Econometrics model using STATA for the economics of the environment
STATA is one of the renowned software packages used in econometrics. Most American universities and colleges use STATA as the statistical tool for elective and core courses like economics. Reputable companies across the globe value the knowledge of this important software tool. Most of them have listed the knowledge of STATA as an added advantage for candidates. If you are a student pursuing a course related to economics and statistics, you must first ace your STATA assignment before you can land your dream career. In this article, we have explained how you can use STATA to demonstrate the application of the travel cost method. We have worked with data and applied simple econometrics methods to solve easy willingness-to-pay questions. In this sample econometrics homework, we have taken the case of a research consultant hired by the state of North Carolina in the United States to evaluate a new policy meant to improve environmental quality at the Albermarle and Pimlico Sounds. The aim is to use STATA to crunch numbers and generate a report that gauges whether introducing the new policy to control fishing and agricultural practices polluting habitats in the state is economically viable.
The descriptive statistics show that the average income in this data set was US$ 32,542.48 with the minimum income reported being US$ 5000 and the highest income reported being US$ 85,000. After running a crude regression model to factor in all the 20 variables, at 5% significance level, the results show that the variables Worfull, the years of education, sex if the respondent was male, whether an individual was married, the number of household members, the reported knowledge of the sounds area are key estimators of the income of individuals. The results of the fitted regression model are shown in the table below.
Change is a major part of our lives, whether it be a change in technology, education, health care or social policies. Policies are designed to make decisions that would in turn have an influence on the actions within the scope of coverage. To enable the growth of an economy, and the change in the living standards positively within a nation, it is of great essence that there should be successful policy implementation at all levels of government. For example, in the United States, the state and local governments make key investment decisions on infrastructure and many other areas which in turn will determine the long-run capacity of the entire economy(Nunn et al., 2019).
Changes in policies on most occasions are advantageous to an organization, country, or entity. This study aims to look at the recreational benefits of a change in policy that is aimed at improving the environmental quality in the State of North Carolina specifically in Albemarle and Pimlico sounds. This is an estuarine system that is designed to preserve the integrity of the entire estuarine ecosystem, with a special emphasis on improving water quality in the region's rivers and sounds and restoring America’s Estuaries("Home | APNEP", 2022). A change in policy to improve this would lead to various recreational benefits for Americans. The main objective is to identify the estimates of the recreational benefits by looking at secondary data.
The data used in this study is secondary data of a survey collected by a team of experts on current and prospective recreation participation using a representative sample of the 800,000households living in the area. The data contains 20 variables and 765 sampled observations. The data dictionary of the variables within the data set is shown below.
Table 1 Data dictionary
|trips1||Number of trips taken to the Sounds in the previous season – current quality|
|trips2||Number of trips to be taken to the Sounds in the next season – current quality|
|trips2q||Number of trips to be taken to the Sounds in the next season – higher quality|
|income||Income in US$|
|worfull||=1 if respondents work fulltime|
|educ||Years of education|
|sex||=1 if respondent is male|
|married||=1 if respondent is married|
|house||Number of household members|
|county||ID of the county in Pimlico region|
|know||Reported knowledge of the Sounds area (1=low to 4=high)|
|concern||Reported concern for the Sounds area (1=low to 4=high)|
|support||Reported support for the restoration of the Sounds area (1=low to 4=high)|
|effect||Reported perceived success of the restoration of the Sounds area (1=low to
|pimlico||=1 if the interview was carried out in Pimlico|
|DP||Distance to Pimlico Sound in miles|
|DA||Distance to Albemarle Sound|
|DF||Distance to substitute site (Cape Fear)|
To meet the research objectives of this study, we would fit a multiple linear regression model to this data and obtain the best model with the variables of the study. Linear regressions would allow us to identify the relationship between the variables of the study while controlling for the effect of other variables thus it is key in determining the estimates of recreational benefits of the policy change (Olsen et al., 2020). This study considers income as the dependent variable and as the recreational benefits of the change in policy. This will be regressed against other variables to check for the variable's estimates of the best fit and identify the relationship between these variables and the dependent variable.
The descriptive statistics show that the average income in this data set was US$ 32,542.48 with the minimum income reported being US$ 5000 and the highest income reported being US$ 85,000. After running a crude regression model to factor in all the 20 variables, at a 5% significance level, the results show that the variables Worfull, the years of education, sex if the respondent was male, whether an individual was married, the number of household members, the reported knowledge of the sounds area are key estimators of the income of individuals. The results of the fitted regression model are shown in the table below.
Table 2 Regression model coefficient estimates
From table 2 above, we can see the relationship between the variables and the dependent variable income. All these variables affect positively the income of individuals. The corresponding effect on income if the respondents agree to work full time while all other factors are held constant would lead to an increase in the income of the individual by US$ 5,115.43. The same can be said of married individuals exhibiting the highest effect size on the income of individuals to be US$ 12,019.02 with a unit change. As the reported knowledge increases within the sound area, there is a reported change of US$ 2,035.37. At a 5% significance level, our model is adequate with 31.6% of the variation in the dependent variable being explained by the independent variables of the study.