In 2023, average monthly real wages in Russia increased by *** percent year-over-year. The real wage growth rate slowed down in recent years, gradually declining from *** percent in 2018.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

The Earnings Slowdown: What It Means for Investors

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This paper challenges the notion that on-the-job training investments are quantitatively important for workers' welfare and argues that on-the-job training may not increase lifetime income by more than 1 percent. I argue that it is very difficult to reconcile the slowdown in wage growth late in a worker's career with optimizing behavior unless the technology for learning on the job is such that it generates very low gains from training. The analysis is based on a nonparametric methodology for estimating the learning technology from wage profiles; the results are arrived at by comparing the lifetime income when the worker optimally invests in his human capital to the one where he does not make any investments. (JEL: E24, J24, J31)

Nucor projects Q4 earnings to miss Wall Street expectations due to slower steel mill activity and lower average selling prices.

This dataset contains replication files for "The Fading American Dream: Trends in Absolute Income Mobility Since 1940" by Raj Chetty, David Grusky, Maximilian Hell, Nathaniel Hendren, Robert Manduca, and Jimmy Narang. For more information, see https://opportunityinsights.org/paper/the-fading-american-dream/. A summary of the related publication follows. One of the defining features of the “American Dream” is the ideal that children have a higher standard of living than their parents. We assess whether the U.S. is living up to this ideal by estimating rates of “absolute income mobility” – the fraction of children who earn more than their parents – since 1940. We measure absolute mobility by comparing children’s household incomes at age 30 (adjusted for inflation using the Consumer Price Index) with their parents’ household incomes at age 30. We find that rates of absolute mobility have fallen from approximately 90% for children born in 1940 to 50% for children born in the 1980s. Absolute income mobility has fallen across the entire income distribution, with the largest declines for families in the middle class. These findings are unaffected by using alternative price indices to adjust for inflation, accounting for taxes and transfers, measuring income at later ages, and adjusting for changes in household size. Absolute mobility fell in all 50 states, although the rate of decline varied, with the largest declines concentrated in states in the industrial Midwest, such as Michigan and Illinois. The decline in absolute mobility is especially steep – from 95% for children born in 1940 to 41% for children born in 1984 – when we compare the sons’ earnings to their fathers’ earnings. Why have rates of upward income mobility fallen so sharply over the past half-century? There have been two important trends that have affected the incomes of children born in the 1980s relative to those born in the 1940s and 1950s: lower Gross Domestic Product (GDP) growth rates and greater inequality in the distribution of growth. We find that most of the decline in absolute mobility is driven by the more unequal distribution of economic growth rather than the slowdown in aggregate growth rates. When we simulate an economy that restores GDP growth to the levels experienced in the 1940s and 1950s but distributes that growth across income groups as it is distributed today, absolute mobility only increases to 62%. In contrast, maintaining GDP at its current level but distributing it more broadly across income groups – at it was distributed for children born in the 1940s – would increase absolute mobility to 80%, thereby reversing more than two-thirds of the decline in absolute mobility. These findings show that higher growth rates alone are insufficient to restore absolute mobility to the levels experienced in mid-century America. Under the current distribution of GDP, we would need real GDP growth rates above 6% per year to return to rates of absolute mobility in the 1940s. Intuitively, because a large fraction of GDP goes to a small fraction of high-income households today, higher GDP growth does not substantially increase the number of children who earn more than their parents. Of course, this does not mean that GDP growth does not matter: changing the distribution of growth naturally has smaller effects on absolute mobility when there is very little growth to be distributed. The key point is that increasing absolute mobility substantially would require more broad-based economic growth. We conclude that absolute mobility has declined sharply in America over the past half-century primarily because of the growth in inequality. If one wants to revive the “American Dream” of high rates of absolute mobility, one must have an interest in growth that is shared more broadly across the income distribution.

Graph and download economic data for Average Hourly Earnings of All Employees, Leisure and Hospitality (CES7000000003) from Mar 2006 to Jun 2025 about leisure, hospitality, earnings, establishment survey, hours, wages, employment, and USA.

