Economic Structure, Trade Openness, and Gendered Employment in

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absolute and relative well-‐being (Irene van Staveren, Diane Elson, Caren Grown, and ..... 2000; Daniela Casale 2004; Carmen Diana Deere 2005). On the ...
      Economic  Structure,  Trade  Openness,  and  Gendered  Employment  in   Sub-­‐Saharan  Africa       Evelyn  Wamboye   Business  Department   Pennsylvania  State  University   DuBois,  PA  15801  USA   [email protected]     Stephanie  Seguino   Department  of  Economics   University  of  Vermont   Burlington,  VT  05401  USA   [email protected]     December  15,  2012     Abstract     More  than  thirty  years  into  the  third  era  of  globalization,  scholars  are  in  a  position  to   evaluate  the  distributive  effects  of  the  policy  shifts  that  have  led  to  greater  economic   integration.  One  region  of  the  world  for  which  there  is  little  robust  empirical  evidence  on   gendered  employment  effects  is  sub-­‐Saharan  Africa  (SSA).  This  paper  empirically  explores   these  issues  for  38  SSA  countries,  and  for  two  sub-­‐groups  (mineral  exporters  and  non-­‐oil   non-­‐mineral  exporters).  Our  purpose  is  to  identify  whether  there  is  an  impact  of  economic   and  trade  structure  on  women’s  relative  access  to  work.  We  use  fixed  effects  (FE)  and  two   stage  least  squares  (TSLS)  estimation  techniques  on  an  unbalanced  panel  data  for  the   period  of  1991-­‐2010.  Our  findings  suggest  that  trade  liberalization  has  gendered   employment  effects,  with  the  direction  depending  on  the  structure  of  the  economy.   However,  the  more  robust  finding  is  that  a  country’s  infrastructure  plays  a  determining   role  in  gendered  labor  market  outcomes  in  SSA  since  the  early  1990s.   Key  words:  Trade,  economic  structure,  gender,  employment,  sub-­‐Saharan  Africa.      JEL  code:  F14,  F15,  F16,  J21.      

   

Economic  Structure,  Trade  Openness,  and  Gendered  Employment  in   Sub-­‐Saharan  Africa    

 

I.    

Introduction  

More  than  thirty  years  into  the  third  era  of  globalization,  scholars  are  in  a  position  

to  evaluate  the  distributive  effects  of  those  policy  shifts  that  have  led  to  greater  economic   integration.  Feminists  have  made  a  major  contribution  to  the  literature,  evaluating  the   effects  of  liberalization  of  trade,  investment,  and  finance  on  several  aspects  of  women’s   absolute  and  relative  well-­‐being  (Irene  van  Staveren,  Diane  Elson,  Caren  Grown,  and   Nilufer  Cagatay  2007).  Gender  effects  are  complex.  There  is  evidence,  for  example,  that   semi-­‐industrialized  countries  have  registered  rapid  gains  in  women’s  share  of  employment   but  conditions  of  work  have  tended  to  be  precarious  (Lourdes  Benería  2003),  and   employment  gains  have  reversed  as  countries  move  up  the  industrial  ladder  (Sheba  Tejani   and  William  Milberg  2010).    

One  region  of  the  world  for  which  there  is  as  yet  little  robust  empirical  evidence  on  

gendered  employment  effects  is  sub-­‐Saharan  Africa  (SSA).  Women  in  low-­‐income   agricultural  economies  in  SSA  tend  to  be  concentrated  in  subsistence  agriculture,  to  a   lesser  extent  in  agro-­‐processing  in  the  manufacturing  sector,  and  in  informal  sector  work.   The  latter  concentration  is  more  indicative  of  residual  unemployment  than  of  a  livelihood   choice  (Jorge  Arbache,  Alexandra  Kolev,  and  Ewa  Filipina  2010).  Men  are  more  likely  to   obtain  their  livelihood  in  export  sectors  such  as  cash  crops  and  mineral  extraction.   Economic  roles  tend  to  be  relatively  rigid,  and  women  lack  the  resources  to  respond  to  

 

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incentives  to  export,  suggesting  that  labor  is  relatively  immobile  between  sectors  (World   Bank  2012).      

Given  rigid  gender  roles,  the  structure  of  gender  job  segregation  in  the  export  

sector,  and  women’s  lack  of  resources  to  facilitate  labor  mobility  between  sectors  in  SSA,   one  possibility  is  that  men’s  employment  opportunities  may  have  increased  more  than   women’s  in  contrast  to  outcomes  in  semi-­‐industrialized  economies.  The  effect  of  trade   integration  will  also  depend  on  how  import  liberalization  impacts  women’s  employment   opportunities.  Insofar  as  imported  goods  compete  with  the  types  of  goods  women  produce   as  subsistence  farmers,  rural  waged  workers,  or  in  the  manufacturing  sector,  we  might   expect  negative  effects  on  women’s  income-­‐generating  opportunities.  That  said,  if  over   time,  economies  adapt  and  labor  supply  adjusts  to  new  opportunities,  and  if  in  fact  growth   is  stimulated  by  economic  integration,  the  longer  run  effects  on  women’s  economic   opportunities  may  be  positive.    

This  paper  empirically  explores  these  issues  for  38  SSA  economies  and  for  two  sub-­‐

groups  of  non-­‐oil  producing  SSA  countries  –  mineral  exporters  and  non-­‐mineral  exporters   –  using  fixed  effects  (FE)  and  two  stage  least  squares  (TSLS)  estimation  techniques.  Our   purpose  in  analyzing  gendered  employment  effects  in  this  way  is  to  identify  whether  there   is  an  impact  of  economic  and  export  structure  on  women’s  relative  and  absolute  access  to   work.  We  also  explore  the  gender  impact  of  physical  infrastructure  via  its  potential  to   reduce  their  unpaid  care  work,  freeing  time  to  spend  in  employment.  Our  results  show  that   while  trade  has  significant  effects  –  largely  negative  –  infrastructure  has  a  strong  positive   effect  on  women’s  relative  and  absolute  employment.  Our  results  are  robust  to  different   estimation  techniques,  model  specifications,  and  samples.    

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II.  Background  and  Literature  Review   The  1980s  witnessed  an  era  of  trade,  investment,  and  financial  liberalization  for  many   developing  countries.  The  goal  of  this  significant  macroeconomic  policy  shift  away  from   targeted  subsidies,  managed  exchange  rates,  and  import  protection  was  to  promote   competitiveness  in  global  markets,  and  thus  to  shift  the  structure  of  production  away  from   domestic  production  towards  exports.  If  successful,  proponents  argued,  these  policies   would  successfully  address  balance  of  payments  problems  and  stimulate  economic  growth,   with  employment  also  expanding.      

In  sub-­‐Saharan  Africa,  as  in  other  developing  countries,  the  structural  adjustment  

programs  that  have  shaped  macroeconomic  policy  and  performance  since  the  1980s   include  real  currency  depreciation,  import  tariff  and  non-­‐tariff  barrier  reductions,   accompanied  by  liberalization  of  foreign  direct  investment  (FDI).  In  addition,  in  the  earlier   years  of  liberalization,  International  Monetary  Fund  (IMF)  and  World  Bank  conditionalities   included  cuts  to  public  sector  budgets  and  thus  employment,  with  impacts  on  food   subsidies,  health  care  facilities,  and  education.  The  recommendations  of  these  international   financial  institutions  (IFIs)  were  premised  on  the  view  that  sub-­‐Saharan’s  economic  future   depended  on  an  outward-­‐oriented  program  of  raw  materials  exports.      

In  response,  tariff  rates  fell  by  50%  from  1985  to  early  2000s  in  SSA.  There  is  some  

variation  across  countries  in  the  extent  of  tariff  reduction  (although  no  particular  pattern   by  economic  structure).  For  example,  in  West  Africa,  tariffs  averaged  38%  in  the  mid-­‐1980s   as  compared  to  20%  in  southern  Africa  (Babatunde,  Musibau  Adetunji  2009).  By  the  end  of   the  2000s,  the  median  tariff  for  all  SSA  was  13%,  but  South  Africa  and  Mauritius  are   outliers,  with  tariffs  averaging  8%  and  7%,  respectively.1  Despite  extensive  tariff  reduction,    

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SSA’s  share  of  global  trade  has  steadily  fallen  from  3.3%  in  the  1970s  to  just  1.8%  in  the   late  2000s.  Of  particular  note  is  that  SSA’s  agricultural  exports  have  precipitously  declined   from  over  one  quarter  of  merchandise  exports  in  the  1980s  to  just  15%  in  the  2000s   (UNCTAD  2009).      

Liberalization  was  also  intended  to  spur  foreign  direct  investment  (FDI)  in  SSA.  But  

this  did  not  materialize.  FDI  to  SSA  as  a  share  of  total  developing  country  FDI  fell   substantially  from  the  1980s  onward,  from  21%  in  the  1970s  to  just  11%  in  the  2000s.   Most  FDI  in  SSA  went  to  oil-­‐exporting  and  resource-­‐rich  countries,  particularly  Angola,   Nigeria,  and  South  Africa  (Jomo  Kwame  Sundaram,  Oliver  Schwank,  and  Rudiger  von  Arnim   2011).  There  is  some  evidence  that  lack  of  diversification  of  the  export  structure  in  some   SSA  economies  has  also  contributed  to  volatility  in  foreign  exchange  earnings,  dampening   the  benefits  that  such  liberalization  might  otherwise  offer  (Michael  Bleaney  and  David   Greenaway  2001).    

Even  if  trade  and  growth  were  stimulated  as  a  result  of  liberalization,  women  might  

not  benefit  absolutely  or  relatively.  Research  on  the  gender  effects  of  openness  in  SSA  has   emphasized  the  (negative)  impact  of  declines  in  public  sector  spending  on  women’s  well-­‐ being,  resulting  from  privatization  and  decreased  public  sector  spending  in  part   attributable  to  the  decline  in  tariff  revenues  (Christina  Gladwin  1991).  Cuts  in  social   spending  and  infrastructure  investments  can  have  a  negative  impact  on  women’s  unpaid   labor  time,  girls’  access  to  schooling,  and  maternal  mortality,  with  implications  for  labor   market  participation.      

In  one  of  the  few  cross-­‐country  econometric  studies  on  this  topic,  Mina  Baliamoune-­‐

Lutz  (2007)  explores  the  effect  of  trade  liberalization  on  gender  equality  of  literacy  in  the    

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SSA  region  compared  to  non-­‐SSA  developing  countries.  The  author  conducts  a  cross-­‐ country  analysis  employing  OLS  and  three-­‐stage  least  squares  estimations  to  account  for   potential  endogeneity  of  trade  and  growth.  She  presents  robust  statistical  evidence  that   higher  integration  into  world  markets  causes  gender  inequality  of  literacy  to  increase  in   SSA.    

The  channels  by  which  openness  affects  women’s  absolute  and  relative  employment  

outcomes  differ  from  the  transmission  mechanisms  of  literacy.  Mainstream  trade  theory   hypothesizes  that  liberalization  stimulates  growth,  which  may  result  in  an  increase  in   women’s  employment  and  share  of  employment.  However,  even  in  the  absence  of  growth   effects  from  trade  openness,  there  may  be  gendered  employment  effects  due  to  the   different  distributions  of  men  and  women  across  tradable  and  nontradable  sectors.  This,     coupled  with  the  rigidity  of  gender  roles  that  inhibit  substitutability  between  male  and   female  labor,  suggests  the  potential  for  gendered  effects  of  trade  liberalization,  although   those  effects  may  or  may  not  contribute  to  gender  equality.    

