This is a take-home final exam for AEB 3550 for Spring 2020. It is an individual assignment worth 100 points.
statistics
Description
An Analysis of H2A Farm Labor in the US and Florida (100 points)
This is a
take-home final exam for AEB 3550 for Spring 2020. It is an individual
assignment worth 100 points. For full credit, you must submit a typed word
document or pdf on e-learning no later than the due date/time. No points will
be awarded for tables and graphs not present in the final document. If you have
any questions about this exam, you must ask Dr. Sharp and NOT classmates. No
work may be shared among students and any honor code violations will be
submitted to the Dean of Students which could result in a delay in your program
of study.
Summary: This assignment is cumulative and represents the
application of a broad spectrum of descriptive and inferential statistics
learned throughout the semester to a H2A Farm Labor dataset. After successfully
completing this final exam, students should be able to:
1) Identify
scales of measure of data and coherently organize economic data using Microsoft
excel.
2) Write a
professional report in a word document based on statistical analysis.
3) Create and
interpret pivot tables, relative frequency charts and 5 number summaries for
qualitative and quantitative data using excel.
4) Calculate
confidence intervals, test statistics and probabilities and interpret probabilistic
outcomes for random variables. Conduct hypothesis tests under various
distributional assumptions.
5) Generate
charts and graphs in order to interpret the relationship between variables of
interest for an economic/agricultural phenomenon.
6) Use excel
to develop simple and multiple regression models and draw conclusions related
to agricultural labor markets.
Step 1: Familiarize yourself with the data
Agricultural work in the US is often seasonal in nature and
as such, the labor requirements can vary based on time of the year. At the same
time, the work done by farm laborers often benefits from some level of
experience which may be challenging for most Americans to fulfil given the
nature of the work. As such, we often turn to international workers who are
both skilled in the work required and available for short bursts (i.e.
seasonal). The H2A program is a way to facilitate the connection between US
farmers and foreign workers for seasonal work within many agricultural
production settings (see https://www.farmers.gov/manage/h2a). The
dataset provided for this project provides information on H2A requests, pay,
hours, work type and sector, as well as other interesting information. In order
to make this project a bit more manageable, I have provided to you an excel
file with the following tabs:
|
OriginalData |
This data
was downloaded by Dr. Sharp earlier this month and while some of the data was
eliminated as it did not constitute H2A data or there were data entry errors,
this tab is the basis from which all other smaller datasets are generated
(using pivot tables) |
|
FloridaAggregate |
From the
original data, I have created a smaller subset of data that only includes
Florida H2A applications. |
|
USAgSector |
This data is
broken down by sector and sub-sector in agriculture and represents number of
workers, average hours, average months of experience and average pay rate by
agricultural sector. |
|
States |
This data is
broken down by state and again represents workers requested, workers
certified, average hourly expectation, average pay, and average experience
requirement by state. |
|
PayRate |
This pivot
table breaks down worker requirements and pay by pay cycle. |
|
Employers |
This tab
breaks down average pay (hourly only) based on the type of employer who
requested the H2A worker. The two types of requestors were labor contractors
and individual employers. |
Step 2: Write a professional report analyzing H2A Farm Labor
in the US and Florida
I.
(1 points) Professional title page with name, class,
date and topic
II.
(4 points) Summary of the dataset, types of statistics
provided in the report and important lessons learned from the H2A labor
dataset.
III.
(10 points) Discuss whether each of the following
variables you work with in your data set is categorical or quantitative and the
scale of measure of each:
a. Sector/Subsector
b. Workers
required/workers certified
c. Average
Hours
d. Average
Months of Experience
e. Average Pay
Rate
f. State
g. Pay rate
h. Employers
(H2A contractor or individual employer)
IV.
(5 points) Florida Aggregate—Geographical variation
and status
|
|
Create a joint probability table using
tools in excel. In your FloridaAggregate tab, select all of your data (ctrl +
a) àInsert àPivot table. Your
row variable should be EMPLOYER_CITY, your columns CASE_STATUS and your sum
values NBR_WORKERS_REQUESTED. You should show values as “% of Grand Total.”
Are there
any cities (this is a very loose use of the word “cities”) that seem to have
more applications than others based on the marginal probabilities? What is
the most common determination? What constitutes the highest joint
probability. Is that surprising given the marginal probabilities? |
