United States SBP: Utilities (UL): COVID-19 Impact: Large Negative Effect data was reported at 8.700 % in 11 Apr 2022. This records a decrease from the previous number of 9.300 % for 28 Mar 2022. United States SBP: Utilities (UL): COVID-19 Impact: Large Negative Effect data is updated weekly, averaging 8.400 % from Nov 2020 (Median) to 11 Apr 2022, with 36 observations. The data reached an all-time high of 21.400 % in 13 Dec 2021 and a record low of 3.500 % in 16 Nov 2020. United States SBP: Utilities (UL): COVID-19 Impact: Large Negative Effect data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s United States – Table US.S035: Small Business Pulse Survey: by Sector: Weekly. Beg Monday (Discontinued).

The data set evaluates the relationship between water surface area and angler effort on Devil's Lake, North Dakota. Over the last 30+ years, water levels have expanded/contracted in Devil's laking owing to variation in climate (precipitation). Positive changes (i.e. expansion) in the lake surface area results in increased fish production and angling opportunities that positively influence angler effort and the local economy.

United States SBP: COVID-19 Impact: Large Negative Effect data was reported at 30.200 % in 04 Oct 2020. This records a decrease from the previous number of 30.400 % for 27 Sep 2020. United States SBP: COVID-19 Impact: Large Negative Effect data is updated weekly, averaging 35.950 % from Apr 2020 (Median) to 04 Oct 2020, with 18 observations. The data reached an all-time high of 51.400 % in 26 Apr 2020 and a record low of 30.200 % in 04 Oct 2020. United States SBP: COVID-19 Impact: Large Negative Effect data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s United States – Table US.S044: Small Business Pulse Survey: Weekly, Beg Sunday (Discontinued).

NoSQL Database Market Outlook 2032

The global NoSQL database market size was USD 5.9 Billion in 2023 and is likely to reach USD 36.6 Billion by 2032, expanding at a CAGR of 30% during 2024–2032. The market growth is attributed to the rising adoption of NoSQL databases by industries to manage large amounts of data efficiently.



Increasing adoption of digital solutions by businesses is augmenting the NoSQL database industry. Businesses continue using the unique capabilities that NoSQL databases bring to their data management strategies. The NoSQL solutions work without any predefined schemas, thus, offering more flexibility to businesses that need to handle and manage ever-evolving data types and formats.

The factors behind the accelerating growth of the NoSQL database market include the omnipresence of internet-related activities, a surge in big data, and others. NoSQL database solutions present exceptional scalability and offer superior performance while managing extensive datasets. Moreover, the shift from conventional SQL databases to NoSQL databases to handle big-data and real-time web application data augmented the market.

Impact of Artificial Intelligence (AI) on the NoSQL Database Market

Artificial Intelligence (AI) has a significant impact on the NoSQL databases market by creating a surge in data volume and variety. AI technologies, including machine learning and deep learning, generate and process vast amounts of data, necessitating efficient data management solutions. The integration of AI with NoSQL databases further enhances data analysis capabilities and enables businesses to acquire valuable insights and make informed decisions. Therefore, the rise of AI technologies is propelling the market.

Non-Relational Databases, commonly referred to as NoSQL databases, have gained significant traction in recent years due to their ability to handle diverse data types and structures. Unlike traditional relational databases, non-relational databases do not rely on a fixed schema, which allows for greater flexibility and scalability. This adaptability is particularly beneficial for businesses dealing with large volumes of unstructured data, such as social media content, customer reviews, and multimedia files. As organizations continue to embrace digital transformation, the demand for non-relational databases is expected to rise, further driving the growth of the NoSQL database market.

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Around 30 percent of data center professionals responding to a 2023 survey said that sustainability concerns had impacted their decision-making around edge deployments to a large extent. Edge data centers are generally small in scale, making it difficult for operators to match the sustainability metrics achieved by large-scale facilities.

This data package includes the underlying data and files to replicate the calculations, charts, and tables presented in Aggregate Effects of Budget Stimulus: Evidence from the Large Fiscal Expansions Database. PIIE Working Paper 19-12.

