Analyse of unemployment indicator

Analysis paper: Analyse of unemployment indicator

Question 1 (a)

These are the number of people who are unemployed in 1979 and 1981 respectively.

We obtained percentages in each of the comparison states, and average them before reporting as follow.

Putting into the required format fo % unemployment between 1979-1981

Analyse of unemployment indicator variations apply difference-in-difference models to compare the cities’ average percentage unemployment among blacks and whites between 1979 and 1981 resulting from the effect of immigration while controlling any other sources of variations. The simple linear regression analysis results show that the differences in the cities’ unemployed population percentage between 1979 and 1981 are of average insignificant (p=1.00<0.05) at a 95% confidence level.

Question 1 (b)

The table below shows the average log weekly earnings for the unemployed population by race in 1979 and 1981. The average of Miami is compared with other cities across the years

The difference-in-difference model is used to analyse and account for variations in log weekly income for the unemployed population between 1979 and 1981 that results from the effect of immigration while controlling any other sources of variations. The simple linear regression analysis results shown below suggest that the differences in the log weekly income for the unemployed population between 1979 and 1981 due to immigration are statistically insignificant at a 95% confidence level.

Question 1 (c)

The regression model to be estimated

Y= β0 + β1*[unemployment] + β2*[weekly wage] + β3*[ unemployment* weekly wage] +ε

Regression for whites

Regression for blacks

The standard error that applies, in this case, is 0.0006165. This shows high accuracy of the data compared to the original ground information. This is an indication that the data is viable to analysis and producing of substantial results

Question 1 (e)

The assumption to be made in DD for it to be an accurate analysis is that stability of dummy variable to be in constant form instead of having it as descriptive

Question 1 (e)

City-specific trends

include city-specific assumption check in regression approves some confidence since the data error from the ground result is relatively low since it is a .00 standard error.

Question 1 (f)

Use of non-event in validating DID is a quasi-experimental design that makes use of longitudinal data from treatment and control groups described in part d is essential in obtaining an appropriate counterfactual to estimate a causal effect. DID estimating the effect of a specific intervention or migration treatment by comparing the changes in outcomes over time between a population that is employed and unemployed and that one that is getting weekly pay in Miami compared to other

Question 2 (a)

1. Including country and fixed year effects in the regression model helps in controlling time-invariant observations between 1994 and 2005. This is because the data under consideration is a panel data. Over time, some variables may exhibit autoregressive properties, this leads to another bias in the casual inferences
2. Prediction based on a regression model on a panel data of this nature is characterised with recall bias. This is a type of bias emanate from time series trends and seasonality in the data which was collected from 1994 and 2005.
3. Question 2 (b)

deregulation index needs to have under bearing legislations in order to encourage more investment which in return will stimulate the economic growth. This would probably be through removing of restriction to trade and high migration restriction to bank investors to US

Question 2 (c)

The table below is based on the regression model;

The standard error applying for the first result is relatively close

Question 2 (d)

Question 2 (e)

Question 2 (f)

In the Estimated IV regressions under the three measure of credit growth to the housing gives am estimation of 0.2 percentage growth on house prices within the stipulated period of time. Having causal effect on credit as the main cause of this change since it is an effect contributor on change in the asset price.

Question 2 (h)

Prediction based performed on a regression model on a panel data of this nature is characterised with recall bias. This is a type of bias emanate from time series trends and seasonality in the data which was collected from 1994 and 2005. Performing branching deregulation by addition of elasticity of housing supply makes a great deregulation to the house hold price since it forms an addition advantage or incentive to potential house owners