M A R I YA A L E K S Y N S KA( j o i n t w i t h J a n i n e B e r g )

I L O

D E V E L O P I N G A N D I M P L E M E N T I N G P O L I C I E S F O R A B E T T E R F U T U R E AT W O R K

G E N E VA , J U LY 2 0 1 5

Understanding Firm Demand for Temporary Labour in Developing

Countries

I. Introduction

Temporary employment prompts policy concerns

Most of the research done for developed countries

What factors explain the use of temporary labour in developing countries? Is this part of development or firm preference? Do labour market institutions play a role?

II. Hypotheses

Flexibility Cost saving Technology

o Fluctuations in demand (seasonality, business cycle)

o Competitiono External shocks

Balancing stability and flexibility of workforce:

core vs periphery

o Hiring costs (recruitment and firm-specific training vs screening)

o On-the-job (wages, bonuses, paid leave, social security etc)

o Termination costs (especially as compared to terminating permanent contracts)

o Extent of standardisation

o Computerizationo Firm-specific know-

how

World Bank Enterprises Survey 118 countries, over 72,000 observations, 2006-

2014 Other data sources: WB for GDP, ILO for

unemployment and EPL data

III. Data Description

IV. Descriptive Statistics

Incidence of temporary employment, as % of total wage employment, in private sector, circa 2010

The mean share of temporary workers is 11%Only about 40% of all firms employ temporary

labourAmong those that do, the mean share is

27,5%

Distribution of the firm-level number of temporary employees, as % of total employment, in firms employing

at least 1 temporary worker

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De

nsity

0 .2 .4 .6 .8 1temporary employees as % of all employees, employed by a firm in fiscal yearkernel = epanechnikov, bandwidth = 0.0230

Kernel density estimate

Firms that do not employ temporary labour are smaller in size and those not offering training; they have otherwise

largely similar characteristics to firms employing temporary workers.