A  number  of  country-­‐level  studies  have  been  conducted  on  the  gendered  

employment  effects  of  trade  liberalization  in  SSA-­‐type  economies.  A  general  finding  is  that   men  are  more  likely  to  benefit  from  trade  liberalization  due  to  their  concentration  in   natural  resource  extraction  industries  (Maurizio  Bussolo  and  Rafael  De  Hoyos  2009;   Marzia  Fontana  2007;  Elissa  Braunstein  2012).  In  several  African  economies  where  the   manufacturing  sector  is  less  competitive  than  in  the  Asia  region,  empirical  evidence  shows   that  a  reduction  in  tariffs  on  labor-­‐intensive  imports  has  hurt  women’s  employment   relatively  more  than  men’s  (Ratnakar  Adhikari  and  Yumiko  Yamamoto  2006;  David  Kucera   and  Leanne  Roncolato  2011).      

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Among  the  few  global  cross-­‐country  studies  on  the  labor  effects  of  globalization,  

Lisa  Meyer  (2006)  examines  the  effect  of  liberalization  on  female  labor  force  participation   (but  not  gender  equality  of  labor  force  participation)  for  the  period  1970-­‐95.  The  author   obtains  contradictory  results  and  concludes  they  are  not  robust  enough  to  draw  firm   conclusions.  The  failure  to  obtain  consistent  results  may  be  due  to  an  analysis  that  does  not   differentiate  effects  on  countries  with  very  different  structures  of  production  and  trade.   This  may  make  it  difficult  to  discern  labor  force  participation  effects,  given  that  gender   employment  distribution  across  sectors  is  a  function  of  the  structure  of  the  economy.     Gendered  labor  market  effects  of  globalization  may  in  fact  differ  according  to   economic  structure  and  trade  composition.  In  exporting  sectors  that  are  female-­‐dominated   in  employment,  we  would  expect  openness  to  increase  women’s  access  to  employment,   absolutely  and  relative  to  men.  Typically,  we  have  observed  this  effect  in  countries  whose   shift  to  an  export  orientation  resulted  in  an  expansion  of  labor-­‐intensive  manufacturing   (Guy  Standing  1989;  Günseli  Berik  2000;  Naila  Kabeer  2000).      

In  general,  male  workers  in  SSA  tend  to  be  concentrated  in  primary  commodity  and  

cash  crop  production  (FAO  2011).  In  particular,  men  are  heavily  represented  as  employees   in  ore  and  mineral  extraction  and  in  the  production  of  export  crops  such  as  coffee  and   cocoa.  This  stylized  fact  does  not  imply  women  do  not  participate  in  cash  crop  production.   Rather,  women’s  cash  crop  plots  tend  to  be  smaller  and  have  lower  yields  than  men’s  due   to  resource  constraints,  including  access  to  credit  for  the  purchase  of  inputs  and  means  to   travel  to  market  (Ruth  Vargas  Hill  and  Marcella  Vigneri  2011;  FAO  2012  ).  In  Ghana,  for   example,  women  only  represent  20  percent  of  cocoa  farmers  (Marcella  Vigneri  and  Rebecca  

 

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Holmes  2009).  In  other  cases,  such  as  in  Uganda,  while  both  men  and  women  work  on  cash   crop  production,  men  decide  how  the  resulting  income  should  be  spent  (IFAD  2000).   Table  1  shows  the  sectoral  female  share  of  employment  for  SSA  countries  for  which   such  data  are  available.  Women’s  share  of  employment  is  substantially  lower  on  average  in   the  mining  and  quarrying  sector  (20.5%)  than  in  agriculture  or  manufacturing,  although   there  is  wide  variation  across  countries.  In  contrast,  the  female  share  of  agriculture  is   40.5%,  while  in  manufacturing,  the  share  is  39.5%.  The  relatively  high  share  of  females  in   manufacturing  is  in  part  explained  by  the  labor-­‐intensive  nature  of  this  sector  in  much  of   SSA.     (Table  1  about  here).   Although  on  the  surface,  it  is  tempting  to  hypothesize  that  men  may  differentially   benefit  from  trade  expansion  in  SSA,  the  shift  to  an  outward  orientation  may  lead  to  men   hire  women,  including  female  family  members,  to  work  on  their  cash  crop  plots  (William   Darity,  Jr  1995).  This  possibility  is  supported  by  evidence  that  a  large  share  of  women   engage  in  waged  labor  in  the  rural  sector  of  many  SSA  countries  (John  Sender,  Carlos  Oya,   and  Christopher  Cramer  2006;  Carlos  Oya  and  John  Sender  2009).  Women’s  employment   may  therefore  also  rise  in  response  to  an  expansion  of  exports,  which  may  or  may  not  come   at  the  cost  of  reduced  labor  on  subsistence  plots.     Moreover,  although  trade  liberalization  has  had  a  negative  effect  on  textile   manufacturing  in  a  number  of  SSA  countries  and  thus  women’s  employment,  non-­‐ traditional  agricultural  exports  (NTAEs)  are  an  increasing  (though  small)  share  of  exports,   and  tend  to  employ  female  workers.  To  the  extent  that  agricultural  exports  increase  in   response  to  trade  liberalization,  we  might  expect  women’s  employment  opportunities  in    

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agro-­‐processing  to  improve.  It  thus  remains  an  empirical  question  as  to  whether  women’s   absolute  and  relative  job  opportunities  have  increased  as  a  result  of  trade  liberalization  in   SSA.   This  study’s  contribution  to  the  literature  is  the  analysis  of  the  gendered   determinants  of  employment  in  38  SSA  economies.  Our  analysis  compares  outcomes  in  oil   exporting  countries  and  two  groups  of  non-­‐oil  exporters:  mineral  exporters    (MECs),  and   non-­‐mineral  exporters  (NMEs),  reflective  of  differential  resource  endowments  in  this   region.  Countries  are  categorized  as  follows.  Those  countries  in  our  sample  that  are  not  oil   exporters  are  classified  according  to  the  share  of  ores  and  minerals  in  exports.  Those   countries  above  the  SSA  share  of  10%  are  categorized  as  MECs  and  those  below  as  NMECs.   Table  A.1  in  the  appendix  identifies  countries  in  our  sample  according  to  their  oil,  MEC,  or   NMEC  status.  To  give  a  sense  of  the  different  export  structures  of  these  countries,  for  oil   exporting  countries,  the  mean  share  of  fuel  in  merchandise  exports  over  the  period  1960-­‐ 2010  is  32%.  Ores  and  minerals  are  a  small  share  of  their  exports  at  7.5%.  In  contrast,  the   mean  share  of  ores  and  minerals  in  exports  in  MECs  averaged  over  1960-­‐2010  is  29.9%   compared  to  1.2%  in  NMECs.2     The  dependent  variables  in  our  analysis  are  a)  the  female  minus  the  male   employment-­‐to-­‐population  rate  for  those  15  years  and  over  and  b)  the  female  employment   rate.  (In  the  econometric  analysis,  both  variables  are  measured  in  natural  logs).  Note  that   the  former  dependent  variable  is  measured  such  that  a  larger  gap  indicates  greater  gender   equality  in  employment,  though  this  does  not  allow  us  to  differentiate  whether   improvements  are  due  to  higher  female  employment  rates  or  lower  male  employment   rates.  Panel  A  in  Figure  1  offers  a  graphical  representation  of  trends  in  this  variable  since    

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1991  (the  first  year  for  which  cross-­‐country  SSA  data  are  available)  to  2010.  The  female-­‐ male  gap  has  widened  (women’s  employment  rates  have  increased  more  than  men’s)  over   this  period  in  each  group  of  countries.  The  gap  has  widened  the  most  in  NMECs  (5.1   percentage  points),  compared  to  an  average  of  4.4  percentage  points  in  oil  exporting   countries  and  3.5  percentage  points  in  MECs.  Panel  B  exhibits  secular  trends  in  the  female   employment  rate  in  each  group  of  countries.  Note  the  significantly  higher  female   employment  rate  in  NMECs  and  MECs  as  compared  to  oil  exporting  countries.   (Figure  1  about  here).   We  want  to  underscore  that  any  evidence  of  a  positive  effect  of  increased  trade   volume  on  women’s  employment  does  not  necessarily  infer  an  improvement  in  relative   well-­‐being.  In  many  countries,  as  noted,  the  direct  effects  of  liberalization  of  trade  on   employment  are  contemporaneous  with  cuts  in  public  sector  spending  in  education,  health,   food  and  agriculture  subsidies,  and  infrastructure.  This  in  turn  can  lead  to  “distress”  sales   of  female  labor  in  order  to  replace  lost  income  from  other  sources.  In  that  case,  an  increase   in  women’s  employment  may  signal  a  decline,  not  improvement,  in  well-­‐being.  Moreover,   should  the  ratio  of  female  to  male  employment  rise  with  trade  liberalization,  this  is  no   guarantee  that  women’s  relative  time  poverty  attenuates,  or  that  there  is  sufficient  time  for   the  household  to  contribute  the  desired  amount  of  time  to  reproductive  labor.     II.  Structures  of  Production,  Employment,  and  Trade,  and  Macroeconomic   Performance     We  preface  our  analysis  of  gendered  employment  effects  of  trade  integration  with  a   description  of  the  structure  of  SSA  economies  and  an  exploration  of  trade  and   macroeconomic  trends.      

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2.1.  Structure  of  production    

Services  is  the  largest  sector  in  SSA  countries,  contributing  roughly  half  of  value-­‐added  to   GDP  in  2009.  In  oil  exporting  SSA  economies,  however,  services  is  somewhat  lower  at   37.9%  of  GDP  (Table  2).  The  agricultural  sector  is  nevertheless  considered  the  backbone  of   most  sub-­‐Saharan  economies,  employing  the  majority  of  workers,  regardless  of  economic   structure.  The  greatest  structural  difference  is  observed  in  the  share  of  industrial  sector,   and  more  specifically,  non-­‐manufacturing  industry  value-­‐added  as  a  share  of  GDP.  That   sector  is  21.9%  of  GDP  in  MECs  compared  to  only  9.8%  in  NMECs.  In  contrast,  the   manufacturing  sector,  a  subset  of  the  industrial  sector  (with  a  tendency  to  be  more  female-­‐ intensive  in  employment),  contributes  more  to  GDP  in  NMECs  (13.4%)  than  in  MECs   (9.2%)  or  oil  exporters  (10.2%).      

In  addition  to  data  on  sectoral  employment  shares,  Table  2  also  provides  

information  on  employment  shares  of  all  workers,  as  well  as  the  ratio  of  female  to  male   employment  by  sector,  from  1991  to  2009.  As  can  be  seen,  the  largest  share  of  workers  – fully  two  thirds  –  is  employed  in  the  agricultural  sector  in  NMECs  as  compared  to  the  other   groups.  However,  in  all  groups,  agriculture  is  only  marginally  female-­‐dominated.  In   contrast,  the  industrial  sector  is  male-­‐dominated,  and  substantially  more  so  in  MECs.   Finally,  between  a  quarter  to  one  third  of  all  workers  are  employed  in  the  services  sector,  a   female-­‐dominated  sector  in  all  groups.   (Table  2  about  here).    

