Has output growth been accompanied by employment generation and poverty reduction?poverty reduction?- Which are the growing sectors?- Which are the growing sectors?- In which sector is growth being accompanied by employment generation and / or productivity increases?- 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?- Are the poor benefitting from the observed employment and productivity increases?- Is poverty more responsive to productivity increases or employment increases?- Is poverty more responsive to productivity increases or employment increases?- Which sectors have a bigger effect on poverty?- 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 - Is the incidence of unemployment, underemployment, or low returns among the poor higher than the average? How big are the gaps?the gaps?- What has been the impact of growth and distributional changes over observed poverty? What has been the role of each - What has been the impact of growth and distributional changes over observed poverty? What has been the role of each component of labor income?component of labor income?

Q2: What is the employment and labor income profile of the population?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?- 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…- 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 Q3: What is the role of segmentation, labor supply, skill mismatch and labor demand?labor demand?- Returns to Education - Returns to Education - Composition of the labor force and skills mismatch- Composition of the labor force and skills mismatch- Decision to participate and probability of getting a “good” or “bad” job- Decision to participate and probability of getting a “good” or “bad” job- Estimating labor demand- Estimating labor demand

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 7

Demographic Analysis

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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 Analysis

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Most growth models do not distinguish between output per capita (Ypc) and output per unit of worker (Ype).

peYpcYemployed

Y

pop

Y

pop

popageworking

popageworking

forcelabor

forcelabor

employed

employed

Y

pop

Y __

__

_

_

prawrappmepeYpcY

In a demographic transition this is not necessarily true: In a demographic transition this is not necessarily true: Changes in fertility, mortality and migration patterns yields different Changes in fertility, mortality and migration patterns yields different

dynamics in population and labor force growth rate (for given dynamics in population and labor force growth rate (for given participation rates)participation rates)

emp = employment rateemp = employment rate par = participation ratepar = participation rate wapr = Working age population ratio to wapr = Working age population ratio to

total populationtotal population

Demographic analysis: Fertility and Mortality Rates

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Countries have a window of opportunity to cash in a “demographic dividend” if they take advantage of improvements in the age dependency ratio (adr)

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waprpopwap

popwapadr

y = -0.7393x + 9.8851

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Ferti

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LN (Per capita GDP in US$ of 2000)

Cross Country Fertility Rate and Per Capita Income. 2005

y = -0.1633x + 9.2049

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talit

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Cross Country Mortality Rate and Per Capita Income. 2005

Demographic analysis. Case example: Kenya

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After modeling population dynamics, labor force estimation can be calculated for assumptions on the participation rate Links to education and gender

goals

(20.0) (15.0) (10.0) (5.0) 0.0 5.0 10.0 15.0 20.0

0-45-9

10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-79

80+

Percentage of total population in cohort

Age

gro

up

Demographic Pyramid. Kenya. 2005-2030

2.0

2.5

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2.0%

2.5%

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4.5%

1999

2001

2003

2005

2007

2009

2011

2013

2015

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2019

2021

2023

2025

2027

2029 #

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ildre

n p

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the

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ars

pe

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Kenya: Estimated Total Fertility, Crude Birth and Standard Death rates. 1999-2030

Crude birth rate Standard death rate Total Fertility Rate

0.60.620.640.660.680.7

0.720.740.760.780.8

19

99

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(po

p<

15+

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p>

64)/

po

p15

64

Kenya: Age Dependency Ratio, model results. 1999-2030

Profile of the labor force

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Knowledge of the distribution of working age population and labor Knowledge of the distribution of working age population and labor force is essential to identify productive and non-included groupsforce is essential to identify productive and non-included groups

Profile of the labor force: Country specific and data intensive

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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

Profile of the labor force. Case example: Kenya

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15

Employment Profile Summary. Case Example: Kenya (apologies for tiny font)