Incidence of temporary employment, by sector

M/S Sector Mean percent of temporary workers per

firm

M Textiles 0.24

M Leather 0.32

M Garments 0.27

M Food 0.28

M Metals and machinery 0.25

M Electronics 0.25

M Chemicals and pharmaceuticals 0.22

M Wood and furniture 0.31

M Non-metallic and plastic materials 0.28

M Auto and auto components 0.20

M Other manufacturing 0.25

S Retail and wholesale trade 0.26

S Hotels and restaurants 0.29

S Construction, Transportation 0.39

S Other services 0.26

Incidence of temporary employment, by sector

M/S Sector Mean percent of temporary workers per

firm

M Textiles 0.24

M Leather 0.32

M Garments 0.27

M Food 0.28

M Metals and machinery 0.25

M Electronics 0.25

M Chemicals and pharmaceuticals 0.22

M Wood and furniture 0.31

M Non-metallic and plastic materials 0.28

M Auto and auto components 0.20

M Other manufacturing 0.25

S Retail and wholesale trade 0.26

S Hotels and restaurants 0.29

S Construction, Transportation 0.39

S Other services 0.26

Temporary employees and country income

Afghanistan2008

Albania2007

Albania2013

Angola2006

Angola2010

Antiguaandbarbuda2010

Armenia2009

Armenia2013

Azerbaijan2009

Azerbaijan2013

Bahamas2010

Bangladesh2007

Bangladesh2013

Barbados2010

Belarus2008Belarus2013Belize2010

Benin2009

Bhutan2009

Bolivia2006

Bolivia2010

Bosnia and Herzegovina2009

Bosnia and Herzegovina2013

Botswana2006

Botswana2010

Brazil2009

Bulgaria2007

Bulgaria2009

Bulgaria2013

BurkinaFaso2009

Burundi2006

Cameroon2009

CapeVerde2009

Centralafricanrepublic2011

Chad2009

Chile2006Chile2010

China2012

Colombia2006

Colombia2010

Congo2009

Costarica2010

Croatia2007Croatia2013 Czech Republic2009

Côte d'Ivoire2009DRC2006

DRC2010

DRC2013

Djibouti2013

Dominica2010

DominicanRepublic2010

Ecuador2006ElSalvador2006Elsalvador2010

Eritrea2009 Estonia2009

Ethiopia2011

Fiji2009

Fyr Macedonia2009

Fyr Macedonia2013

Gabon2009Gambia2006

Georgia2008

Georgia2013Ghana2007

Grenada2010Guatemala2006

Guatemala2010

Guinea2006GuineaBissau2006

Guyana2010

Honduras2006

Honduras2010

Hungary2009

Indonesia2009

Iraq2011

Jamaica2010 Kazakhstan2009Kazakhstan2013

Kenya2007

Kenya2013

Kosovo2009

Kosovo2013

Kyrgyz Republic2009

Kyrgyz Republic2013

LaoPDR2009

LaoPDR2012

Latvia2009

Latvia2013

Lesotho2009

Liberia2009

Lithuania2009Lithuania2013

Madagascar2009Malawi2009

Mali2007

Mali2010Mauritania2006

Mauritius2009

Mexico2006

Mexico2010

Micronesia2009

Moldova2009

Moldova2013

Mongolia2009Mongolia2013

Montenegro2009

Montenegro2013

Mozambique2007

Namibia2006Nepal2009

Nepal2013

Nicaragua2006

Nicaragua2010

Niger2009

Nigeria2007

Pakistan2007

Panama2006

Panama2010

Paraguay2006

Paraguay2010

Peru2006

Peru2010

Philippines2009

Poland2009

Romania2009

Romania2013

Russia2009Russia2012

Rwanda2006

Rwanda2011

Samoa2009

Senegal2007

Serbia2009

Serbia2013

Sierra Leone2009

SouthAfrica2007

SriLanka2011

StKittsandNevis2010

StLucia2010

StVincentandGrenadines2010

Suriname2010

Swaziland2006

Tajikistan2008

Tajikistan2013

Tanzania2006

Tanzania2013Timor Leste2009

Togo2009

Tonga2009

TrinidadandTobago2010

Turkey2008

Uganda2006

Uganda2013

Ukraine2008

Ukraine2013

Uruguay2006Uruguay2010

Vanuatu2009

Venezuela2006Venezuela2010

Vietnam2009

Yemen2010

Zambia2007

Zambia2013

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20 22 24 26 28 30GDP PPP, in logs

Correlation: -0.137

Distribution of temporary employees, as per cent of firm’s workforce, by legal regulations governing fixed-term work

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0 .2 .4 .6 .8 1x

FTCs prohibited for permanent tasks FTCs authorized for permanent tasks

Temporary employees and EPL

Afghanistan2008

Afghanistan2014

Angola2006

Angola2010

Antiguaandbarbuda2010

Argentina2010

Armenia2009

Azerbaijan2009

Bangladesh2007

Bangladesh2013

Bulgaria2013

BurkinaFaso2009

Cameroon2009

Centralafricanrepublic2011

Chile2010

China2012 Czech Republic2009

Côte d'Ivoire2009DRC2006

DRC2010

DRC2013

Elsalvador2010

Ethiopia2011

Fyr Macedonia2009

Fyr Macedonia2013

Gabon2009

Georgia2008

Georgia2013 Honduras2010

Hungary2009

Indonesia2009

Lesotho2009

Madagascar2009Malawi2009

Mexico2006

Mexico2010

Moldova2009

Moldova2013

Mongolia2009Mongolia2013

Montenegro2013

Niger2009

Nigeria2007

Panama2006

Panama2010

Peru2010

Philippines2009

Romania2009

Romania2013

Russia2012

Rwanda2011

Senegal2007

Serbia2013

SouthAfrica2007

SriLanka2011

StLucia2010

Tanzania2006

Tanzania2013

Uganda2013

Venezuela2010

Vietnam2009

Yemen2010

Zambia2013

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.2 .4 .6 .8Level of employment protection, regular contracts

Correlation: 0.029

V. Empirical framework

Temp_share_all ijkt = αijk + β1i Xi + β2iYi + jj + kk + tt+ εijkt

Temp_share_all ij - share of temporary labour in firm i operating in sector j country k and year t

Xi - the set of individual baseline firm characteristics

Yi - set of additional individual firm characteristics : flexibility, cost, and technology factors

jj, kk , tt - sector, country, and year, by including the corresponding dummies

εijkt - error term

Estimations Step 1: Internal Factors VARIABLES (1)

  Flexibility

National market 0.00657***

(0.00192)International market 0.0175***

(0.00368)

Informal competition 0.0107***

(0.00160)

Sales volatility 2.11e-14***

-4.14E-15

Employment ratio 0.0136***

(0.00220)Employment ratio (start) 0.000143***

(3.67e-05)Finance is an obstacle 0.00392***

(0.000569)

Constant 0.274***

(0.0211)

Observations 57,033

R-squared 0.184

 VARIABLES (2)

  Cost

Total labour cost0.00286***

(0.000578)

Training 0.0117***

(0.00184)Regulation obstacle

0.00471***

(0.000849)

Education obstacle

0.00283***

(0.000734)

Constant 0.373***

(0.0220)

Observations 48,752

R-squared 0.125

 VARIABLES (3)

 Technolog

yTelecoms problem 0.00562***

(0.000912)Certification -0.0108***

(0.00285)Borrowed technology 0.00535*

(0.00308)Constant 0.397***

(0.0654)

