Theodossiou’s study looked at a large variety of factors and correlations between low-paid jobs, unemployment and individuals’ psychological well-being. At first he presented a survey to unemployed workers asking a variety or questions regarding mental health. I curious however how in depth this survey was. Especially when a study is done on mental health, there can be instances that people with deep rooted mental health a) wouldn’t volunteer to take a survey and b) admit that they have a mental health issue. The same goes for unemployment. People don’t often like to talk about being unemployed. So these are examples of how the data could be affected throughout this study. Asking these questions to yourself while taking data for a study is vital for the results of the experiment. Next, as I read through the study I was wondering if we could find the causes of the two factors (mental health and unemployment) Is it the unemployment  of individuals that can cause more self doubt, or is it the possible mental instability that gets people fired from their jobs? It could very likely be both. AW mentions, that “data is dependent on the individual’s situation- should be considered when we analyze all data.” (Oct. 8, 2018) This especially pertains to the intricacies of mental health and unemployment, because it can be very difficult to find the true causes and effects of unemployment and mental health.

This paper demonstrated times when researchers must make bold claims when operationally defining qualitative variables. For example, they had to make decisions as to how one would asses general happiness pertaining to employment while avoiding confounds. They realized that aging can result in changes to one’s psychological well-being in some but not in others (thus changes across age could be due to factors other than employment), but also had to acknowledge that mental health varies greatly at any given age. When assessing well-being, they used statements pertaining to the person’s “usual” state. In some studies, this could be problematic- given that everyone’s “usual” varies greatly. However, this methodology worked for their experiment because it assessed deviations from their normal state in response to stressors pertaining to employment.

In a more general sense, it was interesting to see the level of detail researchers must consider when attempting to analyze qualitative data (versus it’s more straightforward quantitative counterpart). In a classmate’s post, they referenced the danger of omitted variables being used in order to support a preferred outcome. This reading seems to tackle the other end of the spectrum, in which data must be assessed in a way that best fits the study, but does not manipulate the outcome in a way that would ensure favorable support of the hypotheses.

In exploring the relationship between unemployment and mental distress, the reading highlighted some of the difficulties in collecting data on mental distress. They brought up many other variables that could play a role in explaining why unemployed individuals may be suffering from mental distress, such as their marital status. Because of this, it was hard for the researcher to determine the causal relationships and if mental distress was the effect of unemployment or vice versa. I think that the likelihood for reverse causation, and confounding variables are errors for causal inference that are at a much higher stake in observational and survey psychology experiments.

In relation to JN’s post, who asked “can any data be truly representative of the situation it’s attempting to model?”, I think this study would suggests no. It gives you, somewhat, an idea of the relationship explored in the research question, which I think the researcher explored in detail. Although, I still struggle with the fact that data can be representative of mental distress because it varies from person to person and is something so personal to the individual. I think the psychology is a difficult and tricky field in which outside variables can strongly influence data results.

In the Theodossiou article, the author tried to answer if not having a job affects well-being more than having a low paying one. The study was done in 1992 Britain, which I would argue might skew the data towards joblessness not having as big of an affect on their life as it would in other countries. There is a running joke, which is widely accepted in Great Britain, that British people bottle up their feelings and don’t express them fully, even when given the opportunity to. In this case, I think that the jobless people when asked might under-report how much being unemployed negatively affects them. I think the study would report very different data in countries that are more accepting of sharing ones feelings. I also believe that the study was unclear in how they asked the subjects to report how they felt, because they were only given a scale of 1-4, and the wording they used for the scaling seemed very similar from one level to another. This could skew the data even further, because if a participant doesn’t fully understand what they’re responding to, how can that data be accurate? Overall, I felt like this study was important towards explaining human nature and the value we put on work, but the way in which it was conducted seems to detract from the validity of their claim that it is better for your well-being to have a low-paying job than none at all.

In MLC’s post “Unemployment on Psychological Well-Being” the most important issue to me was discussed. I think it is very important to have questions that provide accurate data, and I don’t think the way in which they gathered their data was the best way to do so. I would’ve liked if the scale was greater, like 1-10, so that you could see more variation in answers to see the relative difference in responses.

First, looking back at the past blog posts on North Dublin, I shared similar worries about the validity of the study.  Whoever wrote “Reading Response #2: Mortality in the North Dublin Union during the Great Famine” hit the nail on the head by saying they were concerned that they only looked at workhouse, perhaps creating bias in the study.

For this week, I think there is an issue with study.  However, the issue is different than the study above.  I think the methods employed were fine.  However,  I worry the conclusion taken from their results seems a little too strong.  Meaning, given the data, they derived meaning from it very well, but I think  what they inferred from this sample (the survey), to all the population (everyone) is somewhat misleading.  I think to do that, they would have needed to use a entity and time fixed effect model.   I do not mean to discount their results, but with all economics papers, this should be taken with a grain of salt.

