Slum Upgrading beyond incubation: exploring the dilemmas of nation-wide large scale policy interventions in Brazil´s growth acceleration programme (PAC)

The Indian industrial policy made a major transition towards liberalization in the mid-1980s with the proponents of liberalization expecting not only a general increase in the efficiency of Indian industry but also improvement terms of innovative performance. Extensive industrial studies, as well as macro-level data, suggest that liberalization in the field of industrial licensing and foreign technological collaborations has resulted in large-scale entry of new firms across different segments of the economy. In this context, this chapter makes an attempt to review the promotion-oriented industrial policies of the Indian Engineering industry and also trace the industrial growth from 1950-51 onwards. It has been observed that there were mainly two breaks (kinked points) during this period, one in 1965-66 and the other in 1984-85. A review of policies suggests that these breaks were associated with major shifts in policies of the government. The study indicates that the first break came through industrial policies of the government with a focus on the heavy industries during the initial phases, while the other break came during 1984-85, which could be attributed to changes in policies from a restrictive one in the mid-'60s and '70s to a liberalized one in this sector in the '80s.

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Social Medical Capital: How Patients and Caregivers Can Benefit From Online Social Interactions

Journal of Medical Internet Research ◽

10.2196/16337 ◽

2020 ◽

Vol 22 (7) ◽

pp. e16337 ◽

Cited By ~ 2

Author(s):

Pietro Panzarasa ◽

Christopher J Griffiths ◽

Nishanth Sastry ◽

Anna De Simoni

Keyword(s):

Social Interactions ◽

Large Scale ◽

Big Data Analytics ◽

Online Health Communities ◽

Policy Interventions ◽

Large Scale Network ◽

Scale Network ◽

Effective Policy ◽

Health Communities ◽

Network Studies

The rapid growth of online health communities and the increasing availability of relational data from social media provide invaluable opportunities for using network science and big data analytics to better understand how patients and caregivers can benefit from online conversations. Here, we outline a new network-based theory of social medical capital that will open up new avenues for conducting large-scale network studies of online health communities and devising effective policy interventions aimed at improving patients’ self-care and health.

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The irrational interplay between partisanship, beliefs about the severity of the COVID-19 pandemic, and support for policy interventions

10.31234/osf.io/4cgfw ◽

2020 ◽

Author(s):

Lucia Freira ◽

Marco Sartorio ◽

Cynthia Boruchowicz ◽

Florencia Lopez Boo ◽

Joaquin Navajas

Keyword(s):

Large Scale ◽

Public Attitudes ◽

Cost Benefit ◽

The United States ◽

Policy Implications ◽

Policy Support ◽

School Closures ◽

Large Variability ◽

Policy Interventions ◽

Identity Motives

The COVID-19 pandemic is a global crisis that has forced governments around the world to implement large-scale interventions such as school closures and national lockdowns. Previous research has shown that partisanship plays a major role in explaining public attitudes towards these policies and beliefs about the severity of the crisis. However, the cognitive roots of this phenomenon remain poorly understood. In principle, partisan gaps in policy support could emerge from cost-benefit analyses from individuals with dissimilar perceptions about the severity of the pandemic, as proposed by rational models of partisan behavior. Alternatively, polarized responses may be driven by social identity motives that are unrelated to individual beliefs, as predicted by theories of tribal partisanship. Here, we tested the predictions of these two models across four experiments (N=1980) performed in four different countries (Argentina, Uruguay, Brazil, and the United States). Participants forecasted the number of COVID-19 deaths in their country after considering either a high or low number. Then, they rated their agreement with a series of interventions. This anchoring procedure, which experimentally induced a large variability in the forecasted number of deaths, did not modify policy preferences. Instead, we observed that partisanship independently modulated the optimism of forecasts and participants’ support for COVID-19 policies. These results, which are against the predictions of the rational partisanship model, have strong policy implications. In particular, our findings suggest that communication strategies aimed at informing the public about the severity of the pandemic will not substantially change levels of support for COVID-19 interventions.

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Multipath Forecasting: the Aftermath of the 2020 American Crisis

10.31235/osf.io/f37jy ◽

2021 ◽

Author(s):

Peter Turchin

Keyword(s):

Large Scale ◽

Weather Prediction ◽

Dynamic Modelling ◽

Social Breakdown ◽

Political Crises ◽

Policy Interventions ◽

The Usa ◽

Parameterized Model ◽

Human Costs ◽

State Breakdown

Recent years have seen major political crises throughout the world. Most recently, the US was swept by a wave of protests, urban riots, and violent confrontations between left- and right-wing extremists. Understanding how future crises will unfold and assessing the resilience of different countries to various shocks is of foremost importance in averting the human costs of state breakdown and civil war. In a recent publication (Turchin et al. 2018) we proposed a novel transdisciplinary approach to modeling social breakdown, recovery, and resilience. This approach builds on recent breakthroughs in macrosocial dynamics (and specifically structural-demographic theory), statistical analysis of large-scale historical data, and dynamic modelling. Our main goal is to construct a series of probabilistic scenarios of social breakdown and recovery. We called this approach—similar to ensemble forecasting in weather prediction—multipath forecasting (MPF). In this article I develop a “prototype” of the MPF engine with the goal of illustrating the utility a fully developed version may have. I first apply the computational model to the period of American history from the beginning of the nineteenth to the end of the twentieth century, with the goal of parameterizing the model and testing it against data. Then I use the parameterized model to forecast the dynamics of instability in the USA beyond 2020 and illustrate how the MPF engine can be used to explore the effects of different policy interventions.