BNPL user growth in the United States is expected to slow down significantly in the years following 2024, although the source does not state why. This is according to a forecast made in August 2024 and released a few months later. It predicts that in 2024, over ** percent of the U.S. population will have accessed and used some form of buy now, pay later. The slowdown contrasts figures on BNPL transaction value, which is expected to keep growing. Sources connect economic challenges with a potentially growing use of BNPL in the United States, although there is no concrete proof of such a connection. As of 2024, the BNPL market remains unregulated.

We solve the standard production function with constant elasticity of substitution (CES) for its labour augmenting technology term. We make capital stock data and insert them together with data from Penn World Tables (PWT9.1). This provides labour augmenting technology levels and growth rates for alternative elasticities of substitution for 70 countries, 1950‐2017. The percentage growth rates of labour‐augmenting technical change (LATC) are shown to fall over time (productivity slowdown) for all elasticity values in a panel data analysis. They converge to a panel average of 2.67% and 1% depending on the inclusion of human capital and the elasticity of substitution assumed. The standard growth result of a GDP growth rate equal to that of LATC and labour input holds only for LATC based on low elasticities of substitution indicating that the economies are not in steady‐states. The correlation of LATC growth rates with total factor productivity growth from PWT9.1 is strongest (0.893) for LATC data based on an elasticity of substitution of 0.8. Matching the labour/capital share ratios from CES functions with those of PWT9.1 reveals a range of elasticities of substitution for each country, mostly between 0.8 and 1.2 or somewhat lower for developing countries. If the MPL‐to‐wage ratio is 1.6, the elasticities of substitution vary around 0.8. Using the human‐capital corrected LATC growth with CES = 0.8, 13 of 69 countries have a productivity slowdown defined as growth rate below mean in the long run; the USA is not among them indicating that the US productivity slowdown is mainly one of human capital. Dynamics of coefficient of variation and kernel density distributions for LATC growth rates shows that there is neither technological convergence nor divergence.

In 2015, the wages of employees of the consolidated production human health sector in the Netherlands increased by 591 million euros (+1.87 percent) since 2014. While the growth in this industry is slowing down, with 32.2 billion euros, the wages of employees is at its peak in the observed period. Notably, the wages of employees in this industry continuously increased over the last years.Find more statistics on other topics about the Netherlands with key insights such as wages of employees of the electric equipment sector and wages of employees of the water supply & waste sector.

We solve the standard production function with constant elasticity of substitution (CES) for its labour augmenting technology term. We make capital stock data and insert them together with data from Penn World Tables (PWT9.1). This provides labour augmenting technology levels and growth rates for alternative elasticities of substitution for 70 countries, 1950‐2017. The percentage growth rates of labour‐augmenting technical change (LATC) are shown to fall over time (productivity slowdown) for all elasticity values in a panel data analysis. They converge to a panel average of 2.67% and 1% depending on the inclusion of human capital and the elasticity of substitution assumed. The standard growth result of a GDP growth rate equal to that of LATC and labour input holds only for LATC based on low elasticities of substitution indicating that the economies are not in steady‐states. The correlation of LATC growth rates with total factor productivity growth from PWT9.1 is strongest (0.893) for LATC data based on an elasticity of substitution of 0.8. Matching the labour/capital share ratios from CES functions with those of PWT9.1 reveals a range of elasticities of substitution for each country, mostly between 0.8 and 1.2 or somewhat lower for developing countries. If the MPL‐to‐wage ratio is 1.6, the elasticities of substitution vary around 0.8. Using the human‐capital corrected LATC growth with CES = 0.8, 13 of 69 countries have a productivity slowdown defined as growth rate below mean in the long run; the USA is not among them indicating that the US productivity slowdown is mainly one of human capital. Dynamics of coefficient of variation and kernel density distributions for LATC growth rates shows that there is neither technological convergence nor divergence.

