Effectiveness and supply effects of high-coverage rent control policies

Concerns about housing affordability are widespread in cities worldwide, prompting discussions about rent control policies. This paper studies the effects of a rent control policy adopted in Catalonia in 2020 that applied to some but not all municipalities. The policy virtually covered all the rental market and forced ads and tenancy agreements to specify the applicable rent cap to ensure enforcement. To identify the causal effect of the rent control regulation on the rental market, we exploit register microdata of tenancy agreements and implement difference-in-differences regressions and event-study designs. Our results indicate that the regulation reduced average rents paid by about 4% to 6%. We do not find evidence of a reduction in the supply of rental units, as measured by the number of signed and ended agreements or the active stock of rental units. We implement several robustness tests to address identification concerns related to Covid-19. Our results suggest that rent control policies can effectively reduce rental prices without necessarily shrinking the rental market.

Understanding the Predictors of Low Take-Up of the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC): A Nationwide Longitudinal Study.

INTRODUCTION: The Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) is among the largest U.S. social safety net programs. Although strong evidence exists regarding the benefits of WIC, take-up (i.e., participation among eligible individuals) has steadily declined in the past decade. This study addresses gaps in our knowledge regarding predictors of WIC take-up during this time. METHODS: Data were drawn from the 1998-2017 waves of the National Health Interview Study (NHIS), a serial cross-sectional study of the U.S. POPULATION: The analytic sample included 23,645 children and 10,297 women eligible for WIC based on self-reported demographic characteristics. To investigate predictors of WIC take-up, we regressed self-reported WIC receipt on a range of individual-level predictors (e.g., age, nativity, income) and state- level predictors (e.g., unemployment rate, governor's political affiliation) using multivariable logistic regression. In secondary analyses, results were additionally stratified by race/ethnicity, time period, and age (for children). RESULTS: For both women and children, older maternal age and higher educational attainment were associated with decreased take-up of WIC. Associations differed by race/ethnicity, time period, and state characteristics including caseload of other social programs (e.g., Medicaid). DISCUSSION: Our study identifies groups that are less likely to take up WIC benefits for which they are eligible, thereby contributing important evidence to inform programs and policies to increase WIC participation among groups with lower take-up. As WIC evolves past the COVID-19 pandemic, special attention will be needed to ensure that resources to encourage and support the participation of racially and economically marginalized individuals are equitably distributed.

Do Early Warning Systems Help High School Students Stay on Track for College? Mixed Methods Evaluation of the Ninth Grade Success Initiative

As the transition point between middle and high school, ninth grade can either set a student up for long-term success or diminish a student's likelihood of graduating high school altogether. The Ninth Grade Success Initiative is a dropout prevention program, piloted in five Washington State high schools in 2019–2020. We evaluated the effects on student outcomes and found that the program led to improvements in course grades, behavioral outcomes, more effective targeting of services to higher-need students, and better preparation for the COVID-19 transition to virtual learning. © 2023 Kappa Delta Pi.

The role of the California tier system in controlling population mobility during the COVID-19 pandemic.

BACKGROUND: Policies to restrict population mobility are a commonly used strategy to limit the transmission of contagious diseases. Among measures implemented during the COVID-19 pandemic were dynamic stay-at-home orders informed by real-time, regional-level data. California was the first state in the U.S. to implement this novel approach; however, the effectiveness of California's four-tier system on population mobility has not been quantified. METHODS: Utilizing data from mobile devices and county-level demographic data, we evaluated the impact of policy changes on population mobility and explored whether demographic characteristics explained variability in responsiveness to policy changes. For each California county, we calculated the proportion of people staying home and the average number of daily trips taken per 100 persons, across different trip distances and compared this to pre-COVID-19 levels. RESULTS: We found that overall mobility decreased when counties moved to a more restrictive tier and increased when moving to a less restrictive tier, as the policy intended. When placed in a more restrictive tier, the greatest decrease in mobility was observed for shorter and medium-range trips, while there was an unexpected increase in the longer trips. The mobility response varied by geographic region, as well as county-level median income, gross domestic product, economic, social, and educational contexts, the prevalence of farms, and recent election results. CONCLUSIONS: This analysis provides evidence of the effectiveness of the tier-based system in decreasing overall population mobility to ultimately reduce COVID-19 transmission. Results demonstrate that socio-political demographic indicators drive important variability in such patterns across counties.