Male Female Total Male Female Total Male Female Total Male Female Total Male Female Total Male Female TotalWorking age population (15-64 years) 5,380,666 5,807,895 11,188,561 2,396,212 2,348,069 4,744,281 7,776,878 8,155,964 15,932,842 7,176,937 7,677,228 14,854,165 2,201,258 2,212,051 4,413,309 9,378,195 9,889,279 19,267,474 -Inactive, of which: 1,389,271 1,556,271 2,945,542 303,020 329,284 632,304 1,692,291 1,885,555 3,577,846 2,065,411 2,375,246 4,440,657 514,533 836,815 1,351,348 2,579,944 3,212,061 5,792,005 -discouraged (1) 26,438 34,549 60,987 7,976 20,482 28,457 34,414 55,031 89,444 89,989 264,331 354,320 26,012 147,144 173,156 116,001 411,475 527,476 -full time student, of which: 707,684 564,061 1,271,745 235,779 209,189 444,967 943,462 773,250 1,716,712 1,517,564 1,263,106 2,780,670 319,335 274,057 593,392 1,836,899 1,537,163 3,374,062 *15-19 years 606,328 497,491 1,103,819 202,705 175,429 378,134 809,033 672,920 1,481,953 1,180,569 1,052,381 2,232,950 231,778 185,732 417,510 1,412,347 1,238,113 2,650,460 *20-24years 92,077 61,035 153,113 26,164 30,214 56,378 118,241 91,249 209,490 274,606 187,501 462,107 68,644 71,484 140,128 343,250 258,986 602,235 -Active 3,991,396 4,251,624 8,243,020 2,093,192 2,018,785 4,111,977 6,084,588 6,270,409 12,354,997 5,111,526 5,301,982 10,413,508 1,686,725 1,375,236 3,061,961 6,798,251 6,677,218 13,475,469 *Employed, of which: 3,652,290 3,804,842 7,457,132 1,821,518 1,246,958 3,068,476 5,473,808 5,051,800 10,525,608 4,614,363 4,750,246 9,364,609 1,424,758 1,006,299 2,431,057 6,039,121 5,756,545 11,795,666 -youth (15-29 years) 1,350,585 1,374,667 2,725,252 567,115 571,217 1,138,332 1,917,700 1,945,884 3,863,584 1,873,131 1,815,940 3,689,071 534,986 451,733 986,719 2,408,117 2,267,673 4,675,790 *Unemployed, of which: 339,105 446,781 785,887 271,673 771,827 1,043,500 610,779 1,218,609 1,829,387 497,163 551,736 1,048,899 261,966 368,937 630,903 759,129 920,673 1,679,802 -youth (15-29 years) 198,621 216,599 415,221 158,373 527,484 685,858 356,994 744,084 1,101,078 344,388 391,541 735,930 196,372 280,483 476,855 540,761 672,024 1,212,785

Employed, by status in employment 3,652,290 3,804,842 7,457,132 1,821,518 1,246,958 3,068,476 5,473,808 5,051,800 10,525,608 4,614,363 4,750,246 9,364,609 1,424,758 1,006,299 2,431,057 6,039,121 5,756,545 11,795,666 -Paid employment 1,192,167 367,797 1,559,964 1,348,239 604,737 1,952,976 2,540,406 972,534 3,512,940 1,427,573 615,306 2,042,879 970,084 460,796 1,430,880 2,397,657 1,076,102 3,473,759 -Business Owner 183,947 177,241 361,188 203,268 222,719 425,987 387,214 399,960 787,175 70,011 34,973 104,984 64,955 32,513 97,468 134,967 67,486 202,453 -Own Account Worker / Unpaid Family Worker 2,136,343 3,143,015 5,279,358 213,590 388,007 601,597 2,349,933 3,531,022 5,880,955 2,836,362 3,317,524 6,153,886 356,546 395,627 752,172 3,192,908 3,713,151 6,906,058 -Apprentice 8,690 2,972 11,662 4,075 0 4,075 12,765 2,972 15,737 4,964 4,581 9,545 2,282 2,231 4,513 7,246 6,812 14,058 -Other 31,274 28,367 59,641 34,416 5,903 40,319 65,690 34,270 99,960 37,316 27,007 64,323 7,349 7,477 14,826 44,665 34,484 79,149 -Missing (no data on status) 99,870 85,450 185,320 17,930 25,592 43,522 117,800 111,041 228,841 238,137 750,855 988,992 23,542 107,656 131,198 261,679 858,511 1,120,190