If you use the data, please cite as: Cohen-Setton, Jeremie, Egor Gornostay, and Colombe Ladreit de Lacharrière (2019). Aggregate Effects of Budget Stimulus: Evidence from the Large Fiscal Expansions Database. PIIE Working Paper 19-12. Peterson Institute for International Economics.

COVID-19 vaccines are widely available in wealthy countries, yet many remain unvaccinated. We report on two studies (U.S. and France) with millions of Facebook users that tested strategies central to vaccination outreach: (1) health professionals addressing common concerns and (2) motivating “ambassadors” to encourage vaccination in their social networks. We can reject very small effects of any intervention on new first doses (0.16pp - U.S., 0.021pp - France), with similar results for second doses and boosters (U.S.). During the Omicron wave, messaging aimed at the unvaccinated or those tasked with encouraging others did not change vaccination decisions.

The GCAM-USA model was used to evaluate the incremental national and regional emission impacts of widespread electric vehicle adoption in the US through 2050. This dataset includes the model outputs that were used to develop figures and tables for the related manuscript. This dataset is associated with the following publication: Ou, Y., N. Kittner, S. Babaee, S.J. Smith, C. Nolte, and D. Loughlin. Evaluating long-term emission impacts of large-scale electric vehicle deployment in the US using a human-Earth systems model. Applied Energy. Elsevier B.V., Amsterdam, NETHERLANDS, 300: 117364, (2021).

US Tariff Impact on Market

US tariffs on semiconductor components used in data center chips could impact the overall cost of production. As the demand for GPUs and other advanced chips used in data centers grows, tariffs on components such as processors, memory units, and storage chips could raise production costs.

This price increase may be passed onto end consumers, particularly large data centers, which account for 64.1% of the market. Given the growing importance of data processing in sectors like BFSI (which accounts for 23.0% of the market), these tariffs could slow down investments in upgrading existing infrastructure.

While the North American market currently leads, the rising costs could lead to increased competition from global manufacturers, reducing the market share in the U.S. However, as demand for high-performance computing continues, these short-term challenges may be offset by long-term growth driven by the increasing reliance on cloud services and data-intensive applications.

US Tariff Impact on Sectors

• GPU Chips: 4%-6%
• Data Center Chips (General): 5%-7%
• Semiconductor Components: 3%-5%

Economic Impact

Tariffs on semiconductor components could increase production costs for data center chips, raising prices across sectors, particularly in large data centers. This would impact enterprises relying on large-scale data storage and processing, particularly in high-demand sectors like BFSI, potentially slowing the pace of infrastructure upgrades and investments.

Geographical Impact

North America, which currently leads the market with 38.4% share, may face slowed growth due to higher prices caused by tariffs on imported components. The U.S. could experience reduced competitiveness in the global market, as manufacturers in other regions with fewer tariffs could offer more affordable alternatives.

Business Impact

Businesses in the data center chip sector may face lower profit margins due to increased production costs from tariffs. Companies might be forced to pass the increased costs onto customers, which could affect demand, particularly among smaller enterprises or those in price-sensitive industries, potentially slowing market growth.

➤➤ Request sample reflecting US tariffs @ https://market.us/report/data-center-chip-market/free-sample/

Climate change has been shown to influence lake temperatures globally. To better understand the diversity of lake responses to climate change and give managers tools to manage individual lakes, we modelled daily water temperature profiles for 10,774 lakes in Michigan, Minnesota and Wisconsin for contemporary (1979-2015) and future (2020-2040 and 2080-2100) time periods with climate models based on the Representative Concentration Pathway 8.5, the worst-case emission scenario. From simulated temperatures, we derived commonly used, ecologically relevant annual metrics of thermal conditions for each lake. We included all available supporting metadata including satellite and in-situ observations of water clarity, maximum observed lake depth, land-cover based estimates of surrounding canopy height and observed water temperature profiles (used here for validation). This unique dataset offers landscape-level insight into the future impact of climate change on lakes. This data set contains the following parameters: Thermal metrics, Spatial data, Temperature data, Model drivers, Model configuration, which are defined below.

US Tariff Impact on Market

US tariffs on imported components used in data center physical security solutions could have a significant impact on the overall market. With the solution segment dominating the market, the increased cost of critical components such as surveillance cameras, biometric scanners, and access control systems may raise the price of these security solutions.

