Economics of Poverty Week 4
Economics of Poverty Week 4 ECON 2456
Popular in Economics of Poverty
Popular in Economics
This 4 page Class Notes was uploaded by Aaron Notetaker on Friday September 23, 2016. The Class Notes belongs to ECON 2456 at University of Connecticut taught by D. Kennedy Jr in Fall 2016. Since its upload, it has received 24 views. For similar materials see Economics of Poverty in Economics at University of Connecticut.
Reviews for Economics of Poverty Week 4
Report this Material
What is Karma?
Karma is the currency of StudySoup.
You can buy or earn more Karma at anytime and redeem it for class notes, study guides, flashcards, and more!
Date Created: 09/23/16
Economics of Poverty Week 4 (Review of basic stats cont.) Frequency – number of times a value of data occurs Relative frequency – ratio (fraction or proportion) of number of times a value of data occurs. RF = f / n where RF = relative frequency f = frequency and n = sample size Constructing Histogram for frequency distribution – lists all classes and number of values that belong in that class (grouped data). Can more easily summarize large data sets. The graph (histogram itself) Consists of continuous boxes. Horizontal axis is labeled with what the data represents. Vertical axis is labeled with frequency or relative frequency. Rule of thumb to use a histogram with 100 or more values in a data set - Decide number of classes (bigger the data set, more classes) - Calculate class width - Starting point (lower limit) Frequency polygons – line graphs for frequency distribution. Create one extra class at each end of the x-axis (to begin and end the polygon at 0 frequency/touching the x-axis). [as the number of classes increase, the polygon eventually becomes a smooth curve]. Shapes of histograms - Symmetric bell shaped (1 center peak) - Symmetric bimodal (2 peaks, having 2 modes) - Skewed right - Skewed left - Uniform/rectangular Mean – average, median, mode – most frequent value The (sample) mean is significantly influenced by outliers and to a skewness of a distribution. Estimates the (unknown) population mean The location and value of the median are not the same thing. The median is not influenced by outliers or the distribution. A proper measure of the center (whether to look at mean, median or mode) should be chosen based on the study question and when looking at the available data The law of large numbers – if you take samples of larger and larger size from any pop, than the mean of the sample, is more likely to get closer and closer to the actual population mean. Standard deviation (SD) – how far data values are from their mean. Provides a measure of the overall variation in a data set. Always positive or zero. Small when data is concentrated, large when data has a large spread of values. If the sample is a good representative of the population, SD of the sample (s) is likely very close to the population SD (sigma). Affected by outliers and skewness (making SD larger) Deviation – ‘x – mean’ (distance to the mean) Variance – average of the squares of the deviations (steps for calculating SD and other formulas found in lecture slides, stats textbook or a simple google search) The pth percentile of the data is such a value that p percent of the observations fall at or below it. th Median is the 50 percentile th st 25 percentile – 1 quartile (Q1) 75 percentile – 3 quartile (Q3) Interquartile range (IQR) – Indicates the spread of the middle half of the data. The difference between Q1 and Q3. (50% of observations are located between Q1 and Q3) Random sampling – each member of a pop initially has an equal chance of being selected for the sample (Simple random sample) Sampling errors – could be caused by having too small of a sample size, as well as other things Non-sampling errors – defective counting device or other factors not directly related to the sample Sampling bias – when a sample is collected from a pop and some members of the pop are not as likely to be chosen as others. (could misrepresent the population being studied) Standard error – measures the sampling variability of a statistic – how much the stat varies from one sample to another (Standard Error of the Mean, Standard deviation of the sampling distribution of the mean) Central Limit Theorem – if you keep drawing larger and larger samples, and calculate their means, the sample means form their own normal distribution (sampling distribution) Empirical rule – if the data distribution is unimodal, bell-shaped and symmetric certain observations apply. QUANTIFYING POVERTY AND INEQUALITY Poverty – if the total income for a household (family and/or unrelated individual in the house) falls below the relevant poverty threshold, then the household (and every individual in it) is considered in poverty The Census Bureau uses a set of money income thresholds that vary by family size and composition to determine who is in poverty In 2015, 13.4% of Americans were in poverty, up from 12.75% in 2004 (more detailed breakdown of poverty demographics in lecture slides or from the Census Bureau) 2 approaches to identify who is poor. (Important criticism of both approaches is their concern with income and consumption) 1. Absolute method – draws a line based on certain factors that could tell between poor and not poor. Any line would be subjective and open to criticism. Measured in relation to amount of money necessary to meet basic needs. Not concerned with broader quality of life. 2. Relative approach – considers someone poor who is at the lower end of the distribution of income. Deciding what ‘lower end’ is open to criticism. In relation to economic status of other members of the society. People are poor if they fall below prevailing standards of living in a given society. Income poverty – when a family’s income fails to meet a Federally established threshold, which varies across countries. International standard of extreme poverty – less than 1$ a day Data suggests that poverty falls most heavily on minorities, single-parent families, and children. The US Poverty Threshold was first developed in 1963-64 by Mollie Orshansky. Developed 2 sets of poverty threshold - Derived from the Agriculture Department’s economy food plan - Derived from its less-stringent low-cost food plan To get from food plan costs to estimates of minimum necessary expenditures for all items, Engel’s Law is used. Used a survey that showed 1/3 of after-tax income of families is used on food. Developed poverty threshold based on this ratio. (another survey showed ¼ of after-tax income). She made the assumption that the family would be able to cut back its food and non-food expenditures by the same proportion. The poverty threshold for a family was set at 3x the cost of the economy food plan. (criticisms of this threshold proposed later.)
Are you sure you want to buy this material for
You're already Subscribed!
Looks like you've already subscribed to StudySoup, you won't need to purchase another subscription to get this material. To access this material simply click 'View Full Document'