The  structure  of  production  in  SSA  has  shifted  substantially  over  the  1960-­‐2010  

period.  Figure  2  compares  trends  in  value-­‐added  as  a  percentage  of  GDP  by  sector.  Panel  A   shows  a  steady  decline  in  agriculture  as  a  share  of  GDP  since  the  mid-­‐1960s,  despite  the    

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expectation  that  trade  liberalization  might  expand  the  demand  for  agricultural  exports  and   therefore  output.  The  service  sector  in  SSA  and  other  developing  countries  is  typically   associated  with  informal  sector  employment,  much  of  which  is  disguised  or  residual   unemployment.  Services  as  a  share  of  GDP  has  been  rising  in  both  MECs  and  NMECs,   though  by  the  early  1990s,  the  growth  in  this  sector’s  share  was  more  accentuated  in   NMECs.  (Services  also  includes  public  sector  employment,  but  given  the  decline  in  public   sector  budgets  in  much  of  SSA,  it  is  not  possible  to  attribute  the  rising  share  of  this  sector  in   GDP  to  public  sector  output  growth).  In  contrast,  the  share  of  this  sector  in  GDP  has  fallen   since  1975  for  oil  exporters.     (Figure  2  about  here).   As  Panel  C  in  Figure  2  suggests,  non-­‐manufacturing  industrial  output  as  share  of   GDP  began  to  increase  in  MECs  and  oil  exporters  by  1990.  Its  share  is  significantly  lower  in   NMECs,  and  has  been  less  variable  than  in  the  other  groups,  but  also  shows  a  very  modest   increase  since  the  early  1990s.  These  trends  coincide  with  trade  liberalization  that  began  in   the  mid-­‐to  late-­‐1980s  in  many  SSA  countries.  The  manufacturing  sector,  in  contrast,   demonstrates  a  significant  divergence  in  structural  trends  between  oil  exporters,  MECs  and   NMECs  (Panel  D).  While  in  oil  exporters  and  MECs,  the  onset  of  trade  liberalization  is   contemporaneous  with  a  decline  in  manufacturing’s  share  of  GDP,  in  NMECs,  a  continued   upward  trend  is  evident.  That  upward  trend,  however,  is  very  anemic,  and  bodes  poorly  for   future  economic  growth  insofar  as  the  manufacturing  sector  is  often  the  driver  of  economy-­‐ wide  productivity  growth  (José  Antonio,  Codrina  Rada,  and  Lance  Taylor  2009).            

 2.2  Trends  in  Imports  and  Exports       12  

Similar  to  many  developing  countries,  trade  policy  reforms  started  in  the  mid  to  late  1980s   in  SSA  as  part  of  the  overall  structural  and  macroeconomic  policy  reforms  induced  by   World  Bank  conditionality  on  loans.  These  reforms  entailed  elimination  of  all  subsidies  for   exports  and  the  reduction  of  import  tariffs  as  well  as  their  restructuring  to  a  more   simplified  form.  In  response  to  these  policy  shifts,  an  upward  trend  in  the  ratio  of  exports   and  imports  to  GDP  is  evident  in  all  three  groups  of  countries  (Figure  3,  Panel  A).      

As  Panel  B  in  Figure  3  shows,  however,  all  groups  had  suffered  from  widening  trade  

deficits  prior  to  the  liberalization  period,  and  liberalization  has  not  improved  that  state  of   affairs  for  NMECs  and  MECs.  The  emergence  of  a  large  trade  imbalance  in  these  two  groups   was  initially  in  part  related  to  the  rising  price  of  foreign  crude  oil  imports,  and  continued  as   a  result  of  fluctuating  primary  commodity  prices  on  the  world  markets.  While  oil  exporting   countries  have  fared  better,  they  demonstrate  remarkable  volatility  in  the  current  account   balance.    

To  understand  these  trends  in  more  detail,  we  examine  the  shift  in  the  structure  of  

exports  and  imports  in  three  key  areas  –  food,  ores,  and  manufacturing.  These  three  sectors   are  particularly  important  to  our  analysis  insofar  as  trends  will  have  an  impact  on  female   and  male  employment,  with  women  more  likely  to  be  involved  in  food  and  manufacturing   production  (agro-­‐processing)  while  men  are  more  concentrated  in  ores  and  minerals.      

Net  food  exports  as  a  percentage  of  all  merchandise  exports  have  been  in  decline  in  

all  groups  since  1962  (Figure  4,  Panel  A).  That  downward  trend  slowed  by  the  early  1990s   in  NMECs  and  oil  exporters  but  continued  in  MECs.  Net  manufacturing  exports  as   percentage  of  total  net  merchandise  exports  have  experienced  a  secular  increase  since   1962  (although  still  in  deficit),  with  a  brief  interruption  in  the  early  1990s  in  NMECs  (Panel    

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B).  Finally,  in  Panel  C,  there  is  a  notably  volatile  secular  increase  in  net  ore  exports  in  MECs   in  contrast  to  stagnant  trends  in  NMECs  and  oil  exporters  since  1992  where  net  ore  exports   are  close  to  zero.    

    (Figures  3  and  4  about  here).    

 

2.3.  Trends  in  Macroeconomic  Performance    

Trends  in  trade  openness  and  composition  of  output  and  net  exports  describe  a  country’s   economic  structure.  It  is  also  useful  to  look  at  per  capita  GDP  growth,  which  partially   reflects  demand-­‐side  effects  of  trade.  Long-­‐run  growth  trends  (estimated  using  a  Hodrick-­‐ Prescott  filter)  over  the  period  1960  to  2010  vary  widely  (Figure  5).  Growth  rates   registered  a  notable  downturn  in  the  1980s  in  all  groups,  contemporaneous  with  trade   liberalization.  Subsequently,  growth  rates  have  shown  an  upward  trend  with  oil  exporters   outperforming  MECs  and  NMECs  by  the  2000s.  Average  growth  rates  are,  however,   extremely  low  by  developing  country  standards,  averaging  0.5%  per  annum  for  the  38  SSA   countries  in  our  sample  from  1985-­‐2010,  compared  to  a  rate  of  0.7%  in  the  pre-­‐ liberalization  post-­‐independence  era.3   (Figure  5  about  here).      

The  data  provided  in  this  section  reflect  the  differing  structures  and  structural  

changes  of  these  three  groups  of  countries.  These,  coupled  with  gender  differences  in   patterns  of  employment,  underscore  the  relevance  of  evaluating  the  impact  of  trade   liberalization  according  to  SSA  countries’  economic  and  trade  structure.        

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III.  Gendered  Employment  Effects  of  Trade  Liberalization:  Methodology  and  Empirical   Results  

    3.1  Empirical  Model      

In  the  empirical  analysis,  we  investigate  whether  economic  integration  has  led  to  an   increase  in  women’s  relative  employment  in  SSA  countries.  The  empirical  model  identifies   four  categories  of  factors  that  affect  the  female-­‐male  employment  rate  gap:  1)  GDP  growth,   2)  physical  infrastructure,  which  affects  women’s  care  burden  and  thus  female  labor   supply,  3)  the  degree  of  global  integration,  and  4)  economic  structure.  We  discuss  each  of   these  in  turn.    

We  include  the  growth  rate  of  real  GDP  per  capita  to  account  for  the  effects  of  

aggregate  demand  on  gendered  employment.  The  direction  of  the  effect  of  aggregate   demand  on  female-­‐male  employment  gap  may  vary.  For  example,  evidence  from  South   Africa,  Mexico,  and  Argentina  shows  that  women  engage  in  distress  sales  of  labor  in  times   of  economic  hardship.  This  countercyclical  surge  arises  due  to  the  decrease  in  household   income  from  male  breadwinners  (Mercedes  Gonzalez  de  la  Rocha  1988;  Marcella  Cerrutti   2000;  Daniela  Casale  2004;  Carmen  Diana  Deere  2005).  On  the  other  hand,  with  economic   growth,  women  may  have  increased  relative  job  opportunities  due  to  their  lower  wages.      

We  also  control  for  influences  on  women’s  relative  labor  supply.  The  unpaid  care  

burden,  which  can  constrain  women’s  ability  to  participate  in  production,  is  influenced  by  a   country’s  physical  infrastructure.  Electrification,  clean  water,  transport,  and   communication  infrastructure  help  to  lessen  the  time  women  spend  in  unpaid  labor   (Pierre-­‐Richard  Agénor,  Otaviano  Canuto,  and  Luiz  Pereira  da  Silva  2010).    In  our   estimations,  we  capture  infrastructure  with  two  variables  –  the  percentage  of  the    

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population  with  access  to  improved  sanitation  facilities  and  telephone  lines  per  100  people.    

Improved  sanitation  is  expected  to  have  a  positive  effect  on  the  gap  between  female  

and  male  employment  through,  for  example,  improved  overall  health  outcomes  and   reduced  time  spent  by  women  as  caregivers.  The  effect  of  telephone  lines  on  relative  access   to  employment  is  indirect,  and  this  variable  proxies  for  other  direct  measures  of  time-­‐ saving  infrastructure  improvements  for  which  data  are  more  sparse  –  roads,  electricity   consumption,  and  births  attended  by  skilled  health  personnel  (the  latter  is  a  measure  of   social  rather  than  physical  infrastructure).  For  the  38  countries  in  our  sample,  the   correlations  of  telephones  with  roads,  electricity  consumption,  and  births  attended  by   skilled  health  personnel,  respectively,  are  0.257,  0.902,  and  0.638.    

As  the  literature  has  noted,  policies  that  contribute  to  global  economic  integration  

can  affect  the  structure  of  production  as  well  as  employment  opportunities  by,  for  example,   shifting  productive  economic  resources  from  the  non-­‐tradables  sector  to  the  tradables   sector.  Although  it  would  be  ideal  to  have  time  series  trade  policy  data  on  tariffs  and  non-­‐ tariff  trade  barriers  (NTBs),  such  data  are  not  widely  available.4  We  therefore  rely  on   outcome  measures.  In  particular,  economic  integration  is  measured  as  trade’s  share  of  GDP   (the  sum  of  exports  and  imports  as  a  %  GDP),  and  in  separate  regressions,  we  disaggregate   this  variable,  using  exports  and  imports  as  a  percentage  of  GDP.      

There  are  two  reasons  for  which  we  are  motivated  to  measure  the  effects  of  imports  

and  exports  separately.  Entering  exports  and  imports  as  separate  arguments  allows  for  net   exports  to  be  negative  (thereby  capturing  the  demand-­‐side  effects  of  trade  openness),  even   if  trade  as  a  percentage  of  GDP  is  positive.  Second,  research  has  identified  export  sectors  in   developing  countries  as  feminized  due  to  their  low-­‐skill  labor  intensity  and  women’s    

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relatively  lower  wages  (Standing  1989).    Nonetheless,  the  group  of  countries  in  this  study  is   unique  in  the  sense  that  besides  being  net  importers,  their  export  sectors  are  characterized   by  primary  commodity  production  and  agriculture-­‐based  manufacturing,  with  potentially   different  effects  on  gendered  employment.  The  sign  of  the  coefficients  on  exports  and   imports  is  therefore  unpredicted  prior  to  our  empirical  investigation.      

