Millennium and Sustainable Development Goals Paper The Length should not exceed 3500 words (excluding references). Please answer ONE of the following que

Millennium and Sustainable Development Goals Paper The Length should not exceed 3500 words (excluding references).

Please answer ONE of the following questions (please note additional guidelines I upload):

How have women’s organisations assisted in promoting human (resource) development capabilities, as well as empowerment, in organisations and/or in societies more widely? Your discussion should critically assess theoretical debates about the nature and complexity of empowerment in diverse geographical regions. (You are encouraged to draw on case examples of NGOs/INGOs and Civil Society Organisations).
This essay comprises two parts: a) briefly assess why the Millennium Development Goals have failed to deliver on equality promises; and b) critically examine the viability of the Sustainable Development Goals with respect to their gender goals and commitments. In so doing, explore how appropriate governance, reporting, multiple social actor collaboration, and measurement strategies can be implemented to attain equity by 2030 (with regard to part (b) you can present either a broad argument or focus on one country/region to use as an example when composing your answer).
Critically examine why Gender National Action Plans (GNAPs) are important for social transformation and economic growth. Choosing TWO countries Gender/Equality National Action Plans: a) assess what is meant by GNAP in each country context; b) critically assess the national systems and institutional structures (or ‘mechanisms’) each country has put in place that are tasked with ensuring that country actually works toward gender equality; and c) suggest practical strategies that can improve gender representation/participation, especially in areas of political leadership and decision-making (not necessarily at national level, but consider these factors at all levels of organisations and society).
“[D]espite the [breadth of Rwandan national ICT-related] efforts regarding the inclusion of both women and men in the domain of ICT, Rwanda still shows a number of weaknesses. For example, the ICT sector remains mostly male-dominated. Gaps have been identified at the level of how ICTs are used to promote rights for women and girls, empower women for different kind of activities, increase women’s participation in decision making and provide women’s employment in ICT and related sectors.” (Mumporeze and Prieler, 2017: 1288). Making particular reference to a country case study of your choice, critically review Gender and ICT skills development. Include in your discussion, the particular role of organizations in facilitating knowledge and skills development among women and girls.
Explain how globalization is a gendered process and why its impact varies, or is uneven, across different countries or regions (or organizations), and thus results in unequal opportunities and constraints for women and men. Suggest practical HRD policies that can counter the inequities of globalization processes in order to incorporate voices of the marginalised and encourage all stakeholders to strive for equality and social justice. Your discussion should include two countries or take a regional focus. It may also focus on one policy area such as poverty, work, health, environment, property, violence or take a more general perspective.
“You cannot change norms if you are only talking to women and girls about changing the norms that restrict their voice and agency. You have to broaden that conversation” (from ‘Empowering Women & Girls’ panel discussion; video link available on Blackboard). Discuss, by using examples from developing country/economy contexts, or choose a specific country or region, and pay particular attention to: a) the risks or obstacles faced by challenging, questioning, or even defying social norms or mores; b) the contemporary role of men in Gender and Development; and (c) in broadening ‘the conversation’ suggest, with justification, what items should be on the current agenda of such a discussion?

ADDITIONAL GUIDELINES: (IMPORTANT)

a) In your answers you are encouraged to demonstrate your understanding of different gender and development theories and concepts by using illustrative case examples from different geographic regions or countries (e.g. the Middle East as a region; or Nigeria and Lesotho, or China and Malaysia as examples of contrasting/comparable countries). You should clearly indicate this in the introduction to your essay.

b) Your essays should use literature from across this unit’s lectures and handbook with which to build your arguments and answers; it is expected that a high degree of appropriate reading will be clear and obvious in your essays.

c) You are encouraged to utilise gender development/inequality indicators produced by the WEF, UN, World Bank, and research institutions such as Freedom House, the Asian Development Bank, the African Development Bank, or the Islamic Development Bank, as well as country government reports and private/public organization reports/data. An assignment workshop will be held near the end of the programme in May where tutorial support will be available to assist with essay plans. You can, of course, make contact with the course convenor or any of the individual session lecturers.

d) The essay questions do not necessarily correspond to individual lectures and you are encouraged, too, to draw on a variety of material presented in the module’s lectures, reading lists, and tutorials as long as it is appropriate to the essay question you are answering.

