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The studies of descriptive political representation demonstrate that the share of women amongst local elected officials increases, but mayors are still predominantly men. This paper contributes to the literature on the link between the descriptive and substantive representation of women at the local level. It investigates the influence of mayors’ gender on the development of local childcare policies in Poland. We employ quasi-experimental research schemes (difference-in-differences and generalised synthetic control) to study changes in childcare provision and public spending on nurseries and kindergartens. We merged electoral data (changes on the mayoral position) and registry data on local budget expenditures and service availability covering a period of more than 16 years. We do not find any systematic causal link, suggested by the extant literature on substantive representation, between the election of a female mayor and the expansion of childcare services.
With the green, circular, and low-carbon concept, eco-industrial parks are regarded as key drivers for maximizing environmental and economic benefits. Based on the panel data of 276 cities in China from 2007 to 2018, this paper regards the establishment of eco-industrial parks as a quasi-natural experiment, and employs the difference-in-differences method to test the impact of eco-industrial parks on urban haze pollution. We find that eco-industrial parks significantly reduce urban haze pollution and the conclusion holds with robustness tests. Heterogeneity analysis shows that the effect of eco-industrial parks on haze pollution is more pronounced in eastern and resource-based cities. Finally, mechanism analysis indicates that eco-industrial parks reduce urban haze pollution mainly by promoting technological innovation, upgrading industrial structure, and strengthening urban environmental regulations.
This chapter focuses on causal inference in healthcare, emphasizing the need to identify causal relationships in data to answer important questions related to efficacy, mortality, productivity, and care delivery models. The authors discuss the limitations of randomized controlled trials due to ethical or pragmatic considerations and introduce quasi-experimental research designs as a scientifically coherent alternative. They divide these designs into two broad categories, independence-based designs and model-based designs, and explain the validity of assumptions necessary for each design. The chapter covers key concepts such as potential outcomes, selection bias, heterogeneous treatment effects bias, average treatment effect, average treatment effect for the treated and untreated, and local average treatment effect. Additionally, it discusses important quasi-experimental designs such as regression discontinuity, difference-in-differences, and synthetic controls. The chapter concludes by highlighting the importance of careful selection and application of these methods to estimate causal effects accurately and open the black box of healthcare.
What was the effect of war outcomes on key indicators of state formation in a post-war phase? In this chapter I demonstrate that victors and losers of war were set into different state capacity trajectories after war outcomes were revealed. I do this using a set of cutting-edge causal inference techniques to analyse the gap in state capacity that was generated between winners and losers in the time-period of most stringent warfare (1865-1913). After substantiating that the outcomes of these wars were determined by exogenous or fortuitous events, I provide a short description of my treatment—i.e., defeat—and outcomes—i.e., total revenues and railroad mileage as key indicators of state infrastructural capacity. My estimator, a difference-in-differences model, shows defeat had a negative long-term impact on state capacity which remains remarkably robust even after relaxing key assumptions. Finally, I use the synthetic control method to estimate how state capacity in Paraguay and Peru would have evolved in a counterfactual world where these countries were spared the most severe defeats in late nineteenth-century Latin America.
from
Part II
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The Practice of Experimentation in Sociology
Davide Barrera, Università degli Studi di Torino, Italy,Klarita Gërxhani, Vrije Universiteit, Amsterdam,Bernhard Kittel, Universität Wien, Austria,Luis Miller, Institute of Public Goods and Policies, Spanish National Research Council,Tobias Wolbring, School of Business, Economics and Society at the Friedrich-Alexander-University Erlangen-Nürnberg
Vignette experiments are vignettes are brief descriptions of social objects including a list of varying characteristics, on the basis of which survey respondents state their evaluations or judgments. The respondents’ evaluations typically concern positive beliefs, normative judgments, or their own intentions or actions. Using a study on the gender pay gap and an analysis of trust problems in the purchase of used cars as examples, we discuss the design characteristics of vignettes. Core issues are the selection of the vignettes that are included out of the universe of possible combinations, the type of dependent variables, such as rating scales or ranking tasks, the presentation style, differentiating text vignettes from a tabular format, and issues related to sampling strategies.