Finally,  we  use  manufacturing  and  agriculture  value-­‐added  as  a  share  of  GDP  to  

capture  gender  employment  effects  arising  from  changes  in  sectoral  demand  and  in   economic  structure.  Given  the  intensity  of  women’s  employment  in  the  agricultural  and   manufacturing  sectors,  we  might  expect  that  an  increase  in  agriculture  and  manufacturing   in  GDP  would  widen  the  female-­‐male  employment  gap.  We  would  therefore  expect  positive   signs  on  the  coefficients  of  these  two  variables.      

 Based  on  this  discussion,  our  estimated  equation  for  the  determinants  of  the  

female-­‐male  employment  gap  is:                 lnGapit = ! + "1Grit + "2 ln Sanit + "3 lnTelit + " 4 lnTradeit + "5 ln Mfgit + "6 ln Agit + #it              (1)                           where  ln  is  the  natural  log,  Gap  is  the  female  minus  the  male  employment-­‐to-­‐population   rate  in  country  i  at  time  t,  Gr  is  the  growth  rate  of  GDP  per  capita,  San  is  the  percentage  of   the  population  with  improved  sanitation  facilities,  Tel  is  the  number  of  telephone  lines  per   100  people,  Trade  is  the  sum  of  exports  and  imports  as  a  percentage  of  GDP,  Mfg  and  Ag  are   manufacturing  and  agriculture  value-­‐added  as  percentages  of  GDP,  respectively,  and  ε  is   the  error  term.  We  run  another  set  of  regressions  where  we  substitute  exports  (X)  and   imports  (M)  for  the  trade  variable.    

 

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3.2  Data  and  Econometric  Results  

  Data  are  taken  from  World  Bank’s  World  Development  Indicators  and  African  Development   Indicators,  with  employment  variables  drawn  from  the  ILO’s  Key  Indicators  of  the  Labor   Market  database.  Descriptions  and  data  sources  of  the  model  variables  are  shown  in  Table   A.2.  Descriptive  statistics  of  all  variables  used  in  our  analysis  are  provided  in  Table  A.3.5    

 

We  use  two  estimation  methods  in  our  analysis:  fixed  effects  (FE)  and  two  stage  

least  squares  (TSLS)  on  an  unbalanced  panel  for  the  period  1991-­‐2010.  Fixed  effects   estimation  captures  country-­‐specific  factors  influencing  gendered  employment  not   otherwise  captured  by  the  independent  variables.  One  assumption  of  the  FE  model  is  that   the  time  variant  characteristics  are  unique  to  each  country  and  that  they  are  not  correlated   with  another  country’s  characteristics.  This  assumption  holds  if  the  countries’  error  terms   are  not  correlated.  However,  if  the  error  terms  are  correlated,  the  assumption  does  not   hold  and  the  FE  model  cannot  be  used.  Consequently,  we  conduct  the  Hausman   specification  test  in  order  to  determine  whether  to  use  random  or  fixed  effects.  The  test   rejects  the  null  hypothesis  that  the  difference  in  random  and  fixed  effects  coefficients  are   not  systematic,  thereby  affirming  fixed  effects  as  the  model  of  choice.  To  control  for   potential  heteroskedasticity,  we  report  results  based  on  robust  standard  errors.       TSLS  estimation  is  added  as  a  robustness  check  and  to  account  for  potential   endogeneity  of  explanatory  variables.    In  particular,  as  the  gender  and  growth  literature   has  shown,  the  degree  of  gender  equality  may  itself  influence  the  rate  of  economic  growth   (Stephanie  Seguino  2000;  Stephan  Klasen  and  Francesca  Lamanna  2009;  Amarakoon   Bandara  2012),  and  may  also  respond  to  changes  in  the  trade  share.  We  therefore   instrument  GDP  growth  in  the  TSLS  estimations  with  the  growth  rate  of  gross  fixed  capital    

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formation,  the  growth  rate  of  OECD  economies  to  capture  trade  effects,  6  the  gap  between   the  female  and  male  labor  force  participation  rates,  and  the  ratio  of  female  to  male  primary   education.  The  latter  two  variables  are  measured  in  natural  logs.  Standard  with   instrumental  variable  estimation  techniques,  we  calculate  both  the  Cragg-­‐Donald  Wald  F   and  the  Sargan  statistics  to  test  for  weak  instruments  and  overidentifying  restrictions.  Each   of  the  regressions  produced  test  statistics  that  exceeded  the  relevant  critical  values,   indicating  that  instruments  are  sufficiently  strong  and  the  regressions  are  not   overidentified.      

In  the  baseline  regressions,  the  female-­‐male  employment  gap  is  specified  as  a  

function  of  the  independent  variables  outlined  in  equation  (1).    We  use  three  sets  of  data:   (1)  the  full  sample  of  38  SSA  countries,  including  6  oil  exporters,  (2)  15  NMECs,  and  (3)  17   MECs.  Because  of  the  small  sample  size  of  the  oil  exporting  countries,  we  do  not  provide   separate  regressions  for  this  group.7  Table  3  reports  the  results  for  female-­‐male   employment  rate  gap  effects  of  trade,  controlling  for  other  factors  that  affect  the   employment  gap.  In  Table  4,  we  report  results  from  disaggregating  trade  with  exports  and   imports  entered  as  separate  arguments,  followed  by  results  from  further  disaggregation  to   sub-­‐categories  of  exports  and  imports.  

 

(Table  3  about  here).   GDP  per  capita  growth  rates  capture  the  employment  effects  of  changes  in  aggregate   demand.  While  aggregate  employment  rates  are  typically  pro-­‐cyclical,  gendered  effects   have  been  found  to  vary  across  countries.  In  the  recent  financial  crisis,  for  example,  male   employment  rates  fell  more  than  female  rates  in  the  United  States,  whereas  in  some   developing  countries  that  export  labor-­‐intensive  manufactures,  women’s  employment  was    

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more  negatively  affected.  Our  results  suggest  counter-­‐cyclical  movements  in  the  female-­‐ male  employment  gap  in  the  full  sample,  but  only  in  the  TSLS  regression.  The  coefficient  on   this  variable  is  neutral  in  the  NMECS  and  MECs,  regardless  of  the  model  specification.  The   apparent  counter-­‐cyclical  movements  reflected  in  the  full  sample,  but  absent  in  the   subsamples,  might  be  due  to  the  inclusion  of  the  oil  exporting  countries.     The  coefficients  on  sanitation  access,  one  of  our  infrastructure  development  proxies,   are  significant  and  positive  across  all  model  specifications  for  the  full  sample  and  NMECs.  A   10%  increase  in  the  share  of  the  population  with  access  to  improved  sanitation  facilities,   for  example,  improves  women’s  relative  employment  by  0.23  –  0.4%  in  the  full  sample,  and   0.78  –  0.83%  in  NMECs.  The  effect,  in  contrast,  is  negative  but  only  in  the  TSLS  results  for   MECs.  Telephone  lines,  our  second  infrastructure  proxy,  is  also  significant,  and  is  positive   for  all  samples,  with  a  10%  increase  in  telephone  lines  per  100  people  contributing  to  a   0.3–  0.5%  increase  in  the  female-­‐male  employment  gap  (in  favor  of  women’s  employment).   This  is  consistent  with  the  hypothesis  that  better  infrastructure  has  differentially  positive   effects  on  women’s  employment  chances,  possibly  via  a  reduction  in  women’s  unpaid  care   burden.     The  regression  results  indicate  that  increased  global  integration  as  measured  by   trade’s  share  in  GDP  has  no  significant  effect  on  the  female-­‐male  employment  gap  in  the  full   sample  (Table  3,  columns  1  and  2).    However,  when  we  consider  NMECs  and  MECs   separately,  we  find  contradictory  effects  of  the  trade  share  of  the  female-­‐male  employment   gap.  Those  effects  are  negative  in  NMECs,  a  result  that  holds  both  in  the  FE  and  TSLS   estimations,  while  they  are  positive  in  MECs  but  only  in  the  TSLS  estimation.  In  other   words,  if  the  share  of  trade  in  GDP  increases  by  10%,  the  disparity  in  female-­‐male    

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employment  disparity  will  narrow  (in  favor  of  men’s  employment)  by  approximately  0.78  –   0.97%  in  NMECs,  but  widen  by  0.5%  in  MECs.     These  results  differ  from  what  might  be  expected.  In  MECs  where  mining  is  a  larger   share  of  GDP  and  is  a  male-­‐dominated  sector,  we  would  have  expected  a  negative  effect  on   the  female-­‐male  employment  gap.  Apart  from  this  unexpected  result,  the  fact  that  the   results  differ  between  these  two  groups  underscores  the  relevance  of  exploring  trade   effects  by  economic  structure.     Finally,  manufacturing  and  agriculture  shares  in  GDP  are  used  to  measure  effects  of   changes  in  sectoral  demand.  As  the  share  of  manufacturing  in  GDP  rises  in  NMECs,  the   employment  gap  widens  in  favor  of  women,  but  in  MECs,  the  opposite  is  the  case,  only  in   the  TSLS  regressions.  This  result  for  NMECs  is  expected  since  manufacturing  in  that  group   tends  to  be  both  labor-­‐intensive  goods  production  (such  as  garments  in  Mauritius)  as  well   as  agro-­‐processing.    The  case  of  MECs  is  different,  insofar  as  manufacturing  as  a  share  of   GDP  has  been  falling  since  the  early  1990s.  The  negative  coefficient  on  this  variable  would   imply  that  women’s  employment  in  this  sector  has  fallen  more  than  men’s,  as  a  result.    