e) You must compile a properly formatted References List (Harvard method) for this assignment citing appropriate GAD literature cited in this course. You should also reference any organisational documents and/or websites you have utilised. This list is excluded from your overall word count.

f) You must use references that within 10 years, avoid outdated references. GENDER, GROWTH AND
POVERTY:
Empirical Methods and Evidence
David Lawson
Senior Lecturer Public Policy and
Development Economics
GDI University of Manchester
Structure of Presentation
Gender and Growth Theory & Practice
Gender and Poverty Theory and Practice
Typical Approach
Non Typical Approaches (Beyond
Headship)
Time Poverty
Key Findings and Policy Perspective
Conclusion/Other Issues – Assignment

Gender and Growth: Theory
Gender gaps in education matter:
1. distortion and/or declining marginal returns; 2.
externality of female education; 3. demographic
effects
Gender gaps in employment matter:

1. distortion; 2. makes labor-intensive growth strategy more
difficult; 3. employment and bargaining power; 4.
employment and governance

Gender gaps matter for women as producers:
1. distortion due to unequal access to assets and
inputs; 2. distortions regarding adoption of new
technologies; 3. inefficiency due to high and unequal
time burdens in household economy
Gender and Growth Theory:
Gender Gaps – Education
Prim ary Gross Enrolm ent Rate Secondary Gross Enrolm ent Rate Average Years of Attainm ent
1975
Region
1999
1975
1999
1970
b
1995
Fem ales Males Fem ales Males Fem ales Males Fem ales Males Fem ales Males Fem ales Males
East Asia & Pacific
108
Europe & Central Asia
..
121
..
106
93
105
35
95 ..
49
..
60
65
3.06
4.54
5.85
6.84
80
81
8.09
8.93
9.67
9.20
Latin America & Caribbean
97
100
130
133
34
35
87
80
3.52
4.14
5.58
5.91
Middle East & North Africa
64
99
91
99
24
44
67
72
1.39
2.75
4.21
5.74
South Asia
58
91
91
110
15
33
41
57
1.08
2.95
2.94
5.31
45
66
73
85
6
13
23
28
1.56
2.60
2.82
3.98
Sub-Saharan Africa
a
a
Latest available data on primary GERs are from 1998 and on secondary GERs from 1996.
b
Attainment data include schooling beyond secondary. Since data are from Barro and Lee (2000), the regional classification includes
some countries w ith per capita incomes too high to be included in the World Bank’s database (the one used for the GERs).
Source: World Development Indicators central database and Barro and Lee (2000).
Gender gaps initially large, have been reduced a little. Levels of
female education now the worst in the developing world.
General education crisis mostly to blame (positive exceptions).
Effects of Gaps: Education
Table 2: Estimating the Effect of Gender Inequality in Education on
Growth Differences between Uganda and Botswana and East Asia
1960-2000
1990-2000
Direct
Total
Direct
Total
Botswana
Effect of Gender Inequality in 1960
0,45
1,14
0,29
0,73
Ratio of Gender Inequality in
Growth of Education
0,13
0,18
-0,06
-0,08
Total
0,58
1,32
0,23
0,65
Effect of Gender Inequality in 1960
0,18
0,46
0,14
0,36
Ratio of Gender Inequality in
Growth of Education
0,28
0,37
-0,02
-0,02
Total
0,46
0,84
0,12
0,34
East Asia
Effects of Gaps: Education
24 (of 36) countries failing to reach MDG3
are in Sub Saharan Africa.
Growth costs: 0.3-0.5% lower 2005-2015
‚Fertility Costs‘: 0.1-0.4 higher TFR in
2015
‚Child mortality costs‘: 5-26/1000 in 2015
Questions: To what extent will the rapid
expansion of education in some countries
mitigate these costs?

Gender and Employment
Evidence quite weak (comparability problems,
endogeneity)
Growth costs of gender gaps may be as large as (or
larger than) growth costs of gender gaps in education.
Gender gaps in formal sector employment prevent
realization of labour-intensive growth strategies
(exceptions: Mauritius, Lesotho, Tunisia)
Particular barriers: high fertility, gender gaps in
education, discrimination in formal labor markets,
difficulty to combine work with childcare.

