Men Eat More In Front Of Women?

An article written by Julie Beck of The Atlantic on November 19, 2017, outlines a study that claims “Men Overeat to Impress Women”. The article explains how the study, by Cornell University, recorded observations of men and women at an all you can eat pizza buffet, and measured how much food they ate. The study found that men ate “93 percent more pizza and 86 percent more salad” when they were with women compared to when they were with other men. The study cited that they were probably doing it to impress women, “Our observation of men ‘eating heavily’ is sensibly viewed in an evolutionary perspective as men ‘showing off’”.

The entire article makes this study seem like it was very sound and that the results were clear. It also made it seem like the reasoning for it was clear. In an interview with Kevin Griffin, the lead author of the study, he said “overeating might function as a comparable kind of signal that a person is healthy enough that they can engage in unhealthful behavior of excessive eating (and still end up okay).” The thing about this statement is it’s a hypothesis. The article makes it seem like it’s a fact, based on the fact that that quote is written in it twice, but it doesn’t talk anywhere about any other possibilities besides impressing women or trying to signal to women that they are strong.

When I went through and read the study, I got a lot more information from it than I got from the article. For example, the article made it seem like they just creepily watched people what pizza, never taking surveys from them or even letting them know they were being surveyed. However, in the abstract, it states “Additionally, while women do not eat significantly differently as a function of the sex of their dining partners, women eating with men tended to estimate themselves to have eaten more and reported feeling like they were rushed and overate.” It’s actually interesting to me that the article does not talk about this, because it seems like a pretty major finding for the study, but I guess the Atlantic just wanted to focus on men overeating. However, this quote brings up an important point that the article did not – the people were all surveyed as follows: “When participants had finished with their meals, a research assistant met them at the cash register to ask them to complete a survey that asked each of them to estimate the number of calories of pizza they consumed as well as their level of (dis)agreement on a nine-point scale with the statements “I overate,” “I felt rushed,” and “I am physically uncomfortable.” A picture of some of the graphs they used can be found below.

Screen Shot 2017-12-03 at 5.27.01 PMScreen Shot 2017-12-03 at 5.27.52 PM

I personally feel that this study was much more interesting than the article made it out to be, particularly with how people predicted how many calories they ate. When women ate with women, they predicted they ate an average of about 350 less calories compared to when they ate with men, even though they only ate slightly less with women. In addition, when women ate with men, they felt much fuller than if they ate with women, probably having to do with the perceived calorie intake thing. According to their study, men also ate about twice as much with women than when they ate with men.

This study, in my opinion, has some holes. For example, this is not a representative population at all. They did this study at one pizza buffet at lunch time over the course of two weeks. How do we know that maybe men in this area don’t eat breakfast as much as women, so maybe that’s why they ate more or something? The sample size was just 133 people. As someone whose worked in resturaunts, either that not everyone who went into the pizza buffet at lunch was surveyed, or maybe it was a really slow resturaunt. Maybe they had really bad pizza, so that’s why so few people were surveyed in the teo weeks, and maybe men don’t complain about gross pizza in front of women but they would in front of men. I know I’m being quite skeptical about this, but I guess I just don’t agree with how the data was collected and for them to be making as profound statements such as the article makes. The conclusion stated that:

“Future research into “eating heavily” among males should examine the relative importance of female mate choice and intrasexual competition and consider whether this pattern holds in societies where relative thinness is not prized (e.g., Tovee et al. 2006); however, our behavioral findings drawn from a naturalistic field study introduce an important pattern through its rejection of the hypothesis that men tend to eat more in the company of other men.”

The study accepts and acknowledges that more research should be done and proposes more things it could be gone on, and says that they have “introduced an important pattern”. The article written by the Atlantic made it seem like such more sound research compared to “introduced an important pattern”. Honestly, I have nothing against the study, because they definitely aren’t saying their research is more important or has more gravity than it has. However, the article by the Atlantic about the study, is a bit of an exaggeration.