Agriculture,  on  the  other  hand,  has  a  negative  effect  in  the  TSLS  regressions  across  

all  samples.  In  contrast,  effects  are  insignificant  in  the  FE  models.  One  way  to  interpret   these  results  is  that,  as  the  agricultural  sector  has  shrunk  in  SSA,  women’s  employment   rates  are  again  more  negatively  affected.  This  might  suggest  that  contract  farming  and  cash   crop  exports,  both  stimulated  by  trade  liberalization,  have  resulted  in  more  employment   opportunities  for  men  than  women,  despite  women’s  participation  as  workers  in  the  non-­‐ traditional  export  (NTAE)  sector.8  

 

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When  trade  is  disaggregated  into  imports  and  exports  (Table  4),  the  effects  of   economic  growth  differ,  with  growth  having  a  positive  effect  on  the  employment  gap  but   only  in  NMECs  and  only  in  the  TSLS  regressions.  Our  measures  of  physical  infrastructure,   sanitation  and  telephone  lines,  continue  to  have  positive  significant  effects  across  samples   in  the  TSLS  regressions.  The  expansion  of  exports  as  a  share  of  GDP  has  a  negative  effect  on   the  female-­‐male  employment  gap  in  all  regressions  across  all  samples.  That  is,  men’s  job   advantage  widens  with  export  expansion.  For  example,  if  the  share  of  exports  in  GDP  is   increased  by  10%,  the  female-­‐male  employment  disparity  will  narrow  by  roughly  0.3%  in   the  full  sample,  between  0.29–0.36%  in  NMECs,  and  between  0.2–0.35%  in  MECs.  The   expansion  of  imports  exerts  a  negative  effect  on  the  employment  gap  in  NMECs,  while  a   positive  effect  in  the  full  sample  and  MECs.  This  can  result  from  the  composition  of  imports,   which  are  likely  capital-­‐intensive  in  MECs  and  therefore  less  likely  to  affect  women’s  job   access.  In  contrast,  women  are  more  likely  to  be  employed  in  import-­‐competing  industries   in  NMECs  than  MECs,  accounting  for  the  result  that  a  10%  increase  in  imports  as  a  share  of   NMECs’  GDP  results  in  a  0.46–0.58%  decline  in  the  employment  gap  in  favor  of  men.  Finally,   coefficients  on  manufacturing  and  agriculture  are  broadly  similar  to  those  reported  in   Table  3.     As  the  literature  has  widely  noted,  the  substitution  of  male  for  female  labor  tends  to   be  inelastic  in  import  and  export  industries  due  to  gender  norms.  For  that  reason,  we  re-­‐ run  these  regressions,  disaggregating  each  to  assess  the  effects  of  food  and  manufacturing   (more  female-­‐intensive  in  employment),  and  ores  and  minerals  which  is  male-­‐intensive.   The  regression  results  are  for  the  full  sample  only,  since  disaggregation  of  exports  and   imports  obviates  the  relevance  of  disaggregating  countries  according  to  economic  structure.    

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Food  exports  are  found  to  confer  no  employment  advantages  for  women,  nor  do  food   imports  disadvantage  them  relative  to  men.  Ore  exports  have  a  neutral  effect,  but  ore   imports  raise  women’s  employment  relative  to  men,  possibly  because  male  workers  are   disadvantaged  as  ore  imports  rise.  Both  manufacturing  exports  and  imports  tend  to  worsen   women’s  share  of  jobs.  The  effects  are  quite  small  for  manufacturing  exports,  but  are  larger   for  manufacturing  imports,  although  only  significant  in  the  TSLS  regressions.     (Table  4  about  here).   To  sum  up  the  results  of  this  section,  although  trade  liberalization  has  been   associated  with  a  feminization  of  employment  in  some  developing  economies,  this  effect   may  not  occur  if  production  shifts  in  favor  capital-­‐intensive  industries,  non-­‐agricultural   sectors,  or  male-­‐dominated  agricultural  crops  (Marzia  Fontana,  Susan  Joekes,  and  Rachel   Masika  1998;  Nilufer  Cagatay  2001).  Women  in  SSA  who  tend  to  be  in  low-­‐skilled    or  labor-­‐ intensive  jobs  and  concentrated  in  the  informal  and/or  subsistence  agricultural  sector   appear  to  have  benefited  less  from  the  wave  of  trade  liberalization  that  started  in  the  1980s.   In  contrast,  we  find  that  the  strongest  and  most  robust  effects  on  women’s  increased  share   of  employment  stem  from  infrastructure  investments  that  reduce  their  care  burden.9  It   may  very  well  be  that  the  ability  of  women  to  engage  in  trade-­‐induced  employment   possibilities  is  shaped  by  their  care  burden.  That  is,  it  is  likely  that  infrastructure   improvements  are  a  complement  to  women’s  relatively  greater  access  to  employment   induced  by  trade  expansion.         III.  Effects  of  Trade  Liberalization  on  Women’s  Employment  Rates    

 

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The  concern  about  the  relative  effects  of  trade  liberalization  on  women’s  employment  are   motivated  by  a  rich  literature  underscoring  the  role  of  bargaining  power  in  influencing   negotiations  between  adults  over  resources  and  labor  at  the  household  level.  That  said,  we   might  also  be  interested  in  the  absolute  effect  of  trade  on  women’s  employment  changes.   This  matters  for  several  reasons.  First,  a  basic  tenet  of  economic  development  is  that  it   should  contribute  to  broadly  shared  well-­‐being  and  we  are  thus  interested  to  know   whether  the  policy  shift  that  leads  to  expanded  trade  in  GDP  in  fact  benefits  women’s   access  to  work,  even  if  the  effect  is  weaker  than  on  men’s  employment.  Second,   improvements  in  women’s  absolute  well-­‐being  have  been  found  to  have  significant   beneficial  effects  on  children’s  outcomes,  with  positive  effects  on  long-­‐run  growth.   In  this  section,  we  briefly  present  results  obtained  from  estimating  equation  (1)   with  the  female  employment-­‐to-­‐population  rate  as  the  dependent  variable,  measured  in   natural  logs.  Again,  we  estimate  our  model  with  FE  and  as  a  robustness  check  and  to   account  for  potential  endogeneity  of  GDP  growth,  we  re-­‐run  the  regressions  with  TSLS.    

Table  5  summarizes  those  results.  It  is  notable  that  GDP  growth  is  neutral  in  its  

effects  on  female  employment  across  all  samples  and  estimation  methods.  Growth  rates  in   SSA  have  been  weak  at  best,  and  the  inability  of  our  data  set  to  identify  benefits  for   women’s  employment  may  therefore  not  be  surprising.  In  contrast,  both  of  the   infrastructure  variables  exert  the  strongest  positive  effect  on  women’s  employment  of  all  of   the  independent  variables  in  our  model.  The  results  are  significant  in  the  full  and  NMEC   samples.  They  are  not  significant  in  the  MECs  but  that  could  be  due  to  multicollinearity   with  the  manufacturing  share  of  GDP  variable.10  The  combined  effect  of  a  10%  increase  in  

 

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sanitation  and  telephone  infrastructure  yields  a  0.8%  improvement  in  women’s   employment  rates  in  the  full  sample  and  1.0%  in  NMECs.     (Table  5  about  here).   The  effect  of  trade  as  a  share  of  GDP  is  neutral  in  the  full  sample,  negative  in  NMECs,   and  positive  in  MECs  (but  only  in  the  TSLS  regression).  This  suggests  that  the  negative   effect  of  trade  on  the  female-­‐male  employment  gap  (Table  3)  cannot  simply  be  attributed  to   men’s  differential  ability  to  gain  employment  with  trade  expansion.  Rather,  the  results   suggest  that  women’s  absolute  employment  rates  decline  with  trade  openness  in  NMECs.   Why  might  this  be  so?  One  possibility  is  that  trade  liberalization,  associated  with  structural   adjustment  programs  that  reduce  public  sector  expenditures  may  have  contributed  to   women’s  care  burden  in  terms  of  food  preparation,  health  care,  and  other  activities  not   otherwise  captured  by  our  physical  infrastructure  variables.  In  that  case,  trade  is  acting  as   a  proxy  for  a  broader  array  of  neoliberal  macro-­‐level  policies.  As  such,  then,  it  would   underscore  the  importance  of  looking  at  the  impact  of  a  broad  set  of  macroeconomic   policies  rather  than  attempting  to  isolate  the  effects  of  a  single  policy.    Other  noteworthy  results  from  these  regression  analyses  are  the  negative  effects  of   manufacturing  as  a  share  of  GDP  on  women’s  employment  as  well  as  the  inverse   relationship  between  agriculture’s  share  of  GDP  and  women’s  employment  rates.    Although   perhaps  counterintuitive,  given  the  assumption  of  agriculture  as  a  female-­‐dominated   sector,  this  can  be  explained  by  the  shift  from  agriculture  to  services  in  SSA,  where  women   are  even  more  heavily  concentrated.       V.  Conclusion  

 

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There  are  limits  to  what  can  be  discerned  from  aggregate  cross-­‐country  analyses.  Many   authors  conclude  their  research  with  a  call  for  further  research  and  we  are  no  different.  To   understand  macro  linkages  more  fully,  such  studies  must  be  complemented  by  country-­‐ specific  analyses.  That  said,  there  are  some  important  conclusions  that  can  be  drawn  from   the  results  presented  here.     The  rich  gender  and  development  literature  that  has  developed  over  the  last  25   years  made  its  intellectual  mark  by  identifying  the  gendered  impact  of  macroeconomic   policies.  The  adoption  of  neo-­‐liberal  policies,  often  induced  by  World  Bank  and  IMF  loan   conditionalities,  had  been  found  to  have  unintended  negative  impacts  on  women’s  well-­‐ being  in  SSA,  linked  primarily  to  public  expenditure  cuts.  Further,  as  so  presciently  noted   by  Diane  Elson  (1991),  women’s  care  burden  constrained  their  relative  ability  to  respond   elastically  to  new  employment  opportunities.11  And  of  course,  as  has  been  found  in  a   number  of  countries,  including  SSA,  trade  liberalization  has  been  contemporaneous  with   worsened  current  account  balances,  as  imports  outpaced  exports.     Now  25  years  later,  we  are  in  a  better  position  to  assess  the  gender  employment   effects  of  structural  changes  deriving  from  to  trade  liberalization.  Although  labor  supply   responses  might  be  inelastic  in  the  shorter  run,  there  is  the  possibility  that  labor,  including   female  labor,  has  adapted  in  the  longer  run  to  take  advantage  of  structural  changes.   Our  results  convey  a  mixed  picture,  however.  Table  6  summarizes  the  highlights  of   the  results.  Holding  constant  other  factors,  trade  expansion  has  had  a  negative  effect  on   women’s  absolute  and  relative  employment  chances  in  NMECs.  The  effects  are  positive  in   MECs,  but  not  robust  across  estimation  methods.  When  we  disaggregate  trade  variables   into  exports  and  imports,  and  then  sub-­‐groups,  gender  effects  differ  across  sectors  and    

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countries  of  differing  economic  structures.  For  example,  both  imports  and  exports  have  a   negative  effect  on  women’s  relative  employment  rates  in  NMECs  and  MECs,  but  imports   have  a  positive  effect  in  MECs.  At  a  more  granular  level  of  analysis,  we  find  that  food   imports  and  exports  produce  a  neutral  gendered  employment  effect,  but  manufacturing   exports  and  imports  lower  women’s  employment  relative  to  men’s  (that  is,  men  gain   more).   (Table  6  about  here).   Perhaps  the  most  substantive  finding  in  this  study,  however,  is  that  infrastructure   improvements  are  positively  correlated  with  women’s  absolute  and  relative  employment   rates.  The  econometric  analysis  used  in  this  research  is  designed  to  decompose  the   determinants  of  employment  into  discrete  factors.  Given  this,  our  results  show  that  if  we   hold  infrastructure  constant,  we  still  have  gendered  trade  effects.  That  said,  there  is  some   likelihood  that  it  is  the  composite  of  gender  conditions  in  a  country  that  mediate  the   relationship  between  trade  and  gendered  employment.12  To  have  introduced  interaction   terms  to  capture  this  effect,  however,  was  deemed  too  demanding  for  this  dataset,  given   cross-­‐country  differences  in  employment  surveys  (Table  1)  and  measurement  of   infrastructure.13  It  bears  reiterating,  however,  that  the  elasticity  of  labor  supply  is   gendered,  and  women’s  care  burden  has  an  important  effect  on  that  elasticity.   Finally,  this  paper  is  a  contribution  to  the  ongoing  debates  and  discussion  on  the   gendered  effects  of  macroeconomic  policies,  including  rules  on  trade.  It  underscores  the   need  for  economic  analysis  that  is  gender-­‐aware,  in  the  sense  of  recognizing  that  economic   activity  works  through  and  within  gendered  relationships,  including  those  in  the  labor  

 

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market.  Given  the  impact  of  gender  on  economic  growth  and  development,  a  better   understanding  of  the  policies  required  to  diminish  gender  inequality  is  essential.    

