Econ
Kolh
Draft1ofPaper_jeanpierrej2NSMComments_attempt_2024-04-17-18-22-11_draft_econ.docxJean-Pierre
The Relationship Between the Rate of Unemployment and Non-Institutional Population in California
There's a lot of work that needs to be done. See my comments, and I've provided some resources as well.
Abraham and Kearney 2018.pdf ECON 349 Lecture 4 Employment, Hours, and Wages.pptx
Moffitt and Employment to Population Ratio.pdf Who is out of the Labor Force.pdf
INTRODUCTION
Unemployment is a major problem affecting various countries and cities in the world. Many governments are currently trying to tackle the problem of unemployment. Employment can be defined as a situation where a section of the labour force in a certain area is of working age, they are able and available for work but unable to find work. This population is actively looking for jobs. Not the entire population of an area that is not working is defined as unemployed; it is only the population that is actively looking for work. A job can include any income-generating activity from full-time employment, part-time work, or self-employment. The rate of unemployment can be calculated directly as a percentage of the unemployed persons in the labour force available. The labour force available is the sum of the unemployed and employed persons within a particular population. This paper tries to examine if the general population of an area or the non-institutional population of an area is directly related to the rate of unemployment in the area. The study population in this case is California, USA. California is a city that has a population of 31,124,355 people as of 2023. The population of the city has been increasing quite steadily over the last three decades. The percentage of the entire non-institutional population that is in employment (the labour force) has been fluctuating over the years. Several factors might be leading to this fluctuation. Qualitative and quantitative methods are used in this research to determine the relationship between the non-institutional population and the rate of unemployment in the city. Comment by Moellman, Nicholas Scott: I think you mean unemployment here. Comment by Moellman, Nicholas Scott: I would use American English standard spelling (labor). Not wrong, just stylistic choice. Comment by Moellman, Nicholas Scott: I would save any definitions for later, and focus on what your paper is doing. Comment by Moellman, Nicholas Scott: CA is a state Comment by Moellman, Nicholas Scott: So, this relationship seems very direct. And, there is a lot of literature looking at changes in the LFPR over time. I think you need some more motivation here to let me know why this is something I should be reading about.
BACKGROUND
California is one of the cities that have significant disparities between the non-institutional population and the labour force. Labour force participation can also be called the labour force, the sum of the employed people and those who are actively looking for work. The labour force participation in California has been quite low over the last four decades. The COVID-19 pandemic saw the levels reach some very low levels of 60.7% compared to 63.1% in the year 2012. However, past pandemic labour force participation is still lower than pre-pandemic participation. In 2023, we can see a labour force participation of 62% compared to a high of 67% in the year 2000 and a high of 67.7% in the year 1989 (U.S. Bureau of Labor Statistics, 2024). Labour force participation is vital for the economic well-being of the people and the economy of the region. These reductions in the labour force compared to the high population are a combination of many factors such as economic recessions, changes in demography, aging population, and macroeconomic forces among others. Comment by Moellman, Nicholas Scott: I won’t comment every time, but go through and double check and make sure you are referencing CA as a state. Comment by Moellman, Nicholas Scott: You mean non-institutionalized Comment by Moellman, Nicholas Scott: No, these are different things. Comment by Moellman, Nicholas Scott: Why? Expand here.