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Trends in Intersectoral Wages in Pakistan

The Pakistan Development Review ◽

10.30541/v31i4iipp.1243-1253 ◽

1992 ◽

Vol 31 (4II) ◽

pp. 1243-1253

Author(s):

Faiz Bilquees

Keyword(s):

International Migration ◽

Return Migration ◽

Large Scale ◽

A Priori ◽

Government Employees ◽

Real Wages ◽

Wage Rates ◽

Policy Interventions ◽

The Government ◽

Formal And Informal Sectors

In Pakistan intersectoral wage trends have been analysed by Guisinger and Hicks (1978); Irfan (1982) and Irfan and Ahmed (1985). The studies show that over the period 1970 to 1984 real wages of the large-scale manufacturing, construction and agriculture sectors increased while that of the government employees declined significantly. The study shows international migration to be one of the major determinants of the rise in real wages, in addition to important policy interventions. The present study is an extension of Irfan and Ahmed's work. It has been undertaken for two reasons. First there is great scarcity of empirical evidence on this very important issue. Second, the more important factor is the sharp reversal in one of the major variables - out migration. Since 1981 there has been net return migration. This phenomenon a priori is expected to upset the labour market and the wage rates in the opposite direction. The study is planned as follows: Section II describes the trends in real wages between the formal and informal sectors.! Section III describes the factors behind the observed trends in real wages, and finally Section IV gives the conclusions of the study.

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There Will Be Blood: Oil Curse, Fossil Dependency and Petro-Addiction

New Formations ◽

10.3898/newf:103.02.2021 ◽

2021 ◽

Vol 103 (103) ◽

pp. 10-42

Author(s):

Michael Watts

Keyword(s):

Large Scale ◽

Oil And Gas ◽

Resource Curse ◽

Resource Dependency ◽

Extractive Industries ◽

Policy Debate ◽

Oil Consumption ◽

Policy Interventions ◽

Oil Curse ◽

Oil And Gas Sector

There is an active academic and policy debate over whether and how oil producers – as exemplars of a larger set of Global South development problems associated with 'resource dependency' – can be associated with a number of 'pathologies' or deficits (corruption, poor economic growth, conflict) that are seen as expressions of a much-wider global addiction to petroleum and natural gas. Equally, there is a vibrant set of regulatory and policy interventions designed to render the oil and gas sector more transparent and accountable through modalities like the extractive industries transparency initiative (EITI). In both cases, the language of dependency and addiction is endemic. The socalled 'resource curse' and oil's commodity status as 'the devil's excrement' are exemplary expressions of oil's apparently seductive yet catastrophic properties. Oil dependency and oil addiction have become central to the discourse – a planetary discourse in effect, of the Anthropocene and forms of life within it. This article explores how discourses of dependency and addiction have been put to work, and with what effect, in the debate around the oil and gas global assemblage. It shows how in the case of dependency (and here it is largely the dependency associated with oil-producing or petro-states such as Saudi Arabia or Nigeria) there are often unacknowledged and deep registrations of the word's meanings which are embedded in liberal governance. Much of this dependency talk, I will argue, locates the problem in a series of failings (which oil both overdetermines and facilitates) associated with liberal views of the self, of political economy and the state. In the case of oil dependency as an addiction, I attempt to draw out how an understanding of addiction as a social (and systemic) issue, rather than a property of individual consumers or the pathological-addictive character of particular commodities, sheds light on how oil is built into hydrocarbon capitalism, and what it will take to, as it were, break the habit of large-scale oil consumption.

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Machine Learning-Based Slum Mapping in Support of Slum Upgrading Programs: The Case of Bandung City, Indonesia

Remote Sensing ◽

10.3390/rs10101522 ◽

2018 ◽

Vol 10 (10) ◽

pp. 1522 ◽

Cited By ~ 14

Author(s):

Gina Leonita ◽

Monika Kuffer ◽

Richard Sliuzas ◽

Claudio Persello

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Large Scale ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Support Vector ◽