Observations 27,336R-squared 0.121

Estimations Step 2: External FactorsMacro Macro, EPL MACRO,

EPL, FTCMACRO,

EPL, FTC, coverage

Low-middle income -0.00134 0.00213 -0.00683 -0.0573***(0.0154) (0.0216) (0.0229) (0.0131)

Upper-middle income -0.0503*** -0.0517** -0.0503** -0.0766***(0.0165) (0.0196) (0.0200) (0.0186)

High-income -0.0512*** -0.0463** -0.0555** -0.142***(0.0155) (0.0213) (0.0248) (0.0212)

GDPgrowth 0.000910 0.000280 0.000323 0.00128(0.00130) (0.00183) (0.00154) (0.00139)

GDPgrowth_3y_lag -0.000388 0.000811 -0.000832 0.000984

(0.000720) (0.000900) (0.00117) (0.00205)Unemployed 0.00101 0.000458 -0.000362 -0.00186

(0.000743) (0.000929) (0.00107) (0.00112)EPLEX 0.0161 0.0914 0.175

(0.0724) (0.0581) (1.156)EPL Coverage 0.106

(0.493)EPLEX*Coverage -0.0572

(1.227)FTC prohib perm -0.0359** -0.0819***

(0.0163) (0.0139)FTC dur unlim 0.00361 0.00812

(0.0143) (0.0165)Constant 0.148*** 0.112** 0.116** 0.0942

(0.0278) (0.0519) (0.0552) (0.472)

Observations 39,126 19,940 19,940 14,803R-squared 0.137 0.139 0.144 0.158

Estimations Step 3: Disagregations by Sector and Country Income,

selected results

 VARIABLES (1) (2) (3) (4)

Manuf Services Lower income Upper income

         

International market 0.0215*** 0.00690 0.0225*** 0.0117**

(0.00445) (0.0119) (0.00672) (0.00487)

Sales volatility 2.11e-14*** -3.54e-14 2.29e-14** 2.07e-14***

-4.14E-15 (2.86e-14) (9.74e-15) (5.79e-15)Employment ratio (start) 0.000137*** 8.07e-05 0.000240** 0.000100**

(4.28e-05) (0.000107) (0.000112) (4.52e-05)Regulation obstacle 0.00483*** 0.00160 0.00431*** 0.00340***

(0.00106) (0.00171) (0.00157) (0.00105)Education obstacle 0.00337*** 0.00161 0.00421*** 0.00175*

(0.000940) (0.00139) (0.00131) (0.000920)

Observations 27,978 12,698 18,337 22,339

R-squared 0.171 0.245 0.185 0.174

Key message: relevance of micro-factors varies across sectors rather than across levels of

development

Estimations Step 3: Disagregations by Sector and Country Income,

selected results  (1) (2) (3) (4)

Manuf Services Lower income Upper income          Valid grounds 0.0695*** 0.0676*** 0.148*** 0.255***

(0.0257) (0.0247) (0.0315) (0.0482)Prohibited grounds 0.0303 0.00604 0.0387 0.0782***

(0.0245) (0.0208) (0.0261) (0.0236)Trial period -0.0119 -0.0390 -0.108*** 0.0472*** (0.0217) (0.0215) (0.0288) (0.0116)Procedural requirements 0.0213 0.00627 -0.0401 -0.00673

(0.0303) (0.0201) (0.0415) (0.0146)Notice period 0.106 0.0189 -0.00941 0.152** (0.0919) (0.0663) (0.0833) (0.0700)Severance / redundnacy pay -0.0782 -0.0103 -0.0474 -0.0714 (0.0474) (0.0343) (0.0267) (0.0471)Redress 0.0288 0.0554 0.0839** -0.0248

(0.0271) (0.0277) (0.0313) (0.0239)FTC prohib perm -0.0524*** -0.0506*** -0.0909*** -0.0416***

(0.0185) (0.0159) (0.0226) (0.0125)FTC dur unlim 0.00756 -0.0177 -0.0157 0.0191

(0.0219) (0.0179) (0.0213) (0.0117)Constant 0.134* 0.298*** 0.197* -0.0436

(0.0693) (0.0600) (0.0993) (0.0542)

Observations 13,409 10,314 11,126 12,597R-squared 0.131 0.219 0.180 0.168Key message: relevance of macro-factors varies across

levels of development rather than across sectors of activity

Final remarks

We tested the relevance of flexibility, cost, and technology factors for the firm use of temporary labour in developing countries

We confirmed that these factors, when measured at the micro level, are at work, similarly to developed countries

At the macro level, the relevance is only partial: regulations governing FTC matter, but regulations governing termination of regular contracts have only a limited relevance; macroeconomic fluctuations have limited relevance

Relevance of micro factors varies across sectors rather than across levels of development

Relevance of macro factors varies across levels of development rather than across sectors of activity, perhaps suggesting that compliance and enforcement issues are at stake

Thank You

aleksynska@ilo.org