Gender and Employment
EAP
1960
1970
1980
1990
2000
male economic activity rate, 15-64 (MACT)
90.69
87.82
86.41
85.71
84.94
female economic activity rate, 15-64 (FACT)
41.33
46.25
52.85
56.47
59.67
female share of labor force, 15-64 (FLFT)
28.52
32.41
36.13
38.66
40.31
female employee rate (EMPLF)
0.17
0.22
0.29
0.3
male employee rate (EMPLM)
0.39
0.43
0.46
0.45
SA
male economic activity rate, 15-64 (MACT)
92.5
90.4
88.6
87.61
86.22
female economic activity rate, 15-64 (FACT)
48.61
47.84
47.22
47.88
50.87
female share of labor force, 15-64 (FLFT)
30.71
31.28
31.82
32.9
35.28
female employee rate (EMPLF)
0.05
0.06
0.1
0.08
male employee rate (EMPLM)
0.27
0.3
0.34
0.27
SSA
male economic activity rate, 15-64 (MACT)
92.65
91.34
89.75
88.59
87.49
female economic activity rate, 15-64 (FACT)
69.62
68.59
67.2
66.44
66.1
female share of labor force, 15-64 (FLFT)
43.45
43.59
43.53
43.56
43.48
female employee rate (EMPLF)
0.12
0.09
0.09
0.03
male employee rate (EMPLM)
0.46
0.27
0.26
0.08
MENA
male economic activity rate, 15-64 (MACT)
88.84
85.39
82.03
81.02
81.21
female economic activity rate, 15-64 (FACT)
21.56
23.21
25.54
27.50
33.70
Gender Gaps and Inequality
OPPG work: Largest effect of gender gaps on
poverty via growth effects.
Women live in poor and rich households alike.
Inequality effects can only arise if gender gaps are
more substantial (or consequential) among the
poor than the rich.
In Africa: gender gaps in agriculture particularly
problematic for poor women; gender gap fertility
link larger among poor households. Both could be
inequality-enhancing (hypothesis).

Thoughts?
Questions?
Your Experiences of Gender and Growth in
Your Country?

Gender and Growth
Policy Implications
Remove gender gaps in education.
Reduce gender gaps in formal sector
employment (in combination with laborintensive growth strategy).
Equalize access to assets and inputs in
agriculture.
Ease time burden for women through
fertility decline and improvements in
household infrastructure.

GENDER AND POVERTY:
Empirical Methods and Evidence
Introduction
Household Data Typically Underused for
Gender Analysis – restricted to Income/Cons
Many Household Survey – Nationally based
LSMS, DHS,
Gender Analysis Can Be Used to:
– Address the Gender Dimensions of Poverty,
– Identify Trends in Key Variables – ie.
Vulnerability, Who Control Spending Power in
HHold etc,
– Support the Integration into of PEAP etc. and
Design of Future HHold Surveys

Methodological Problems
General
Economic roles of women only partially visible in
official statistics (can be remedied).
Effects of women‘s labor on non-income dimensions
of well-being not included (can be remedied).
Economic constraints (in particular time constraints)
not visible (can be remedied).
Micro data
Poverty (typically) measured at the household level
Researchers are lazy – they stay at the MHH and
WHH and do not use their imagination + reserach
methods quantitative and qualitative – `Q2`
What We Typically Find From
Previous Household Data Analysis