Statistics in times of increasing uncertainty

The statistical community mobilised vigorously from the start of the 2020 SARS-CoV-2 pandemic, following the RSS's long tradition of offering our expertise to help society tackle important issues that require evidence-based decisions. This address aims to capture the highlights of our collective engagement in the pandemic, and the difficulties faced in delivering statistical design and analysis at pace and in communicating to the wider public the many complex issues that arose. I argue that these challenges gave impetus to fruitful new directions in the merging of statistical principles with constraints of agility, responsiveness and societal responsibilities. The lessons learned from this will strengthen the long-term impact of the discipline and of the Society. The need to evaluate policies even in emergency, and to strive for statistical interoperability in future disease surveillance systems is highlighted. In my final remarks, I look towards the future landscape for statistics in the fast-moving world of data science and outline a strategy of visible and growing engagement of the RSS with the data science ecosystem, building on the central position of statistics.

Adaptive learning in containment measures: evaluation of policy interventions during the 2020 waves of Covid-19 in Italy

The COVID-19 pandemic hit Italy very harshly in two waves, the first in spring 2020 and the second between the autumn and the winter of the same year. Data show some major differences between the two phases;in particular, the first wave caused fewer infections but had a higher fatality rate. These pandemic evolutions, together with modified social conditions, called for a rapid adaptation of containment measures, i.e. stricter and homogeneous in the first wave, flexible and diversified in the second wave. The interrupted time series analysis applied to daily data on new cases yields positive results for both interventions in flattening the infection curve. The policies achieved almost the same percentage of positive cases avoided in the two waves. Adaptive and diversified policies based on learning from previous results seem to be suitable for this kind of decision-making in conditions of uncertainty. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

COVID-19 and employment relief programs: A tale of spatially blind policies for a spatially driven pandemic

We use the case of Chile to analyze the effectiveness of a spatially blind employment relief program (hereafter referred to as the LPE program) established by the Chilean government and implemented during the COVID-19 pandemic. Chile is an interesting case because on the one hand its nonpharmaceutical interventions were spatially driven by health indicators based on small geographical areas;hence, producing sizeable regional and temporal variation of the local conditions induced by the COVID-19 pandemic. On the other hand, the LPE program was designed and implemented nationally without distinction of local labor market or pandemic conditions, and each firm could decide whether to enroll in the program. By exploiting the spatial-temporal variation of exogenously imposed lockdowns and using a difference-in-differences panel data framework, we find that the LPE program was only effective for a group of regions in the country but, more importantly, that the LPE program was less effective during lockdowns. Moreover, the requirements of the LPE program were vague and did not target specific populations or entities. Consequently, our results suggest that women, informal and small firm workers, and most economic sectors throughout the country were less able to take advantage of the benefits of this program. © 2023 Wiley Periodicals LLC.

Use of a multi-method approach to rapidly assess the impact of public health policies at the state and local level: a case study of flavored e-cigarette policies.

BACKGROUND: E-cigarettes are the most-commonly used tobacco product by youth since 2014. To prevent youth access and use of e-cigarettes, many U.S. states and localities have enacted policies over a relatively short period of time. The adoption of these policies has necessitated timely data collection to evaluate impacts. METHODS: To assess the impact of flavored e-cigarette policies in select states and local jurisdictions across the United States, a multi-method, complementary approach was implemented from July 2019 to present, which includes analyses of cross-sectional online surveys of young people ages 13-24 years with retail sales data. RESULTS: From February 2020 through February 2023, cross-sectional surveys have been conducted in three cities, one county, and eight states where policy changes have been enacted or are likely to be enacted. Data collection occurred every six months to provide near real-time data and examine trends over time. Additionally, weekly retail sales data were aggregated to showcase monthly sales trends at the national level and for the selected states. DISCUSSION: This rapid and efficient method of coupling online survey data with retail sales data provides a timely and effective approach for monitoring a quickly changing tobacco product landscape, particularly for states and localities where rapidly-available data is often not available. This approach can also be used to monitor other health behaviors and relevant policy impacts.