One of the factors contributing to the lower labour force participation in California is the aging population. The population may seem high in a certain area but most of the people in the area are past working age and are retired (Ochsen, 2021). In the year 2000, the active labour force in employment was at 67% but today it is around 62%. Most of the actively employed people or people looking for jobs have since aged and are no longer in the labour force. Another contributing factor to the low labour force participation is the gender gap. The gender gap is mostly necessitated by motherhood (Lafortune, 2024). The gender norm is that the mother stays home and looks after the children when the father goes to work. Thus, the gender gap is largest for women who have children and a partner. In most cases in California where the father of the children is working and able to provide for the family, the mother does not find the need to work. Among working-age people in California, women are 13% less likely to work than men. This is due to responsibilities placed on women and non-equal opportunities for men and women. There are also significant gaps caused by race in California. Black men and Latina women have a lower likelihood of finding work in California. In general, foreign-born adults have lower participation than the native-born adults. A major contributor to these differences is educational differences between the various races. For eligible employees, college graduates have a workforce participation rate of 90% with minimal differences between the races. Eligible employees who lack a high school diploma have a lower participation rate of about 74% for Latinos and 48% for black people (Lafortune, 2024). Education attainment is a major factor affecting the labour force participation rate and closing the education attainment gap would improve participation. Comment by Moellman, Nicholas Scott: I’m going to attach some notes from my poverty class that discuss LFPR. You need to do a lot of work to motivate changes in LFPR. The data I showed you has labor force data for all states for a wide variety of years. Why are you only focusing on CA? Why would you not utilize all of the data?
METHODOLOGY
Data was obtained from the U.S. Bureau of Statistics, local area unemployment statistics. The data for California ranges from 1976 to 2023 (U.S. Bureau of Labour Statistics, 2024). The data includes the civilian non-institutional population and the civilian labour force. The civilian labour force includes a percentage of the civilian labour force to the total civilian non-institutional population, the number of employed people and the percentage of the total civilian non-institutional population, the number of unemployed people and the percentage to the total civilian non-institutional population, and lastly the rate of unemployment. A column for the number of people not participating in the workforce was inserted and the figures were computed. The relationship between the population change, the rate of employment, the unemployed people and the people not participating in the workforce was determined.
Jean-Pierre
More research work was done qualitatively to determine why the population has many people who are not active in the labour force and the effects. Having many people in the workforce who are not willing to work makes it difficult for the authorities to keep accurate employment data and even plan for employment opportunities. The causes and effects of people not participating in the labour force have been listed. Comment by Moellman, Nicholas Scott: You really need to think about what you are doing here. There is a direct and mechanical relationship between population and the LFPR over the years. There’s no need for you to go in and recalculate the unemployment rate or LFPR, these are things that can be readily found. Why are you asking and answering this question? I get no indication from what is presented here.
DATA
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California: Employment status of the civilian noninstitutional population, |
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1976 to 2023 annual averages |
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FIPS Code |
State and area |
Year |
Civilian non-institutional population |
Number of people not participating in the Workforce |
Civilian labour force |
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Total |
Percent of population |
Employment |
Unemployment |
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Total |
Percent of population |
Total |
Rate |
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06 |
California |
1976 |
15,823,750 |
5,929,514 |
9,894,236 |
62.5 |
8,985,601 |
56.8 |
908,635 |
9.2 |
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06 |
California |
1977 |
16,277,917 |
5,900,212 |
10,377,705 |
63.8 |
9,509,331 |
58.4 |
868,374 |
8.4 |
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06 |
California |
1978 |
16,761,667 |
5,851,090 |
10,910,577 |
65.1 |
10,131,743 |
