FOOD SECURITY AND LOW‐INCOME HOUSEHOLDS IN THE MALAYSIAN EAST COAST ECONOMIC REGION: AN EMPIRICAL ANALYSIS

This research, conducted to analyze the level of food security among poor and low‐income households in the east coast economic region of Malaysia, uses primary data of 460 families from the E‐Kasih poor households database, based on a cluster random sampling technique. Food security levels were measured using the United States Agency for International Development Household Food Insecurity Access model. Findings indicate that 52.8% of households are food secure, 23.3% mildly food insecure, 14.3% moderately food insecure, and 9.6% are severely food insecure. These findings are very important to assist policymakers to achieve Vision 2020 and the targets of Malaysian National Plans regarding food security, socioeconomic development, and the alleviation of poverty. The involvement of private sector and community‐based organizations are important to combat short run, seasonal, and event‐related risks, as well as for the development of appropriate mitigation and adaptation options to ensure sustainable food security at household level in Malaysia.     

Refining Targeting against Poverty Evidence from Tunisia*

We introduce a new methodology to target direct transfers against poverty. Our method is based on estimation methods that focus on the poor. Using data from Tunisia, we estimate ‘focused’ transfer schemes that highly improve anti‐poverty targeting performances. Post‐transfer poverty can be substantially reduced with the new estimation method. For example, a one‐third reduction in poverty severity from proxy‐means test transfer schemes based on OLS method to focused transfer schemes requires only a few hours of computer work based on methods available on popular statistical packages. Finally, the obtained levels of undercoverage of the poor are particularly low.     

Simplified Jackknife Variance Estimates for Fuzzy Measures of Multidimensional Poverty

In this paper, we present a practical methodology for variance estimation for multi‐dimensional measures of poverty and deprivation of households and individuals, derived from sample surveys with complex designs and fairly large sample sizes. The measures considered are based on fuzzy representation of individuals' propensity to deprivation in monetary and diverse non‐monetary dimensions. We believe this to be the first original contribution for estimating standard errors for such fuzzy poverty measures. The second objective is to describe and numerically illustrate computational procedures and difficulties in producing reliable and robust estimates of sampling error for such complex statistics. We attempt to identify some of these problems and provide solutions in the context of actual situations. A detailed application based on European Union Statistics on Income and Living Conditions data for 19 NUTS2 regions in Spain is provided.     

Multiple Visits and Data Quality in Household Surveys

In order to increase data quality some household surveys visit the respondent households several times to estimate one measure of consumption. For example, in Ghanaian Living Standards Measurement surveys, households are visited up to 10 times over a period of 1 month. I find strong evidence for conditioning effects as a result of this approach: In the Ghanaian data the estimated level of consumption is a function of the number of prior visits, with consumption being highest in the earlier survey visits. Telescoping (perceiving events as being more recent than they are) or seasonality (first‐of‐the‐month effects) cannot explain the observed pattern. To study whether earlier or later survey visits are of higher quality, I employ a strategy based on Benford's law. Results suggest that the consumption data from earlier survey visits are of higher quality than data from later visits. The findings have implications for the value of additional visits in household surveys, and also shed light on possible measurement problems in high‐frequency panels. They add to a recent literature on measurement errors in consumption surveys (Beegle et al., 2012 , Gibson et al., 2015 ), and complement findings by Zwane et al. ( 2011 ) regarding the effect of surveys on subsequent behaviour.     

Identifiability of mean‐reverting measurement error with instrumental variable

In the context where one main regressor is measured with error and at least one instrumental variable is available for the correction of measurement error, this paper provides, to the best of our knowledge, a first point‐identification result on the variance of measurement error, the variance of latent variable, and their covariance. We show that the parameters are identified if the regression model is not de facto linear. We illustrate the method in an application to identify mean‐reverting measurement error, a typical issue in reported income where the measurement error of income is negatively correlated with the true income.     

Estimating time variation in measurement error from data revisions: an application to backcasting and forecasting in dynamic models

Over time, economic statistics are refined. This implies that data measuring recent economic events are typically less reliable than older data. Such time variation in measurement error affects optimal forecasts. Measurement error, and its time variation, are of course unobserved. Our contribution is to show how estimates of these can be recovered from the variance of revisions to data using a behavioural model of the statistics agency. We illustrate the gains in forecasting performance from exploiting these estimates using a real‐time dataset on UK aggregate expenditure data. Copyright © 2009 John Wiley & Sons, Ltd.     

Quadratic Engel Curves with Measurement Error: Evidence from a Budget Survey*

This paper examines the importance of accounting for measurement error in total expenditure in the estimation of Engel curves, based on the 1994 Ethiopian Urban Household Survey. Using Lewbel's [Review of Economics and Statistics (1996 ), Vol. 78, pp. 718–725] estimator for demand models with correlated measurement errors in the dependent and independent variables, we find robust evidence of a quadratic relationship between food share and total expenditure in the capital city, and significant biases in various estimators that do not correct for correlated measurement errors.     