This could lead to higher costs for data centers, particularly large data centers, which account for 43.5% of the market share. Furthermore, the IT and telecommunications sector, a significant user of data center security solutions, could experience delays and cost increases due to tariff-related disruptions.

Although these tariffs might cause short-term price hikes, the long-term growth in demand for physical security in data centers is likely to continue as security concerns grow alongside the increasing data volumes handled by large centers.

US Tariff Impact on Sectors

• Surveillance Solutions: 4%-6%
• Access Control Systems: 5%-7%
• Biometric Systems: 3%-5%

Economic Impact

Tariffs could raise the cost of components critical for data center physical security solutions, including cameras and biometric systems. This price increase may affect both suppliers and consumers, especially in large data centers, leading to higher capital and operational costs for data storage and management facilities.

Geographical Impact

North America, being the dominant market for data center physical security, will be significantly impacted by tariffs on imported security components. These tariffs could slow down the growth of data center infrastructure, particularly in the U.S., where advanced technology and high-security measures are crucial for maintaining data integrity.

Business Impact

Businesses in the data center physical security market could face reduced profit margins as increased tariffs on imported components lead to higher costs. Smaller companies may struggle to absorb these costs, which could impact competition. Larger players may pass on the cost increases to customers, affecting overall adoption.

➤➤ Request sample reflecting US tariffs @ https://market.us/report/data-center-physical-security-market/free-sample/

United States SBP: Retail: COVID-19 Impact: Large Negative Effect data was reported at 25.200 % in 04 Oct 2020. This records an increase from the previous number of 24.800 % for 27 Sep 2020. United States SBP: Retail: COVID-19 Impact: Large Negative Effect data is updated weekly, averaging 32.200 % from Apr 2020 (Median) to 04 Oct 2020, with 18 observations. The data reached an all-time high of 53.100 % in 26 Apr 2020 and a record low of 24.800 % in 27 Sep 2020. United States SBP: Retail: COVID-19 Impact: Large Negative Effect data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s United States – Table US.S036: Small Business Pulse Survey: by Sector: Weekly, Beg Sunday (Discontinued).

Abstract

To educate consumers about responsible use of financial products, many governments, non-profit organizations and financial institutions have started to provide financial literacy courses. However, participation rates for non-compulsory financial education programs are typically extremely low.

Researchers from the World Bank conducted randomized experiments around a large-scale financial literacy course in Mexico City to understand the reasons for low take-up among a general population, and to measure the impact of this financial education course. The free, 4-hour financial literacy course was offered by a major financial institution and covered savings, retirement, and credit use. Motivated by different theoretical and logistics reasons why individuals may not attend training, researchers randomized the treatment group into different subgroups, which received incentives designed to provide evidence on some key barriers to take-up. These incentives included monetary payments for attendance equivalent to $36 or $72 USD, a one-month deferred payment of $36 USD, free cost transportation to the training location, and a video CD with positive testimonials about the training.

A follow-up survey conducted on clients of financial institutions six months after the course was used to measure the impacts of the training on financial knowledge, behaviors and outcomes, all relating to topics covered in the course.

The baseline dataset documented here is administrative data received from a screener that was used to get people to enroll in the financial course. The follow-up dataset contains data from the follow-up questionnaire.

Geographic coverage

Mexico City

Analysis unit

-Individuals

Universe

Participants in a financial education evaluation

Kind of data

Sample survey data [ssd]

Sampling procedure

Researchers used three different approaches to obtain a sample for the experiment.

The first one was to send 40,000 invitation letters from a collaborating financial institution asking about interest in participating. However, only 42 clients (0.1 percent) expressed interest.

The second approach was to advertise through Facebook, with an ad displayed 16 million times to individuals residing in Mexico City, receiving 119 responses.

The third approach was to conduct screener surveys on streets in Mexico City and outside branches of the partner institution. Together this yielded a total sample of 3,503 people. Researchers divided this sample into a control group of 1,752 individuals, and a treatment group of 1,751 individuals, using stratified randomization. A key variable used in stratification was whether or not individuals were financial institution clients. The analysis of treatment impacts is based on the sample of 2,178 individuals who were financial institution clients.