60.4 |
778,834 |
7.1 |
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06 |
California |
1979 |
17,214,167 |
5,925,983 |
11,288,184 |
65.6 |
10,586,188 |
61.5 |
701,996 |
6.2 |
|
06 |
California |
1980 |
17,687,333 |
6,082,176 |
11,605,157 |
65.6 |
10,806,362 |
61.1 |
798,795 |
6.9 |
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06 |
California |
1981 |
18,068,500 |
6,250,085 |
11,818,415 |
65.4 |
10,939,299 |
60.5 |
879,116 |
7.4 |
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06 |
California |
1982 |
18,426,583 |
6,270,394 |
12,156,189 |
66.0 |
10,932,263 |
59.3 |
1,223,926 |
10.1 |
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06 |
California |
1983 |
18,725,000 |
6,422,714 |
12,302,286 |
65.7 |
11,094,344 |
59.2 |
1,207,942 |
9.8 |
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06 |
California |
1984 |
19,162,167 |
6,550,962 |
12,611,205 |
65.8 |
11,634,051 |
60.7 |
977,154 |
7.7 |
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06 |
California |
1985 |
19,644,083 |
6,675,108 |
12,968,975 |
66.0 |
12,035,026 |
61.3 |
933,949 |
7.2 |
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06 |
California |
1986 |
20,065,167 |
6,727,699 |
13,337,468 |
66.5 |
12,436,979 |
62.0 |
900,489 |
6.8 |
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06 |
California |
1987 |
20,525,167 |
6,784,578 |
13,740,589 |
66.9 |
12,939,298 |
63.0 |
801,291 |
5.8 |
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06 |
California |
1988 |
20,983,083 |
6,847,149 |
14,135,934 |
67.4 |
13,390,021 |
63.8 |
745,913 |
5.3 |
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06 |
California |
1989 |
21,459,000 |
6,921,914 |
14,537,086 |
67.7 |
13,797,192 |
64.3 |
739,894 |
5.1 |
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06 |
California |
1990 |
22,598,254 |
7,456,539 |
15,141,715 |
67.0 |
14,267,872 |
63.1 |
873,843 |
5.8 |
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06 |
California |
1991 |
22,780,860 |
7,650,379 |
15,130,481 |
66.4 |
13,949,676 |
61.2 |
1,180,805 |
7.8 |
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06 |
California |
1992 |
23,048,542 |
7,744,681 |
15,303,861 |
66.4 |
13,871,386 |
60.2 |
1,432,475 |
9.4 |
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06 |
California |
1993 |
23,149,223 |
7,886,954 |
15,262,269 |
65.9 |
13,809,100 |
59.7 |
1,453,169 |
9.5 |
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06 |
California |
1994 |
23,194,655 |
7,928,274 |
15,266,381 |
65.8 |
13,943,532 |
60.1 |
1,322,849 |
8.7 |
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06 |
California |
1995 |
23,269,337 |
7,996,755 |
15,272,582 |
65.6 |
14,068,313 |
60.5 |
1,204,269 |
7.9 |
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06 |
California |
1996 |
23,453,185 |
8,053,437 |
15,399,748 |
65.7 |
14,268,104 |
60.8 |
1,131,644 |
7.3 |
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06 |
California |
1997 |
23,829,617 |
8,023,354 |
15,806,263 |
66.3 |
14,795,185 |
62.1 |
1,011,078 |
6.4 |
Table 1: Employment status of the civilian noninstitutional population in California (U.S. Bureau of Labour Statistics, 2024)
Comment by Moellman, Nicholas Scott: Rather than these graphs, just pull from FRED. There’s no benefit here.
Graph 1: The change in population over the years
Jean-Pierre
Graph 2: The change in rate of employment over the years
RESULT
The rate of unemployment is calculated as a factor of the unemployed people in the population to the participating labour force (MasterClass, 2022). Comment by Moellman, Nicholas Scott: This is superfluous and not a result. This is just you describing how these statistics are calculated.
So, R=
Where R is the rate of unemployment
Participating labour force= TP-PNPLF
Where TP is the Total Population
PNPLF is the People not participating in the labour force.
So, R=
R (TP-PNPLF) =Unemployed people
TP= (Unemployed people) + PNPLF
This shows that while population of California and all other areas is inversely proportional to the rate of unemployment, it is directly proportional to the unemployed people and dependent on the people not participating in the labour force.
Looking at Graph 1 the population increase has been steadily increasing in California from 1976 to 2023. This is normal since population growth in many areas is always on a gradual rise. Looking at Graph 2, the rate of unemployment has been decreasing, although with some fluctuations, with the increase of population in the years. In 1976 when the population was at a lowest of 15,823,750, the rate of unemployment was quite high at 9.2%. In 2023 when the population was at a high of 31,124,355 the rate of unemployment was at a low of 4.8% (U.S. Bureau of Labour Statistics, 2024). The major fluctuation that is seen in the year 2020 is because of the COVID-19 pandemic where the rate of unemployment was quite high, but the population was still on a steady rise. The economic regression of 2009 to 2012 also affected the trend where many people lost their jobs, but the population of California was still at a steady rise.