A new methodology for multidimensional poverty measurement based on the capability approach

We propose a methodology based on the use of clustering techniques derived from data analysis and multi-attribute decision analysis methods aiming at purposeful multidimensional poverty measurement. Our contribution to methodological knowledge insists on the necessity to build “meaningful measurements” for policy making and policy implementation. Our standpoint underlines the necessity to consider the problem of poverty measurement as a decision problem and to tackle its measurement issue with that in mind. We also show that such an exercise can be useful to develop a better operational definition of poverty and to solve the aggregation issues.     

Evaluating asset‐pricing models using the Hansen–Jagannathan bound: a Monte Carlo investigation

We use recent statistical tests, based on a ‘distance’ between the model and the Hansen–Jagannathan bound, to compute the rejection rates of true models. For asset‐pricing models with time‐separable preferences, the finite‐sample distribution of the test statistic associated with the risk‐neutral case is extreme, in the sense that critical values based on this distribution deliver type I errors no larger than intended—regardless of risk aversion or the rate of time preference. We also show that these maximal‐type‐I‐error critical values are appropriate for both time and state non‐separable preferences and that they yield acceptably small type II error rates. Copyright © 2002 John Wiley & Sons, Ltd.     

The Effect of Income Distribution on the Ability of Growth to Reduce Poverty: Evidence from Rural and Urban African Economies

The present study examines the extent to which income distribution affects the ability of economic growth to reduce poverty, based on 1990s data for a sample of rural and urban sectors of African economies. Using the basic‐needs approach, an analysis‐of‐covariance model is derived and estimated, with the headcount, gap, and squared gap poverty ratios serving as the respective dependent variables, and the Gini coefficient and PPP‐adjusted incomes as explanatory variables. The study finds that the responsiveness of poverty to income growth is a decreasing function of inequality, albeit at varying rates for the three poverty measures: lowest for the headcount, followed by the gap and fastest for the squared gap. The ranges for the income elasticity in the sample are estimated at: 0.02–0.68, 0.11–1.05, and 0.10–1.35, respectively, for these poverty measures. Furthermore while, on average, the responsiveness of poverty to income growth appears to be the same between the rural and urban sectors, there are substantial sectoral differences across countries. The results suggest the need for country‐specific emphases on growth relative to inequality.     

How Does Childbirth Alter Intrahousehold Resource Allocation? Evidence from Japan*

Exploiting unique panel data that include direct measurements of resource allocation within households, we investigated the impact of childbirth on intrahousehold allocation for married Japanese couples. Based on a collective model of the household, we developed reduced‐form and structural‐form estimation equations that allow us to focus on private goods to track the changes in intrahousehold resource allocation. We found one additional child is associated with a reduction in the wife's private expenditure share by at least two percentage points. This may be because she substitutes more say in decisions on the children for her own private expenditure share.     

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.     

Mobility and the dynamics of poverty in Iran: Evidence from the 1992–1995 panel survey

In the last three decades, revolutionary Iran has experienced large shocks to its political and economic system with likely effects on poverty, inequality, and economic mobility. While poverty has declined, inequality has remained relatively high and stable over nearly four decades. In this paper, for the first time, we examine poverty and inequality in a dynamic context using a 4-year panel data, collected during 1992–1995. We show that short-term income mobility is relatively high, which helps mitigate persistent high inequality. We offer a range of estimates of transition probabilities, indicating that, for example, someone in the lowest (highest) quintile has between 25% and 50% chance of moving up (down) the income ladder. Focusing on the dynamics of poverty, we distinguish between short- and long-term poor and between chronic and transient poverty. Surprisingly, we find that chronic poverty is a more serious problem in urban than rural areas, while transient poverty is geographically more uniformly distributed. Finally, using Tobit and quantile regression, we examine the correlates of these two types of poverty. Both chronic and transient poverty are higher for households headed by women and by younger and less educated men. While minorities suffer more from transient poverty, they are less likely to be chronically poor. We discuss the implications of these findings for policy to alleviate chronic and transient poverty.     

Health Effects of Air Pollution: A Statistical Review

We critically review and compare epidemiological designs and statistical approaches to estimate associations between air pollution and health. More specifically, we aim to address the following questions:

• 1 Which epidemiological designs and statistical methods are available to estimate associations between air pollution and health?
• 2 What are the recent methodological advances in the estimation of the health effects of air pollution in time series studies?
• 3 What are the the main methodological challenges and future research opportunities relevant to regulatory policy?

In question 1, we identify strengths and limitations of time series, cohort, case‐crossover and panel sampling designs. In question 2, we focus on time series studies and we review statistical methods for: 1) combining information across multiple locations to estimate overall air pollution effects; 2) estimating the health effects of air pollution taking into account of model uncertainties; 3) investigating the consequences of exposure measurement error in the estimation of the health effects of air pollution; and 4) estimating air pollution‐health exposure‐response curves. Here, we also discuss the extent to which these statistical contributions have addressed key substantive questions. In question 3, within a set of policy‐relevant‐questions, we identify research opportunities and point out current data limitations.     