Employed, by employer 3,652,290 3,804,842 7,457,132 1,821,518 1,246,958 3,068,477 5,473,808 5,051,800 10,525,609 4,614,363 4,750,246 9,364,609 1,424,758 1,006,299 2,431,057 6,039,121 5,756,545 11,795,666 -Public sector 404,773 109,904 514,677 674,836 274,186 949,022 1,079,610 384,090 1,463,699 328,077 128,913 456,991 347,191 123,299 470,490 675,268 252,213 927,481 -Private, modern 314,400 114,822 429,222 410,745 203,622 614,367 725,146 318,444 1,043,589 265,343 133,621 398,963 176,979 95,705 272,684 442,322 229,325 671,647 -Informal sector 1,182,811 939546.3 2,122,357 635400.1 569187.6 1,204,588 1,818,211 1,508,734 3,326,945 877,992 971,279 1,849,270 767,071 670,362 1,437,433 1,645,063 1,641,641 3,286,704 -Small agricultural sector, pastoralist activities 1,705,223 2,570,994 4,276,217 65,451 86,550 152,001 1,770,674 2,657,544 4,428,218 3,119,818 3,497,375 6,617,193 110,835 94,384 205,219 3,230,653 3,591,759 6,822,412 -Other employer 44,654 68,337 112,991 35,086 113,413 148,499 79,740 181,750 261,490 13,946 11,803 25,749 21,571 21,973 43,544 35,517 33,776 69,293 -Missing (no data on employer) 429 1,239 1,668 0 0 0 429 1,239 1,668 9,188 7,255 16,443 1,111 575 1,686 10,299 7,831 18,130

Employed, by activity (*) 3,652,290 3,804,842 7,457,132 1,821,518 1,246,958 3,068,476 5,473,808 5,051,800 10,525,608 4,614,363 4,750,246 9,364,609 1,424,758 1,006,299 2,431,057 6,039,121 5,756,545 11,795,666 -Primary 2,379,508 3,208,097 5,587,604 86,821 100,405 187,226 2,466,329 3,308,502 5,774,830 3,094,387 3,753,313 6,847,700 96,777 93,467 190,244 3,191,164 3,846,780 7,037,944 -Secondary 214,430 30,905 245,335 265,752 40,719 306,471 480,182 71,624 551,806 327,974 69,974 397,948 272,925 48,398 321,323 600,898 118,372 719,270 -Tertiary 1,058,352 565,840 1,624,193 1,468,946 1,105,834 2,574,780 2,527,298 1,671,674 4,198,972 1,192,003 926,959 2,118,962 1,055,056 864,434 1,919,490 2,247,059 1,791,393 4,038,452Public Sector 404,773 109,904 514,677 674,836 274,186 949,022 1,079,610 384,090 1,463,699 328,077 128,913 456,991 347,191 123,299 470,490 675,268 252,213 927,481 -Primary 83,534 12,197 95,731 27,546 9,779 37,325 111,079 21,976 133,056 47,071 17,063 64,135 12,809 2,572 15,381 59,880 19,636 79,515 -Secondary 18,080 742 18,822 26,254 507 26,761 44,334 1,249 45,584 13,932 505 14,437 27,504 3,713 31,217 41,436 4,218 45,655 -Tertiary 303,159 96,964 400,124 621,036 263,900 884,936 924,196 360,864 1,285,060 267,074 111,345 378,419 306,878 117,014 423,892 573,952 228,359 802,311Private Sector 3,247,517 3,694,939 6,942,455 1,146,682 972,772 2,119,454 4,394,199 4,667,711 9,061,909 4,286,286 4,621,333 8,907,619 1,077,567 883,000 1,960,567 5,363,853 5,504,332 10,868,186 -Primary 2,295,974 3,195,900 5,491,874 59,275 90,626 149,901 2,355,249 3,286,525 5,641,775 3,047,316 3,736,249 6,783,565 83,968 90,895 174,863 3,131,284 3,827,144 6,958,429 -Secondary 196,350 30,163 226,513 239,498 40,212 279,710 435,847 70,375 506,222 314,042 69,469 383,510 245,421 44,685 290,106 559,462 114,154 673,616 -Tertiary 755,193 468,876 1,224,069 847,909 841,934 1,689,844 1,603,102 1,310,810 2,913,913 924,929 815,614 1,740,543 748,178 747,420 1,495,598 1,673,107 1,563,034 3,236,141

Employed, by education attainment 3,652,290 3,804,842 7,457,132 1,821,518 1,246,958 3,068,477 5,473,808 5,051,800 10,525,609 4,614,363 4,750,246 9,364,609 1,424,758 1,006,299 2,431,057 6,039,121 5,756,545 11,795,666 -Primary education 2,138,695 2,098,032 4,236,727 570,302 506,078 1,076,380 2,708,997 2,604,110 5,313,107 2,647,994 2,719,135 5,367,129 559,598 412,262 971,860 3,207,592 3,131,397 6,338,989 -Secondary education 997,548 627,739 1,625,287 1,062,300 592,892 1,655,192 2,059,848 1,220,631 3,280,479 1,318,040 981,688 2,299,728 719,084 466,507 1,185,592 2,037,124 1,448,196 3,485,320 -Tertiary education 26,834 16,089 42,924 121,334 35,356 156,691 148,169 51,446 199,614 41,163 13,081 54,244 83,277 29,450 112,727 124,440 42,531 166,971 -No education / Missing (no data on attainment) 489,213 1,062,982 1,552,195 67,582 112,633 180,215 556,795 1,175,615 1,732,409 607,166 1,036,341 1,643,507 62,799 98,080 160,879 669,965 1,134,422 1,804,386