 

https://www.theatlantic.com/health/archive/2015/11/men-overeat-to-impress-women/416760/

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Cars Falling From The Sky: Analyzing My Own Paper

 

For this week’s blog post, I will be analyzing my group physics paper and the data collected in it. For a disclaimer, I would just like to say we are only in physics 1, but I will be writing about this like it was an actual published paper. In addition, this was only the rough draft of our paper, but I still think it turned out pretty well, even though in this analysis I will be tearing it apart and explaining how it doesn’t actually mean anything.

Our physics paper was about a certain scene in Furious 7, in which cars are ejected from an airplane at 12,000 feet. We believed that the cars looked like they fell for much longer than they would have in real life, so we did the calculations to see how long the car would have actually fallen, and based on how long they said it fell, how far it actually would have fallen.

The first problem with this paper is that many assumptions were made that could have compromised the end physics result the paper came to. For example, air drag had to be calculated, and we weren’t sure how to calculate the air drag if the car was spinning, so “[We] assumed that the car maintained a single orientation with the nose of the car facing down.” In the movie, the car spun a lot whilst it fell, so if we were going to do a real, accurate calculation of the car falling, a simple rectangle doing a nosedive does not suffice.

In addition to this, we took the time of falling from a trailer video, and in the middle of the cars falling, there is a picture saying when the movie was coming out. Obviously, the movie stretched out the time of how long the cars were falling, but it does not seem like they’re making the implication that the cars were falling the whole time they showed. In other words, the movie was not implying that the cars were falling in real time, and we acted like the movie was implying that. The whole paper is based off that one implication, which, when reevaluated, is not even accurate. So it’s possible that even though we calculated the car would have fallen in less time, the filmmakers were not trying to even make people think they were in the air for longer than they were.

Another issue with this paper is that it only analyzes the one scene from Furious 7. If it’s proven or disproven that this one scene is accurate, it’s impossible to make any predictions or assumptions about any other movies or even any other scenes in this movie because of it. If we wanted to actually have a good purpose, we would have analyzed more movie scenes, or more scenes of the Fast and Furious series. Unfortunately, there isn’t a huge conclusion to be made about this paper, the only possible conclusion is that the one scene wasn’t accurately portrayed. Which, that conclusion was made, but due to the air drag and the fact that it doesn’t seem like the movie is implying that the cars fell for such a period of time, doesn’t seem to be a sound conclusion.

The mathematics of how this was calculated was indeed correct. The formulas were accurate to how they should have been, but it is possible that some variables were not taken into place. Unfortunately, we had to make a lot of assumptions about things like the mass of the car, the coefficient of drag, and the air density. If any of those assumptions, that were just numbers pulled from the internet, were incorrect, the calculations would be off, thus giving us a different time of falling than it actually would have been. Due to all the assumptions, the margin of error is much greater than it would be for something with less assumptions. However, the methodology for this is very accurate. In the conclusion, the paper states  “The methodology used to figure out how long it took the car to fall and how far the car would have fallen could apply to other objects falling from the sky.”  This is correct. While figuring out the calculations for a car may have been difficult, the methodology in the paper could be used for a rectangular prism falling from a known height at a known coefficient of drag and air density, and then the falling time would have been more accurate. In that sense, this paper does a lot of good, because that is the main application of this paper.

All and all, this paper made a lot of assumptions that possible made huge problems in their calculations. The methodology was correct, however, assuming that the car didn’t spin and using numbers for the air density off the internet could have messed up the calculations. In addition, the whole paper was trying to prove that the cars would not fall for as long as they did in the movie clip, but in the clip there is a random screen in the middle advertising when the movie would come out – making it impossible to figure out how long the filmmakers are assuming the car falls for anyways. In addition to this, the paper doesn’t have a very broad reach, and it only analyzed one movie clip, making it impossible to make any assumptions about the whole movie or any other movies.

 

A copy of this paper can be found at https://drive.google.com/file/d/1RaQ1tR5c_B-tpp5haLbZcdrRBPT2DkyS/view?usp=sharing

 

Furious-7-Poster.jpg

Has the No Child Left Behind Act Increased Diagnoses of ADHD?

