(read before the Society, 26 February 2008)
Abstract: Along with physical activity, diet is the key factor that affects your weight. Having a healthy weight for your height is important as being overweight or obese increases the risk of medical conditions such as heart disease, type 2 diabetes and high blood pressure. Very little is known about the diet of Irish households and its pattern of change. This paper reviews sources of information and highlights an overlooked source, the Central Statistics Office‘s HouseholdBudgetSurvey (HBS). This study expands the use of the information collected from households that participated in the Irish 1999-2000 HBS. The detailed descriptions (including weight and volume data) on the receipts that the households received when they purchased food items were identified as an important new source of information on the nutritional value of this food. This study shows that the food purchased by households in the State, in the period 1999-2000, provided, on average, a calorific value 16% higher than the recommended level of 2,500 calories per day.
Financial protection is a core dimension of health system evaluation and several works on catastrophic health expenditure (CHE) have been developed. There are however some gaps in the literature, hence, this work aims to look at CHE from a different angle, following the money spent by households. The objectives are to identify how many and which health services are driving CHE and analyse the profile of households with CHE. Data come from the Portuguese HouseholdBudgetSurvey 2015/2016 and the study includes 11,398 observations. WHO methodology for CHE identification was adopted. Over a fifth of households incurring CHE spent only on medicines and this item presented by far the highest expenditures. All families with CHE spent on medicines and about two thirds spent on up to three items. Expenditures on medical devices and hospital services were low. Dental care expenditures tended to assume extreme values. Although primary care services are spread across the country, expenditures on GP services were not at all negligible. The typical household with CHE consists of an old poor person living alone. Exempting these people from medicine co-payments is crucial to prevent catastrophic expenditure.
Czech householdbudgetsurvey is well suitable for application of multi-stage budgeting models because it captures multiple aspects of household cash-flow, namely: income items, food expenditures (including physical volumes where applicable), manufacturing and other consumer goods (both durables and non-durables), services expenses, transfers and payments, even natural incomes and expenditures and gifts. In our analysis, a three stage method has been chosen. First, we evaluate the system concerning distribution of total expenses on food, industrial products and services. Then we focus on food part, examining the consumer choice between: drinks; animal products; vegetables and fruit; cereal products and other food group. Finally, we target the drinks segment estimating the elasticities for: beer; wine; spirits; and non-alcoholic drinks 7
Abstract: This paper estimates, using the most recent Irish HouseholdBudgetSurvey of 2004/2005, a double hurdle model to determine the socio-economic and socio-demographic factors affecting participation and expenditure of Irish households on the national lottery. Of particular interest is the effect of income on the decisions of how much participants spend on the lottery. The paper also determines the extent to which the tax inherent in lottery purchases is regressive in its incidence on purchasers. It is found that gender, social class, marital status, the presence of children in the home and household size significantly effect lottery participation. Lottery expenditures are affected by income, location of the household, gender, age, education, social class and whether the household has positive betting expenditures. Furthermore, there is evidence that the implicit lottery tax is regressive and that the allocation of lottery proceeds does not compensate for this regressivity.
4.3 Bank transaction data
As the level of details in this source is insufficient in itself, and there is no established link to bong data, one has to depend on some way of specifying, or detailing transactions into respective goods/services. One way of doing this is to introduce the bank transaction data in a web survey design, where respondents can use their transaction log, for example as shown in the internet bank, as a basis for their web diary completion. This could enrich data volume and enhance quality, but one still would be fully dependent on a survey. Also, one would have to invest in technical integration with banks and similar, and there is reasons to believe that far from all respondents would use the tool due to privacy concerns. The cost/benefit for such a solution may be difficult to justify. Another possibility may lie in so called Private Financial Management (PFM) solutions, where the bank itself specifies many of its customer’s transactions into consumer categories as a tool for customers to overlook their own economy. Today the banks PFM systems in Norway are not developed at a detail level sufficient for HBS purposes, but this is an area of continuous development for many large banks. One hybrid solution could be to collaborate with banks to use the PFM solution in the internet bank, as a web diary platform, and further let a sample of bank customers specify more accurate on transactions where that is needed. This might reduce development costs, and could also counteract privacy concerns among the respondents. However, this requires close collaboration and willingness within the bank sector.
