Neoliberal Redistributive Policy: The U.S. Net Social Wage in the 21st Century

In this paper, I examine the trends of fiscal transfers between the state and workers during 1959 - 2012 to understand the net impact of redistributive policy in the United States. This paper presents original net social wage data from and analysis based on the replication and extension of Shaikh and Tonak (2002). The paper investigates the appearance of a post-2001 variation in the net social wage data. The positive net social wage in the 21st century is the result of a combination of factors including the growth of income support, healthcare inflation, neoliberal tax reforms, and macroeconomic instability. Growing economic inequality does not appear to alter the results of the net social wage methodology

A Geographic Mosaic of Climate Change Impacts on Terrestrial Vegetation: Which Areas Are Most at Risk?

<div><p>Changes in climate projected for the 21^st century are expected to trigger widespread and pervasive biotic impacts. Forecasting these changes and their implications for ecosystem services is a major research goal. Much of the research on biotic responses to climate change has focused on either projected shifts in individual species distributions or broad-scale changes in biome distributions. Here, we introduce a novel application of multinomial logistic regression as a powerful approach to model vegetation distributions and potential responses to 21^st century climate change. We modeled the distribution of 22 major vegetation types, most defined by a single dominant woody species, across the San Francisco Bay Area. Predictor variables included climate and topographic variables. The novel aspect of our model is the output: a vector of relative probabilities for each vegetation type in each location within the study domain. The model was then projected for 54 future climate scenarios, spanning a representative range of temperature and precipitation projections from the CMIP3 and CMIP5 ensembles. We found that sensitivity of vegetation to climate change is highly heterogeneous across the region. Surprisingly, sensitivity to climate change is higher closer to the coast, on lower insolation, north-facing slopes and in areas of higher precipitation. While such sites may provide refugia for mesic and cool-adapted vegetation in the face of a warming climate, the model suggests they will still be highly dynamic and relatively sensitive to climate-driven vegetation transitions. The greater sensitivity of moist and low insolation sites is an unexpected outcome that challenges views on the location and stability of climate refugia. Projections provide a foundation for conservation planning and land management, and highlight the need for a greater understanding of the mechanisms and time scales of potential climate-driven vegetation transitions.</p></div

Projected vegetation change at selected sites.

<p>Illustration of Bray-Curtis distances between baseline and future vegetation vectors (similar to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130629#pone.0130629.g003" target="_blank">Fig 3</a>) for two selected pixels across our model domain. The slope of this relationship was used as a measure of the sensitivity of projected vegetation change in relation to climate, with mean annual temperature as a proxy for changes in JJA, DJF, and CWD. Much of the scatter around each regression line represents additional effects of PPT. Red illustrates a site with high sensitivity (slope = 0.298) and blue a site with low sensitivity (slope = 0.104).</p

Magnitude of projected vegetation change.

<p>Bray-Curtis distance between future and baseline vegetation distributions, based on frequency distributions illustrated in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130629#pone.0130629.g003" target="_blank">Fig 3</a>, in relation to mean annual temperature of future climates. Colors indicate change in precipitation under future climates (red = negative, blue = positive).</p

Vegetation types.

<p>List of 22 vegetation types included in this study under the ‘1G’ model, assignment to physiognomic classes, and percent of the study region covered by each type (based on [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130629#pone.0130629.ref029" target="_blank">29</a>]). Numbers after each vegetation type indicate alphabetical sort order, used in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130629#pone.0130629.s003" target="_blank">S3 Fig</a> See list of dominant taxa in each vegetation type in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130629#pone.0130629.s010" target="_blank">S1 Table</a>.</p><p>¹Collectively, these occupy 61% of the terrestrial area in San Francisco Bay Area.</p><p>²Cool = 2.68%; Moderate = 5.61%; Warm = 18.3%; Hot = 9.33% (for ‘4G’ model, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130629#sec013" target="_blank">Methods</a>)</p><p>Vegetation types.</p

Projected changes in vegetation location and abundance.

<p>Changes in the location and abundance of the 22 vegetation types illustrated for the GFDL-A2-2070-2099 future. a) Change in distance to coast vs. proportional change in abundance (sum of relative frequencies across all pixels); b) change in elevation vs. proportional change in abundance; c) Change in distance to coast vs. change in elevation.</p

Modeled frequency of vegetation types.

<p>Relative frequency of 22 vegetation types across the San Francisco Bay Area, parameterized for the historical baseline period and then projected for alternative climates based on the recent climate (1981–2010) and 54 possible futures. Future climates are arranged in order of increased warming in mean annual temperature from bottom to top. Changes in MAT and PPT, averaged across the region for each climate scenario, are shown on the left.</p

Exposure vs. sensitivity of vegetation to climate change.

<p>Variation in exposure vs. sensitivity, showing the much greater range of variation in the latter. The product of the two axes is the measure of potential vulnerability of vegetation in response to climate change, shown by the isoclines.</p

Projected change in selected vegetation types.

<p>Changes in relative abundance of four vegetation types, plotted relative to mean annual temperature (MAT) of each of the climate scenarios. Colors indicate change in precipitation (see legend in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130629#pone.0130629.g004" target="_blank">Fig 4</a>). See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130629#pone.0130629.s006" target="_blank">S6 Fig</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130629#pone.0130629.s014" target="_blank">S5 Table</a> for results for all 22 vegetation types.</p

Historical and projected climate means.

<p>Thirty year climatic means vs. mean annual temperature for the historic (1951–1980, red) and recent (1981–2010, blue) periods and 54 possible futures (black) based on 18 different model/forcing scenarios and three time periods (2010–2039, 2040–2069, 2070–2099). See values in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130629#pone.0130629.s012" target="_blank">S3 Table</a>.</p