Use the following code to load the GSS dataset into R: load(url(\"http://bit.ly/

Question

Use the following code to load the GSS dataset into R: load(url("http://bit.ly/dasi_gss_data")) The name of the dataset that you load is gss. For example, you can see a list of the variable names using the following commands: attach(gss) names(gss) *Note: I decided that the name of my variable will be conarmy and that I will focus on Confidence in Military which I think is my independent variable approved by the instructor *Update: My dependent variable is CONINC and my independent variable of interest is CONARMY. Try subsetting on 33 year or a range of years and make sure your CONARMY has values for that period. It's possible that question was not asked every year. If you see only NAs for for that variable when you run table (), change the years selected Use str(name of your variable) to find out whether your variable is categorical ("factor" type in R) or numerical ("int" type in R). Note that this dataset includes data from many years. In your analysis it you are required to first subset the data for a particular year (or years) and analyze only data pertaining to those years. This might be especially useful if you're using a variable from a survey question that was only asked in certain years. Data Analysis Project Components You will build a multiple regression model that predicts family income levels based on your variable of interest, after controlling for four demographic variables: age, sex, race, and educ. Your dependent variable (DV) is coninc (family income in constant dollars). The independent variable of interest you choose can be numerical or categorical. It must be approved by the instructor by April 26th. Use exploratory and inferential methods and techniques we learn in this class to answer your research question, and summarize your findings into a report. Your goal is to submit a completely reproducible project that conveys that you have mastered the MLR method that we have learned in class and that helps you answer your research question. Part 1: Introduction What is your research question? Example: After controlling for age, sex, race, and educ, does being self-employed have a significant effect on family income?

Explanation / Answer

ery little is known about the economics of household level activities in pastoral production systems (Etcher and Baker, 1982). Despite this, interventions are frequently proposed which call for increased cash expenditures. The implications of this for different groups of households (poor, rich) needs to be assessed. An intervention may require increased labour input. Will there be enough labour and if so will sufficient food be available to sustain the energy requirements of increased effort? An intervention may call for culling of nonproductive and old animals from herds and flocks. What is the implication of this for the security and viability of different groups of pastoral households (rich, poor)? What needs of pastoral households can be manipulated to provide incentives for culling? It is in such a context that information on household income and expenditures is required.

Testing and evaluating the impact of interventions as well as assessing the welfare of pastoralists require benchmark data on income and expenditure patterns of different groups of pastoral households .

Unlike agriculturalists, pastoral households depend more on market transactions to satisfy their subsistence needs. Our own work in Kenya shows that even during the wet season the Maasai obtain up to 50% of their calorie intake from purchased food. This figure can increase to 70% during the dry season. Household income and expenditure data are useful for determining the demand of pastoralists for purchased goods and social services and for assessing the teens of trade between pastoralists and the rest of the economy. A very good example is given by Swift (1979) for Somali pastoralists. He analysed the barter terms of trade between pastoral products sold by pastoralists and those they purchase by constructing a pastoral cost of living index. He concluded that by the early 1970s their terms of trade had deteriorated and had "led the pastoral economy into a precarious position".

Collection of households income and expenditure data

In addition to producing milk and meat for their own consumption, pastoral households engage in a variety of transactions involving livestock, livestock products, cash and other items (such as crops, handicrafts etc.), to fulfill different goals. Animals and their products are sold to provide cash. They are given or lent to kin and friends to strengthen social ties and ensure long-term security. They may be similarly received. Animals may be exchanged for social reasons or to increase the productive capacity of herds and flocks.

In order to determine the entire household budget, income and expenditure studies should be designed in such a way as to include not only cash income and expenditures but also these important transactions. Usually, the quantities of these transactions are known and their values can be determined by using prices the items would have attracted had they been sold.

Sampling the target population

In any society the most important factor that influences patterns of household income and expenditure is the wealth status of the household. It is self-evident that the consumption of poor and rich households is markedly different. It is therefore essential that study samples adequately represent the gradient of wealth observed in the pastoral group under study. This may not present a problem in situations where a whole village or encampment or target population is studied as done by the ILCA teams in Mali and Kaduna. Even then villages and camps may be formed on the basis of social classes. Care should be taken in selecting sample villages and camps so that results can be generalisable to a known population type. In situations where the coverage in area is more extensive one has to resort to sampling the population as done by the ILCA teams in Kenya and Ethiopia. In that case a stratification of the population into wealth categories is essential (Bee Grandin (1983) for a detailed discussion of wealth effects and a rapid method of wealth ranking).

Given such a stratification of the target population, the available resources for collecting data and the time-frame and nature of the study, standard sampling procedures can be employed to determine the size of the sample and choosing them (Cochran, 1963).

Types of data required

Inventory of resources

Once the sample households in the study have been determined, an initial inventory of the human and livestock population needs to be made. Here care should be taken so that animals owned by the household but which are away from the main herd or flock at the time of the inventory are included. Animals not owned by the household but borrowed from others should be identified and recorded as such. Similarly members of the household who are away at the time of the census should be included and temporary visitors excluded. An inventory of major household goods also gives a good indication of investment and consumption patterns.

This information is vital for two reasons. First, it quantifies the wealth status of the sample household. Second, it provides the basic population data to perform per capita computations without which meaningful comparative analysis cannot be made.

Household income and expenditure items

For designing the data collection formats background information is required on the nature of items that form the income and consumption baskets of the pastoral households to be studies as these vary from culture to culture. A comprehensive list of these items should be established from the researcher's personal knowledge or from informal surveys involving a few pastoralists and shopkeepers in the area or a combination of these. In addition to standardising the format for enumeration, it is also a good device to facilitate recall by respondents.

The income items include :

- livestock and livestock products (animals. milk, ghee, hides and skins, manure etc.)
- agroforestry products (crops, wood, charcoal, honey etc.)
- cottage industry products (handicraft, beer, medicinal herbs etc.)
- other forms of employment (trade)
- other cash inflows (remittance, borrowing)

Cash expenditure items can be grouped as:

- food
- health and hygiene
- clothing transport
- livestock
- livestock inputs durable household goods
- others (cash outflows such as loans given).

Frequency of data collection

Extracting information on household budgets, especially expenditures, is extremely difficult because one has to rely on the memory of the respondents to recall such data. Information on pastoral households' income is by far easier to get because most of it is derived from the sale of animals, which they remember very well. The feet that such sales happen very infrequently facilitates recall. On the other hand expenditures, especially on food items, occur so frequently in irregular amounts that recall becomes difficult.

In collecting household income and expenditure data, the shorter the time span the respondent is requested to recall the more accurate is the information obtained. Researchers have used different frequencies of collecting such data ranging from one-shot surveys asking questions to estimate income and expenditure for a specified period of time (e.g. per month or per week etc.), to continuous daily recording for a long period of time (a year or more). Within ILCA, the Kenya team has used a frequency of once a month, the Kaduna and Mali teams twice weekly. Of course the latter is more reliable but is, however, very expensive. Unless it is combined with the conduct of other research requiring daily observations, it may not be solely justified on the basis of the precision gained. Even after using this method in Mali, Swift (1983) writes:

'It is likely that enumerators were only partially sucessful in recording these details of household daily transactions. There was inevitably some resistance to such detailed questioning and at times clearly false information was given or important transactions were forgotten or concealed.'

Our own experience in Kenya using a once-a-month recall method proved that respondents could account only for about 70% of their cash income. In conjunction with a nutrition study, which required food intake data on a daily basis, household expenditures were monitored for a month on a daily basis on selected households. A comparison of the two methods showed that the monthly recall accounted for 73% of the total expenditures recorded on a daily basis.

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