professional/wow-inequalities
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity

feat(script): Add shortened infra and techn change section

Browse source
This commit is contained in:
Marty Oehme 2024-07-29 16:22:41 +02:00
parent 9b8d8f04ef
commit f0f6e8c821
Signed by: Marty
GPG key ID: EDBF2ED917B2EF6A
1 changed files with 58 additions and 0 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

58
manuscript/article.qmd
View file

@ -670,6 +670,64 @@ Additionally, they often fail to account for the practical challenges to policy
Recognizing such shortcomings is crucial when assessing the reliability and applicability of their findings,
as will be discussed in considering study robustness.
<!-- ### Informal Economy -->
### Infrastructure and technological change
@Kuriyama2021 analyse the effects of Japan's energy sector decarbonisation efforts on spatial inequalities.
While employment in general is positively affected, especially rural sectors benefit from increased employment probability.
They identify the renewable energy sector's strong entrenchment in rural areas for large-scale projects such as wind, geothermal or large-scale solar power generation.
At the same time, for the context of Japan some new inequalities may be generated between different regions due to new barriers such as limited energy transmission line capacities or potentially mismatched locations of demand and supply.[^Kuriyama-limits]
[^Kuriyama-limits]: The strong connection to Japanese context also provides one limitation to the study's generalizability, along with carrying strong assumptions about initial and future employment numbers and power generation amounts.
@Stock2021 explores both inclusive and exclusionary effects of infrastructure development,
including training and temporary employment for local semi-/unskilled labour under the 'gender inclusive' development of a solar park in India,
in an observational study on achieving micro-level equality through regional uplifting.
They primarily conclude an increase in inequality through socio-economic exclusion and especially exacerbated impacts for women of lower castes.
Redistributive potential was stymied through capture by (female) village elites,
while only an insignificant amount of women from local villages found employment at the park,
predominantly from the dominant caste.[^stock-notes]
Though not able to make causal inferences because of the study design,
the author suggests this may be an example of institutional design neglecting structural power relations and individual agency, especially intersectional between gender and caste.
[^stock-notes]: While acquiring basic additional skills, none of the women participating in training remained connected to the operators of the solar park and none were hired, which disconnects the targeting of training from that of the eventual labour force participants.
<!-- explicitly spatial policies -->
Focusing on transport infrastructure investments, @Adam2018 model the effects on household income inequalities in rural Tanzania.
The effectiveness hinges significantly on the financing scheme employed:
Rural households are generally worse off when developments are deficit-financed or paid through tariff revenues,
while benefiting from financing through consumption taxes or by external aid.
The study can not find a Pareto optimum for any of the investment measures for rural and urban locations simultaneously,
with some worse off in each scheme as much of the increased equity stems from rural workers' movement out of quasi-subsistence agriculture to other locations or other sectors.[^adam-limits]
[^adam-limits]: While the study does create causal inferences its modelling approach limits it to representing the limited subset of these financing schemes,
as well as to the assumption of no additional population growth for the results to hold.
@Blumenberg2014 examines the spatially motivated 'Moving to Opportunity' programme's impacts on employment for disadvantaged US households,
comparing it to those of owning an automobile.
No increase in employment probability was found for participants,
while vehicle ownership correlated with improved employment outcomes for low-income households and in 'transit-rich' areas.[^blumenberg2014]
While access to improved transit is related to employment probability, the move to a transit-rich area alone does not increase it significantly,
reflecting potentially a certain required threshold before it facilitates employment,
or individuals' strategic relocation to make better use of public transport.
Ultimately, the findings signal the need for improved individual access to automobiles in disadvantaged households or extensive transit network upgrade which have to cross an efficiency threshold.
[^blumenberg2014]: The programme provided vouchers to randomized households for relocation to a geographically unrestricted area,
or specifically to a low-poverty area (treatment group),
some of which were also located in 'transit-rich' areas well-connected to public transport systems.
The study's strong oversampling of female participants (98%) may obscure intersectionality between spatial and gender disadvantages,
as well as retaining some possibility for endogeneity bias through unobserved factors such as individual motivation or ability.
<!-- technological change -->
Looking at the effects of technological change such as the introduction of legal access to contraceptive measures for women in the US instead,
@Bailey2012 evaluate the effects on the gendered hourly working wage distribution.
Of the overall closing of the pay gap between 1980 and 2000, this access from an early age contributed nearly 10 percent in the 1980s and over 30 percent in the 1990s,
thus contributing an estimated third of total female wage during throughout.
Primarily, the study identifies greater educational attainment, occupational upgrading, increased individual agency over human capital and career investments and increased labour market experience due to fewer early exits as the underlying mechanisms.[^Bailey-limits]
[^Bailey-limits]: With the structure of the data, the study cannot capture access to contraception beyond age 20, restricting the window of analysis especially on women under 21, as well as not being able to control for exogenous social multiplier effects such as changed employer hiring or promotion patterns, changed marriage and childbearing expectations or overarching paradigmatic norms concerning women's work.
## Agency factors
# Robustness of evidence