What does Variation in Survey Design Reveal about the Nature of Measurement Errors in Household Consumption?

We randomly assigned eight different consumption surveys to obtain evidence on the nature of measurement errors in estimates of household consumption. Regressions using data from more error‐prone designs are compared with results from a ‘gold standard’ survey. Measurement errors appear to have a mean‐reverting negative correlation with true consumption, especially for food and especially for rural households.     

The Low‐Pay No‐Pay Cycle: Are There Systematic Differences across Demographic Groups?

We investigate transitions between unemployment, low‐paid employment and higher‐paid employment using dynamic panel data methods applied to household panel data. We find state dependence in both unemployment and low‐paid employment and evidence of a low‐pay no‐pay cycle. However, we also find significant differences in effects across population subgroups. Typically, the young and better‐educated face lower penalties from unemployment and low‐paid employment. Further, low‐paid employment is preferable to unemployment for women regardless of their demographic characteristics, but for men who have only completed secondary schooling, low‐paid employment actually decreases the chances of entering higher‐paid employment by more than does unemployment.     

Identification and estimation of a class of household production models

We consider a class of household production models characterized by a dichotomy property. In these models the amount of time spent on household production does not depend on the household utility function, conditional on household members having a paid job. We analyse the (non‐parametric) identifiability of the production function and the so‐called jointness function (a function describing which part of household production time is counted as pure leisure). It is shown that the models are identified in the two‐adult case, but not in the single‐adult case. We present an empirical application to Swedish time‐allocation data. The estimates satisfy regularity conditions that were violated in previous studies and pass various specification tests. For this data set we find that male and female home production time are q‐substitutes. Copyright © 2003 John Wiley & Sons, Ltd.     

Dual Credit Markets and Household Usage to Finance: Evidence from a Representative Chinese Household Survey

Using a new and representative data set of Chinese household finance, we document household usage and costs of finance, along with their correlates. As in many developing countries, informal credit is a crucial element of household finance, and interest‐free informal loans based on reciprocal personal relationships are highly prevalent in our sample. Not surprisingly, wealth tends to be associated with greater usage of both formal and informal finance. Political connections, extensive social networks and certain household demographic characteristics (such as size) are all positively associated with formal or informal credit usage (or both). Overall, our findings show signs that a dual credit market is emerging in China, with the poor, politically unconnected, and those with larger family sizes still heavily reliant on informal finance, most of which are interest‐free, while younger, wealthier households with better political connections and financial knowledge are increasingly using formal finance.     

Evidence on the Demand for Mortgage Debt by Owner-Occupants

This paper explores the relationship between household mortgage debt burdens and housing consumption, periodic income, nonhousing wealth, the income tax position of the household, expected mobility, and other micro-level characteristics that proxy for household risk preferences and life cycle effects. We use 1985 and 1989 American Housing Survey data to estimate mortgage debt level equations simultaneous with house value equations, controlling for the contemporaneous nature of these two choices. We find that larger debt levels are positively associated with greater value residences and with the level of household income. Numerous household level demographic characteristics are also systematically related to mortgage demand. Of particular interest are our findings that the use of mortgage debt is affected significantly by the rate of tax savings on mortgage interest deductions and by the expected mobility of the household.     

Direct Estimation of Equivalence Scales and More Evidence on Independence of Base

We explore a direct approach to estimating household equivalence scales from income satisfaction data. Our method differs from previous approaches to using satisfaction data for this purpose in that it can be used to directly fit and evaluate closed‐form and non‐parametric equivalence scales of any desired form. Its flexibility makes it easy to consider specific aspects such as income dependence or more specific information on household composition (such as whether household members live in a partner relationship). We estimate and evaluate a number of scales used in the literature. If the equivalence scale is assumed to be independent of income and to depend only on household size, we do not reject the validity of the widely used square‐root scale at conventional significance levels. We also test GESE and GAESE restrictions (Donaldson and Pendakur, 2003, 2006) and investigate in detail to what extent household economies of scale depend on income. Our results suggest that the income dependence differs fundamentally across household types (rising economies of scale for ‘family’ households, falling economies of scale for multi‐adult households without children and no income dependence for other households).     

Alternative Approaches to Multilevel Modelling of Survey Non‐Contact and Refusal

We review three alternative approaches to modelling survey non‐contact and refusal: multinomial, sequential, and sample selection (bivariate probit) models. We then propose a multilevel extension of the sample selection model to allow for both interviewer effects and dependency between non‐contact and refusal rates at the household and interviewer level. All methods are applied and compared in an analysis of household non‐response in the United Kingdom, using a data set with unusually rich information on both respondents and non‐respondents from six major surveys. After controlling for household characteristics, there is little evidence of residual correlation between the unobserved characteristics affecting non‐contact and refusal propensities at either the household or the interviewer level. We also find that the estimated coefficients of the multinomial and sequential models are surprisingly similar, which further investigation via a simulation study suggests is due to non‐contact and refusal having largely different predictors.     