The treatment group received an invitation to participate in the financial education course and the control group did not receive this invitation. Those who were selected for treatment were given a reminder call the day before their training session, which was at a day and time of their choosing.

Mode of data collection

Face-to-face [f2f]

Research instrument

The follow-up survey was conducted between February and July 2012 to measure post-training financial knowledge, behavior and outcomes. The questionnaire was relatively short (about 15 minutes) to encourage participation.

Interviewers first attempted to conduct the follow-up survey over the phone. If the person did not respond to the survey during the first attempt, researchers offered one a 500 pesos (US$36) Walmart gift card for completing the survey during the second attempt. If the person was still unavailable for the phone interview, a surveyor visited his/her house to conduct a face-to-face interview. If the participant was not at home, the surveyor delivered a letter with information about the study and instructions for how to participate in the survey and to receive the Walmart gift card. Surveyors made two more attempts (three attempts in total) to conduct a face-to-face interview if a respondent was not at home.

Response rate

72.8 percent of the sample was interviewed in the follow-up survey. The attrition rate was slightly higher in the treatment group (29 percent) than in the control group (25.3 percent).

NEWSQL In Memory Database Market Outlook

The global market size for NEWSQL In Memory Databases was estimated at USD 3.8 billion in 2023 and is projected to reach USD 10.9 billion by 2032, growing at a remarkable compound annual growth rate (CAGR) of 12.3% during the forecast period. The growth of this market is primarily driven by the increasing demand for high-speed data processing and real-time analytics across various industries. As businesses continue to generate vast amounts of data, there is a growing need for efficient database management solutions that can handle these large data volumes with low latency. The adoption of NEWSQL In Memory databases, which combine the scalability of NoSQL with the ACID compliance of traditional SQL databases, is thus on the rise.

The demand for real-time data analytics and processing is a significant growth driver for the NEWSQL In Memory Database market. As industries such as BFSI, healthcare, and retail increasingly rely on data-driven decision-making processes, the need for fast and efficient database solutions becomes paramount. NEWSQL In Memory databases provide the ability to process large datasets quickly, enabling businesses to gain insights and make decisions in real time. This is particularly crucial in sectors like finance and healthcare, where timely information can significantly impact outcomes.

The advent of technologies such as artificial intelligence (AI), machine learning (ML), and Internet of Things (IoT) also fuels the growth of the NEWSQL In Memory Database market. These technologies generate immense amounts of data, requiring robust database solutions that can handle high-throughput and low-latency transactions. NEWSQL In Memory databases are well-suited for these applications, providing the necessary speed and scalability to manage the data efficiently. Furthermore, the rising adoption of cloud computing and the shift towards digital transformation in various industries further bolster the market's expansion.

Another crucial factor contributing to the market's growth is the increasing emphasis on customer experience and personalized services. Businesses are leveraging data to understand customer behavior, preferences, and trends to offer tailored experiences. NEWSQL In Memory databases enable organizations to analyze customer data in real time, enhancing their ability to provide personalized services. This is evident in the retail sector, where businesses use real-time analytics to optimize inventory, improve customer engagement, and boost sales.

In-Memory Grid technology plays a pivotal role in enhancing the performance of NEWSQL In Memory databases. By storing data in the main memory, In-Memory Grids significantly reduce data retrieval times, allowing for faster data processing and real-time analytics. This capability is particularly beneficial in scenarios where rapid access to data is crucial, such as in financial transactions or healthcare diagnostics. The integration of In-Memory Grid technology with NEWSQL databases not only boosts speed but also improves scalability, enabling businesses to handle larger datasets efficiently. As industries continue to demand high-speed data processing solutions, the adoption of In-Memory Grids is expected to rise, further driving the growth of the NEWSQL In Memory Database market.

On a regional level, North America holds a significant share of the NEWSQL In Memory Database market, driven by the presence of major technology companies and early adoption of advanced database solutions. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, owing to the rapid digitalization and increasing investments in technology infrastructure. Europe also shows substantial potential, with a growing focus on data-driven strategies and compliance with stringent data regulations.