Individual-based epidemiological model of COVID19 using location data

Because human movement spreads infection, and mobility is a good proxy for other social distancing measures, human mobility has been an important factor in the COVID19 epidemic. Therefore, the control of human mobility is one of the countermeasures used to suppress an epidemic.As a notable feature, COVID19 has had multiple waves (subepidemics). Understanding the causes of the start and end of each wave has important implications for a policy evaluation and the timely implementation of countermeasures. Some of the waves have been correlated with the changes in mobility, and some can be attributed to the emergence of new variants. However, the start and end of some of the waves are difficult to explain through known factors.To evaluate the effect of human mobility, we built a stochastic model incorporating individual movements of 500,000 people obtained from anonymized, user-approved location data of smartphones throughout Japan. Instead of using aggregate values of human mobility, our model tracks the movements of individuals and predicts the infection of all persons within the entire country. Although the model only has a single static parameter, it successfully reproduced the occurrence of three waves of the number of confirmed cases within the study period of March 01 to December 31, 2020 in Japan. It was previously difficult to explain the end of the second wave and the start of the third wave in the study period by human mobility alone. Our results suggest the importance of tracking individual movements instead of relaying the aggregate values of human mobility. © 2022 IEEE.

School's Out? Simulating Schooling Strategies During COVID-19

Multi-agent based systems offer the possibility to examine the effects of policies down to specific target groups while also considering the effects on a population-level scale. To examine the impact of different schooling strategies, an agent-based model is used in the context of the COVID-19 pandemic using a German city as an example. The simulation experiments show that reducing the class size by rotating weekly between in-person classes and online schooling is effective at preventing infections while driving up the detection rate among children through testing during weeks of in-person attendance. While open schools lead to higher infection rates, a surprising result of this study is that school rotation is almost as effective at lowering infections among both the student population and the general population as closing schools. Due to the continued testing of attending students, the overall infections in the general population are even lower in a school rotation scenario, showcasing the potential for emergent behaviors in agent-based models. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

The Impact of Conditional Cash Transfers on Poverty, Inequality, and Employment During COVID-19: A Case Study from Brazil.

The policy responses to the COVID-19 pandemic varied widely between countries. Understanding how effective these responses were is important to improve preparedness for future crises. This paper investigates how one of largest scale conditional cash transfer COVID relief policies in the world-the Brazilian Emergency Aid (EA)-impacted poverty, inequality, and the labor market amidst the public health crisis. We use fixed-effects estimators to analyze the impact of the EA on labor force participation, unemployment, poverty, and income at the household level. We find that inequality, measured by per capita household income, reduced to a historical low and was accompanied by substantial poverty declines-even as compared to pre-pandemic levels. Furthermore, our results suggest that the policy has effectively targeted those in most need-temporarily reducing historical racial inequalities-while not incentivizing reductions in labor force participation. Absent the policy, adverse shocks would have been significant and are likely to occur once the transfer is interrupted. We also observe that the policy was not enough to curb the spread of the virus, suggesting that cash transfers alone are insufficient to protect citizens.