Toward a New Model for Probabilistic Household Forecasts

Household projections are key components of analyses of several issues of social concern, including the welfare of the elderly, housing, and environmentally significant consumption patterns. Researchers or policy makers that use such projections need appropriate representations of uncertainty in order to inform their analyses. However, the weaknesses of the traditional approach of providing alternative variants to single "best guess" projection are magnified in household projections, which have many output variables of interest, and many input variables beyond fertility, mortality, and migration. We review current methods of household projections and the potential for using them to produce probabilistic projections, which would address many of these weaknesses. We then propose a new framework for a household projection method of intermediate complexity that we believe is a good candidate for providing a basis for further development of probabilistic household projections. An extension of the traditional headship rate approach, this method is based on modelling changes in headship rates decomposed by household size as a function of variables describing demographic events such as parity specific fertility, union formation and dissolution, and leaving home. It has moderate data requirements, manageable complexity, allows for direct specification of demographic events, and produces output that includes the most important household characteristics for many applications. An illustration of how such a model might be constructed, using data on the U.S. and China over the past several decades, demonstrates the viability of the approach.     

On the Use of Auxiliary and Paradata for Dealing With Non‐sampling Errors in Household Surveys

In this paper, we consider the use of auxiliary and paradata for dealing with non‐response and measurement errors in household surveys. Three over‐arching purposes are distinguished: response enhancement, statistical adjustment, and bias exploration. Attention is given to the varying focus at the different phases of statistical production from collection, processing to analysis, and how to select and utilize the useful auxiliary and paradata. Administrative register data provide the richest source of relevant auxiliary information, in addition to data collected in previous surveys and censuses. Due to their importance in effective dealings with non‐sampling errors, one should make every effort to increase their availability in the statistical system and, at the same time, to develop efficient statistical methods that capitalize on the combined data sources.     

Mismeasured Household Size and Its Implications for the Identification of Economies of Scale: Correction

We correct an error from Halliday (2010) in which we explored the degree to which household size is measured with errors.     

Durable Purchases over the Later Life Cycle

We investigate life‐cycle patterns of demand for services from household durables using UK panel data. We take careful account of prices, demographics, labour supply and health. Demand for consumer electronics rises with age, while the demand for household appliances is flat. These findings contrast with the well documented decline in non‐durable consumption at older ages, and suggest that studies that estimate the overall discount rate from nondurable consumption may underestimate consumer patience and the savings required to fund retirement. We also find important non‐separabilities between the demand for durables, labour supply and health status.     

WEALTH EFFECTS AND MACROECONOMIC DYNAMICS

The effect of wealth on consumption is an issue of long‐standing interest to economists. Conventional wisdom suggests that fluctuations in household wealth have driven major swings in economic activity both in the United States and abroad. This paper considers the so‐called consumption wealth effects. There is an extensive existing literature on wealth effects that has yielded some insights. For example, research has documented the relationship between aggregate household wealth and aggregate consumption over time, and a large number of household‐level studies suggest that wealth effects are larger for households facing credit constraints. However, there are also many unresolved issues regarding the influence of household wealth on consumption. We review the most important of these issues and argue that there is a need for much more research in these areas as well as better data sources for conducting such analysis.     

Determinants of price mark‐up tolerance for green electricity – lessons for environmental marketing strategies from a study of residential electricity customers in Germany

This paper develops hypotheses on the effects of various attitudinal and perceptual variables as well as socio‐demographic characteristics of residential electricity customers on an individual's willingness to pay a mark‐up for electricity generated from renewable energy sources compared with the price due for electricity from conventional sources. The hypotheses are tested with data from a standardized telephone survey of 238 household electricity consumers in Germany. 53.4% of the participants are willing to pay a mark‐up for green electricity. 26.1% report a price tolerance equal to a 5–10% increase in their current electricity bill. Binary logistic and ordinal regression analyses indicate that price tolerance for green electricity is particularly influenced by attitudes (1) towards environmental issues and (2) towards one's current power supplier, (3) perceptions of the evaluation of green energy by an individual's social reference groups, (4) household size and (5) current electricity bill level. The findings are used to derive suggestions for energy related informational activities of public institutions, green marketing strategies of energy companies and future consumer research regarding demand for pro‐environmental goods. Copyright © 2009 John Wiley & Sons, Ltd and ERP Environment.     