Part II of the book shifts in focus from primary transformation to party transformation. This chapter tests whether primaries can change parties under the most likely conditions: Republican factional primaries in the Tea Party era. Using a difference-in-differences design, this chapter shows that in this most visible case, pressure from the reactionary Republican faction in primary elections served to reorient the party rightwards. It therefore demonstrates that, under the right circumstances, primaries can contribute to partisan polarization in Congress.
The literature has shown that, in developing countries, large cash transfers to older people improve the wellbeing of the recipients and their families. While social pensions have recently emerged in East Asia to deliver small cash benefits to older people, there is little consistent evidence of their effects. We examine the effects of the Basic Pension Scheme, a social pension in South Korea, on income and consumption poverty among older adults. We apply a difference-in-differences event study design and other complementary approaches to data covering the full period of program development from 2006 to 2021. The results show that the social pension decreases income poverty but not consumption poverty. While this study analysed the best data currently available, using better-quality data in future research would enable more robust analysis. Further research is also warranted to find how to improve the effectiveness of a non-contributory pension programme as a tool for reducing income and consumption poverty among older adults.
Chapter 6 turns to affirmative action. I begin with a discussion of two affirmative action-based hypotheses, one instrumental and the other symbolic. Both hypotheses point to these race-targeted policies as explanations for the reclassification reversal. I then test these hypotheses in several ways. First, I analyze priming and list experiments to probe for evidence of strategic manipulation in response to affirmative action. Second, I return to the municipal panel dataset and conduct a difference-in-difference analysis of state-level affirmative action on identification. And finally, I analyze an original panel dataset of university students, constructed from embargoed surveys held by the Ministry of Education in Brazil, to compute difference-in-difference estimates of the effects of affirmative action usage on the identifications of university applicants. Overall, evidence is mixed and inconsistent. Evidence suggests that, as part of the broader array of policies that expanded education, affirmative action does boost the effects of education. But the reclassification reversal cannot be reduced to, nor solely explained by, affirmative action policies.
The COVID-19 pandemic and subsequent policy response to mitigate disease spread had far-reaching impacts on health and social well-being. In response, the Supplemental Nutrition Assistance Program (SNAP) underwent several pandemic-era modifications, including a 15 % monthly benefit increase on January 1, 2021. Research documenting the health effects of these SNAP modifications among low-income households and minoritized groups who were most impacted by the economic fallout during the first years of the pandemic is lacking. We aimed to estimate the health effects of the 15 % SNAP benefit increase in January 2021, among SNAP-eligible US households.
Design:
We estimated the effects of the SNAP increase on food insufficiency, mental health, and financial well-being using a rigorous quasi-experimental difference-in-differences (DID) analysis.
Setting:
August 19, 2020, to March 29, 2021.
Participants:
Participants were drawn from the national US Census Bureau Household Pulse Survey waves 13–27 (n 44 477).
Results:
Compared with SNAP-eligible non-recipients, SNAP-eligible recipients experienced decreased food insufficiency (–1·9 percentage points (pp); 95 % CI –3·7, –0·1) and anxiety symptoms (–0·09; 95 % CI –0·17, –0·01), and less difficulty paying for other household expenses (–3·2 pp; 95 % CI –4·9, –1·5) after the SNAP benefit increase. Results were robust to alternative specifications.
Conclusions:
Expansions of federal nutrition programmes have the potential to improve health and financial well-being. This study provides timely evidence to inform comprehensive safety net nutrition policies during future economic crises and public health preparedness response plans.
This study examines the impact of county-level immigration enforcement of section 287(g) mandates on the number of businesses in the United States. Using the difference-in-differences model, we find that the implementation of 287(g) negatively affected the total number of businesses. We find that counties with 287(g) agreements experienced a 6 percent decrease in the total number of businesses per 1000 county population and this negative effect appeared to be more prominent in businesses with a higher number of employees. Our findings shed light on the complex impacts of immigration policies on businesses, especially those reliant on immigrant labor.