Mixed Evidence on Women Headed Households (WHHs) are
Statistically Poorer.
Buinic and Gupta (38 of 61) WHH>MHH
SSA – (Lawson and Wodon) every LSMS survey country with
poverty no’s – 2/3rds WHH>MHH
Ye (19 SSA countries – After Adjusting For Smaller Household
Size found 10 or 18 MHH>WHH).
Non Income – Typically Find WHHs:
Fewer Land Assets,
Lower income,
Inequalities in Educational Attainment
Dependant Upon Remittances
Gender and Poverty –
Uganda 1999
Household Head – Marital Status
1999
MHH
WHH
Unmarried
Married
Divorced
Widowed
18.0%
17.6%
33.4%
44.6%
19.6%
28.8%
25.6%
40.0%
Gender and Poverty – 1999
Proportion Below Poverty LIne
50.00%
WHH – Higher Incidence
of Poverty
45.00%
40.00%
35.00%
30.00%
MHH
25.00%
WHH
20.00%
15.00%
10.00%
5.00%
0.00%
Unmarried
Married
Divorced
Marital Status
Widowed
Gender and Chronic Poverty
Figure 1 – Poverty Dynamics in Uganda (fro m Lawso n, M cKay and Okidi P o verty Status Repo rt 2003)
18.9%
Poor in all Periods
40.9%
Never Poor
29.7%
Moving out of Poverty
10.4%
Moving
Into
Poverty
Gender and Chronic Poverty
MHH
Poverty Status
Married Divorced
16.7%
Chronic Poor 20.0%
33.3%
Moving out of Poverty 29.8%
6.7%
Moving into Poverty 10.0%
43.3%
Never In Poverty 40.2%
WHH
Married
28.0%
27.9%
17.6%
26.5%
Divorced
10.6%
19.1%
14.9%
46.8%
Married WHHs – Chronic Poverty
45.0%
40.0%
High Proportions of Women Headed
Households are Chronically Poor
and Move into Poverty, Fewer are
Never in Poverty
Male Headed
Households
Women Headed
Households
35.0%
Percentage
30.0%
25.0%
20.0%
15.0%
10.0%
5.0%
0.0%
Chronic Poor
Moving into Poverty
Poverty Status
Never In Poverty
Beyond Headship Based
Poverty

Individual Level Consumption Data is not common
But We Can Calculate:

Female dominance in the households – calculate the proportion of
women in the household and cross reference with poverty,
or consider DHS data that provides valuable insights with respect
to whether women have control over assets, and personal
decisions.
or One could also consider the number of male/female earners in a
household and cross reference with poverty.

Individual level data is common for non monetary defined
measures
Non ‘Monetary’ Defined Gender
Analysis
Household Level
Assets
Demographics
Individual Level
Demographics
Access to Health Care
Control Over Expenditure
Other Options for Informing Us – Education,
HIV/AIDS Awareness, Income Sources,
Time Allocation, Violence, Vulnerability
Assets
Chronic Poor
ASSETS MHH
Land Area (1992) 2.84
WHH
Moving Out of
Moving Into
Poverty
Poverty
MHH WHH MHH WHH
2.17 2.81 2.53 2.54 2.25
Land Area (% 27.1% -14.3% 85.8% 46.3% 6.3% -33.3%
Increase)
Land Area (1999)
3.61
1.86
5.22 3.70 2.70
1.50
Assets- (Change in Land Area
1992-99)
100.00%
80.00%
Percentage Change
60.00%
40.00%
MHH
WHH
20.00%
0.00%
Chronic Poor
Moving Out of Poverty
-20.00%
-40.00%
Poverty Status
Moving Into Poverty
Proportionate Change In Health Care
Demand (1999-2002)- By Income Quartile
Proportionate Change in Demand for Health
Care, When Sick
4.5%
More Poor Women than Poor Men have
Increased Health Care Usage
3.5%
2.5%
Men
1.5%
Women
0.5%
-0.5%
Lowest Income
Quartile
Second Lowest
Income Quartile
Second Highest
Income Quartile
-1.5%
Income Quartiles
Highest Income
Quartile
Women (and their husbands) Control Over Money
Relative to a Women’s Earnings Contribution to
Household Expenditure
P e rc e n t a g e C o n t o l O v e r E x p e n d it u re
90.0%
As Women Contribute Higher Proportions of
Household Expenditure They Have Less Control
Over How it is Spent
80.0%
70.0%
60.0%
50.0%
Self (Women) only
40.0%
Husband Only
30.0%
20.0%
10.0%
0.0%
Almost none/none
Less than half
Half or more
Proportion of Earnings Used for Household Expenditure
All
EmpowermentH.

The WEF report from 2006 and 2014 show a drastic
decline in empowerment for health, education, politics and
economic participation.

Uganda had overall score of 46 in 2006 and 88 in 2014any explanations? civil unrest, current anti gay etc.?

Empowerment Index – When you disaggregate by Family
Decisions (ranking quite low number – good) – Violence
(High – bad) – Access to Resources (High- bad)

http://reports.weforum.org/global-gender-gap-report2014/economies/#economy=UG
Gender and Time Burden
Time use studies show (generally) higher work
burden for women.
e.g. Finnish Stats on Gender and Time Use
http://www.stat.fi/tup/julkaisut/tiedostot/julkaisulu
ettelo/yeli_time_1979-2010_2012_8288_net.pdf
Developing Countries : High fertility and poor
household infrastructure (water and fuel)
significant component of time burden.
Time burden prevents greater involvement in
formal sector activities or labor-intensive
agricultural activities.