Child labor 277,972 216,311 494,283 6,049 33,457 39,506 284,021 249,767 533,789 284,625 222,064 506,689 5,898 20,263 26,162 290,523 242,328 532,850 -5-9 years 97,738 78,751 176,489 2,287 2,634 4,921 100,025 81,385 181,410 91,353 63,990 155,342 1,320 6,620 7,940 92,672 70,610 163,282 -10-14 years 180,234 137,559 317,794 3,762 30,823 34,585 183,996 168,382 352,378 193,272 158,075 351,347 4,579 13,643 18,222 197,851 171,718 369,569

Employed with earnings below poverty line (2) 530,344 219,825 750,168 478,829 298,086 776,916 1,009,173 517,911 1,527,084 650,622 367,570 1,018,192 345,799 238,786 584,585 996,421 606,356 1,602,777Underemployment, time-related (work<28 hours/week) (3) 959,467 1,406,522 2,365,989 188,393 145,920 334,313 1,147,860 1,552,442 2,700,302 913,146 1,224,208 2,137,354 159,433 166,147 325,580 1,072,579 1,390,355 2,462,935 -of which: youth (15-29 years) time-underemployment 411,667 531,941 943,608 55,572 68,514 124,086 467,239 600,455 1,067,694 387,505 483,073 870,578 60,065 56,198 116,263 447,570 539,271 986,841Underemployment, employed want different work (4) 750,437 346,000 1,096,437 417,538 234,689 652,227 1,167,975 580,688 1,748,663 n/a n/a n/a n/a n/a n/a n/a n/a n/a -of which: youth (15-29 years) underemployment 444,913 245,253 690,167 216,373 135,589 351,962 661,286 380,842 1,042,128 n/a n/a n/a n/a n/a n/a n/a n/a n/aSearch unemployment (5) 93,501 57,072 150,573 107,680 129,951 237,631 201,182 187,022 388,204 407,174 286,826 694,000 235,632 221,138 456,769 642,806 507,964 1,150,769

Good quality employment (6) 661,824 147,973 809,796 869,410 306,651 1,176,061 1,531,234 454,623 1,985,857 776951.1 247736.3 1,024,687 624285.5 222009.6 846,295 1,401,237 469,746 1,870,983

Informal employment, by status 1,182,811 939,546 2,122,357 635,400 569,188 1,204,588 1,818,211 1,508,734 3,326,945 877,992 971,279 1,849,270 767,071 670,362 1,437,433 1,645,063 1,641,641 3,286,704 -Business employer 183,947 177,241 361,188 203,268 222,719 425,987 387,214 399,960 787,175 26,273 16,798 43,071 43,672 27,907 71,579 69,945 44,705 114,650 -Own account worker / unpaid family worker 734,767 683,491 1,418,258 156,850 237,966 394,816 891,617 921,457 1,813,074 535,777 638,775 1,174,552 276,080 331,783 607,863 811,857 970,558 1,782,416 -Paid employment 241,825 69,640 311,464 255,357 96,034 351,391 497,182 165,673 662,855 282,732 180,769 463,501 425,100 230,241 655,340 707,832 411,009 1,118,841 -Other informal 22,273 9,175 31,448 19,925 12,469 32,393 42,198 21,644 63,841 33,210 134,937 168,147 22,219 80,432 102,651 55,429 215,369 270,798

Total 2005/06KIHBS'05/06ILFS'98

Rural Urban Total 1998 Rural Urban

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Earnings Profile Summary. Case Example: Kenya