To investigate the causal link between the North American Free Trade Agreement (NAFTA) unrestricted sugar trade agreement signed in 2008 between the USA and Mexico and the diabetes prevalence across all fifty US states.
Design:
A quasi-experimental research design to investigate the causal effect of the NAFTA unrestricted sugar trade agreement on diabetes prevalence. Our study utilises a comprehensive panel dataset spanning from 2000 to 2016, comprising 1054 observations. To conduct our analysis, we applied both the difference-in-differences and event-study methodologies.
Setting:
All the states in the USA.
Participants:
The fifty states in the USA.
Results:
After the enactment of the NAFTA sugar trade agreement between the USA and Mexico in 2008, most states witnessed an increase in diabetes prevalence. The annual impacts displayed significant variation among states, with percentage increases spanning from 0·50 to 2·28 %.
Conclusions:
States with a higher percentage of their population living below the poverty line, a larger Black resident population and a lower proportion of high school graduates had more significant increases in diabetes prevalence attributed to the NAFTA sugar trade agreement.
In this chapter, I analyze data on over 300 individual members of the communist regimes in Bulgaria, Czechoslovakia, East Germany, Hungary, Poland, and Romania. I explore how an abrupt post-Stalinist transition in the wake of the Soviet dictator’s death affected elite cohesion and the relationship between ruling coalitions and their coercive subordinates. Specifically, I test whether breakdowns in elite cohesion led to more punishment of coercive agency chiefs, and their more frequent removal from office. My test of this argument exploits both variation in elite cohesion across Stalinist and post-Stalinist regimes, and variation in Soviet authority over different types of coercive agents. I analyze original data on members of communist ruling coalitions to estimate survival models of their tenures. I find that the tenures of Defense Ministers and secret police chiefs were similar under Stalinist coalitions, but secret police chiefs had significantly shorter tenures than Defense Ministers under post-Stalinist coalitions.
In this part of the book, I move from a comparative historical analysis of Poland and East Germany in Part II to an analysis of quantitative data drawn from all the socialist dictatorships of Bulgaria, Czechoslovakia, East Germany, Hungary, Poland and Romania. The purpose of the following two chapters is to test whether the argument developed in Chapter 2 can travel beyond the Polish and East German cases examined above to explain variation in the turnover of coercive elites and the size of coercive institutions across the region from 1945 to 1989.
In this chapter, I test the effects of post-Stalinist transitions on two important measures of agency capacity: officers employed and individuals registered as secret informants by coercive agencies. I present an original cross-national dataset on officer and informant numbers for every coercive agency in communist Central and Eastern Europe from 1945 to 1989. I show that countries that experienced post-Stalinist transitions had similarly sized coercive agencies to other states before 1953, but these agencies shrank thereafter while others continued to grow. I then estimate a series of difference-in-difference models to test the effect of post-Stalinist transitions on agency size. I find that agencies under post-Stalinist regimes had significantly smaller coercive agencies after Stalin’s death. This confirms the theoretical logic laid out in Chapter 2 in a broader setting than the comparative historical analyses of Poland and East Germany in Chapters 4 and 5. Although the number of cases and coverage of data here are limited, my results suggest that the logic of elite cohesion and coercive capacity laid out in Chapter 2 is applicable to a wide range of authoritarian regimes.
Scholars, policymakers, and citizens alike remain invested in the impact of infectious diseases worldwide. Studies have found that emerging diseases and disease outbreaks burden global economies and public health goals. This article explores the potential link between measles outbreaks and various forms of civil unrest, such as demonstrations, riots, strikes, and other anti-government violence, in four central African countries from 1996 to 2005. Using a difference-in-differences model, we examine whether disease outbreaks have a discernible impact on the prevalence of civil unrest. While our findings indicate that the relationship between disease and civil unrest is not as strong as previously suggested, we identify a notable trend that warrants further investigation. These results have significant implications for health and policy officials in understanding the complex interplay between state fragility, civil unrest, and the spread of disease.