 

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Cagatay,  Nilufer.  2001.  “Trade,  Gender  and  Poverty”.  UNDP,  NY.     Darity,  William,  Jr.  1995.  “The  Formal  Structure  of  a  Gender-­‐segregated  Low-­‐income   Economy.”  World  Development  23(7):  1963-­‐1968.     Deere,  Carmen  Diana.  2005.  Feminization  of  Agriculture?  Economic  Restructuring  in  Rural   Latin  America.  Occasional  Paper  No.1,  UNRISD,  Geneva.     Elson,  Diane.    1991.  “Male  Bias  in  the  Development  Process:  The  Case  of  Structural   Adjustment.”  In  D.  Elson  (Ed.),  Male  Bias  in  the  Development  Process,  pp.  164-­‐90.   Manchester:  Manchester  University  Press.   Fontana  Marzia,  Susan  Joekes,  and  Rachel  Masika.  1998.  “Global  Trade  Expansion  and   Liberalisation:  Gender  Issues  and  Impacts.  BRIDGE  Development-­‐Gender  Report  No.  42,   Institute  of  Development  Studies,  Brighton,  UK.     Fontana,  Marzia  and  Luisa  Natali.  2008.  “Gendered  Patterns  of  Time  Use  in  Tanzania:  Public   Investment  in  Infrastructure  Can  Help.”  Paper  prepared  for  the  IFPRI  Project  on  Evaluating   the  Long-­‐Term  Impact  of  Gender-­‐focused  Policy  Interventions.     Food  and  Agriculture  Organization  (FAO).  2011.  The  State  of  Food  and  Agriculture:  Women   in  Agriculture.  Rome:  Author.     Fontana,  Marzia.  2007.  “Modeling  the  Effects  of  Trade  on  Women,  at  Work  and  at  Home:   Comparative  Perspectives.”  In  Irene  van  Staveren,  Diane  Elson,  Caren  Grown  and  Nilufer   Cagatay  (Eds.),  The  Feminist  Economics  of  Trade.  London:  Routledge.     Gladwin,  Christina  (Ed.).  1991.  Structural  Adjustment  and  African  Women  Farmers.   Gainsville,  FL:  University  of  Florida  Press.     Gonzalez  de  la  Rocha,  Mercedes.  1988.  “Economic  Crisis,  Domestic  Reorganisation,  and   Women’s  Work  in  Guadalajara.”  Bulletin  of  Latin  American  Research  7(2):  207–223.     Hill,  Ruth  Vargas  and  Marcella  Vigneri.  2011.  “Mainstreaming  Gender  Sensitivity  in  Cash   Crop  Market  Supply  Chains.”  ESA  Working  Paper  No.  11-­‐08.     Kabeer,  Naila.  2000.  The  Power  to  Choose.  London:  Verso.     Klasen,  Stephan  and  Francesca  Lamanna.  2009.  “The  Impact  of  Gender  Inequality  in   Education  and  Employment  on  Economic  Growth:  New  Evidence  for  a  Panel  of  Countries.”   Feminist  Economics  15(3):  91-­‐132.     Kucera,  David  and  Leanne  Roncolato.  2011.  “Trade  Liberalization,  Employment,  and   Inequality  in  India  and  South  Africa.”  International  Labour  Review  150  (1–2):  1-­‐41.    

 

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Jomo,  Kwame  Sundaram,  Oliver  Schwank,  and  Rudiger  von  Arnim.  2011.  “Globalization  and   Development  in  sub-­‐Saharan  Africa.”  UN  DESA  Working  Paper  No.  102.   Meyer,  Lisa.  2006.  “Trade  Liberalization  and  Women’s  Integration  into  National  Labor   Markets:  A  Cross-­‐Country  Analysis.”  Social  Indicators  Research  75(1):  83-­‐121   Ocampo,  José  Antonio,  Codrina  Rada,  and  Lance  Taylor.  2009.  Growth  and  Policy  in   Developing  Countries:  A  Structuralist  Approach.  New  York:  Columbia  University  Press.     Oya,  Carlos  and  John  Sender.  2009.  “Divorced,  Separated  and  Widowed  Women  Workers  in   Rural  Mozambique.”  Feminist  Economics  15(2):  1-­‐31.     Seguino,  Stephanie.  2000.  “Gender  Inequality  and  Economic  Growth:  A  Cross-­‐Country   Analysis.”  World  Development  28(7):  1211-­‐30.   Sender,  John,  Carlos  Oya  and  Christopher  Cramer.  2006.  “Women  Working  for  Wages:   Putting  Flesh  on  the  Bones  of  a  Rural  Labour  Market  Survey  in  Mozambique.”  Journal  of   Southern  African  Studies,  Vol.  32,  No.  2,  pp.  313-­‐333.     Shaxson,  Nicholas.  2005.  “New  Approaches  to  Volatility:  Dealing  with  the  ‘Resource  Curse’   in  sub-­‐Saharan  Africa.”  International  Affairs  81(2):  311-­‐324.     Standing,  Guy.  1989.  “Global  Feminization  through  Flexible  Labor.”  World  Development   17(7):    1077–1095.     Tejani,  Sheba  and  William  Milberg.  2010.  “Global  Defeminization?  Industrial  Upgrading,   Occupational  Segmentation  and  Manufacturing  Employment  in  Middle-­‐Income  Countries.”   SCEP  Working  Paper  2010-­‐1,  Schwartz  Center  for  Economic  Policy  Analysis  &  Department   of  Economics,  New  School  for  Social  Research,  New  York.     United  Nations  Children’s  Fund  (UNICEF).  1999.  Women  in  Transition.  Florence,  Italy:   UNICEF  Innocenti  Research  Centre.     United  Nations  Conference  on  Trade  and  Development  (UNCTAD).  2009.  UNCTAD   Handbook  of  Statistics.  Geneva:  UNCTAD.     van  Staveren,  Irene,  Diane  Elson,  Caren  Grown,  and  Nilufer  Cagatay  (Eds.).  2007.  Feminist   Economics  of  Trade.  London:  Routledge.     Vigneri,  Marcella  and  Rebecca  Holmes  2009.  “When  Being  More  Productive  Still  Doesn’t   Pay:  Gender  Inequality  and  Socio-­‐Economic  Constraints  in  Ghana’s  Cocoa  Sector.”  Paper   presented  at  the  FAO-­‐IFAD-­‐ILO  Workshop  on  Gaps,  Trends  and  Current  Research  in  Gender   Dimensions  of  Agricultural  and  Rural  Employment,  Rome.       World  Bank.  2012.  World  Development  Report  2012.  Washington,  DC:  World  Bank.        

31  

      Table  1.  Female  Share  of  Employment  by  Sector  in  SSA   Agriculture   &  Fishing  

Mining  &   Quarrying  

Manu-­‐       facturing  

Data   source  

Year  of   survey  

Botswana  

38.7%  

12.3%  

55.4%  

1  

2006  

12+  including   armed  forces  

Ethiopia  

32.1%  

10.6%  

50.4%  

1  

2006  

10+  

Lesotho  

39.3%  

20.3%  

63.4%  

1  

2000  

10+  

Madagascar  

49.7%  

45.0%  

23.3%  

2  

2005  

6+  

Mali  

29.8%  

26.1%  

50.1%  

1  

2004  

15+  

Mauritius  

27.4%  

0.0%  

43.2%  

1  

2008  

12+  

Namibia  

36.8%  

21.9%  

49.1%  

1  

2004  

15-­‐69  

Nigeria  

36.6%  

20.9%  

52.5%  

1  

2007  

Civilians  (no  age   specified)  

Senegal  

34.3%  

18.5%  

17.0%  

2  

2006  

15+  

Sierra  Leone  

51.4%  

14.0%  

21.4%  

3  

2004  

10+  

South  Africa  

33.1%  

11.0%  

32.1%  

1  

2008  

15-­‐64  

Tanzania  

51.8%  

47.1%  

34.1%  

1  

2002  

15+  

Uganda  

55.8%  

33.5%  

33.4%  

1  

2003  

10+  

Zambia  

49.6%  

5.5%  

27.0%  

3  

2000  

12+  

   

Ages  included  in   sample  

Sample  mean                 40.5%   20.5%   39.5%   Note:  Date  sources  are  1)  labor  force  survey,  2)  household  survey,  and  3)  population  census.     Source:  International  Labor  Organization,  Laborstat,  Table  2B,  accessed  December  8,  2012.  

 

 

32  

Table  2.  Sectoral  Shares  of  Value-­‐Added  as  %  of  GDP  and  Employment      

 

Sectoral  shares  of  output,  2009        

Agriculture   Services   Industry   Non-­‐Mfg.  Industry   Mfg.  

 

NMECs  

MECs  

Oil  

  22.4   54.3  

  21.4   48.5  

  25.9   37.9  

23.3   9.8   13.4  

30.1   22.8   9.4  

36.2   26.0   10.2  

    64.7   9.5   25.2  

    55.8   10.8   33.2  

    50.7   10.0   35.5  

    1.01   0.74   1.07  

    1.05   0.46   1.15  

    1.00   0.65   1.14  

       

              Employment  Shares,  1991-­‐2009     Agriculture       Industry       Services             F/M  Employment  Shares,  1991-­‐2009   Agriculture       Industry       Services      

Note:   Industry   is   comprised   of   manufacturing   and   non-­‐manufacturing   output.   Non-­‐manufacturing   industry   value-­‐added  includes  mining,  construction,  electricity,  water  and  gas.     Source:  World  Development  Indicators  (2012),  online.  