This article was titled, “The Not-So-Hidden Cause Behind the A.D.H.D Epidemic” written by MAGGIE KOERTH-BAKER  for the New York Times on October 15, 2013.  Maggie outlines how children are diagnosed at a much higher rate than they used to, likely due to “changes in the way we school our children, in the way we interact with doctors and in what we expect from our kids” (Koreth-Baker, 2013, New York Times). However, this is the most broad claim she made. She went deeper into a study that she did not source about how the No Child Left Behind act is what caused these changes in how we school our kids, interact with doctors, and expectations of the kids.

The author makes some drastic claims, but throughout her article does not cite any sources. Some of the statistics she puts out there include “Of the 6.4 million kids who have been given diagnoses of A.D.H.D., a large percentage are unlikely to have any kind of physiological difference that would make them more distractible than the average non-A.D.H.D. kid.” With specific numbers as these, you should probably state somewhere they came from, but she didn’t state who even figured that out. As she wrote this as someone with ADHD, who was diagnosed as an adult, she tried to illustrate that it’s hard to get diagnosed as an adult, but children can be diagnosed in one visit to their pediatrician. She talked about how some scientist named Hinshaw found that geographically, the distribution of kids who have ADHD is not distributed equally, and believed that the No Child Left Behind Act, which links school funding to standardized testing scores, to be the cause of such (Koreth-Baker, 2013, New York Times). By blaming the No Child Left Behind act for kids having ADHD, without any foundation for this claim or even citing this man’s study, it may leave people to believe that the No Child Left Behind Act pressures teachers and parents to make sure kids are well behaved in class and can focus on everything. This makes the implication that the NCLB act forces kids who don’t have ADHD to be on medicine so they do better in school. She later goes on to say “Nationwide, the rates of A.D.H.D. diagnosis increased by 22 percent in the first four years after No Child Left Behind was implemented. To be clear: Those are correlations, not causal links.”  (Koreth-Baker, 2013, New York Times). The author seems to confuse the words “correlation” with “causation”, seeming to imply that this is causation. However, correlation and a causal link are basically the same thing. They happen to be happening at the same time, but the NCLB act is not necessarily the cause of that.

This article was incredibly biased and didn’t seem to have any sort of foundation to me. She was making intense claims apparently some guy had figured out, but when I did research I could not find his specific study to cross check what she was saying. However, I found a similar study, that stated

“NCLB-initiated consequential accountability reforms were associated with more ADHD diagnoses among low-income children, consistent with increased academic pressures from NCLB for this subgroup. In contrast, psychotropic medication laws were associated with fewer ADHD diagnoses, because they may indirectly reduce diagnoses via restrictions on recommending or requiring medication use. Future research should investigate whether children most affected by these policies are receiving appropriate diagnoses.”

Again, they act like the ADHD diagnosis are directly caused by the laws. However, they simply studied the laws in each state and when they came into effect, and how many kids per state had been diagnosed with ADHD and how those numbers were changing. They stated that the reason for the increase in diagnoses and the relationship as follows:

“Children with ADHD show substandard academic achievement (13,14). Consequential accountability may indirectly result in more ADHD diagnoses, because diagnosed children are often treated with prescription medications, which are associated with small increases in standardized achievement scores (15), albeit with mixed evidence for improvements in school grades and grade retention (16). School districts are motivated to promote diagnoses, such as ADHD, to gain testing accommodations or even to exclude such children from formal academic testing, even though the latter became more difficult with implementation of NCLB (1719).”

They make some very intense claims, but there was no grounds for such claims. They say the school districts want kids to get diagnosed because it benefits them, however, they have no evidence for how school districts are convincing parents to get their kids diagnosed.

If this study and article was true, it would mean that kids are on medicine due to policies. While the rates of ADHD is rising, it seems like a stretch to blame it on the school districts encouraging diagnoses to get kids to do better in school or exclude them from testing.