Type Analysis

The NEWSQL In Memory Database market can be segmented by type into operational and analytical databases. Operational databases are designed to handle real-time transaction processing, making them ideal for applications that require fast and efficient data entry and retrieval. These databases are commonly used in industries such as finance, retail, and telecommunications, where the ability to process transactions quickly is critical. The demand for operational NEWSQL In Memory databases is growing as businesses increasingly rely on real-time data for decision-making and operational efficiency.

Many experts speculate that US antitrust policy towards horizontal mergers has been too lenient. We estimate the price effects of Whirlpool's acquisition of Maytag to provide new evidence on this debate. We compare price changes in appliance markets most affected by the merger to markets where concentration changed much less or not at all. We estimate price increases for dishwashers and relatively large price increases for clothes dryers, but no price effects for refrigerators or clothes washers. The combined firm's market share fell across all four affected categories, and the number of distinct appliance products offered for sale fell. (JEL G34, K21, L11, L41, L68)

Cloud Database and DBaaS Market Outlook 2032

The global cloud database and DBaaS market size was USD 18.99 Billion in 2023 and is likely to reach USD 75.07 Billion by 2032, expanding at a CAGR of 16.5% during 2024–2032. The market is driven by the increasing dependency of the business operations on digital technology, across the world.



Increasing digitalization and the need for efficient data management are expected to drive the cloud database and Database as a Service (DBaaS) market, during the forecast period. The market is experiencing a surge in demand due to the rising need for cost-effective and scalable database solutions. Technological advancements, such as the integration of AI and machine learning, are further propelling the market. These technologies enhance the efficiency and reliability of cloud databases, thus reducing operational costs and improving data accessibility.

Growing adoption of cloud services across various industries is another significant factor contributing to the expansion of the market. Industries such as BFSI, healthcare, and retail are increasingly relying on cloud databases and DBaaS for efficient data storage and management. These services offer scalability, flexibility, and security, making them an ideal choice for businesses looking to optimize their data management processes.



Rising investments in big data analytics and IoT are another driving factor for the market. The need for efficient data storage and management systems is increasing as businesses generate and analyze vast amounts of data. Cloud databases and DBaaS play a crucial role in this process, ensuring the smooth storage, management, and analysis of large data sets.

Impact of Artificial Intelligence (AI) in Cloud Database and DBaaS Market

The use of artificial intelligence is likely to boost the cloud database and DBaaS market. AI's advanced analytics capabilities enable the extraction of meaningful insights from vast amounts of data, thereby enhancing decision-making processes. The predictive abilities of AI facilitate proactive system maintenance, minimizing downtime and optimizing operational efficiency. Moreover, AI's machine learning algorithms adapt to changing data patterns, ensuring robust data management and security. The integration of AI also allows for automated database tuning, resulting in improved performance and significant cost savings.

As of December 2024, the most significant data breach incident in the United States was the Yahoo data breach that dates back to 2013-2016. Impacting over three billion online users, this incident still remains one of the most significant data breaches worldwide. The second-biggest case was the January 2021 data breach at Microsoft, involving about 30 thousand companies in the United States and around 60 thousand companies around the world.

Replication code for "Local Effects of Large New Apartment Buildings in Low-Income Areas" by Brian Asquith, Evan Mast, and Davin Reed

This paper analyzes a randomized experiment that uses novel strategies to promote handwashing with soap at critical points in time in Peru. It evaluates a large-scale comprehensive initiative that involved both community and school activities in addition to communication campaigns. The analysis indicates that the initiative was successful in reaching the target audience and in increasing the treated population’s knowledge about appropriate handwashing behavior. These improvements translated into higher self-reported and observed handwashing with soap at critical junctures. However, no significant improvements in the health of children under the age of 5 years were observed.