Pivotal – Melinda Gates
http://pivotalventures.org/
Time Poverty of Girls and Women
Hence the Definition of Time Poverty Differs
according to the objectives of the research
And only consider in their definition domestic duties
+ collecting firewood etc. (e.g. not working duties)

Time Poverty and Infrastructure:
Lesotho Case Study
Individual level time use data
Time Poverty – The time an individual
spends on productive activities such as
working, farming, domestic and other duties
(collecting firewood etc.)
Time Poor > 1.5 median hours (little prior
work for

Working,
Employ
ment
Farm,
Live
stock
Fish
Firewood
&
Water
Cooking &
Domestic
Social
Activites
%
Time
Poor
1.81
1.37
0.42
2.80
1.95
7.88
Male (all)
2.10
2.55
0.23
1.48
2.16
8.28
15-24
0.81
2.81
0.27
1.49
2.14
8.14
25-34
3.34
2.27
0.26
1.42
2.15
10.37
35-44
3.72
2.04
0.13
1.46
2.02
8.97
Female (all)
1.62
0.54
0.56
3.73
1.81
6.78
15-24
0.79
0.35
0.55
3.66
1.83
6.23
25-34
2.27
0.40
0.52
3.96
1.61
8.49
35-44
2.55
0.54
0.49
3.76
1.69
8.80
All
Age
Time Poverty – Lesotho
Males
Females
Infrastructure
Water within 30 mins -0.032 (-0.545)
Water 30-59 mins -0.046 (-0.724)
Public transport 30mins
0.004 (0.135)
Public transport 30- -0.016 (-0.462)
59mins
-0.096 (-1.881)*
-0.072 (-1.484)
-0.040 (-1.602)
-0.055 (-2.085)**
Gender Gaps in Time Burden
– Time Poverty

Considerable differences in access to land (and security
of tenure). i.e. Infrastructure is Important
Evidence on inequality in access to inputs (esp.
modern inputs and credit).
Unequal overall time burden (particularly of household
time).
Gender gaps in formal sector employment prevent
realization of labour-intensive growth strategies
(exceptions: Mauritius, Lesotho, Tunisia)
Particular barriers: high fertility, gender gaps in
education, discrimination in formal labor markets,
difficulty to combine work with childcare.
Moving Poverty and Gender
Research Forward
Move beyond headship and static poverty
(female dominance in households, proportions working,
poverty dynamics – even without panels –DHS data)
Look across households AND WITHIN (INTRA-)
households
DHS and entire countries household data are hugely
underused
Labour Mkt Participation, Intergenerational
Transmission of Poverty (IGT), Gender/Violence
Q-SQUARED methods – beyond econometric and
participatory – THE STORY WHH SELL
ASSETS/HIV?ASSET SMOOTHING WHY? – Land
Tenure – Land Act/Lowering Female Bargaining
Power

Policy Implications/Conclusion

Micro based analysis can be used to Inform PEAP/PSRP
Can/Should move away from MHH/FHH
Much more research needed to identify these effects
clearly (including the particular channels through which
they operate and where they are most pertinent).
Can use this to; Reduce gender gaps in formal sector
employment and ease time burden for women through
fertility decline and improvements in household
infrastructure.
Methods Descripitve, Econometric, `Q-Squared‘
Uganda PEAP experience – THE ACTUAL PROCESS
OF INCLUDING RESERACH – DOES IT HAPPEN
Other Issues
Gender/Taxation – Gender Budgeting
PRSP’s – Policy Making

Tax Examples
Are Personal Income Tax and Indirect Taxes and Wealth Taxes
always Proportional?
If not Why?
Think – Gender and Tax
Strotksy (1997) – personal taxation.
– For example, countries may levy different tax rates on men and
women, with a higher rate being applied to married women, as
was done, for instance, in South Africa until 1995
– Allocation of tax exemptions/allowances to husbands but not
to wives, as in Jordan
Budlender (2002) for South Africa and Wanjala et. al (2006) for
Kenya. However, much of the work only looks at the incidence
of indirect taxes by income group, and makes the inference
through gender household head measures (i.e. greater
proportions of female headed household are poorer than men)
that concludes women are then disadvantages