Yesterday was National Mental Health Awareness Day, making this most recent article all the more relevant. I. Theodossiou’s findings surprised me for a few reasons. Firstly, I was surprised by his conclusions about gender, and the fact that women reported being less affected by unemployment. In a society that generally stereotypes women with mental instability, Theodossiou’s findings clearly exposed this as a stereotype. Additionally, given the prominence of mental health awareness among young people, I was surprised that Theodossiou’s results set middle-aged people as experiencing higher odds of lower mental health conditions. These conclusions that refuted the generalizations that I held reminded me of Devin’s comment when he remarked on how the study was statistical, not based on a theory or preconceptions. By coming from a statistical approach, Theodossiou was able to avoid biases in his data collection and analyzation. His lack of bias is perhaps what allowed his findings to counter many stereotypes about those who experience mental health conditions.

In Theodossiou’s article, he examines the relationships between unemployment and low paying jobs with a psychological well-being score. In one part of his analysis, Theodossiou looks at different employment characteristics to see how they affect well-being. I thought the inclusion and the results of people out of the labor force were interesting. Since this study was conducted in a relatively strong economic period, I would assume most people out of the labor market would be due to choice and their psychological well-being would not be affected. I was surprised to see that being out of the labor force had significant effects on well-being for certain subgroups.

I thought JN had an interesting stance on this article and although I don’t agree with all of their opinions, I thought that they brought up a good point regarding the subjectiveness of the well-being ranking. I think there is a possibility that a confounding variable could exist that factors into how people subjectively rank their well-being and their ability to find employment. But in general, this article provides a very interesting way of looking at how different factors related to employment status affect the odds of being in a low or high level of psychological well-being.

In I. Theodossiou’s article from the journal of health economics, regressors such as low pay and unemployment are measured against psychological well-being. The dependent variable, psychological well-being, are numerically quantified using feelings such as dissatisfaction, unhappiness, and low self-esteem. These individual feelings are rated on a integer scale. In the article, dummy variables are brought up as a possible measure to help the author draw conclusions about the data. In OLS regressions, dummy variables are used to illustrate the absence or presence of some categorical effect that may be expected to shift the outcome. A dummy variable takes on a value of 0 or 1. The significance of a regression using dummy variables is it now becomes binary and the coefficient in front of the slope is defined as the treatment effect. The treatment effect is this predicted difference among the two groups in the regression.

In MLC’s post on “Unemployment on Psychological Well-Being”, the author highlights the process of collecting categorical data and quantifying this data somehow. One example of a rating scale the author used that was present in the article is the following: not at all (1), no more than usual (2), rather more than usual (3), much more than usual (4). My only issue with this type of analysis is the difficulty in labeling these distinct categories. In OLS regression, I prefer using continuous variables instead of discrete.

After reading the effects of low pay and unemployment on psychological well-being, I learned that unemployment actually has a major impact on a persons well-being. Literature suggests that many people derive purpose from work and not just income. This was shown to be true in the results. If we look at the regressions ran; the unemployment variable always had a strong, statistically significant, effect on a persons well-being. I found this to be fascinating and I will be sure to remember this the next time I meet a jobless person.

However, looking at an October 7, 2018 post by NB, omitted variable bias may still exist. I do not know the motivation behind the data used. I believe the authors did a great job trying to cover all the bases: married, age, age^2, children, ect. However, for physiological well  being there is not a one size fits all solution so I believe we need to be cautious of these results.

I would also be very interested to see how these results change on an update. I know the economy has evolved and many more people opt out of a full time job and choose to drive for Uber instead.

Lastly, I think it is important to note that survey results may not tell the whole story. People can lie on the forms or only certain people will actually participate in the survey. Both of which cause bias.

Citations

Theodossiou, Ioannis. “The effects of low-pay and unemployment on psychological well-being: a logistic regression approach.” Journal of health economics 17.1 (1998): 85-104.

NB, “Omitted Variable Bias Simpson’s Paradox.” Nick Beatis Blog October 7th 2018

Theodossiou articulates that unemployment has a negative effect on psychological well-being, and then by natural flow of reasoning, he further made the assumption that most unemployment was involuntary.  Theodossiou says that the parameters of measuring the psychological wellbeing were not limited to “caseness scores” which are numerical values associated with some type of psychological testing. He did his analysis by collecting categorical data (ordinally ranked in 4 levels). His categories were; feeling under strain, losing confidence,thinking of being a worthless person, as well as happiness levels and ability and motivation to do day to day activities.  Each ordinal ranking was associated with a value, [e.i. not at all (1), no more than usual (2), rather more than usual (3), much more than usual (4)]. I think that these categorical variables are accurate/useful for this collecting data because the data is subjective to the person answering the questions- and the researchers were looking for subjective data.

Additionally, as Elizabeth said in her blog post, even when looking at the data across various subgroups (age, gender, married/divorced, etc) as to avoid Simpson’s paradox, it all seemed to be relatively following the same trend, but the reasoning behind each trend could be explained differently.   I would agree with Elizabeth that this article does a good job of categorizing data, and analyzing based on many variables and across many groups.