Fulton, B. D., Scheffler, R. M., & Hinshaw, S. P. (2015). State Variation in Increased ADHD Prevalence: Links to NCLB School Accountability and State Medication Laws. Psychiatric Services, 66(10), 1074-1082. doi:10.1176/appi.ps.201400145

Koerth-Baker, M. (2013, October 15). The Not-So-Hidden Cause Behind the A.D.H.D. Epidemic. Retrieved November 12, 2017, from http://www.nytimes.com/2013/10/20/magazine/the-not-so-hidden-cause-behind-the-adhd-epidemic.html

 

Is Anyone Accurate About Abortion Statistics?

When you think of abortions, or at least, the stereotype of someone who gets an abortion, is a young, single woman who was not on birth control can either not afford to have the baby or is not in an emotional position to raise a baby. However, the people who are getting abortions are not just that isolated group.

The Guttmacher Institute is an organization dedicated to collecting facts and statistics about abortions, state policies and regulations on contraceptives, sti’s, and pregnancies. They even have a database for all this information, outlining any detail I could think of about worldwide sexual health related issues, from how many abortion clinics there are to public expenditures for family planning to the socioeconomic characteristics of women who get abortions. Each fact has a link to where they got the information from, most of which are scientific journals published by them. Due to the nature of what data they are collecting, it’s largely left leaning, especially considering how much funding they get from Planned Parenthood. Some sources state that the way they collect their data is quite sketchy.

Often, for forgien countries, the way the Guttmacher Institute collects their data is through health professional surveys, in which they survey facilities and professionals on how many abortions there were. The content of the surveys and the professionals surveyed is unknown (Marc, 2012, Pathos). This information was based on a counter study that indicated all the flaws the Guttmacher institute had with collecting their data. In the Mexico Distrito Federal in 2007, they estimated there were between 137,145 and 194,875 induced abortions. In reality, there was just 10,137, and this was found out because the facilities were required to report to the government the exact numbers of how many abortions there were. The counter paper and this news article, however, only talk about this one instance. They do not talk about all the other regions of Mexico and if the estimates were correct or incorrect. Although it’s obviously arguable that the way they collect their data is not the best, it’s hard to imagine a better way, and without knowing any other circumstances where they were proved to be vastly exaggerating statistics, it’s hard to believe either side.

The statistics I was wanting to look at was not the Mexico statistics, but the ones in the United States in 2014. On their website, in the summary/background, it says:

“Between April 2014 and June 2015, we collected information from 8,380 respondents obtaining abortions at 87 facilities. We used a four-page, self-administered questionnaire available in English and Spanish… Participating facilities provided a total of 11,024 abortions during the survey period, yielding a response rate of 76%… Facilities eligible for participation included clinics and physicians’ offices that provided at least 30 abortions in 2011 (according to the Guttmacher Institute’s 2011 Abortion Provider Census9); hospitals were excluded from the survey because of past recruitment and logistical challenges. In 2011, hospitals accounted for 4% of all abortions,9  and it is unlikely that their exclusion biased the sample.”

They collected their own data like this because they said the CDC had holes in their data, and each state had different requirements for how to report the numbers and some states weren’t even in their report (Guttmacher Institute).

The way they collected this data seemed logical and like it would be accurate. However, due to the fact they’re funded by Planned Parenthood, it seems that they would have quite the incentive to fudge the numbers in a way that favors Planned Parenthood. It’s incredibly hard to get statistics about abortions, but maybe a better way would be by comparing and contrasting their numbers to those of the CDC, seeing which are vastly different, and seeing if there’s any way to combine the data.

The data anybody presents about abortions and the socioeconomic status, marital status, age, and otherwise has the potential to vastly shift people’s opinions of abortions. I made a table of the most interesting facts I saw from the Guttmacher Institute in 2014 compared to the CDC data from 2013. Here it is:

Guttmacher Institute (2014) CDC (2013) Comparison
“Nineteen percent of pregnancies (excluding miscarriages) in 2014 ended in abortion.[1]” “… 200 abortions per 1,000 live births.” I don’t really know if the CDC meant 200/1000 or 200/1200. Both are similar and close to 19%.
“Twelve percent of abortion patients in 2014 were adolescents” “In 2013, adolescents aged <15 and 15–19 years accounted for 0.3% and 11.4% of all abortions, respectively” Those are the same proportions.
“More than half of all U.S. abortion patients in 2014 were in their 20s: Patients aged 20–24 obtained 34% of all abortions, and patients aged 25–29 obtained 27%.[6]” “Women aged 20–24 and 25–29 years accounted for 32.7% and 25.9% of all abortions, respectively” Guttmacher is one percentage higher than the CDC.
“In contrast, women aged 30–34, 35–39, and ≥40 years accounted for 16.8%, 9.2%, and 3.6% of all abortions” I find it very interesting that Guttmacher did not present any data about women over 30.
“White patients accounted for 39% of abortion procedures in 2014, blacks for 28%., Hispanics for 25% and patients of other races and ethnicities for 9%.[6]” “Non-Hispanic white women and non-Hispanic black women accounted for the largest percentages of abortions (37.3% and 35.6%, respectively). Hispanic women and non-Hispanic women in the other race category accounted for smaller percentages (19.0% and 8.1%, respectively)” The CDC reported much higher numbers for black women than Guttmacher – a difference of 7.6%. The Guttmacher also reported 6% higher for hispanic people.
“Some 75% of abortion patients in 2014 were poor or low-income.” I really think the CDC should have published information about income.
“Fifty-nine percent of abortions in 2014 were obtained by patients who had had at least one birth.[6]” “Data from the 41 areas that reported the number of previous live births for women who obtained abortions in 2013 indicate that 40.2%, 45.6%, and 14.1% of these women had zero, one to two, or three or more previous live births, respectively” These numbers do seem within a reasonable range of each other.

 

Honestly, for me, the most interesting thing about this table I have just made is how the data was presented. The CDC stated it very factually and scientifically, whilst Guttmacher compared it much more biasedly. For example, in the row about people in their 20’s, Guttmacher starts off with “More than half of all US abortion patients were in their 20’s”, whilst the CDC just states the information and lets you figure out that math yourself. All of the numbers seem within a reasonable range of one another, except for the race thing, where the CDC and Guttmacher report different numbers about Hispanics and non-hispanic black women. Also, even though Guttmacher claimed that the CDC got all their information from the state, the CDC sourced Guttmacher and many other studies in the references. So I guess it makes sense that they have similar numbers. Even though Guttmacher was a little salty about the CDC when they wrote about them, I wonder if they actually combine numbers. It makes me wonder if it’s even possible to get a real accurate reading about abortion statistics.

This paper was originally going to be about who gets abortions, but I found researching if Guttmacher was accurate to be more interesting. At the end of the day I can honestly say: I have no idea. But it’s pretty biased. And one time, apparently, they had measurements for part of Mexico that were way out of bounds.

Is the United States Postal Service hiding data?

The United States Postal Service has not been doing as well as they were previously over the last several years. In fact, it’s been losing millions of dollars. They deeply desire to stay relevant and show the American public that they are an integral part of society. A paper they came out with was pretty biased, showing data from the last decade.

According to a table on the USPS website titled, “A decade of facts and figures” it makes the USPS seem like a big deal due to the amount of packages and money it needs to operate. The most interesting part of this table, to me, is that on their website, and on the pdf, they have slightly different tables titled the same thing. In the PDF that explains the facts and figures, from 2016, they say the annual revenue. This can be seen below, and I drew an arrow at the top where it says this.

Screen Shot 2017-10-20 at 1.12.16 PM

However, in the 2017 edition, they say the annual operating budget. This can be seen below, where again, I drew an arrow. Also, the 2017 edition is not available in PDF form to download, and their website cuts off part of the data from the previous years, which is annoying.

Screen Shot 2017-10-20 at 1.10.22 PMThis leads me to believe that, without calculating anything, they lost more money in 2016 than they had in previous years. Which, when looking at how much revenue they made, they made billions less than they had in 2015. Neither detail both numbers or how much they made/lost for those years. This can be seen below. They show the operating cost, and the breakdown of how much money they made each year, but nowhere does it say the totals for how much they made each year. I think that is the most important piece of information, and hiding it like they are is really biased of them.

