Economic Development and the Organization of Labour: Jobs of the World Project Bandiera
Aim How labour organisation (allocation/nature of jobs) varies btwn countries at different stages of development
Context Setting: Global data
Motivati Labour is the sole endowment of the poor + main factor of production = Understanding whether labour is employed
on efficiently is key to understanding poverty
Data Assembled new data set: Jobs of the World Data built from (IPUMS) & Demographic and Health Surveys (DHS)
IPMUS: population Censuses covering nearly 100 countries
DHA: Fertility/women's health, hh assets, employment (m&f) and includes SSA
Variables:
JWD variables: reports person working, work remuneration, and whether self-employed/wage work
Work defined: any form of productive activity regardless of whether income generated
Wealth defined: asset index ownership of non-productive assets, dwelling qual etc
Results Result 1: Marketisation of work; labour moves from unpaid HH work to paid market work wealth and gender
determine labor allocation but differences by gender are larger than differences by wealth.
- Fig 4: Wealth: In lower income countries, share of ppl in (unpaid) work largest in poorest; In high-income
countries share of ppl in (mainly paid) work largest in high-income group (unpaid work disappears)
- Fig ¾: gender matters more than wealth in variation in paid work = men specialise in paid market work while
poor women take up paid jobs at a lower rate and ’disappear’ from the measured labor force. = U shape is
driven by women = increase in paid work slowly bc still worked more in HH
Result 2: Emergence of firms as organising unit of work pulling workers out of self-employed work at low levels of
development to paid work in firms at higher level of development bc private firms that employ wage workers
Fig 7 Mainly private firms creating wage jobs and so fig 6 shift from unpaid home production to market
happens gradually
Fig 8 shift from agriculture into manufacturing/services across GDP
Fig 10: lowest educational groups experiencing the larger shifts into wage work
Fig 12: poorest in poor countries are the most likely to work - out of necessity and in the worst types of jobs.
Result 3: Increasing specialisation and creation of ’new’ jobs within firms increase w development bc larger
number of specialised occupations available but comes with occupational segregation by gender
- Firms use more tech/management practices = division of labor and a higher degree of specialisation than
what is possible among self-employed workers = boost productivity
- Fig 13: Panel 1: workforce shift out of agriculture into high skilled groups as countries get richer but Panel
2/3: work is gendered in richer countries: women work more in services and men more in crafts.
- Fig 14: richest countries have more occupation types than poorest + specialisation among employees occurs
for both gender but faster for men (Figure 14b).
- Fig 17 - countries become richer, care tasks are increasingly taken on by women.
Reason for gender misallocation:
- Assessed Interventions include vocational training, cash transfers mostly successful in countries w/ higher
share of women in paid work Social norms more important than indv barrier & not due to labour market
wide factors; no corr btwn results and share of men in paid work
Policy Interventions like training is only effective when womens participation is already high and doesn’t address social
implicati norms
ons address social norms to improve labour allocation eg via Information campaigns to overcome self-stabilising norms
by people wont adhere to norm if they knew the real preferences of others
Policies to improve gender allocation shouldn’t treat both genders equally + reinforce traditional gender
roles eg Parental leave policies often favor mothers with longer paid leave, creating financial incentives for
Aim How labour organisation (allocation/nature of jobs) varies btwn countries at different stages of development
Context Setting: Global data
Motivati Labour is the sole endowment of the poor + main factor of production = Understanding whether labour is employed
on efficiently is key to understanding poverty
Data Assembled new data set: Jobs of the World Data built from (IPUMS) & Demographic and Health Surveys (DHS)
IPMUS: population Censuses covering nearly 100 countries
DHA: Fertility/women's health, hh assets, employment (m&f) and includes SSA
Variables:
JWD variables: reports person working, work remuneration, and whether self-employed/wage work
Work defined: any form of productive activity regardless of whether income generated
Wealth defined: asset index ownership of non-productive assets, dwelling qual etc
Results Result 1: Marketisation of work; labour moves from unpaid HH work to paid market work wealth and gender
determine labor allocation but differences by gender are larger than differences by wealth.
- Fig 4: Wealth: In lower income countries, share of ppl in (unpaid) work largest in poorest; In high-income
countries share of ppl in (mainly paid) work largest in high-income group (unpaid work disappears)
- Fig ¾: gender matters more than wealth in variation in paid work = men specialise in paid market work while
poor women take up paid jobs at a lower rate and ’disappear’ from the measured labor force. = U shape is
driven by women = increase in paid work slowly bc still worked more in HH
Result 2: Emergence of firms as organising unit of work pulling workers out of self-employed work at low levels of
development to paid work in firms at higher level of development bc private firms that employ wage workers
Fig 7 Mainly private firms creating wage jobs and so fig 6 shift from unpaid home production to market
happens gradually
Fig 8 shift from agriculture into manufacturing/services across GDP
Fig 10: lowest educational groups experiencing the larger shifts into wage work
Fig 12: poorest in poor countries are the most likely to work - out of necessity and in the worst types of jobs.
Result 3: Increasing specialisation and creation of ’new’ jobs within firms increase w development bc larger
number of specialised occupations available but comes with occupational segregation by gender
- Firms use more tech/management practices = division of labor and a higher degree of specialisation than
what is possible among self-employed workers = boost productivity
- Fig 13: Panel 1: workforce shift out of agriculture into high skilled groups as countries get richer but Panel
2/3: work is gendered in richer countries: women work more in services and men more in crafts.
- Fig 14: richest countries have more occupation types than poorest + specialisation among employees occurs
for both gender but faster for men (Figure 14b).
- Fig 17 - countries become richer, care tasks are increasingly taken on by women.
Reason for gender misallocation:
- Assessed Interventions include vocational training, cash transfers mostly successful in countries w/ higher
share of women in paid work Social norms more important than indv barrier & not due to labour market
wide factors; no corr btwn results and share of men in paid work
Policy Interventions like training is only effective when womens participation is already high and doesn’t address social
implicati norms
ons address social norms to improve labour allocation eg via Information campaigns to overcome self-stabilising norms
by people wont adhere to norm if they knew the real preferences of others
Policies to improve gender allocation shouldn’t treat both genders equally + reinforce traditional gender
roles eg Parental leave policies often favor mothers with longer paid leave, creating financial incentives for