Abstract:Amidst rapid social transformation, the marriage behavior of young people in China are continuously changing toward marriage delayed. What are the factors contributing to the collective changes in marital behavior among Chinese young people? This study used the 2003-2021 China General Social Survey cross-sectional data and hierarchical age-period-cohort cross-classified random-effects model to investigate factors influencing marriage behaviors.The results show: macroeconomic growth was positively correlated with age at first marriage; Yet the wage growth rate was negatively correlated with age at first marriage in lower social classes and positively with marriage probability among farmers and individuals with low education levels. Influenced by individualism,individuals born during the 1980 and 1990 showed a significant increase in the age at first marriage compared to those born in the 1960s and 1970s. The macroeconomic and cultural changes in Chinese society have a profound impact on collective marriage behavior.

In recent years, the temporary employment sector in Germany has played a crucial role in the integration of refugees and foreign workers. These contributions underline its importance for the German labour market. Since 2023, however, the deteriorating economic situation has put considerable pressure on the industry. The economic slowdown led to a noticeable decline in industrial orders, which also reduced the demand for temporary workers. Between 2020 and 2025, industry turnover fell by an average of 0.5% per year. It is expected to fall by 1.9% to 28.4 billion euros in 2025. Profitability in the temporary staffing industry varies considerably depending on the business model. Companies such as Hays, which place specialised professionals such as engineers and IT experts, achieve higher profit margins as these professionals are in high demand and can achieve high hourly rates. The industry is currently facing the challenge of numerous companies having to cut staff in order to save costs. These job cuts are curbing demand for temporary staff and increasing the need for the industry to fundamentally rethink its business models. Even if the reformed Immigration Act could theoretically open up new prospects by facilitating access to international skilled labour, the actual benefit of these measures remains questionable given the general economic situation. In addition, although investments in digital solutions and process optimisation are urgently needed, they place a financial burden on companies and are associated with considerable uncertainty.The industry will face major challenges in the future, while the general economic outlook remains uncertain. Although the ongoing shortage of skilled labour could potentially offer opportunities, economic conditions are currently not ideal for expansion. An average annual decline in turnover of 0.2% is therefore expected over the next five years, meaning that industry turnover in 2030 is likely to amount to 28.2 billion euros. It is uncertain whether the measures taken to date, such as increased international recruitment and improved qualification programmes, will be sufficient to overcome the structural challenges. However, given these uncertainties, the temporary staffing industry should be able to maintain its position as a key provider of flexible labour solutions in Germany by adapting to new market conditions and developing innovative strategies.

In the second quarter of 2025, the growth of the real gross domestic product (GDP) in China ranged at *** percent compared to the same quarter of the previous year. GDP refers to the total market value of all goods and services that are produced within a country per year. It is an important indicator of the economic strength of a country. Real GDP is adjusted for price changes and is therefore regarded as a key indicator for economic growth. GDP growth in China In 2024, China ranged second among countries with the largest gross domestic product worldwide. Since the introduction of economic reforms in 1978, the country has experienced rapid social and economic development. In 2013, it became the world’s largest trading nation, overtaking the United States. However, per capita GDP in China was still much lower than that of industrialized countries. Until 2011, the annual growth rate of China’s GDP had constantly been above nine percent. However, economic growth has cooled down since and is projected to further slow down gradually in the future. Rising domestic wages and the competitive edge of other Asian and African countries are seen as main reasons for the stuttering in China’s economic engine. One strategy of the Chinese government to overcome this transition is a gradual shift of economic focus from industrial production to services. Challenges to GDP growth Another major challenge lies in the massive environmental pollution that China’s reckless economic growth has caused over the past decades. China’s development has been powered mostly by coal consumption, which resulted in high air pollution. To counteract industrial pollution, further investments in waste management and clean technologies are necessary. In 2017, about **** percent of GDP was spent on pollution control. Surging environmental costs aside, environmental issues could also be a key to industrial transition as China placed major investments in renewable energy and clean tech projects. The consumption of green energy skyrocketed from **** exajoules in 2005 to **** million in 2022.

Ericsson AB's Q4 earnings fall short due to unmet sales rebound in India, despite positive investor sentiment and a significant share rally.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

CASA: Poised for Growth or Headed for Downturn?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Alamo Ascending (ALG): Primed for Growth or at Risk of a Downturn?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data