代做ECON7030 Descriptive Research Part II代做Statistics统计

ECON7030 Descriptive Research Part II- Report

Task

In an empirical project the research question is answered by formulating a hypothesis and testing this hypothesis using appropriate data and relevant statistical techniques. In this assignment, you will focus on data & descriptive research. Accordingly, you will:

1. State your research hypothesis.

2. Describe the data relevant for testing your research hypothesis.

3. Clean and process the data. Construct the relevant variables. Provide a clear description of relevant variables.

4. Create a table of summary statistics (or a chart) following your OP/or report chapter.

5. Interpret the results reported in the relevant table (or chart).

Structure of the report: Descriptive research

Title page. Title of OP, your SID, Semester #, Year, word count.

SECTION 1: Research QUESTION AND A clear and concise DESCRIPTION OF THE DATA/SAMPLE

Step 1. Clearly state your research question and hypothesis.

Step 2. Describe the data/sample (following YOUR OP). At the least, you must state the following:

√ Data source (name of the survey) (e.g., IPUMS, CPS, HILDA)

√ Survey Location (e.g., Nigeria)

√ Data source (name of the survey) [e.g., DHS].

√ Sampling strategy (e.g., describe relevant sampling strategy/survey design – available in the data sites)

√ What information was collected in the survey? (e.g., DHS provides representative data on population, health, HIV, and nutrition at the household level/& individual level (for women and men aged 15-49 years old, and for children aged 0-14 years).

√ Year of study (e.g., “I … used cross section data for 2008 for Nigeria.”  Or I used panel data from 20 waves of HILDA – 2002-2020).

√ Which sample are you using? (I am using DHS Women’s sample).

√ What is your unit of analysis? (Individual? Household? Country?) [e.g., I obtained individual-level anthropometric and sociodemographic data for women of reproductive age:  15-49 years old].

√ Sampling size (e.g., The Nigerian component contains responses from 7,000–35,000 women in each year. I used a sample of 10,746 women)

√ Study design: cross section, panel, repeated cross section, time series (e.g., study design for my research is repeated cross section).

Note: Clearly indicate if your replication data is different from/or similar to OP data.

[Don’t worry if this section substantially overlaps with A3]

Section 2. DaTA Mining, Variable construction and description of key variables.

i. How did you clean and process the data to construct the relevant variables?

Describe how you cleaned the raw data to construct the relevant variables (here you dicuss how you dealt with missing values, outliers, no-response questions etc.).

ii. Clearly describe the relevant variables of your proposed empirical analysis.

Key variables. For example: equivalisedhousehold measures of income, consumption, wealth.

For journal papers, discuss how you constructed the key independent variable, dependent /control variables. For example:

the key outcome variable (dependent variable) of interest in

my proposed model is whether an individual is

overweight/or obese. “i divided body weight by height

squared to calculate each individual’s body mass index

(bmi) and transformed this into a dichotomous indicator of

whether an individual had a bmi above pre-established

overweight and obese thresholds set by the world health

organization (who 2015) based on risks to cardiovascular

health: 25 kg/m2 (overweight or obese) and 30 kg/m2

(obese)” (Barlow, 2021).

Key explanatory variable. For example: the key explanatory variable in my model is ‘expected years of schooling’ measured by number of years of schooling completed by an individual at the time of the survey

(based on DHS variable v133) (Barlow, 2021).

Key control variables. For example: “I control for individual level characteristics such as age, income, occupation;family background characteristics, and dummy variables for regions in Nigeria” (Barlow, 2021) For variable descriptions see the table of summary statistics reported in the next section.

SECTION 3. Report CHARTS/summary statistics of variables

You will replicate a chart (from the report) or the table of summary statistics from your OP.

• If you are directly using OP data (same sample/setting/time-period), then simply reproduce the table from OP.

If you are using a different sample /different setting/or time-period), then report the summary statistics using your sample.

You must:

√ Clearly label tables and figures, adding appropriate notes to make it clear what variables are included and how they are constructed.

√ Present tables and figures professionally. Use relevant Stata codes (e.g., esttab / outreg /putexcel commands and/or graph export commands for figures) to produce tables/figures. DONOT copy- paste Stata output directly into the body of the assignment.

√ Figures, tables, and graphs should be informative and self-explanatory.

SECTION 4. Discussion/Interpretation of results

Interpret the table or the figure you have reported above. The discussion must refer to the broader literature where relevant.

Here’s an example of a table of summary statistics following Barlow (2021).

“Table  1 summarizes  their demographic and anthropometric characteristics.  On  average, 33.6% of the sample was overweight or obese across all survey years. This number is slightly higher than the proportion reported in previous  analyses  of  DHS data,  which  likely  reflects  the  slightly  older  age  of  my  sample (Kampala and Stranges 2014; Neupane et al. 2015). ”

Here’s an example of a chart from a report (Productivity Commission, 2024).

Figure 2.5 – The effects of COVID 19 on households varied across the distribution.

Annual change in equivalised household disposable income by decile, 2018-19 to 2021 22

“Job  losses  at  the start  of  the pandemic meant that labour income for the bottom decile decreased significantly. Yet substantial government support payments led to large increases in the disposable incomes of lowincome households relative to middle and higherincome households, leading to an initial decline in overall income inequality (figure 2.5, 2018-19 to 2019-20). In particular, recipients of JobSeeker initially received an additional $550 per fortnight under the Coronavirus Supplement, almost doubling the previous size of the payment (AIHW 2021,pp. 84–85). ”