The continuous growth of the global human population results in increased use and change of landscapes, with infrastructures like transportation or energy facilities, being a particular risk to large carnivores. Environmental Impact Assessments were established to identify the probable environmental consequences of any new proposed project, find ways to reduce impacts, and provide evidence to inform decision making and mitigation. Portugal has a wolf population of around 300 individuals, designated as an endangered species with full legal protection. They occupy the northern mountainous areas of the country which has also been the focus of new human infrastructures over the last 20 years. Consequently, dozens of wolf monitoring programs have been established to evaluate wolf population status, to identify impacts, and to inform appropriate mitigation or compensation measures. We reviewed Portuguese wolf monitoring programs to answer four key questions: do wolf programs examine adequate biological parameters to meet monitoring objectives? is the study design suitable for measuring impacts? are data collection methods and effort sufficient for the stated inference objectives? and do statistical analyses of the data lead to robust conclusions? Overall, we found a mismatch between the stated aims of wolf monitoring and the results reported, and often neither aligns with the existing national wolf monitoring guidelines. Despite the vast effort expended and the diversity of methods used, data analysis makes almost exclusive use of relative indices or summary statistics, with little consideration of the potential biases that arise through the (imperfect) observational process. This makes comparisons of impacts across space and time difficult and is therefore unlikely to contribute to a general understanding of wolf responses to infrastructure-related disturbance. We recommend the development of standardized monitoring protocols and advocate for the use of statistical methods that account for imperfect detection to guarantee accuracy, reproducibility, and efficacy of the programs. Methods We reviewed all major wolf monitoring programs developed for environmental impact assessments in Portugal since 2002 (Table S1, Supplementary material). Given that the focus here is on the adequacy of targeted wolf monitoring for delivering conclusions about the effects of infrastructure development, we reviewed only monitoring programs that were specifically designed for wolves and not those concerned with general mammalian assessment. The starting point was a compilation from the 2019-2021 National Wolf Census (Pimenta et al., 2023), where every wolf monitoring program that occurred between 2014 and 2019 in Portugal was identified. The list was completed with projects that started before 2014 or after 2019 based on personal knowledge, inquires to principal scientific teams, governmental agencies, and EIA consultants. Depending on duration, wolf monitoring programs can produce several, usually annual, reports that are not peer-reviewed and do not appear on standard search engines (e.g., Web of Science or Google Schoolar) but are publicly available from the Portuguese Environmental Agency (APA – www.apambiente.pt). We conducted an online search on APA´s search engine (https://siaia.apambiente.pt/) and identified a total of 30 projects. For each of these projects, we were interested in the first and the last report to identify any methodological changes. If the last report was not present, we reviewed the most recent one. If no report was present, we requested it from the team responsible. Our investigation centred on characterizing and quantifying four components of wolf monitoring programs that are interlinked and that should be ideally determined by the initial objectives: (1) biological parameters, i.e., what wolf parameters were studied to assess impacts; (2) study design, i.e., what sampling schemes were followed to collect and analyse data; (3) data collection, i.e., which sampling methodology and how much effort was used to collect data; and (4) data analysis, i.e., how data were analysed to estimate relevant parameters and assess impact. Biological parameters were identified and classified under two categories: occurrence and demography, which broadly correspond to the necessary inputs to assess impacts like exclusion effect and changes in reproductive patterns. Occurrence-related parameters refer to variables used to measure the presence or absence of wolves, whereas demographic parameters refer to variables that intend to measure population-level effects such as abundance, density, survival, or reproduction. We also recorded whether any effort was made to quantify prey population distribution or abundance as recommended in the guidelines. For study design, we reviewed the sampling design of the project, with specific focus on the spatial and temporal aspect of the study such as total area surveyed, the definition of a sampling site within this region (i.e., resolution), the duration of the study and the number of sampling seasons. The goal here was to determine whether the sampling scheme used was appropriate for assessing infrastructure impacts on wolf distribution or demography, depending on what the focus was. For data collection, we identified the main data collection methodologies used and the corresponding sampling effort. By far the most frequent method used is sign surveys, and specifically scat surveys, and for these studies we recorded whether genetic identification of species or individuals based on faecal DNA was attempted. We compare how sampling effort varies by the various inference objectives and, as above, assess which, if any, project or data collection approach is most likely to produce evidence of impact. We divided the Analysis component into two groups: single-year and multi-year analyses. For single-year analysis we identified how monitoring projects used data to make inferences about the state biological parameters of interest and discuss the associated strengths and weaknesses. For multi-year analyses, we recorded how differences or trends were quantified and associated with infrastructure impacts, commenting on the statistical robustness of the analyses used across the projects.