The Age–Period–Cohort Problem in Hedonic House Prices Models

The age–period–cohort problem has been studied for decades but without resolution. There have been many suggested solutions to make the three effects estimable, but these solutions mostly exploit non-linear specifications. Yet, these approaches may suffer from misspecification or omitted variable bias. This paper is a practical-oriented study with an aim to empirically disentangle age–period–cohort effects by providing external information on the actual depreciation of housing structure rather than taking age as a proxy. It is based on appraisals of the improvement values of properties in New Zealand to estimate the age-depreciation effect. This research method provides a novel means of solving the identification problem of the age, period, and cohort trilemma. Based on about half a million housing transactions from 1990 to 2019 in the Auckland Region of New Zealand, the results show that traditional hedonic prices models using age and time dummy variables can result, ceteris paribus, in unreasonable positive depreciation rates. The use of the improvement values model can help improve the accuracy of home value assessment and reduce estimation biases. This method also has important practical implications for property valuations.

When less conditioning provides better estimates: overcontrol and endogenous selection biases in research on intergenerational mobility

The counterfactual approach to causality has become the dominant approach to understand causality in contemporary social science research. Whilst most sociologists are aware that unobserved, confounding variables may bias the estimates of causal effects (omitted variable bias), the threats of overcontrol and endogenous selection biases are less well known. In particular, widely used practices in research on intergenerational mobility are affected by these biases. I review four of these practices from the viewpoint of the counterfactual approach to causality and show why overcontrol and endogenous selection biases arise when these practices are implemented. I use data from the German Socio-Economic Panel Study (SOEP) to demonstrate the practical consequences of these biases for conclusions about intergenerational mobility. I conclude that future research on intergenerational mobility should reflect more upon the possibilities of bias introduced by conditioning on variables.

Child stature, maternal education, and early childhood development in Nigeria

Data from the 2016–17 Multiple Indicator Cluster Survey from Nigeria are used to study the relationship between child stature, mother’s years of education, and indicators of early childhood development (ECD). The relationships are contrasted between two empirical approaches: the conventional approach whereby control variables are selected in an ad-hoc manner, and the double machine-learning (DML) approach that employs data-driven methods to select controls from a much wider set of variables and thus reducing potential omitted variable bias. Overall, the analysis confirms that maternal education and the incidence of chronic malnutrition have a significant direct effect on measures of early childhood development. The point estimates based on the ad-hoc specification tend to be larger in absolute value than those based on the DML specification. Frequently, the point estimates based on the ad-hoc specification fall inside the confidence interval of the DML point estimates, suggesting that in these cases the omitted variable bias is not serious enough to prevent making causal inferences based on the ad-hoc specification. However, there are instances where the omitted variable bias is sufficiently large for the ad hoc specification to yield a statistically significant relationship when in fact the more robust DML specification suggests there is none. The DML approach also reveals a more complex picture that highlights the role of context. In rural areas, mother’s education affects early childhood development both directly and indirectly through its impact on the nutritional status of both older and younger children. In contrast, in urban areas, where the average level of maternal education is much higher, increases in a mother’s education have only a direct effect on child ECD measures but no indirect effect through child nutrition. Thus, DML provides a practical and feasible approach to reducing threats to internal validity for robust inferences and policy design based on observational data.

Research on Hysteresis Modeling and Compensation Method of Giant Magnetostrictive Force Sensor

Linearity is an important index for evaluating the performance of various sensors. Under the Villari effect, there may be some hysteresis between the input force and the output voltage of a force sensor, meaning that the output will be multivalued and nonlinear. To improve the linearity and eliminate the hysteresis of such sensors, an output compensation method using a variable bias current is proposed based on the bidirectional energy conversion mechanism of giant magnetostrictive material. First, the magnetization relationship between the input force, bias current, and flux density is established. Second, a nonlinear neural network model of the force-magnetization hysteresis and a neural network model for the compensation control of the force sensor are established. These models are trained using the magnetic flux density-force curve and the magnetic flux density-current curve, respectively. Taking the optimal linearity as the objective function, the bias current under different input forces is optimized. Finally, a bias current control system is developed and an experimental test platform is built to verify the proposed method. The results show that the proposed variable bias current hysteresis compensation method enables the linearity under the return of the force sensor to reach 1.6%, which is around 48.3% higher than under previous methods. Thus, the proposed variable bias current method effectively suppresses the hysteresis phenomenon and provides improved linearity for giant magnetostrictive force sensors.

