scholarly journals Economic disparities and suicides: The dynamic panel data analyses of 50 states in the United States

2021 ◽  
Vol 5 (1) ◽  
pp. 020-029
Author(s):  
Swan Bruce Q

The economic inequalities associated with suicide risks among 50 states in the United States were identified in this paper to form the dynamic panel data set from 1981 to 2016. The effects of growing income inequalities on suicides in the Unites States were estimated using the Arellano–Bond method. This paper is the first to associate the social inequalities with suicides using the state-level dynamic panel data in America. It is found that the change of unemployment rates significantly and positively impact the changes of the overall suicides rates, female and male suicides rates. The changes of Top 10% income index are uniformly positive to the change of female, male and overall state-level suicide rates. The Gini index has positive correspondence within the overall and female groups, along with the insignificantly vague evidence within the male groups. The potential endogeneity problem inferring from the fixed effect estimation has been also investigated accordingly. JEL Classification: A13, A14, I18.

Abstract: Suicides have been the second leading cause of deaths among adolescents in the United States in 2016. This paper aims to find qualitative and quantitative evidence of the relationship between socioeconomic inequalities and adolescent suicides. The suicide risk factors among all states are identified to form the pooled dynamic panel dataset from 1990 to 2016. To our knowledge, this paper is the first to find that social inequalities are significantly related to American adolescent suicides using the state-level dynamic panel data. Changes of unemployment rates have the consistent and significantly positive impacts on changes of adolescent suicides rates. Changes of Top 10% income index are uniformly positive to changes of adolescent suicide rates. Gini indices have inconsistently positive correspondence to adolescent suicide rates. Furthermore, high school graduation rates are insignificantly and negatively associated with adolescent suicide rates in the United States.


10.2196/26081 ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. e26081
Author(s):  
Theresa B Oehmke ◽  
Lori A Post ◽  
Charles B Moss ◽  
Tariq Z Issa ◽  
Michael J Boctor ◽  
...  

Background The COVID-19 pandemic has had profound and differential impacts on metropolitan areas across the United States and around the world. Within the United States, metropolitan areas that were hit earliest with the pandemic and reacted with scientifically based health policy were able to contain the virus by late spring. For other areas that kept businesses open, the first wave in the United States hit in mid-summer. As the weather turns colder, universities resume classes, and people tire of lockdowns, a second wave is ascending in both metropolitan and rural areas. It becomes more obvious that additional SARS-CoV-2 surveillance is needed at the local level to track recent shifts in the pandemic, rates of increase, and persistence. Objective The goal of this study is to provide advanced surveillance metrics for COVID-19 transmission that account for speed, acceleration, jerk and persistence, and weekly shifts, to better understand and manage risk in metropolitan areas. Existing surveillance measures coupled with our dynamic metrics of transmission will inform health policy to control the COVID-19 pandemic until, and after, an effective vaccine is developed. Here, we provide values for novel indicators to measure COVID-19 transmission at the metropolitan area level. Methods Using a longitudinal trend analysis study design, we extracted 260 days of COVID-19 data from public health registries. We used an empirical difference equation to measure the daily number of cases in the 25 largest US metropolitan areas as a function of the prior number of cases and weekly shift variables based on a dynamic panel data model that was estimated using the generalized method of moments approach by implementing the Arellano-Bond estimator in R. Results Minneapolis and Chicago have the greatest average number of daily new positive results per standardized 100,000 population (which we refer to as speed). Extreme behavior in Minneapolis showed an increase in speed from 17 to 30 (67%) in 1 week. The jerk and acceleration calculated for these areas also showed extreme behavior. The dynamic panel data model shows that Minneapolis, Chicago, and Detroit have the largest persistence effects, meaning that new cases pertaining to a specific week are statistically attributable to new cases from the prior week. Conclusions Three of the metropolitan areas with historically early and harsh winters have the highest persistence effects out of the top 25 most populous metropolitan areas in the United States at the beginning of their cold weather season. With these persistence effects, and with indoor activities becoming more popular as the weather gets colder, stringent COVID-19 regulations will be more important than ever to flatten the second wave of the pandemic. As colder weather grips more of the nation, southern metropolitan areas may also see large spikes in the number of cases.