 

33  

   Variables  

Table  3.  Gender  Employment  Effects  of  Trade  Liberalization   (Dependent  variable:  ln[Female  –  Male  Employment  Rate  Gap])     Full  sample   NMECs   MECs   FE   TSLS   FE   TSLS   FE   TSLS   (1)   (2)   (3)   (4)   (5)   (6)  

GDP  growth  

-­‐0.009   (0.020)  

-­‐0.168**   (0.079)  

0.018   (0.026)  

0.123   (0.085)  

0.015   (0.029)  

-­‐0.093   (0.111)  

  Sanitation  

0.041*   (0.022)  

0.023**   (0.011)  

0.083***   (0.020)  

0.078***   (0.013)  

0.029   (0.035)  

-­‐0.078***   (0.024)  

  Telephones  

0.030***   (0.010)  

0.048***   (0.005)  

0.020   (0.017)  

0.031***   (0.008)  

0.013   (0.010)  

0.043***   (0.008)  

  Trade   as  %  GDP  

-­‐0.007   (0.017)  

-­‐0.006   (0.009)  

-­‐0.078***   (0.019)  

-­‐0.097***   (0.013)  

0.023   (0.022)  

0.051***   (0.011)  

  Mfg.   as  %  GDP  

0.012   (0.015)  

0.003   (0.009)  

0.033   (0.019)  

0.027**   (0.012)  

-­‐0.011   (0.024)  

-­‐0.038***   (0.013)  

  Agric.   as  %  GDP  

-­‐0.028   (0.017)  

-­‐0.023***   (0.008)  

-­‐0.050*   (0.027)  

-­‐0.050***   (0.012)  

-­‐0.032   (0.025)  

-­‐0.044***   (0.011)  

 Constant    No.  of  observations   R2  

-­‐0.290***   (0.095)   654  

    441  

0.24  

0.338  

-­‐0.079   (0.130)  

No.  of  countries  

255  

    208  

0.486  

0.526  

-­‐0.263   (0.182)   303  

    175  

0.158  

0.393  

38   30   15   13   17   13   Note:  All  variables  with  the  exception  of  per  capita  GDP  growth  are  measured  in  natural  logs.  Values  in  the   parenthesis  are  standard  errors.  A  single  asterisk  (*)  denotes  significance  at  the  10%  level,  two  asterisks  (**)   at  the  5%  level,  and  three  asterisks  (***)  at  the  1%  level.  Instruments  in  the  TSLS  regressions  are:  the  growth   rate  of  gross  fixed  capital  formation,  OECD  GDP  growth  rates,  the  log  of  the  female-­‐male  gap  in  labor  force   participation  rates,  and  the  log  of  the  ratio  of  female  to  male  gross  primary  enrollment  rates.  

 

 

 

34  

Table  4.  Gender  Employment  Effects  of  Trade  Liberalization,  Exports  and  Imports  Disaggregated   (Dependent  variable:  ln[Female  –  Male  Employment  Rate  Gap])  

 

Full  sample      

Variables  

GDP  growth     Sanitation     Telephones    Exports      Imports         Food  exports     Food  imports     Ore  exports     Ore  imports     Mfg.  exports     Mfg.  imports    Mfg.  as  %  GDP    Agric.  as  %  GDP     Constant     No.  of  observations   R2   No.  of  countries  

NMECs  

Full  sample   (Disaggregated  X  and   M)  

MECs  

FE  

TSLS  

FE  

TSLS  

FE  

TSLS  

FE  

TSLS  

(1)  

(2)  

(3)  

(4)  

(5)  

(6)  

(7)  

(8)  

0.010   (0.020)  

-­‐0.108   (0.079)  

0.026   (0.030)  

0.155*   (0.090)  

0.042   (0.037)  

0.019   (0.109)  

0.016   (0.051)  

-­‐0.147   (0.090)  

0.036*   (0.021)  

0.013   (0.012)  

0.080***   (0.023)  

0.073***   (0.014)  

0.030   (0.034)  

0.072   (0.023)  

0.047**   (0.022)  

0.045***   (0.013)  

0.030***   (0.010)  

0.048***   (0.005)  

0.020   (0.017)  

0.031***   (0.008)  

0.012   (0.010)  

0.038***   (0.007)  

0.037***   (0.011)  

0.050***   (0.005)  

-­‐0.027**   (0.013)  

-­‐0.026***   (0.008)  

-­‐0.029**   (0.013)  

-­‐0.036***   (0.011)  

-­‐0.035**   (0.016)  

-­‐0.020*   (0.011)  

       

0.023   (0.015)  

0.023**   (0.009)  

-­‐0.046**   (0.021)  

-­‐0.058***   (0.016)  

0.052**   (0.024)  

0.069***   (0.012)  

       

       

       

       

       

0.008   (0.007)  

    0.003   (0.004)  

       

       

       

       

-­‐0.006   (0.010)  

0.003   (0.007)  

       

       

       

       

0.0002   (0.003)  

0.0002   (0.002)  

       

       

       

       

0.001   (0.008)  

0.011***   (0.004)  

       

       

       

       

-­‐0.002   (0.004)  

-­‐0.007***   (0.002)  

                                               

                   

   

       

       

       

       

-­‐0.021   (0.023)  

-­‐0.032**   (0.014)  

0.011   (0.014)  

    0.006   (0.009)  

0.033   (0.020)  

0.027**   (0.012)  

-­‐0.011   (0.022)  

-­‐0.021   (0.014)  

0.008   (0.019)  

-­‐0.016   (0.012)  

-­‐0.036**   (0.016)  

-­‐0.032***   (0.008)  

-­‐0.052*   (0.028)  

-­‐0.052***   (0.012)  

-­‐0.040   (0.024)  

-­‐0.053***   (0.011)  

-­‐0.043*   (0.023)  

-­‐0.034***   (0.011)  

-­‐0.129   (0.132)  

       

-­‐0.222   (0.171)  

       

-­‐0.210   (0.171)  

-­‐0.270***   (0.090)   654  

    441  

255  

208  

303  

175  

429  

    308  

0.261  

0.37  

0.484  

0.519  

0.211  

0.451  

0.356  

0.52  

38  

30  

15  

13  

17  

13  

34  

28  

Note:  All  variables  with  the  exception  of  per  capita  GDP  growth  are  measured  in  natural  logs.  Values  in  the  parenthesis   are  standard  errors.  A  single  asterisk  (*)  denotes  significance  at  the  10%  level,  two  asterisks  (**)  at  the  5%  level,  and   three  asterisks  (***)  at  the  1%  level.  Instruments  in  the  TSLS  regressions  are:  the  growth  rate  of  gross  fixed  capital   formation,  OECD  GDP  growth  rates,  the  log  of  the  female-­‐male  gap  in  labor  force  participation  rates,  and  the  log  of  the   ratio  of  female  to  male  gross  primary  enrollment  rates.  

 

35  

Table  5.  Female  Employment  Effects  of  Trade  Liberalization   (Dependent  variable:  ln[Female  Employment-­‐to-­‐Population  Rate])     Full  sample       Variables   GDP  growth    Sanitation    Telephones     Trade   as  %  GDP     Mfg.   as  %  GDP    Agric.  as  %  GDP     Constant     of  observations   No.   R2   No.  of  countries  

NMECs  

MECs  

FE   (1)  

TSLS   (2)  

FE   (3)  

TSLS   (4)  

FE   (5)  

TSLS   (6)  

0.010   (0.029)   0.060***   (0.022)   0.022**   (0.010)   -­‐0.011   (0.019)   0.009   (0.016)   -­‐0.047**   (0.022)   3.993***   (0.103)   654  

-­‐0.056   (0.088)   0.043***   (0.013)   0.038***   (0.006)   -­‐0.017   (0.011)   0.006   (0.011)   -­‐0.046***   (0.009)  

0.156   (0.108)   0.084***   (0.016)   0.028***   (0.010)   -­‐0.097***   (0.017)   0.028*   (0.016)   -­‐0.077***   (0.015)       208  

0.030   (0.050)   0.056   (0.039)   0.0003   (0.010)   0.018   (0.020)   -­‐0.020   (0.019)   -­‐0.065***   (0.022)   3.999***   (0.175)   303  

-­‐0.004   (0.140)   0.015   (0.030)   0.012   (0.010)   0.037***   (0.014)   -­‐0.059***   (0.017)   -­‐0.064***   (0.014)  

    441  

0.027   (0.037)   0.087***   (0.018)   0.019   (0.014)   -­‐0.072***   (0.021)   0.032   (0.019)   -­‐0.072   (0.043)   4.245***   (0.182)   255  

0.253  

0.332  

0.434  

0.46  

0.231  

0.314  

38  

30  

15  

13  

17  

13  

    175  

Note:  All  variables  with  the  exception  of  per  capita  GDP  growth  are  measured  in  natural  logs.  Values  in  the   parenthesis  are  standard  errors.  A  single  asterisk  (*)  denotes  significance  at  the  10%  level,  two  asterisks  (**)   at  the  5%  level,  and  three  asterisks  (***)  at  the  1%  level.  Instruments  in  the  TSLS  regressions  are:  the  growth   rate  of  gross  fixed  capital  formation,  OECD  GDP  growth  rates,  the  log  of  the  female-­‐male  gap  in  labor  force   participation  rates,  and  the  log  of  the  ratio  of  female  to  male  gross  primary  enrollment  rates.    

                   

36  

Table  6.  Summary  of  TSLS  Regression  Results  on  Trade  and  Infrastructure      

Female-­‐Male  Employment   Gap   Full   sample  

 

 

NMECs  

MECs  

−  

−***  

+***  

Exports  

−***  

−***  

Imports  

+**  

Sanitation  

 

 

NMECs  

MECs  

−  

−***  

+***  

+***  

−*  

+  

−**  

−***  

+***  

+  

−***  

+***  

+*  

+***  

+***  

+***  

+***  

+  

Telephone  lines  

+***  

+***  

+***  

+***  

+***  

+  

Manufacturing   share  of  GDP  

+  

+***  

−  

+  

+*  

−***  

−***  

−***  

−***  

−***  

−***  

−***  

Trade  

Agriculture   share  of  GDP            

 

Female  Employment   Rate  

 

37  

Full   Sample  

      Figure  1.  Trends  in  Female  and  Male  Employment-­‐to-­‐Population  Rates  by  Structure  in  SSA,   1991-­‐2010     Panel  A:  Female-­‐Male  Employment  Rate   -­‐5.0   1991  

1994  

1997  

2000  

2003  

2006  

2009  

-­‐10.0   -­‐15.0   -­‐20.0   -­‐25.0   -­‐30.0  

Oil  

   

NMEC  

MEC  

 

  Panel  B:  Female  Employment-­‐to-­‐Population  Rate    

64   59   54   49   44   1991  

1994  

1997  

Oil  

       

 

2000  

NMEC  

 

2003  

2006  

2009  

MEC  

   

38  

  Figure  2.  Trends  in  Structure  of  Production,  1960-­‐2010     Panel  A:  Agriculture  Value-­‐Added  as  Share  of  GDP  

  55   50   45   40   35   30   25   20   15   1960   1965   1970   1975   1980   1985   1990   1995   2000   2005   2010  

Oil  

NMEC  

MEC  

 

  Panel  B:  Services     60   55   50   45   40   35   30   1960   1965   1970   1975   1980   1985   1990   1995   2000   2005   2010  

Oil  

NMEC                                    

 

39  

MEC  

 

  Panel  C:  Non-­‐manufacturing  Industry  

    40   35   30   25   20   15   10   5   0   1960   1965   1970   1975   1980   1985   1990   1995   2000   2005   2010  

Oil  

NMEC  

MEC  

 

    Panel  D:  Manufacturing  

  17   15   13   11   9   7   5   1960   1965   1970   1975   1980   1985   1990   1995   2000   2005   2010   Oil  

NMEC  

MEC  

 

  Note:  Industry  includes  ISIC  divisions  10-­‐14  and  38-­‐45,  comprises  manufacturing,  mining,   construction,  electricity,  water,  and  gas.  Services  include  ISIC  divisions  50-­‐99,  comprised  of   wholesale  and  retail  trade,  transport,  and  government,  financial,  professional,  and  personal  services.      

 

 

Source:  World  Development  Indicators  (2012),  online.  