In the both editions of the table, they go so in depth about how many packages, the package volume, and how many of each type of package. Sure, that is useful information, but they had many more columns about the amount of types of mail or how many people to the post office. People are more interested in how much money they made and lost rather than how many post offices there are or the amount of “Unique Visitors” (which they never even explain what they are).  It seems biased to me that they state that information so much instead of talking more about the money they made.

The conclusion they seem to make is that they need a lot of money to run, they make money in a lot of ways, and a lot of people come in and mail packages. The conclusion that I, and I believe everyone else wants to make, is how the amount of mail being mailed is changing, and the operating costs and income is changing. In this way, a scatter plot with lines for each data set showing how much mail/volume/each type of mail over the years would be most efficient, then another showing amount of post offices/people going to them, and another showing the revenue. However, these tables are all that is on the website. I feel like they displayed this data in a way that makes it seem like they’re all that, even though their operating budget has not changed, while the amount of money they make and amount of packages they send out has been decreasing.

I understand that they want to continue being a part of the government and existing, but unfortunately, tables that point to weird things and don’t display the data properly makes them seem biased and like they are hiding how badly they are doing. Also, changing what goes in the tables each year because they wish to hide how much they lost is wrong in my opinion.

The social implications of this are that we continue funding a government entity that is losing more and more money each year, but we keep it around because we deem it “necessary” and “important”. I mean, it is important, I am not arguing that. I wanted to focus on the table, but when you scroll down in this PDF, it continues to be completely biased. The next portion “The Top 12 Things You Should Know About the U.S. Postal Service” is almost entirely just saying how great it is. It talks about how it “has the country’s largest retail network— larger than McDonald’s, Starbucks and Walmart combined, domestically” “monitor the well-being of elderly and disabled customers”, employs veterans, has one of the largest civilian vehicle fleets in the world, and how important mail is.  They desperately want the public to continue viewing them as relevant and a good use of taxpayer money, so they don’t want to say anything bad about themselves. However, we do see that as a society, we are moving away from letters, and that the Postal Service is a huge expenditure.

It’s obvious why the USPS wrote this – they wanted us to see that they were still important and relevant. They want us to continue funding them. However, I personally am slightly annoyed at how incredibly biased this piece was, and how they didn’t care to provide the information that they are losing more and more money every year.

Are We Recycling Less?

Screen Shot 2017-10-08 at 5.49.10 PMPlastic lasts forever – it never biodegrades, like cardboard does. Instead, it breaks into tiner and tiner pieces, creating microplastics, effects of which we are not sure. That’s why it’s important to recycle. However, according to a 2014 study by the EPA, the amount tons recycled hardly changed  from 2010 to 2014. EPA put out many reports on the municipal solid waste information they collect, but this will just be focusing on two of them. One of them outlines how they collected the data, and the other one is graphs illustrating the data.

The methodology used to gather the data was rather interesting. The Municipal Solid Waste Generation, Recycling, and Disposal in the United States: A Methodology Report, states,  “Industry data and state level data provide the basis for recovery through recycling and composting.” They have each individual solid waste and recycling center report to them about how much waste and recycling there is annually. In the report, they explained each recycling material and how they calculated the data behind it. They also outlined any data gaps that there may have been, clearing up questions that I had regarding what materials were included in each section of the tables, that will be outlined in the next paragraph. Unlike the fact sheet, they didn’t state anything about why they did these reports or why they were important, they simply had a brief introduction and then explained how they calculated the recycling and waste for each material, with no overall conclusion. Because of how in depth they explained everything, I was pretty on board with the data. Even if there was something they could have done better to collect the data, I understood how each part was collected, and what was omitted from the data. This methodology report is a good way to set people up to believe the data sheet.