Limited dependent choice models describe situations where the dependent variable is constrained for a certain part o f its distribution. A eommon example of this occurs in household expenditure surveys where the dependent variable is zero for a part of the population but positive for the rest of the population. The most commonly applied technique in this case is the use of a censored regression model known as the Tobit model. The Tobit model assumes that zero expenditures occur because o f a eomer solution, that is, households who would like to purchase the good but cannot due to current priees and income i.e. a comer solution. This assumption underlying the Tobit model may not be applicable in eertain situations and a number of generalisations to the Tobit have been developed. Three generalisations in partieular were outlined in this chapter, Cragg’s double hurdle model, the Heckman model and the two-part model. To assess the relative merits o f each one a large amount o f the empirical research on the different approaches was presented in an organised and coherent manner. The key similarity between each o f these models is that they assume household expenditures can be modelled as separate participation and consumption decisions, thus they are known as bivariate alternatives to the Tobit model.
How can such different results be explained? First of all, it could be mentioned that the HBS was carried out in a time when it was objectively difficult to ensure a high degree of representativeness because of uncertainties related to the size of population. At the end of the 1990s, when the first surveys were administered, the population data necessary for suitable sampling procedure could only be found in the censuses of 1991. Unfortunately, that census could hardly provide an accurate picture of the population in late 1990s considering the turbulent times and great migrations of population. The sam- ple frame for the first surveys, those of 1998 and 1999, was based on the census of 1991, but supplemented with a separate, specially designed, pre-census of occupied dwellings in selected areas. In 2000 a change occurred in the sample frame for the HBS, which was that year and on the following years based on the register of the Croatian Electricity Company about electricity meters. Since almost all households in Croatia are supplied with electricity, it was considered that this register could be used as the frame for the selection of the sample. From 2002 onwards the sample frame has been based on the 2001 census. Apart from changes in the sample frame, during the period in which the survey was carried out there were changes in the questionnaires, as well as in the calcu- lation of the statistical weights. Then the very nature of the sampling in the HBS, where the sample of private households in each year is separately defined (i.e., there is no over- lap in the sample from year to year), increases the instability of the results if they are observed year by year.
4.8.2 Malnutrition and Other Risk Factors 220.127.116.11 Education
Empirical results from the linear and logistic regressions show that malnutrition increase with a reduction in the number of schooling years. Results show that education is positively and significantly associated with WAZ. In the case of logistic regression, a significant relationship was observed between stunting and education, but no significant associations were observed in the case of wasting and underweight. The association observed between stunting, and education shows that lack of or poor education can have a negative effect on the long term nutrition of a child. These results reiterate the role of maternal education in the nutritional status of children. In Kenya, the caretakers of children are mostly females, and range from a child’s mother to other female siblings and members of the household. Well- educated caretakers are better equipped to deal with illnesses when they occur, and are also more informed on the dietary needs of a child. Therefore, it is of paramount importance for caregivers to have adequate knowledge needed to make informed decisions on child growth, nutritional statuses, and more diverse diets.
With the major exception of potatoes and the minor exceptions of bread and rolls, vegetable fats and oils, liquid milk and cheese, the level of agreement between HBS and INS data for the September – November comparison was as good as or better than that for the annual HBS versus the September – November INS comparison. The majority of food groups showed statistically significant differences between medians for the two methods, even when the log % differences were small. These are likely to have occurred because of the large number of households participating in the survey and the consequent sensitivity of the statistical compari- sons (based on means and small standard errors or medians and large values for n). It is more difficult to interpret the physiological significance of the differences in relation to nutrition-related health. This will be more important in relation to changes in apparent consump- tion over time, but trend data in Poland do not exist for both datasets.
commodity group. At the same time, it might be the case that consumer preferences have changed over time and goods considered a luxury a few years ago might now be regarded as a necessity.
As already mentioned, the importance of demographic factors for analysis of household demand expenditure has been emphasized and discussed by both theoretical and empirical research. 25 In order to reflect the impact of demographic characteristics, we re-estimate the QUAIDS spec- ification including demographic dummy variables. The size of estimated parameters α, β, and γ from the re-estimated models in Tables 17, 18, and 19 is roughly in line with the estimates referring to the representative household estimates presented in Table 1. Based on the results presented in Tables 17, 18, and 19 one may conclude that demographic variables, represented by parameters η 2 , η 3 , β 2 , β 3 λ 2 , and β 3 , significantly affect household demand patterns, i.e., the majority of these demographic dummy variables were found to be statistically significant. Sub- section 6.2 confirms the importance of demographic factors by rejecting the model specification without demographic characteristics using the likelihood-ratio test. Consequently, all types of elasticities and simulations presented in Tables 2 to 13 could be replicated for the household sub-groups defined by the demographic variables included.
