feat(script): Begin adding spatial inequality section

scoping_review.qmd

@@ -638,6 +638,66 @@ It also finds significantly positive impacts on the human capital of the childre
 This suggests childcare costs being removed through a quasi-subsidy reducing the required childcare time burden on mothers, increasing parental agency and employment choices.
 Some limitations to the study include a relatively small overall sample size, as well as employment effects becoming insignificant when the effect is measured on randomization alone (without an additional instrumental variable).
 
+## Spatial inequality
+
+<!-- non-spatial policy but spatial effects -->
+@Gilbert2001 undertake a study looking at the distributional effects of introducing a minimum wage in Britain, with a specific spatial component.
+Overall it finds little effect on income inequality in the country.
+It finds that the effects on rural areas differ depending on their proximity to urban areas.
+While overall income inequality only decreases a small amount, the intervention results in effective targeting with remote rural households having around twice the reduction in inequality compared to others.
+Rural areas that are accessible to urban markets are less affected, with insignificant impacts to overall income inequality reduction.
+One limit of the study is that it has to assume no effects on employment after the enaction of the minimum wage for its results to hold.
+
+In a study on the impacts of minimum wage and direct cash transfers in Brazil on the country's income inequality,
+@SilveiraNeto2011 especially analyse the way the policies interact with spatial inequalities.
+It finds that incomes between regions have converged during the time frame and overall the cash transfers under the 'Bolsa Familia' programme and minimum wage were accounting for 26.2% of the effect.
+Minimum wage contributed 16.6% of the effect to overall Gini reduction between the regions while cash transfers accounted for 9.6% of the effect.
+The authors argue that this highlights the way even non-spatial policies can have a positive (or, with worse targeting or selection, negative) influence on spatial inequalities,
+as transfers occurring predominantly to poorer regions and minimum wages having larger impacts in those regions created quasi-regional effects without being explicitly addressed in the policies.
+Some limitations include limited underlying data only making it possible to estimate the cash transfer impacts for the analysis end-line, 
+and minimum wage effects having to be constructed from the effects wages equal to minimum-wage.
+
+@Kuriyama2021 look at the effects of Japan's move to decarbonise its energy sector on employment, especially rural employment.
+It finds that, while employment in general is positively affected, especially rural sectors benefit from additional employment probability.
+This is due to the renewable energy sector, while able to utilise urban areas for smaller scale power generation, being largely attached to rural areas for larger scale projects such as geothermal, wind power or large-scale solar generation.
+The study also suggests some possible inequality being created in between the different regions of Japan due to the Hokkaido region having limited transmission line capacity and locational imbalance between demand and potential supplies.
+Limitations include its design as a projection model with multiple having to make strong assumptions about initial employment numbers and their extrapolation into the future,
+as well as having to assume the amount of generated power to increase as a stable square function.
+
+Highlighted by these studies, one issue of spatial inequality especially is that in many cases policies are crafted that are targeted without any spatial component, intended to function nationally.
+These non-spatial policies will, however, carry effects on inequalities that are created or exacerbated by spatial inequalities themselves.
+Ideally, policies can make use of spatial effects without having to include explicit spatial components, 
+as was the case with Brazilian social programmes [@SilveiraNeto2011].
+Often however, spatial targeting considerations have to be explicitly invoked to not lose effectiveness or, worse, create adverse outcomes for specific spatial variations,
+as may be the case for some regions in Japan infrastructure efforts [@Kuriyama2021].
+
+<!-- explicitly spatial policies -->
+@Blumenberg2014 look at the effects of a housing mobility intervention in the United States on employment for disadvantaged households,
+and comparing its impacts to the ownership of a car for the same sample.
+It follows the 'Moving to Opportunity' programme which provided vouchers to randomized households for movement to a geographically unrestricted area or to specifically to a low-poverty area (treatment group),
+some of which are in areas with well-connected public transport opportunities.
+The sample for the study is made up predominantly of women (98%).
+No relationship between programme participation and increased employment probability could be established.
+However, a positive relationship exists between owning an auto-mobile and improved employment outcomes for low-income households,
+as well as including those households that are located in 'transit-rich' areas.
+Access to better transit itself is related to employment probability but not gains in employment -
+the authors suggest this reflects individuals' strategic relocation to use public transit for their job.
+However, moving to a better transit area itself does not increase employment probability, 
+perhaps pointing to a certain threshold required in transit extensiveness before it facilitates employment.
+Ultimately, the findings suggest the need to further individual access to auto-mobiles in disadvantaged households or for extensive transit network upgrade which have to cross an efficiency threshold.
+Some limitations of the study are its models low explanatory power for individual outcomes, more so among disadvantaged population groups,
+as well as some remaining possibility of endogeneity bias through unobserved factors such as individual motivation or ability.
+
+@Adam2018 model the effects of transport infrastructure investments in Tanzania on rural income inequalities and household welfare inequalities, modelled through consumption indicators.
+Generally it finds that the results of public investment measures into transport infrastructure largely depend on the financing scheme used.
+Comparing four financing schemes when looking at the effects on rural households, it finds that they are generally worse off when the development is deficit-financed or paid through tariff revenues.
+On the other hand, rural households benefit through increased income from measures financed through consumption taxes, or by external aid.
+The general finding is that there is no Pareto optimum for any of the investment measures for all locations, 
+and that much of the increases in welfare are based on movement of rural workers out of quasi-subsistence agriculture to other locations and other sectors.
+The study creates causal inferences but is limited in its modelling approach representing a limited subset of empirical possibility spaces,
+as well as having to make the assumption of no population growth for measures to hold.
+
+
 # Conclusion
 
 The section with conclude with reflections on the implications of findings for policy.