How do indicators of household food insecurity measure up? An empirical comparison from Ethiopia

Renewed emphasis on programs and policies aimed at enhancing food security has intensified the search for accurate, rapid, and consistent indicators. Measures of food security are urgently required for purposes of early warning, assessment of current and prospective status of at-risk populations, and monitoring and evaluation of specific programs and policies. Different measures are often used interchangeably, without a good idea of which dimensions of food security are captured by which measures, resulting in potentially significant misclassification of food insecure populations. The objective of this paper is to compare how the most frequently used indicators of food security portray static and dynamic food security among the same sample of rural households in two districts of Tigray State, Northern Ethiopia. Seven food security indicators were assessed: the Coping Strategies Index (CSI); the Reduced Coping Strategies Index (rCSI); the Household Food Insecurity and Access Scale (HFIAS); the Household Hunger Scale (HHS); Food Consumption Score (FCS); the Household Dietary Diversity Scale (HDDS); and a self-assessed measure of food security (SAFS). These indicators provide very different estimates of the prevalence of food insecurity, but are moderately well correlated and depict generally similar food security trends over time. We suggest that the differences in prevalence estimates, and in some cases the weaker than expected correlation, can be explained in three ways. First, the indicators differ in the underlying aspect of food security they attempt to capture. Second, each indicator is likely only sensitive within a certain severity range of food insecurity and these ranges do not always overlap. Third, categorization of the prevalence of food insecurity is strongly dependent on the choice of cut-off points. For valid reasons, “food insecurity” has no accepted gold standard metric against which individual indicators can be gauged, though without one it is difficult to say which indicator performs “best” in correctly and reliably identifying food insecure households. The implication is that using more than one indicator is advisable, and policy makers should be aware of what elements of food insecurity each indicator portrays.     

An econometric analysis of demand for food quantity and quality in urban China

This article offers an analytical framework for studying consumer demand for food quality based on a theory‐consistent demand model, namely, the Exact Affine Stone Index specification. Importantly, it accounts for unobserved consumer and regional heterogeneity, and allows for arbitrary Engel curves. The empirical value of our framework is illustrated in an econometric analysis of demand for food quality in China. Evaluating possible structural changes in consumer food expenditures in China is of paramount importance, given the ever‐increasing global role of China and the implications of the structural food preference changes for the global food system. The major findings emerging from this study indicate that income is an important determinant of demand for food quality and quantity with the relatively more affluent provinces having a higher affinity for food quality.     

Persistence,mean reversion and non-linearities in the US housing prices over 1830–2013

The objective of this study is to provide a direct estimate of the degree of persistence of measures of nominal and real house prices for the US economy, covering the longest possible annual sample of data, namely 1830–2013. The estimation of the degree of persistence accommodates for non-linear (deterministic) trends using Chebyshev polynomials in time. In general, the results show a high degree of persistence in the series along with a component of non-linear behaviour. In general, if we assume uncorrelated errors, non-linearities are observed in both nominal and real prices, but this hypothesis is rejected in favour of linear models for the log-transformation of the data. However, if autocorrelated errors are permitted, non-linearities are observed in all cases, and mean reversion is found in the case of logged prices, though given the wide confidence intervals, the unit root null hypothesis cannot be rejected in these cases.     

Commodity prices and BRIC and G3 liquidity: A SFAVEC approach

This paper investigates the influence of liquidity in the major developed and major developing economies on commodity prices. Liquidity is taken to be M2. A novel finding is that unanticipated increases in the BRIC countries’ liquidity is associated with significant and persistent increases in commodity prices that are much larger than the effect of unanticipated increases in G3 liquidity, and the difference increases over time. Over 1999–2012 BRIC liquidity is strongly linked with global energy prices and global real activity whereas G3 liquidity is not. The impact of BRIC liquidity on mineral and metal prices is twice as large as that of G3 liquidity. Granger casualty goes from liquidity to commodity prices. BRIC and G3 liquidity and commodity prices are cointegrated. BRIC and G3 liquidity and global output and global prices are cointegrated. We construct a structural factor-augmented error correction (SFAVEC) model.     