Incidence by Employment Status and Quintile (tax as % of expenditure)
Male breadwinner
Female breadwinner
Quintile
Total
tax
VAT
Excise
Tax
Fuel Tax
# of HH
Total
Tax
VAT
Excise
Tax
Fuel
Tax
# of HHs
1
*8.17
*6.98
*0.85
0.35
217382
6.9
6.27
0.29
0.33
302659
2
*8.95
*7.4
*1.08
*0.47
333905
8.2
7.27
0.52
0.41
401829
3
*9.64
*7.78
*1.24
0.62
571690
8.72
7.59
0.49
0.64
492950
4
*9.92
*7.53
*1.31
*1.08
1077101
8.73
7.43
0.53
0.77
602627
5
*9.36
*6.97
*0.99
*1.4
1381791
8.08
6.41
0.41
1.25
566430
Total
*9.36
*7.36
*1.12
*0.88
3581869
8.14
7.05
0.45
0.64
2366495
Dual earner
None employed
1
*7.95
*6.73
*0.9
0.33
133016
7
*6.39
*0.39
*0.23
685263
2
*9.24
*7.45
*1.23
*0.57
208823
*7.82
*7.12
*0.44
*0.26
711576
3
*9.5
*7.7
*1.04
*0.76
343163
*8.56
7.63
*0.59
*0.34
650251
4
*10.0
7
*7.78
*1.01
*1.27
505634
*8.96
*7.67
*0.72
*0.56
585065
5
*8.69
6.4
*0.61
*1.68
1036768
*8.72
*6.73
*0.56
*1.43
432227
Total
*9.15
*7.13
*0.89
*1.14
2227405
*7.84
*6.99
*0.49
*0.37
3064381
(Source Casale 2009) *Reports statistical significance in equality of means t-tests with unequal variance at 5% level. Reference category in italics. For
example, tax incidence in African female-breadwinner households is tested against tax incidence in African male-breadwinner households.
Gender Budgeting Further Consdierations
Santiary Products
http://yle.fi/uutiset/osasto/news/tuesdays_papers_fi
nland_vs_swedens_birth_rates_mp_wants_sanitary
_products_vat_cut_big_study_on_finnish_health_st
arts/9432966
UK – Girls Missing School due to costs
http://www.bbc.com/news/uk-39266056

Thoughts/Group/Other

Critique FHH MHH gender poverty
measures
Critique Time Poverty Measures
What other gender and poverty
economic development measures are
there? – Refer to evidence from
developing countries in your answer
Is Divorce rate the best measure of
empowerment?
Reading

Klasen, S. 2002. Low Schooling for Girls, slower Growth for All? World Bank
Economic Review 16: 345-373.
Blackden, C. Mark and Chitra Bhanu. 1999. Gender, Growth, and Poverty Reduction,
Special Program of Assistance for Africa 1998 Status Report on Poverty, World Bank
Technical Paper No. 428, Washington D.C.
M. Blackden, S. Canagarajah, S. Klasen and D. Lawson 2007, ‘Gender and Growth in
Africa: Evidence and Issues’ (PDF), in ‘Advancing Development: Core Themes in
Global Development’, edited by George Mavrotas and Anthony Shorrocks, Palgrave ,
UK .
Budlender, D. (2009), ‘Gender and Personal Income Taxes in South Africa, mimeo:
http://sds.ukzn.ac.za/files/South%20Africa_PIT_Budlender.pdf
Gender Gap Report
http://www3.weforum.org/docs/WEF_GenderGap_Report_2013.pdf
D. Lawson, (2003) ‘Gender Analysis of the Ugandan National Household Surveys
2003)’, Background paper for the revision of Uganda ’s PEAP (available from the
author).
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End of Presentation
david.lawson@manchester.ac.uk
Gender, ICTs and Development: References & Further Reading
March 2019
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on Gender and IS”. New Technology, Work and Employment 19(3), 222-241.
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networks: an exploratory study of Internet campaigns in Iran, Journal of Information
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Andersson, A., & Hatakka, M. (2017). Victim, mother, or untapped resource?
Discourse analysis of the construction of women in ICT policies. Information
Technologies & International Development, 13, 72–86.
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