Shrinking Cities,Shrinking Households,or Both?

Household size decline accounts for a substantial portion of population loss in shrinking cities, yet little research has focused on it. Much of the literature presents a simple growth/decline binary that is largely determined via population figures. In this paper, we highlight the importance and assess the impact of household size changes on population decline, and determine what types of household size declines are more acute in shrinking cities than other locales. We find that elderly households and households with school-aged children are under-represented in shrinking cities, while households with pre-school-aged children are over-represented. More tellingly, we find the biggest source of household-related loss in shrinking cities to be the growth of single-person households now living in houses that were once home to entire families. These findings puncture the binary on which much of the shrinking cities discourse is based. The population dynamics of most cities are subtler than either practitioners or critical scholars assert. We argue that plans and development strategies for shrinking cities should reflect a range of demographic changes, including outmigration and internal household restructuring.     

Poverty Dynamics of Households in Rural China

The objective of our study is to identify patterns and causes of households' transitions into and out of poverty using the long household panel data on rural China in 1989–2009. We propose a discrete‐time multi‐spell duration model that not only corrects for unobserved heterogeneity, but also addresses the endogeneity due to dynamic selection associated with household's livelihood strategies. The household choosing farming or out‐migration as a main livelihood strategy was more likely to escape from persistent poverty than those taking local non‐agricultural employment. The present study emphasizes the central role of agriculture in helping the chronically poor escape from poverty.     

Linear household technologies

This paper discusses, estimates and formally compares the best known procedures for incorporating demographic variables into complete demand systems. In particular, a class of general procedures belonging to Gorman's family of ‘general linear household technologies’ is introduced. Estimation and comparison of different procedures make use of Italian household budget data for the years 1973–1992, incorporating a single demographic variable (family size) into a Generalized Quadratic Almost Ideal Demand System. In our empirical example, however, even the most general household technologies are unable to fully capture the behavioural heterogeneity shown by the data. Copyright © 2000 John Wiley & Sons, Ltd.     

Mixture of Truthful–Untruthful Responses in Public Surveys

When sensitive issues are surveyed, collecting truthful data and obtaining reliable estimates of population parameters is a persistent problem in many fields of applied research mostly in sociological, economic, demographic, ecological and medical studies. In this context, and moving from the so‐called negative survey, we consider the problem of estimating the proportion of population units belonging to the categories of a sensitive variable when collected data are affected by measurement errors produced by untruthful responses. An extension of the negative survey approach is proposed herein in order to allow respondents to release a true response. The proposal rests on modelling the released data with a mixture of truthful and untruthful responses that allows researchers to obtain an estimate of the proportions as well as the probability of receiving the true response by implementing the EM‐algorithm. We describe the estimation procedure and carry out a simulation study to assess the performance of the EM estimates vis‐à‐vis certain benchmark values and the estimates obtained under the traditional data‐collection approach based on direct questioning that ignores the presence of misreporting due to untruthful responding. Simulation findings provide evidence on the accuracy of the estimates and permit us to appreciate the improvements that our approach can produce in public surveys, particularly in election opinion polls, when the hidden vote problem is present.     

DATA GAPS,DATA INCOMPARABILITY,AND DATA IMPUTATION: A REVIEW OF POVERTY MEASUREMENT METHODS FOR DATA‐SCARCE ENVIRONMENTS

Questions that often come up in contexts where household consumption data are unavailable or missing include: what are the best existing methods to obtain poverty estimates at a single snapshot in time? and over time? and what are the best available methods to study poverty dynamics? A variety of different techniques have been developed to tackle these questions, but unfortunately, they are presented in different forms and lack unified terminology. We offer a review of poverty imputation methods that address contexts ranging from completely missing and partially missing consumption data in cross‐sectional household surveys, to missing panel household data. We present the various existing methods under a common framework, with pedagogical discussion on their intuition. Empirical illustrations are provided using several rounds of household survey data from Vietnam. Furthermore, we also offer a practical guide with detailed instructions on computer programs that can be used to implement the reviewed techniques.     

Time Series Based Errors and Empirical Errors in Fertility Forecasts in the Nordic Countries*

We use ARCH time series models to derive model based prediction intervals for the Total Fertility Rate (TFR) in Norway, Sweden, Finland, and Denmark up to 2050. For the short term (5–10 yrs), expected TFR‐errors are compared with empirical forecast errors observed in historical population forecasts prepared by the statistical agencies in these countries since 1969. Medium‐term and long‐term (up to 50 years) errors are compared with error patterns based on so‐called naïve forecasts, i.e. forecasts that assume that recently observed TFR‐levels also apply for the future.