Graph 2 shows that the increase in the total population is dependent on the rate of unemployment and the number of unemployed people. It should be a straight line, but it has some sharp edges due to a third factor which is the people who do not participate in the labour force; this is the regression coefficient that shows the correlation between the total population of an area to the rate of unemployment. The fluctuation of these figures makes it hard to correlate population with unemployment because it is a variable.
CONCLUSION
Unemployment for people in California is either by choice or by circumstance. The labour force includes employed people and others who are willing and able to work but unable to secure jobs. This shows a direct relation between the population of California and the unemployed people as well as the rate of unemployment (Correa, 2024). Assuming that all people are willing and able to work, and the unemployed people find jobs, the rate of unemployment decreases while keeping the population constant. When they cannot find work, the rate of unemployment increases as the population is kept constant. However, one factor affects the equation, the people who do not participate in the labour force.
The people who do not participate in the labour force are either elderly, incapacitated, given other responsibilities, or lack the qualifications to get a job. The elderly can be excused because they most likely did their part in the labour force. Some people lack opportunities to work due to discrimination based on their origin, their level of education, or other responsibilities such as motherhood that prevent them from going to work. This population is what the policymakers need to look at. Mothers can have the chance to work if they can leave their children under care givers; therefore, contributing to the economy, their personal growth, and regional growth. The people in California should also get equal opportunities of learning an equal opportunity to get jobs. When the population increases and the people not participating in the labour force increase, it means that the rate of unemployment is rising, and it should be a reason for worry to the authorities. Unemployment comes with low income, low quality of life, and insecurity (Lee, 2024). It is better to have more people in employment supporting fewer unemployed people rather than have many people not taking part in the labour force and struggling to make ends meet.
Work Cited
Correa, C. (2024, March 7th). Local unemployment uptick continues as California ranks last in nation. Turlock Journal, pp. 1-2.
Julien Lafortune, S. B. (2024). Labor Force Participation in California. Sacramento: Public Policy Institute of California.
Lee, K. (2024, March 1st). Unemployment Casts a Shadow Over California’s Economy. The New York Times, pp. 1-2.
MasterClass. (2022, October 12th). Learn About Unemployment: Definition, Examples, and Causes of Unemployment. Retrieved from MasterClass: Community and Government: https://www.masterclass.com/articles/learn-about-unemployment
Ochsen, C. (2021, February 11th). Age cohort effects on unemployment in the USA: Evidence from the regional level. Papers in Regional Science, pp. 1-2.
Pratap, P. (2021). Public Health Impacts of Underemployment and Unemployment in the United States: Exploring Perceptions, Gaps and Opportunities. Internatinal Journal of Environmental Research and Public Health, 10021-10036.
U.S. Bureau of Labor Statistics. (2024, March 13th). Local Area Unemployment Statistics. Retrieved from U.S. Bureau of Labor Statistics: https://www.bls.gov/lau/rdscnp16.htm
Civilian non-institutional population 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 15823750 16277917 16761667 17214167 17687333 18068500 18426583 18725000 19162167 19644083 20065167 20525167 20983083 21459000 22598254 22780860 23048542 23149223 23194655 23269337 23453185 23829617 24271632 24710730 25116226 25526368 25882406 26210571 26503320 26761750 27012671 27274555 27606374 27946731 28532316 28952845 29295636 29626794 29962936 30283673 30568839 30804622 30963179 31056874 31126813 31018365 31040583 31124355
Unemployment Rate during the years
1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 9.1999999999999993 8.4 7.1 6.2 6.9 7.4 10.1 9.8000000000000007 7.7 7.2 6.8 5.8 5.3 5.0999999999999996 5.8 7.8 9.4 9.5 8.6999999999999993 7.9 7.3 6.4 5.9 5.3 4.9000000000000004 5.5 6.8 6.9 6.2 5.4 4.9000000000000004 5.3 7.3 11.5 12.5 11.9 10.5 9 7.6 6.3 5.5 4.8 4.2 4.0999999999999996 10.1 7.3 4.3 4.8