Screen Shot 2017-10-08 at 5.23.05 PM.pngThe graph above is, in my opinion, the most important graph from Advancing Sustainable Materials Management: 2014 Tables and Figures. It shows over time, how much of each material was recycled each year. From 2010- 2014, it looks as though there is hardly any change at all. However, the other years shown are five – ten years apart. So if we assumed the rate of recycling is linear, and calculated the change over the other years, it shows that the change in how much is recycled in thousands of tons every year is slow. For example, if we took 85,430 (total recycled and composted 2010) and subtracted 79,790 (2005), then divided by 5 (for the linear change each year) we would get 1,128, which is roughly the same change seen each year from 2010-2014, meaning that there isn’t the drastic decrease as this table could lead one to believe.

The societal implications of this are interesting. If we didn’t think deeply about that table, we may think that the rate of recycling is slowing to a halt. Looking at it in graphical form seems to make more sense, and they also had a graph of this data.

Screen Shot 2017-10-08 at 5.31.49 PM.pngThe gray is the amount of thousands of tons that were recycled, in total. There are many ups and downs, but overall, the amount that is being recycled did grow from 1960 to 2014. It would be difficult to use this data to predict what will happen in the future, as there are so many dips it’s simply unclear if it’s going to increase at a rapid rate or not.

The EPA did not make any implications in either the methodological report or the graphs. The methodology report stated where the data came from, and the graphs illustrated the data, but not why it was the way it was or making any predictions for future years. It was all incredibly factual and informational, with very little information besides the data and how it was collected. This leads the reader to make their own choice about if the data is accurate, what it means, and draw their own conclusions about the implications of it. The main point I guess I’d like to make is that we aren’t recycling less, even if at a glance it may look like we are. It would be hard to predict in future years, what trends there are and what may happen based on this data from the EPA.

 

What effect does having more children have on the children?

Many people believe, for religious reasons, as I was told the other day, “Righteous people should have more children.” Statistically, religious people have more children, according to the American Council on Science and Health. The idea is that the worthy people should have more kids to carry on the human race, or spread their religion, and have more “righteous” children. However, does having more children really result in having a better impact on society? Does having more kids make those children more successful?

When people have a lot of kids, they do it because they want to help the world by raising good people into it. Despite how overpopulation is arguably the biggest issue our world faces because it contributes to every other environmental factor, having more kids gives eash kid less attention. According to Swanson (2016), “[With] every additional kid born, the other siblings are more likely to suffer from lower cognitive abilities and more behavioral issues, and have worse outcomes later in life.” This statement is quite vague, because it’s hard to measure “worse outcomes in life”. However, the “lower cognitive abilities” with the “behavioral issues” are probably what cause the worse outcomes. In addition, “The research showed that these effects weren’t a temporary side effect of the birth of a younger sibling, but persisted through childhood and into later life — resulting in lower education, lower earnings, more criminal behavior…” The less children people have, the more likely they are to graduate high school and graduate college. They’re less likely to make as much money as others, and they do more criminal behavior. It makes sense, too, when you have income that is strained over more people, you’re more likely to be in poverty, which is what leads to less education, lower earnings, and criminal behavior anyways. These results are also similar in China, where especially large families in rural villages have the highest drop in education. According to another article from Business Insider on the same study, “On average, children in larger families have lowered parental investment and worse cognitive and non-cognitive outcomes.” This means that when people have more children, they spend less time with their children, and their parents are less “invested”. When parents are less invested in their kids, it’s no wonder why they receive less education and end up making less money. In general, kids born into poverty are likely to suffer. And when you have more kids, you’re more likely to be in poverty.

Neither of these articles seemed biased to me, because they were explaining the research study’s results. If they had been putting arguments of why this data wasn’t accurate, it would be different, but both articles simply talked about the findings from the study, however, the conclusions made are sound. Having less kids drastically impacts how much education they will receive and their cognitive abilities. Living in Utah, the state with the highest fertility rate, it really makes you think about if people who have many kids are really doing good for the planet. This blog post was inspired by people I have met in Utah, who want or have 10 or more kids, because I was concerned if they were really helping the world by having so many kids. I suppose this article doesn’t affect my day to day life, but it gives me more reason to not want to have many children. It’s not the best thing for the world, and it’s not the best things for the kids either.