2000 ◽  
Vol 60 (1) ◽  
pp. 42-66 ◽  
Author(s):  
Janet Currie ◽  
Joseph Ferrie

This article examines the effect of state-level legal innovations governing labor disputes in the late 1800s. This was a period of legal ferment in which worker organizations and employers actively lobbied state governments for changes in the rules governing labor disputes. Cross-state heterogeneity in the legal environment provides an unusual opportunity to investigate the effects of these laws. We use a unique data set with information on 12,965 strikes to show that most of these law changes had surprisingly little effect on strike incidence or outcomes. Important exceptions were maximum hours laws and the use of injunctions.


2021 ◽  
Vol 12 (3) ◽  
pp. 743-777 ◽  
Author(s):  
Shakeeb Khan ◽  
Fu Ouyang ◽  
Elie Tamer

We explore inference on regression coefficients in semiparametric multinomial response models. We consider cross‐sectional, and both static and dynamic panel settings where we focus throughout on inference under sufficient conditions for point identification. The approach to identification uses a matching insight throughout all three models coupled with variation in regressors: with cross‐section data, we match across individuals while with panel data, we match within individuals over time. Across models, we relax the Indpendence of Irrelevant Alternatives (or IIA assumption, see McFadden (1974)) and allow for arbitrary correlation in the unobservables that determine utility of various alternatives. For the cross‐sectional model, estimation is based on a localized rank objective function, analogous to that used in Abrevaya, Hausman, and Khan (2010), and presents a generalization of existing approaches. In panel data settings, rates of convergence are shown to exhibit a curse of dimensionality in the number of alternatives. The results for the dynamic panel data model generalize the work of Honoré and Kyriazidou (2000) to cover the semiparametric multinomial case. A simulation study establishes adequate finite sample properties of our new procedures. We apply our estimators to a scanner panel data set.


2003 ◽  
Vol 42 (4II) ◽  
pp. 987-1014
Author(s):  
Azhar Iqbal ◽  
Muhammad Sabihuddin Butt

The question whether real money causes real output appears to be important for many economists working in the area of macroeconomics and, has been subjected to a variety of modern econometric techniques, producing conflicting results. One often applied method to investigate the empirical relationship between money and real activity is Granger causality analysis [Granger (1969)]. Using this approach, the causality question can be sharply posed as whether past values of money help to predict current values of output. This concept, however, should be clearly distinguished from any richer philosophical notion of causality [cf. Holland (1986)]. Present paper examines the relationship between money (both M1 and M2) and income (Real GDP) for 15 developing countries using a newly developed heterogeneous dynamic panel data approach.1 Sims (1972) postulated “the hypothesis that causality is unidirectional from money to income agrees with the post war U.S. data, whereas the hypothesis that causality is unidirectional from income to money is rejected”. Since then a voluminous literature has emerged testing the direction of causality.2 Some studies have tested the relationship between these variables and the direction of causality for a particular country using time series techniques [e.g., Hsiao (1979) for Canada, Stock and Watson (1989) for U.S. data, Friedman and Kuttner (1992, 1993) for U.S. data, Thoma (1994) for U.S. data, Christiana and Ljungquist (1988) for U.S. data, Davis and Tanner (1997) for U.S. data, Jusoh (1986) for Malaysia, Zubaidi, et al. (1996) for Malaysia, Biswas and Saunders (1998) for India, and Bengali, et al. (1999) for Pakistan]. Other studies have tested the above on a number of countries, for example Krol and Ohanian (1990) used the data for Canada, Germany, Japan and the U.K. Hayo (1999) using data from 14 European Union (EU) countries plus Canada, Japan, and the United States. More recently Hafer and Kutan (2002) used a sample of 20 industrialised and developing countries. This paper contributes to this later strand of the literature, which it extends in three directions. First, it employed a newly developed panel cointegration technique [Larsson, et al. (2001)], to examine the long-run relationship between money and income. Second, the study performs panel causality test, recently developed by Hurlin and Venet (2001), to explore the direction of causality between the said variables. Third, the important contribution of the present study is to test whether relationship between money and income is homogeneous or heterogeneous across countries.