The contemporary Republican Party has been the site of asymmetric partisan entrenchment and factional infighting. We test whether factional pressure from a far-right faction (the Tea Party) exacerbated the party's rightward movement with a granular analysis of Republican factionalism at the congressional district level. We develop a measure of local factionalism using novel datasets of activist presence and primary contests. Then, we conduct a difference-in-differences analysis to assess whether local factionalism in the Tea Party era heightened Republican partisanship and legislative extremism at the district level. We find that districts that experienced factional pressure moved rightward on both measures. These findings help clarify how the Tea Party captured the Republican Party and support a focus on the role of party factions in fomenting partisan conflict.
The Affordable Care Act (ACA) was intended to reduce inequalities in access to healthcare resources. However, a 2012 Supreme Court decision allowed states to opt out of a key component of the policy, leading to even greater variation in Medicaid’s implementation. Using this variation, we estimate the effect of the ACA Medicaid expansion and racial dynamics on federal Medicaid-CHIP transfers received by states at the county level. To do so, we use a difference-in-differences specification and allow the expansion effect to vary across counties with different population shares of Black Americans. We find that Medicaid expansion increases the funds that are sent to counties, but additional analyses show that the racial demographics of a county also serve to influence how federal resources are apportioned. Specifically, the analyses reveal a curvilinear relationship between the proportion of Black residents and the dispersal of funds.
To alleviate the imbalance in demographics, the Chinese government initiated the universal two-child policy nationwide in 2016, which has comprehensively impacted society, especially females. Our study investigates whether this policy has negatively affected workforce employment and income among women in urban areas. Based on the DID (difference-in-differences) method and the Heckman Two-Step Estimation, reliable empirical evidence shows that the universal two-child policy has significantly reduced women’s employment by 4.06% and decreased their labour income by 10.43%. Surprisingly, this policy has decreased the employment among women under 25 years old by 23.99% and has reduced the income of higher educated females by 29.59%. Furthermore, we find that the influence of the universal two-child policy on female employment has gradually increased from 2016 to 2018, and its impact on income has presented an evident time lag.
The onset of the COVID-19 pandemic constituted a large shock to the risk of acquiring a disease that represents a meaningful threat to health. We investigate whether individuals subject to larger increases in objective health risk – operationalized by occupation-based measures of proximity to other people – became more supportive of increased government healthcare spending during the crisis. Using panel data that track UK individuals before (May 2018–December 2019) and after (June 2020) the outbreak of the pandemic, we implement a fixed-effect design that was pre-registered before the key treatment variable was available to us. While individuals in high-risk occupations were more worried about their personal risk of infection and had higher COVID-19 death rates, there is no evidence that increased health risks during COVID-19 shifted either attitudes on government spending on healthcare or broader attitudes relating to redistribution. Our findings are consistent with recent research demonstrating the limited effects of the pandemic on political attitudes.
Telemedicine enables patients to communicate with physicians effectively, especially during the coronavirus disease (COVID-19) pandemic. However, few studies have explored the use of online health care platforms for a comprehensive range of specialties during the COVID-19 pandemic. This study aimed to investigate how telemedicine services were affected by the announcement of human-to-human transmission in China.
Methods:
Telemedicine data from haodf.com in China were collected. A difference-in-differences analysis compared the number of telemedicine use and the number of active online physicians for different specialties in 2020 with the numbers in 2019, before and after the announcement of human-to-human transmission.
Results:
Data from 2 473 734 telemedicine use during the same calendar time in 2020 and 2019 were collected. Telemedicine use in 2020 increased by 349.9% after the announcement of human-to-human transmission in China, and the number of active online physicians increased by 23.2%. The difference-in-differences analysis indicated that the announcement had statistically significant positive effects on the numbers of telemedicine use for almost all specialties, except cosmetic dermatology, pathology, occupational diseases, sports medicine, burn, medical imaging, and interventional medicine.
Conclusion:
Telemedicine services increased significantly after the announcement of human-to-human transmission of COVID-19. Online activities of most specialties increased, except where providers had to conduct in-person testing and provide bedside therapies.