Evaluation of a Prototype Broadband Water-Vapour Profiling Differential Absorption Lidar at Cardington, UK

For a one-month period in summer 2020, a prototype Vaisala broadband differential absorption lidar (BB-DIAL) was deployed at a Met Office research site. It was compared with in-situ observations of humidity (93 radiosonde ascents and 27 of uncrewed aerial vehicle flights) and the Met Office 1.5 km resolution numerical weather prediction (NWP) model: UK Variable resolution model (UKV). The BB-DIAL was able to collect data up to the cloud base, in all-weather situations including rain, when it was possible to reach 3 km. The average maximum height was 1300 m, with 75% of the data reaching 1000 m and 35% extending to 1500 m. Compared with radiosondes, the standard deviation for the water vapour is between 5% and 10%. The comparison with the UKV is very encouraging, with a correlation of 0.90. The error against the radiosonde is smaller than against the UKV, which is encouraging for assimilation the BB-DIAL data in UKV. Some data quality issues, such as an increase in error and variable bias in the region of overlap between the far field and close field, spurious oscillations and an unrealistic dry layer above fog are identified. Despite these issues, the overall results from this assessment are promising in terms of potential benefit, instrument reliability and capturing significant humidity changes in the boundary layer.

Can Panel Data Methodologies Determine the Impact of Climate Change on Economic Growth?

Several major papers have been published over the last ten years claiming to have detected the impact of either annual variations in weather or climate change on the GDPs of most countries in the world using panel data-based statistical methodologies. These papers rely on various multivariate regression equations which include the annual average temperatures for most countries in the world as one or more of the independent variables, where the usual dependent variable is the change in annual GDP for each country from one year to the next year over 30-50 year time periods. Unfortunately, the quantitative estimates derived in these papers are misleading because the equations from which they are calculated are wrong. The major reason the resulting regression equations are wrong is because they do not include any of the appropriate and usual economic factors or variables which are likely to be able to explain changes in GDP/economic growth whether or not climate change has already impacted each country’s economy. These equations, in short, exhibit suffer from “omitted variable bias,” to use statistical terminology.

Love Thy Neighbour? Perceived Community Abidance and Private Compliance to COVID-19 Norms in India

To arrest the spread of COVID-19 infection, strict adherence to frequent hand washing and respiratory hygiene protocols have been recommended. While these measures involve private effort, they provide health gains along with collective community benefits and hence are likely to be driven by pro-social motives like altruism and reciprocity. Using data from 934 respondents collected from April till May 2020 across India, we assess if changes in perceived community compliance can predict changes in individual compliance behaviour. We observe statistically significant and positive relationship between the two, even after accounting for observable and omitted variable bias allowing us to view the results from a plausible causal lens. Further, we find subsequent lockdowns having a detrimental effect on individual compliance though the gains from higher perceived community compliance seem to offset this loss. We also find positive perceptions about community can be particularly effective for people with pre-existing co-morbidities. Our findings underscore the need for multi-level behavioural interventions involving local actors and community institutions to sustain private compliance during the pandemic. We suggest these interventions need to be specially targeted for individuals with chronic ailments and emphasize on community behavioural practices in public messaging. JEL Codes: I12, I18, I19, I31

Doctoral Education, Job Mismatch, and Wage Consequences in South Korea: A Propensity Score Matching Approach

This study extends the previous literature on the wage effects of over-education, focusing on young doctorate holders (DHs). It also contributes to the conventional over-education literature on a causal relationship between over-education and wages by implementing techniques of propensity score matching (PSM). By tackling potential bias as a consequence of omitted variable bias via the PSM strategy, this study provides evidence of the negative influence of over-education on wages (i.e., the over-education wage penalty) once potential sources of bias are adequately considered. While the current analysis is focused on one country, South Korea, its results might be relevant for many other countries that have experienced a rapid expansion in the supply of DHs over recent years.