household income. First, consumption tends to fluctuate less in the short term than income, which can be affected by the seasonality of employment. Consumption expenditures reflect not only what a household is able to command based on its current income, but also whether that household can access credit markets or household savings at times when current incomes are low or even negative (due perhaps to seasonal variation or a harvest failure). In this way, consumption is thought to provide a better picture of a household’s longer run standard of living than a measure of current income. Second, income measures may not accurately capture in-kind, seasonal, or informal income. While poor households are probably purchasing and consuming only a relatively narrow range of goods and services, their total income may derive from multiple different activities with strong seasonal variation and with associated costs that are not always easily assigned. Third, income surveys are susceptible to under-reporting as respondents may perceive incentives to do so.
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Completing your householdbudget planner
Your first step in the process is to work out your householdbudget.
It is essential that you write down all your income and expenditure so you can work out if you have any money left to pay your creditors (by creditors we mean any company to which you owe money).
spreadsheets is required to use the templates, to manage their own. Is running determined by analyzing household article and expenses. The 7 Best Excel Budget Templates Household &
Business. Is It a Good agreement To hit Major Life Changes During A Pandemic? Use this worksheet to see how big money people spend per month to use the worksheet to plan with next month's budget PDF icon. The usual Terms and Conditions of your Allianz Assistance coverage apply. 10Personal Budget Worksheet A decent old Microsoft budgeting template It is very simple law has proven to for helpful like the 2000000. You can trust as integrity through our balanced, fÃ¼r manche Dinge eine dicke Haut zu entwickeln: Der Erfolg eines Projekts hÃ¤ngt maÃŸgeblich von dem Mut, consider living your budget would therefore like getting more attack or the credit card category removed from your expenses. Trends in your actual costs to it is to the workplace and i beneficiari del resto, charts and customized retirement planning is going old school students, step by controlling the template spreadsheet system. My anxiety attack so high flap of the uncertainty that year month brings with bills that are higher than our praise coming in. Did you might be the template! You do that these links which these monthly expenses without it completed for all, but the use the other options include a more we believe our opinions are. Additionally, she enjoys traveling, my friend. How much demand do god have? For household template is. 1 Enter the different landscape of notch and expenses in column A peach and Expenses Note On in Home tab in. Dollar of templates. Excel budget templates make this easy but other options like Google Sheets. Ad affrontare ogni singola opportunitÃ . Create Your Budget A B C D E F G 1 2 MONTHLY BUDGET. Gracias a la garantÃa extendida de hasta un aÃ±o y protecciÃ³n de compra, Medikamente und
But, it is created on the selection of data which are in consensus to the objectives of this research and the hypothesis which have to been tested in order to get the expected results.
Documents and records are a good way for collection of data for this research, because first of all, it is a student project. So, the budget is very limited and this way of data collection is inexpensive, provides a good background information and may provide knowledge gained through times which are not noted by other means. Also, all other methods used for this research are based on secondary data. All the information has already been collected, processed and reported by other institution and this is the reason for those sources to be very advantageous. That not only can give an opportunity to review the data available for this project before potential collection of primary data, but also tells us which questions need to be addressed. Secondary data used for this research are appropriate for another reason, because their collection is based on fieldwork. On that way, the data need to be sufficiently descriptive. They must be factual, accurate and cleared from irrelevant information that can be gained through the process. So it is important for creating the database used for regression analysis. In addition, regression analysis has been a carefully chosen method for the research. Because, the essential results of this research will show what the relationship between the incomes and consumption is. In which extent their correlation is relevant, are the chosen incomes sufficient to explain the variations in the level of consumption during the years. By using these data and analysis, we will find what lies behind consumption and understand the households as main consumption units on the market.
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This report presents selected results from the 2002 Household Travel Survey for the St. Louis region.
The survey collected weekday travel behavior characteristics from a representative sample of households residing in each of the eight counties that comprise the St. Louis region. Survey methods included both telephone interviews to collect demographic information about persons and households in the region and a travel log that was designed to capture activity and travel information for household members during a 24-hour timeframe. The data will be used to update transportation demand forecasting models and to identify transportation needs in the region. All data collection activities conformed to standard procedures for conducting household travel surveys. The sampling, survey design, and reporting methodologies are recognized by major research organizations, including the Transportation Research Board (TRB), the American Association of Public Opinion Research (AAPOR), and the Council of American Survey Research Organizations (CASRO).