Entrepreneurship Policy and SME Development during Pandemic Crisis in Russia

: Small and medium-sized enterprises (SMEs) suffered from government restrictions and a drop in consumer demand in 2020–2021 and therefore became one of the main targets of anti-crisis support worldwide. We aimed to identify trends and factors influencing the SMEs' dynamics in the Russian regions during the coronacrisis, including the impact of entrepreneurship policy. We have verified with the econometric analysis that the SMEs' number reduction was more serious in regions with a large SME sector, with a high proportion of industries potentially affected by the crisis, with stricter anti-pandemic measures. The latter factor had an impact not only on the domestic market, but also on SMEs in neighboring regions, which proves the existence of close ties between enterprises of different regions. However, there are some factors that influenced the SMEs development positively: relatively higher income level, more favourable business climate and larger consumer market. The previously undertaken efforts of the regional authorities to improve the business climate had a positive effect on the SMEs survival during the crisis. Business digitalization turned out to be an effective way to adapt (online services and sales), and state support policies could be more efficient (targeted and accessible) in digitally advanced regions. The agrarian regions due to continued demand for food got through the crisis more easily, while the border regions, focused on foreign trade relations, suffered more. In general, the business performance reduction was smaller in the regions that significantly intensified support. In a group of proactive regions (Tyumen, Belgorod, Ulyanovsk oblast, Crimea,1 etc.), where both general and specific support were increased above the national average, SMEs decrease rate was 1.6% lower. According to our calculations, during crises special attention should be paid to supporting business digitalization, improving regional business climate and increasing the accessibility of markets for SMEs (transport development, import substitution, etc.). These measures can become a significant factor in business development after the events of 2022. © 2022, Pleiades Publishing, Ltd.

Statistics in times of increasing uncertainty

The statistical community mobilised vigorously from the start of the 2020 SARS‐CoV‐2 pandemic, following the RSS's long tradition of offering our expertise to help society tackle important issues that require evidence‐based decisions. This address aims to capture the highlights of our collective engagement in the pandemic, and the difficulties faced in delivering statistical design and analysis at pace and in communicating to the wider public the many complex issues that arose. I argue that these challenges gave impetus to fruitful new directions in the merging of statistical principles with constraints of agility, responsiveness and societal responsibilities. The lessons learned from this will strengthen the long‐term impact of the discipline and of the Society. The need to evaluate policies even in emergency, and to strive for statistical interoperability in future disease surveillance systems is highlighted. In my final remarks, I look towards the future landscape for statistics in the fast‐moving world of data science and outline a strategy of visible and growing engagement of the RSS with the data science ecosystem, building on the central position of statistics.

The Best and Sustainable COVID-19 Policy in the World

This article will scientifically evaluate individual COVID-19 policies of countries around the world, U.S. states, and Japanese prefectures, respectively. The efficacy of the vaccines has been reported in many of the world's top medical journals, but even after more than a year of vaccination, the claims have yet to be met. Human emotions, behaviors, and individual policies can significantly influence the outcome against the pandemic. The evaluation in this article is based on a single determinant of the policy outcome. Scoring policies is based on dividing the number of deaths due to COVID-19 by the population in millions. The lower the score, the better the policy. Unfortunately, scores monotonically increase, so that policymakers can only suppress them but cannot improve or decrease them. Therefore, mistakes by policymakers cannot be corrected in the future and they are fatal forever. The result using three tools will reveal the best COVID-19 policy in the world. The revealed policy should have been or be adopted in individual countries for mitigating and ending the COVID-19 pandemic. This article also suggests what is needed in our society for reducing the unnecessary deaths due to COVID-19.

A COVID-19 Control Policy Evaluation and Prediction Method Based on Clustering

COVID-19 has brought huge losses to the economy all over the world. To solve this problem, we delivered an epidemic situation evaluation and prediction system based on dynamic data clustering, which was established to cluster the epidemic data in different regions and make evaluations and predictions through the Markov chain. Using the method of streaming data to cluster, we set the data at the same cluster as the sampling results from the same distribution to classify the epidemic situation. We used the Markov chain model to estimate the future development of the epidemic situation. According to the characteristics of stream data, the system can avoid the impact of epidemic data not meeting the assumption of independent homodistribution and only assess the epidemic situation based on local areas. © 2022 IEEE.

The management of COVID-19 epidemic: estimate of the actual infected population, impact of social distancing and directions for an efficient testing strategy. The case of Italy

This work focuses on the so called ‘first wave' of COVID-19 epidemic (21 February–10 April 2020) and aims at outlining a viable strategy to contain the COVID-19 spread and efficiently plan an exit from lockdown measures. It offers a model to estimate the total number of actual infected among the population at national and regional level inferring from the lethality rate, to fill the proven gap with the number of officially reported cases. The result is the reference population used to develop a forecasting exercise of new daily cases, compared to the reported ones. The eventual discrepancy is analysed in terms of compliance with the restrictive measures or to an insufficient number of tests performed. This simulation indicates that an efficient testing policy is the main actionable measure. Furthermore, the paper estimates the optimal number of tests to be performed at national and regional level, in order to be able to release an increasing number of individuals from restrictive measures. Copyright © 2022 Inderscience Enterprises Ltd.