 

 

40  

  Figure  3.  Trends  in  Trade  Openness  and  Trade  Deficit  (Surplus),  1960-­‐2010     Panel  a:  Trade  as  %  of  GDP     95   85   75   65   55   45   35   25   1960   1965   1970   1975   1980   1985   1990   1995   2000   2005   2010  

Oil  

NMEC  

MEC  

    Panel  b:  Trade  deficit  (surplus)  as  %  of  GDP    

 

 

  15   10   5   0   1960   1965   1970   1975   1980   1985   1990   1995   2000   2005   2010   -­‐5   -­‐10   -­‐15   -­‐20   -­‐25   -­‐30  

Oil  

 

 

 

NMEC  

MEC  

   

 

41  

Figure  4.  Trends  in  the  Structure  of  Exports  and  Imports     Panel  A:  Net  food  exports  

    90  

70  

50  

30  

10   1962   1967   1972   1977   1982   1987   1993   1998   2003   2008   -­‐10  

-­‐30  

Oil  

NMEC  

MEC  

  Panel  B:  Net  manufacturing  exports            

 

 

 

0   1962   1967   1972   1977   1982   1987   1992   1997   2002   2007   -­‐10   -­‐20   -­‐30   -­‐40   -­‐50   -­‐60   -­‐70   -­‐80   Oil  

NMEC  

                     

42  

MEC  

 

Panel  C:  Net  ore  exports  

  60   50   40   30   20   10   0   1962   1967   1972   1977   1982   1987   1992   1997   2002   2007   -­‐10   Oil  

NMEC  

MEC  

       

Note:  Net  export  sectoral  shares  measure  the  difference  between  sectoral  exports  and  imports  as  a   share  of  GDP.  Food  comprises  the  following  SITC  categories:  food  and  live  animals;  beverages  and   tobacco;  animal  products.  

 

Source:  World  Development  Indicators  (2012),  online.  

   

 

 

43  

    Figure  5.  Long-­‐Run  Per  Capita  GDP  Growth  Trends  in  SSA  Economies,  1961-­‐2010   (Hodrick-­‐Prescott  Filter)         0.04   0.03   0.02   0.01   0.00   1961  

1966  

1971  

1976  

1981  

1986  

1991  

1996  

2001  

2006  

-­‐0.01   -­‐0.02  

Oil  

NMEC  

       

                         

 

44  

MEC  

 

APPENDIX            

Table  A.1.  Sample  Countries  by  Structure  of  Merchandise  Exports    

    Non-­‐oil  Mineral  Exporters   Botswana   Central  African  Republic   Congo,  DR   Gabon   Ghana   Guinea   Mauritania   Mozambique   Namibia   Niger   Rwanda   Senegal   South  Africa   Tanzania   Togo   Zambia   Zimbabwe  

Non-­‐oil  Non-­‐Mineral   Exporters   Benin   Burkina  Faso   Burundi   Eritrea   Ethiopia   Gambia   Guinea  Bissau   Kenya     Lesotho   Madagascar   Malawi   Mali   Mauritius   Swaziland   Uganda    

 

 

45  

  Oil  Exporters   Angola Cameroon Chad Cote d'Ivoire Nigeria Sudan  

 

Table  A.2  Model  Variables  and  Sources     Variable   Data  Source   Female  employment-­‐to-­‐population  rate  minus  male   employment-­‐to-­‐population  rate  (15+)  

Authors’  calculation  based  on  ADI   database  

Female  employment-­‐to-­‐population  rate  (15+)  

ADI  database  

GDP  per  capita  growth  rate   Percent  of  population  with  access  to  improved   sanitation  facilities    

ADI  database  

Telephone  lines  per  100  people  

ADI  database  

Trade    (exports  plus  imports)  as  a  share  of  GDP    

ADI  database  

Manufacturing  value-­‐added  as  %  of  GDP  

ADI  database  

Agriculture  value-­‐added  as  %  of  GDP  

ADI  database  

Exports  of  goods  and  services  as  %  of  GDP  

ADI  database  

Imports  of  goods  and  services  as  %  of  GDP  

ADI  database  

Ores  and  metals  exports  as  %  of  merchandise  exports  

ADI  database  

Ores  and  metals  imports  as  %  of  merchandise  imports  

ADI  database  

Food  exports  as  %  of  merchandise  exports  

ADI  database  

Food  imports    as  %  of  merchandise  imports  

ADI  database  

Manufactures  export  as  %  of  merchandise  exports  

ADI  database  

Manufactured  imports  as  %  of  merchandise  imports   Female  labor  force  participation  rate  minus  log  male   labor  force  participation  rate  (population  15-­‐64)  

ADI  database  

Growth  rate  of  gross  fixed  capital  formation    

Authors’  calculation  based  on  ADI  data  

WDI  database  

Authors’  calculation  based  on  ADI  data   Authors’  calculation  based  on  WDI   Ratio  of  female  to  male  gross  primary  enrollment  rates     data   Growth  rate  of  OECD  GDP  per  capita       WDI  database   Notes:  ADI  is  the  African  Development  Indicators.  WDI  is  the  World  Development  Indicators.  Both  databases   are  from  World  Bank’s  online  database.      

 

 

 

46  

  Table  A.3.  Descriptive  Statistics        

Mean  

Std.   Dev.  

Min  

Max  

Obs.  

Female-­‐male  employment  rate  gap  (log)  

-­‐0.264  

0.254  

-­‐1.154  

0.153  

722  

Female  employment  rate  (log)  

3.997  

0.317  

3.082  

4.459  

722  

Per  capita  GDP  growth  

0.01  

0.056  

-­‐0.64  

0.316  

758  

Sanitation  (log)  

3.18  

0.712  

1.099  

4.489  

748  

Telephone  (log)  

-­‐0.312  

1.298  

-­‐5.157  

3.392  

719  

Trade  as  %  GDP  (log)  

4.146  

0.489  

2.382  

5.344  

727  

Manufacturing  value-­‐added  as  %  GDP  (log)  

2.259  

0.587  

0.916  

3.81  

690  

Agriculture  value-­‐added  as  %  GDP  (log)  

3.108  

0.757  

0.606  

4.232  

696  

Exports  as  %  GDP  (log)  

3.224  

0.644  

1.167  

4.646  

727  

Imports  as  %  GDP  (log)  

3.593  

0.466  

1.979  

4.995  

727  

Ores  exports  as  %  of  merchandise  exports  (log)  

0.889  

2.401  

-­‐5.923  

4.487  

468  

Ores  imports  as  %  of  merchandise  imports  (log)  

0.131  

0.728  

-­‐2.579  

3.698  

471  

Food  exports  as  %  of  merchandise  exports  (log)  

3.057  

1.402  

-­‐4.157  

4.596  

470  

Food  imports  as  %  of  merchandise  imports  (log)   Manufacturing  exports  as  %  of  merchandise  exports   (log)   Manufacturing  imports  as  %  of  merchandise  imports   (log)  

2.778  

0.481  

0.804  

4.134  

471  

2.421  

1.691  

-­‐7.611  

4.553  

467  

4.119  

0.192  

3.384  

4.532  

471  

Female-­‐male  labor  force  participation  rate  gap  (log)  

-­‐0.283  

0.238  

-­‐1.167  

0.039  

722  

Female/male  ratio  primary  enrollment  rates  (log)  

4.442  

0.18  

3.805  

4.803  

637  

Growth  of  gross  fixed  capital  formation    

0.086  

0.182  

-­‐0.777  

0.787  

505  

Growth  rate  of  OECD  GDP  per  capita  

1.306  

1.646  

-­‐4.584  

3.176  

760  

Note:  All  variables  are  measured  in  natural  logs  with  the  exception  of  the  growth  rates  of  per  capita  GDP,   gross  fixed  capital  formation,  and  OECD  growth  rates.            

 

47  

   

ENDNOTES                                                                                                                 1

 This  is  based  on  authors’  calculations  from  African  Development  Indicators  (2012),  online.  

2  This  is  based  on  authors’  calculations  from  World  Development  Indicators  (2012),  online.   3  This  is  based  on  authors’  calculations  using  the  World  Bank’s  African  Development  Indicators  (online).  

Aggregate  economic  activity  (measured  as  variance  of  per  capita  GDP  growth  rates)  has  also  been  more   volatile  in  since  1985.     4

 These  trade  policy  variables,  in  any  case,  have  some  limitations.  Data  on  tariffs,  for  instance,  disregard  the   use  of  NTBs  as  impediments  to  trade.  Also,  it  is  not  always  the  case  that  NTBs  are  strictly  enforced,  especially   in  poor  developing  countries  where  corruption  sometimes  triumphs  over  trade  policies.   5  Two  unit  root  tests  were  performed  on  all  variables,  the  standard  Augmented  Dickey-­‐Fuller  test  and  the  

Phillips-­‐Perron  test.  We  were  able  to  decisively  reject  the  null  hypothesis  of  no  unit  root  for  all  but  two   variables  –the  natural  log  of  telephone  lines  were  100  people  and  the  log  of  the  female  to  male  primary   enrollment  rate  (used  as  an  instrument  for  per  capita  GDP  growth  in  the  TSLS  estimation.  For  those  two   variables,  we  modified  the  specification  to  include  deterministic  drift  (intercept)  and  linear  trend  terms.   Based  on  the  results  of  the  unit  root  tests,  we  conclude  that  this  set  of  variables  can  be  considered  to  be   stationary,  stationary  with  drift,  or  trend  stationary  (that  is,  there  is  no  evidence  of  unit  roots  once  we   account  for  deterministic  factors  that  can  produce  trends  in  the  data).   6  See  Bruckner,  Markus,  and  Lederman  (2012)  for  a  detailed  explanation  of  the  OECD  GDP  growth  rate  

instrument    

7  We  also  ran  regressions  in  which  we  excluded  the  oil  exporters  from  the  full  sample.  This  did  not  

significantly  change  the  results  presented  here  for  the  full  sample.  Results  available  upon  request.   8  The  services  sector  is  the  omitted  sector  here,  and  it  is  useful  to  remember  that  a  good  deal  of  service  sector  

employment  is  informal  and  can  be  construed  as  residual  unemployment.     9

 Though  we  do  not  have  direct  estimates  of  the  effects  on  women’s  care  burden,  numerous  studies  have   found  linkages  between  infrastructure  improvements  and  women’s  care  burden  (Agénor,  Canuto,  and  da   Silva  2010;  Fontana  and  Natali  2008).     10  We  ran  stepwise  regressions  without  the  manufacturing  variable,  and  obtained  positive  and  significant   coefficients  on  sanitation  and  telephones.  Results  available  on  request.   11  Darity  (1995)  too  noted  that  with  the  expansion  of  demand  for  cash  crops,  often  controlled  by  men,  

women’s  labor  time  might  be  further  constrained  as  they  provided  labor  on  male  crops.  This  would  not  only   limit  their  time  for  subsistence  production  but  also  for  off-­‐farm  remunerative  work.       12  We  are  grateful  to  an  anonymous  referee  for  raising  this  point.   13  Substantial  effort  goes  into  data  cleaning  to  ensure  accuracy,  consistency,  and  coherence.  Nevertheless,  this  

is  a  daunting  task,  given  that  SSA  infrastructure  estimates  are  compiled  from  a  variety  of  sources,  including   household  and  enterprise  surveys,  administrative  data  sources,  population  and  housing  censuses  (Africa   Infrastructure  Knowledge  Program  2011).    

 

48