Slum Upgrading ◽

Sequential Feature Selection ◽

Local Acceptance ◽

Consistent Information

The survey-based slum mapping (SBSM) program conducted by the Indonesian government to reach the national target of “cities without slums” by 2019 shows mapping inconsistencies due to several reasons, e.g., the dependency on the surveyor’s experiences and the complexity of the slum indicators set. By relying on such inconsistent maps, it will be difficult to monitor the national slum upgrading program’s progress. Remote sensing imagery combined with machine learning algorithms could support the reduction of these inconsistencies. This study evaluates the performance of two machine learning algorithms, i.e., support vector machine (SVM) and random forest (RF), for slum mapping in support of the slum mapping campaign in Bandung, Indonesia. Recognizing the complexity in differentiating slum and formal areas in Indonesia, the study used a combination of spectral, contextual, and morphological features. In addition, sequential feature selection (SFS) combined with the Hilbert–Schmidt independence criterion (HSIC) was used to select significant features for classifying slums. Overall, the highest accuracy (88.5%) was achieved by the SVM with SFS using contextual, morphological, and spectral features, which is higher than the estimated accuracy of the SBSM. To evaluate the potential of machine learning-based slum mapping (MLBSM) in support of slum upgrading programs, interviews were conducted with several local and national stakeholders. Results show that local acceptance for a remote sensing-based slum mapping approach varies among stakeholder groups. Therefore, a locally adapted framework is required to combine ground surveys with robust and consistent machine learning methods, for being able to deal with big data, and to allow the rapid extraction of consistent information on the dynamics of slums at a large scale.

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How Do Stakeholders Perceive Barriers to Large-Scale Wind Power Diffusion? A Q-Method Case Study from Ceará State, Brazil

Energies ◽

10.3390/en12112063 ◽

2019 ◽

Vol 12 (11) ◽

pp. 2063

Author(s):

Cláudio Albuquerque Frate ◽

Christian Brannstrom

Keyword(s):

Wind Power ◽

Transmission Lines ◽

Large Scale ◽

Q Method ◽

Predictive Capacity ◽

Social Perspectives ◽

Renewable Power ◽

Policy Interventions ◽

High Penetration ◽

Installed Capacity

High penetration of renewable power requires technical, organizational, and political changes. We use Q-method, a qualitative–quantitative technique, to identify and analyze views held by key actors on challenges for large-scale diffusion of wind power in Ceará State, Brazil, an early leader in wind power with 2.05 GW installed capacity. Four quantitatively determined social perspectives were identified with regard to views on challenges for wind power expansion: (1) failing because of the grid; (2) environmental challenges; (3) planning for wind, and (4) participating in wind. Each social perspective emphasizes a different array of barriers, such as cost of new transmission lines, transformation of a hydro-thermal mental model, predictive capacity for wind energy, and the need for participatory forum. Understanding the subjective views of stakeholders is a key first step in eventually reducing these barriers to renewable power penetration through diverse policy interventions.

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A Design-Based Approach to Improve External Validity in Welfare Policy Evaluations

Evaluation Review ◽

10.1177/0193841x16655656 ◽

2016 ◽

Vol 41 (4) ◽

pp. 326-356 ◽

Cited By ~ 10

Author(s):

Elizabeth Tipton ◽

Laura R. Peck

Keyword(s):

Site Selection ◽

External Validity ◽

Large Scale ◽

Welfare Policy ◽

Sample Selection ◽

Randomized Experiments ◽

New Approach ◽

Balanced Sampling ◽

Policy Interventions ◽

Policy Arena

Background: Large-scale randomized experiments are important for determining how policy interventions change average outcomes. Researchers have begun developing methods to improve the external validity of these experiments. One new approach is a balanced sampling method for site selection, which does not require random sampling and takes into account the practicalities of site recruitment including high nonresponse. Method: The goal of balanced sampling is to develop a strategic sample selection plan that results in a sample that is compositionally similar to a well-defined inference population. To do so, a population frame is created and then divided into strata, which “focuses” recruiters on specific subpopulations. Units within these strata are then ranked, thus identifying “replacements” similar to sites that can be recruited when the ideal site refuses to participate in the experiment. Result: In this article, we consider how a balanced sample strategic site selection method might be implemented in a welfare policy evaluation. Conclusion: We find that simply developing a population frame can be challenging, with three possible and reasonable options arising in the welfare policy arena. Using relevant study-specific contextual variables, we craft a recruitment plan that considers nonresponse.

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Social Medical Capital: How Patients and Caregivers Can Benefit From Online Social Interactions (Preprint)

10.2196/preprints.16337 ◽

2019 ◽

Cited By ~ 1

Author(s):

Pietro Panzarasa ◽

Christopher J Griffiths ◽

Nishanth Sastry ◽

Anna De Simoni

Keyword(s):

Social Interactions ◽

Large Scale ◽

Big Data Analytics ◽

Online Health Communities ◽

Policy Interventions ◽

Large Scale Network ◽

Scale Network ◽

Effective Policy ◽

Health Communities ◽

Network Studies

UNSTRUCTURED The rapid growth of online health communities and the increasing availability of relational data from social media provide invaluable opportunities for using network science and big data analytics to better understand how patients and caregivers can benefit from online conversations. Here, we outline a new network-based theory of social medical capital that will open up new avenues for conducting large-scale network studies of online health communities and devising effective policy interventions aimed at improving patients’ self-care and health.

Download Full-text