Suit the Remedy to the Case-The Effectiveness of COVID-19 Nonpharmaceutical Prevention and Control Policies Based on Individual Going-Out Behavior.

Nonpharmaceutical policies for epidemic prevention and control have been extensively used since the outbreak of COVID-19. Policies ultimately work by limiting individual behavior. The aim of this paper is to evaluate the effectiveness of policies by combining macro nonpharmaceutical policies with micro-individual going-out behavior. For different going out scenarios triggered by individual physiological safety needs, friendship needs, and family needs, this paper categorizes policies with significant differences in intensity, parameterizes the key contents of the policies, and simulates and analyzes the effectiveness of the policies in different going-out scenarios with simulation methods. The empirical results show that enhancing policy intensity can effectively improve policy effectiveness. Among different types of policies, restricting the times of going out is more effective. Further, the effect of controlling going out based on physiological safety needs is better than other needs. We also evaluate the policy effectiveness of 26 global countries or regions. The results show that the policy effectiveness varies among 26 countries or regions. The quantifiable reference provided by this study facilitates decision makers to establish policy and practices for epidemic prevention and control.

Economics and economists during the COVID-19 pandemic: a personal view.

As was true for many others, my professional life was turned upside down in the early days of the pandemic. The crisis touched almost every field in economics: international supply chains broke down, economic activity was heavily constrained either by non-pharmaceutical measures to fight the pandemic or by voluntary action, and the labour market experienced unprecedented levels of short-time work and huge (temporary) lay-offs. Governments struggled to provide cash and find ways to compensate affected people and businesses. Financial markets tumbled and monetary policy faced new challenges on top of an already tense situation.

The management of COVID-19 epidemic: estimate of the actual infected population, impact of social distancing and directions for an efficient testing strategy. The case of Italy

This work focuses on the so called 'first wave' of COVID-19 epidemic (21 February-10 April 2020) and aims at outlining a viable strategy to contain the COVID-19 spread and efficiently plan an exit from lockdown measures. It offers a model to estimate the total number of actual infected among the population at national and regional level inferring from the lethality rate, to fill the proven gap with the number of officially reported cases. The result is the reference population used to develop a forecasting exercise of new daily cases, compared to the reported ones. The eventual discrepancy is analysed in terms of compliance with the restrictive measures or to an insufficient number of tests performed. This simulation indicates that an efficient testing policy is the main actionable measure. Furthermore, the paper estimates the optimal number of tests to be performed at national and regional level, in order to be able to release an increasing number of individuals from restrictive measures.

Forecasting crew fatigue risk on international flights under different policies in China during the COVID-19 outbreak.

To predict the risk of fatigue for flight crews on international flights under the new operating model policy of the civil aviation exemption approach policy during the COVID-19 outbreak, and to provide scientific validation methods and ideas for the exemption approach policy. This paper uses the change in flight crew alertness as a validation indicator, and then constructs an alertness assessment model to predict flight crew fatigue risk based on the SAFTE model theory. Then, the corresponding in-flight rotation plans for the flight is designed according to the exemption approach policy issued by the CAAC, the CCAR-121 part policy and the real operational requirements of the airline, respectively, and finally the simulation results is compared by comparing the pilot alertness and cockpit crew alertness under the exemption approach policy and the CCAR-121 part policy with the flight duration. The results show that the flight crew alertness level for the flight in-flight rotation plan simulation designed under the exemption approach policy is higher or closer to the pilot alertness level for operational flights under the CCAR-121 Part policy. This validates the reasonableness and safety of the exemption approach policy issued by the CAAC to meet the requirements of epidemic prevention and control, and provides scientific support and solutions for fatigue monitoring and management.