Regional Inequalities in Consumption Patterns: A Multilevel Approach to the Case of Italy

The main aim of this paper is to evaluate the disparities in the Italian regions on the demand side. In more detail, an attempt will be made to find if the consumption behaviour of Italian households is different in the regions. With this in mind, Istat's 2000 Italian Family Budget data set was analysed. The data in question, which were collected through a two‐stage sample over Italy's 20 regions, contains information regarding the expenses of approximately 23,000 households. In this analysis, both households and regions are considered as units: households are nested in the regions so that the basic data structure is hierarchical. In order to take this hierarchical structure into account, a multilevel model was used, making it possible for parameters to vary randomly from region to region. The model in question also made it possible to consider heterogeneity across different groups (regions), such as stochastic variation. First, regional inequalities were tested using a simple model in which households constituted the first level of analysis and were grouped according to their region (the second level). As a second step, and in order to investigate the interaction between geographical context and income distribution, another model was used. This was cross‐classified by income and regions. The most relevant results showed that there is wide fragmentation of consumption behaviour and, at the same time, various differentiated types of behaviour in the regions under analysis. These territorial differentials become clear from income class and items of consumption.     

Penalized Quantile Regression with Semiparametric Correlated Effects: An Application with Heterogeneous Preferences

This paper proposes new ?₁‐penalized quantile regression estimators for panel data, which explicitly allows for individual heterogeneity associated with covariates. Existing fixed‐effects estimators can potentially suffer from three limitations which are overcome by the proposed approach: (i) incidental parameters bias in nonlinear models with large N and small T ; (ii) lack of efficiency; and (iii) inability to estimate the effects of time‐invariant regressors. We conduct Monte Carlo simulations to assess the small‐sample performance of the new estimators and provide comparisons of new and existing penalized estimators in terms of quadratic loss. We apply the technique to an empirical example of the estimation of consumer preferences for nutrients from a demand model using a large transaction‐level dataset of household food purchases. We show that preferences for nutrients vary across the conditional distribution of expenditure and across genders, and emphasize the importance of fully capturing consumer heterogeneity in demand modeling. Copyright © 2016 John Wiley & Sons, Ltd.     

Decomposing economic mobility transition matrices

We present a decomposition method for transition matrices to identify forces driving the persistence of economic status across generations. The method decomposes differences between an estimated transition matrix and a benchmark transition matrix into portions attributable to differences in characteristics between individuals from different households (a composition effect) and portions attributable to differing returns to these characteristics (a structure effect). A detailed decomposition based on copula theory further decomposes the composition effect into portions attributable to specific characteristics and their interactions. To examine potential drivers of economic persistence in the USA, we apply the method to white males from the 1979 US National Longitudinal Survey of Youth. Depending on the transition matrix entry of interest, differing characteristics between sons from different households explain between 40% and 70% of observed income persistence, with differing returns for these characteristics explaining the remaining gap. Further, detailed decompositions reveal significant heterogeneity in the role played by specific characteristics (e.g., education) across the income distribution.     

Multiple Testing for No Cointegration under Nonstationary Volatility

With cointegration tests often being oversized under time‐varying error variance, it is possible, if not likely, to confuse error variance non‐stationarity with cointegration. This paper takes an instrumental variable (IV) approach to establish individual‐unit test statistics for no cointegration that are robust to variance non‐stationarity. The sign of a fitted departure from long‐run equilibrium is used as an instrument when estimating an error‐correction model. The resulting IV‐based test is shown to follow a chi‐square limiting null distribution irrespective of the variance pattern of the data‐generating process. In spite of this, the test proposed here has, unlike previous work relying on instrumental variables, competitive local power against sequences of local alternatives in 1/T‐neighbourhoods of the null. The standard limiting null distribution motivates, using the single‐unit tests in a multiple testing approach for cointegration in multi‐country data sets by combining P‐values from individual units. Simulations suggest good performance of the single‐unit and multiple testing procedures under various plausible designs of cross‐sectional correlation and cross‐unit cointegration in the data. An application to the equilibrium relationship between short‐ and long‐term interest rates illustrates the dramatic differences between results of robust and non‐robust tests.     

Geographic poverty traps? A micro model of consumption growth in rural China

How important are neighbourhood endowments of physical and human capital in explaining diverging fortunes over time for otherwise identical households in a developing rural economy? To answer this question we develop an estimable micro model of consumption growth allowing for constraints on factor mobility and externalities, whereby geographic capital can influence the productivity of a household's own capital. Our statistical test has considerable power in detecting geographic effects given that we control for latent heterogeneity in measured consumption growth rates at the micro level. We find robust evidence of geographic poverty traps in farm‐household panel data from post‐reform rural China. Our results strengthen the equity and efficiency case for public investment in lagging poor areas in this setting. Copyright © 2002 John Wiley & Sons, Ltd.