ILR Review ◽  
2019 ◽  
Vol 72 (5) ◽  
pp. 1262-1277 ◽  
Author(s):  
Robert W. Fairlie ◽  
Javier Miranda ◽  
Nikolas Zolas

The field of entrepreneurship is growing rapidly and expanding into new areas. This article presents a new compilation of administrative panel data on the universe of business start-ups in the United States, which will be useful for future research in entrepreneurship. To create the US start-up panel data set, the authors link the universe of non-employer firms to the universe of employer firms in the Longitudinal Business Database (LBD). Start-up cohorts of more than five million new businesses per year, which create roughly three million jobs, can be tracked over time. To illustrate the potential of the new start-up panel data set for future research, the authors provide descriptive statistics for a few examples of research topics using a representative start-up cohort.


Author(s):  
Sotiris Vandoros ◽  
Ichiro Kawachi

AbstractPrevious studies have found an association between recessions and increased rates of suicide. In the present study we widened the focus to examine the association between economic uncertainty and suicides. We used monthly suicide data from the US at the State level from 2000 to 2017 and combined them with the monthly economic uncertainty index. We followed a panel data econometric approach to study the association between economic uncertainty and suicide, controlling for unemployment and other indicators. Economic uncertainty is positively associated with suicide when controlling for unemployment [coeff: 8.026; 95% CI: 3.692–12.360] or for a wider range of economic and demographic characteristics [coeff: 7.478; 95% CI: 3.333–11.623]. An increase in the uncertainty index by one percent is associated with an additional 11–24.4 additional monthly suicides in the US. Economic uncertainty is likely to act as a trigger, which underlines the impulsive nature of some suicides. This highlights the importance of providing access to suicide prevention interventions (e.g. hotlines) during periods of economic uncertainty.


Author(s):  
Waleed Said Soliman Faragalla

In this paper, the author investigates the tourism demand function using the dynamic panel data approach in the case of Egypt. The panel data set covers the time period between 1995 and 2014. The individuals are 49 countries as origin countries for tourists, representing 92% of the total tourist arrivals to Egypt. Explanatory variables which affect the tourism demand function were taken into account: lag of dependent variable that leads to dynamic panel data approach, using DIFF-GMM estimator proposed by Arellano and Bond (1991); also, many other explanatory variables like GDP per capita, relative price index, distance, and dummy variable which represent the political situation. One of the important and significant conclusions of the paper is the significant effect of the lagged dependent variable (0.493), which may be explained as “Word of Mouth” to tourists’ decision when choosing the destination.


Demography ◽  
2021 ◽  
Author(s):  
J. David Hacker ◽  
Jonas Helgertz ◽  
Matt A. Nelson ◽  
Evan Roberts

Abstract Children require a large amount of time, effort, and resources to raise. Physical help, financial contributions, medical care, and other types of assistance from kin and social network members allow couples to space births closer together while maintaining or increasing child survival. We examine the impact of kin availability on couples' reproductive success in the early twentieth-century United States with a panel data set of over 3.1 million couples linked between the 1900 and 1910 U.S. censuses. Our results indicate that kin proximity outside the household was positively associated with fertility, child survival, and net reproduction, and suggest that declining kin availability was an important contributing factor to the fertility transition in the United States. We also find important differences between maternal and paternal kin inside the household—including higher fertility among women residing with their mother-in-law than among those residing with their mother—that support hypotheses related to the contrasting motivations and concerns of parents and parents-in-law.


1998 ◽  
Vol 58 (2) ◽  
pp. 345-374 ◽  
Author(s):  
Claudia Goldin

Secondary-school enrollment and graduation rates increased spectacularly in much of the United States from 1910 to 1940; the advance was particularly rapid from 1920 to 1935 in the nonsouthern states. This increase was uniquely American; no other nation underwent an equivalent change for several decades. States that rapidly expanded their high school enrollments early in the period had greater wealth, more homogeneity of wealth, and less manufacturing activity than others. Factors prompting the expansion include the substantial returns to education early in the century and a responsive “state.” This work is based on a newly constructed state-level data set.


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