Male Female All Male Female All Male Female AllWages 6,496 5,235 6,149 6,328 4,595 5,684 -0.4% -1.8% -1.1%Paid employment 6,696 5,457 6,370 6,669 5,863 6,409 -0.1% 1.0% 0.1%Working employer 5,919 3,598 5,481 12,950 10,036 11,995 11.8% 15.8% 11.8%Own account worker 5,435 2,962 4,580 4,902 2,368 3,699 -1.5% -3.2% -3.0%Unpaid family worker 4,208 3,937 4,098 2,883 1,281 2,140 -5.3% -14.8% -8.9%Apprentices 9,302 . 9,302 2,888 3,141 2,998 -15.4% -14.9%Other 4,106 2,370 3,786 4,452 1,411 3,235 1.2% -7.1% -2.2%Not Specified 5,638 5,634 5,637 4,891 3,139 3,884 -2.0% -8.0% -5.2%

Earnings* 6,582 4,837 5,955 7,469 5,152 6,554 1.8% 0.9% 1.4%Paid employment 7,548 5,688 7,061 7,906 6,735 7,531 0.7% 2.4% 0.9%Working employer 9,558 6,510 8,122 24,746 16,651 22,233 14.6% 14.4% 15.5%Own account worker 4,733 3,932 4,371 7,989 5,337 6,855 7.8% 4.5% 6.6%Unpaid family worker 4,228 3,851 4,075 3,208 2,412 2,879 -3.9% -6.5% -4.8%Apprentices 11,628 1,163 9,012 4,722 3,141 4,114 -12.1% 15.3% -10.6%Other 6,814 2,608 5,907 5,805 3,060 4,856 -2.3% 2.3% -2.8%Not Specified 5,888 4,810 5,512 3,616 2,525 3,003 -6.7% -8.8% -8.3%

Wages by EmployerPrivate Sector 6,914 5,785 6,673 10,538 9,083 10,130 6.2% 6.7% 6.1%Public Sector 10,500 9,391 10,177 13,311 13,005 13,211 3.4% 4.8% 3.8%Informal Sector 4,842 2,859 4,360 4,194 2,621 3,473 -2.0% -1.2% -3.2%Small Scale Agriculture 2,441 2,506 2,460 2,338 1,376 2,042 -0.6% -8.2% -2.6%Other Sectors 3,684 1,587 2,498 18,406 12,949 16,041 25.8% 35.0% 30.4%Not Specified 5,517 5,884 5,644 4,301 4,166 4,238 -3.5% -4.8% -4.0%

Earnings by EmployerPrivate Sector 7,900 6,334 7,567 15,466 12,468 14,585 10.1% 10.2% 9.8%Public Sector 11,571 9,520 10,978 15,959 14,209 15,403 4.7% 5.9% 5.0%Informal Sector 5,727 4,374 5,160 9,117 5,414 7,320 6.9% 3.1% 5.1%Small Scale Agriculture 2,864 2,855 2,861 2,029 1,128 1,734 -4.8% -12.4% -6.9%Other Sectors 4,967 1,674 3,116 23,837 19,244 21,834 25.1% 41.7% 32.1%Not Specified 5,816 4,744 5,449 3,391 2,510 2,952 -7.4% -8.7% -8.4%

1998 2005 %Change per YearMean monthly Real Wages and Earnings by Selected Cohorts. KSh. Of 2000

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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

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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

19

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

20

Segmentation: Oaxaca-Blinder Method

ssjsjsj XXXww lnln j,s = labor market segments (say, public sector and private modern j,s = labor market segments (say, public sector and private modern

sector)sector) wwj , j , wwss = earnings in segments j and s = earnings in segments j and s

XXjj, X, Xss = average observed characteristics between segments (say, = average observed characteristics between segments (say,

average education and experience)average education and experience) jj, , s s = Returns to specific characteristics= Returns to specific characteristics

The model says that earning differential across sectors reflect, The model says that earning differential across sectors reflect, differences in observed characteristics and also differences in differences in observed characteristics and also differences in returns to individual characteristics.returns to individual characteristics.

21

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

0lnln0,

1,

0,

1,

j

j

j

j

Emp

Emp

w

wsign

Measure of skills mismatch in non-competitive labor marketsMeasure of skills mismatch in non-competitive labor markets– From calculations of unemployment rates among different educational groups (uFrom calculations of unemployment rates among different educational groups (u ii) compared to ) compared to

overall unemployment rate (u). Also, if unemployment is considered a not adequate measure of 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 (bexcess labor supply, one could add to the ratio figures of “bad jobs” per educational group (b ii) and ) and

overall bad jobs in the economy (b).overall bad jobs in the economy (b).

u

umm ivar

2

11

bu

bumm iivar

2

12

22

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)

iiii ZwH 210*

iii Xw 10

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

H=0 otherwise

iii ZXHP ,/0

iiii vZX 21010

23

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