Quantitative easing and the loan to collateral value ratio

We study optimal monetary policy in a New Keynesian model at the zero bound interest rate where households use cash alongside house equity borrowing to conduct transactions. The amount of borrowing is limited by a collateral constraint. When either the loan to value ratio declines or house prices fall, we observe a decrease in the money multiplier. We argue that the central bank should respond to the fall in the money multiplier and therefore to the reduction in house prices or the loan to collateral value ratio. We also find that optimal monetary policy generates a large and persistent fall in the money multiplier in response to the drop in the loan to collateral value ratio.     

Food,farmers, and the future: Investigating prospects of increased food production within a national context

With international food price shocks in 2008 and 2011, food security became a political priority in many countries. In addition, some politicians have recently adopted a more nationalistic stance. Against that background, this paper critically investigates the prospects of increased food production within a national context. We use a small, high-income country, Norway, as an empirical case. In 2012, the government set a goal of increasing agricultural food production by 20% by 2030. We ask: 1) How has food production in Norway developed before and after the goal was set? 2) What plans do farmers have, and what do they regard as the main obstacles to increased production? We apply a mixed method combining public statistics, a survey, and interviews. We analyze four production systems: a) milk; b) grass-based meat; c) combined pig and grain; and d) grain. These systems represent around 80% of the domestically consumed food produced on farms in Norway. Since 2000, aggregate food production has had a slight downward trend with periodic fluctuations. Based on a political economy approach, we identify land and labor as the most limiting factors. Capital is less of a hindrance and offers a potential for increased production. Farmers have modest expectations of increased production, though. This outlook resonates with the strong integration of agriculture into the wider economy, at both micro and macro levels, making it challenging to implement new policies and change farm practices on a broad basis. Increases in some specific products, however, are realistic.     

Food preferences as a proxy for adventurousness

Food preferences have been demonstrated to be a proxy measure for adventurousness, in general. This study demonstrates that a single variable of food preferences unveils the four dimensions of adventurousness that are applicable in a general tourism context. The findings suggest that adding this simple semantic differential question to visitor surveys can be a powerful tool to evaluate the critical factor of adventurousness in tourism.     

Do housing prices promote total factor productivity? Evidence from spatial panel data models in explaining the mediating role of population density

The issue of what to promote in total factor productivity (TFP) in urban areas has been widely discussed in academia and housing prices and population density are confirmed to be two of the most essential driving factors. However, research into the interaction of housing prices and population density with TFP has been neglected, with no previous studies taking spatial factors into consideration, which may bias the results. From this perspective, using spatial panel data models and employing instrumental variables to solve the endogenous problem, this study examines the impact of housing prices on TFP through the mediating effect of population density for 283 Chinese cities during the period 2000–2013, and confirms that the mediating effect accounts for 18.70 % of the total effect. The results show the positive and significant association of housing prices with TFP and the inverted U-shape of population density. The underlying logic is that housing prices change population density by attracting people with high purchasing power and discouraging those unable to afford housing, whereas increased density helps to promote productivity since the settled inhabitants always have highly developed work skills and are well educated. The influencing mechanism of housing prices on TFP through population density is analyzed, namely the spillover effect. We find that the spillover effect exists in the eastern and central regions, as well as first, second, and third tier cities, while for western regions and fifth tier cities, population mobility and increased in housing prices slows their economic development. There is no evidence of any spillover effect in fourth tier cities. A discussion and suggested policy implications are also provided.     

Estimating the duration of different seasons and their impact on hotel room prices

The purpose of the study is to accurately measure the span of different seasons and its effect on the fluctuation of the occupancy rate and the average room rates in the hospitality sector. While prior studies have concentrated on measuring seasonality using calendar months, this study takes a different approach by measuring seasons in terms of the date count so that the exact starting and ending date of a season can be identified. This step involves adopting a non-parametric methodology that split the time scale into several small parts to obtain a better fit of the relationship and that can detect the starting and end of the season when given specific dates.