Search

India is on the edge of a demographic revolution with a rapidly rising working-age population. For the first time in this study, we investigate the role of the rising working-age population on per capita small savings in post offices and banks net of socio-economic characteristics using state-level panel data compiled from multiple sources for the period 2001–2018. Our comprehensive econometric assessment with multiple robustness checks provides three key findings: (1) per capita private savings is increasing because of India’s growing working-age population; thus, the ‘economic life cycle hypothesis’ is evidentially supported; (2) the demographic factors contribute around one-fourth of the per capita private savings inequality across Indian states; and (3) the demographic window of economic opportunity for India can yield maximum benefits in terms of private savings when accompanied by favourable socio-economic policies on education, health, gender equity, and economic growth.

Polyculture of fish is a common pond-based aquaculture system practiced by small-scale producers in developing countries to improve input use efficiency, and increase productivity and profits. We conduct a cross-country comparison to examine whether the economic and productivity benefits are seen in data of 1,651 ponds from 1,307 fish farming households in three countries: Bangladesh, Egypt, and Myanmar. Among these ponds, polyculture is the prevailing system, as it is practiced in 66% of them. The surveys of these households were completed in 2019. We use propensity score matching to match the ponds based on household and pond characteristics, so that ponds are compared with other similar ponds. Results indicate that the aquaculture revenue and profit of polyculture ponds are higher than those of monoculture ponds by US$4,993 and US$6,985, respectively, per hectare per cycle. The increase is also observed in the systems of tilapia polyculture and rohu polyculture, which are the two most common systems among the sampled farmers. The increase for rohu polyculture at US$7,992 in revenue and US$9,366 in profit per hectare per cycle is higher than the increase for tilapia polyculture at US$4,649 and US$6,649, respectively. However, tilapia polyculture farmers save more harvested fish for household consumption, by 72 kg per cycle, than farmers of other systems. The higher profits for general polyculture, tilapia polyculture, and rohu polyculture are statistically significant after controlling for country-level factors and have high critical value of gamma in the Rosenbaum sensitivity analysis, indicating that these results are robust. This analysis from fish farming households complements the results from pond experiments and can help to inform decision-making in aquaculture policy and training.

We test the hypothesis that protest participation decisions in an adult population of potential climate protesters are interdependent. Subjects (n=1,510) from the four largest German cities were recruited two weeks before protest date. We measured participation (ex post) and beliefs about the other subjects' participation (ex ante) in an online survey, used a randomized informational intervention to induce exogenous variance in beliefs, and estimated the causal effect of a change in belief on the probability of participation using a control function approach. Participation decisions are found to be strategic substitutes: a one percentage-point increase of belief causes a .67 percentage-point decrease in the probability of participation in the average subject., Comment: 21 pages, 6 pages appendix, 4 figures

Islamic foreign exchange forward plays a significant role to mitigate various foreign currency exchange risks. The main challenge that impedes the development and operation of the Islamic foreign exchange forward as a hedging instrument is the behaviour of relying on existing conventional framework with core conception of relying on interest rate and excessive risk taking. This study utilized monthly data from April 2004 to October 2017 of the Malaysian derivatives market. This study found that in the absence of an alternative profit-rate related benchmark and cross border activities, Islamic banks are constrained to use the interest rate benchmark. In the short run, both medium term (6-months) and longer term (12-months) tenures indicate faster speed of adjustment possibility due to higher trading volume and less demand for the medium term for the Islamic foreign exchange forward contract. It implies a need of the Islamic foreign exchange forward as a longer-term hedging instrument and not for a short term speculation and risk-taking purposes, as prohibited by shariah.

As cryptocurrencies develop and circulate at greater rates, countries have appeared to consider the technology as an adoptable medium of exchange. By expanding the influence of cryptocurrencies through adoption, countries raise its impact on the global economy. This paper is the first to apply an augmented version of the gravity model to examine the effects of global cryptocurrency adoption on international trade. This empirical study involves aggregating datasets on U.S. bilateral trade flows, gravity variable statistics, and the adoption of cryptocurrencies. In application of the gravity model, regression analyses are used on the aggregated data to test the magnitude of cryptocurrencies’ impact on trade. Based on the overall findings, the variables for cryptocurrency adoption produce negative coefficients suggesting a negative correlation between the adoption of cryptocurrencies and international trade. The central tendency in the empirical evidence offers the interpretation that countries with weak institutions to promote trade are more likely to adopt cryptocurrencies resulting in a negative association between cryptocurrency adoption and trade.

This review systematizes the emerging literature for causal inference using deep neural networks under the potential outcomes framework. It provides an intuitive introduction on how deep learning can be used to estimate/predict heterogeneous treatment effects and extend causal inference to settings where confounding is non-linear, time varying, or encoded in text, networks, and images. To maximize accessibility, we also introduce prerequisite concepts from causal inference and deep learning. The survey differs from other treatments of deep learning and causal inference in its sharp focus on observational causal estimation, its extended exposition of key algorithms, and its detailed tutorials for implementing, training, and selecting among deep estimators in Tensorflow 2 available at github.com/kochbj/Deep-Learning-for-Causal-Inference.

Using quantile regression models to estimate quantile treatment effects is becoming increasingly popular. This paper introduces the rqr command that can be used to estimate residualized quantile regression (RQR) coefficients and the rqrplot postestimation command that can be used to effortless plot the coefficients. The main advantages of the rqr command compared to other Stata commands that estimate (unconditional) quantile treatment effects are that it can include high-dimensional fixed effects and that it is considerably faster than the other commands.

Cite as:Charith, K., & Davydenko, A. (2021). Informational value of dividend initiations: Impact of cash dividends on share prices of manufacturing companies in Sri Lanka.International Journal of Economics and Finance, 13(3), 13-30. https://doi.org/10.5539/ijef.v13n3p13 Abstract The shareholder wealth consists of dividends and capital gains. The former is considered to be risk averse, whereas the latter is perceived to be risky. The risk return trade off in these two returns drives the investor preference. The objective of a for profit organization is to maximize shareholders’ wealth, however, disbursing dividends may not always be in the best interest of shareholders. Theoretically, retained earnings increase share prices as firms have more funds to be invested. The objective of the study is to measure the stimulus of cash dividends on share prices. We conduct empirical analysis based on data relating to companies listed on the Colombo Stock Exchange (CSE) under the manufacturing sector. As we show in our literature review, in order to reduce the risk of obtaining spurious results, this analysis requires the use of advanced modelling techniques allowing to model non stationarity of time series, as well the presence of control variables and lagged variables. The novelty of our study is in the use of advanced modelling and data visualisation techniques (including the ‘xdPlot’ dataviz framework recently proposed by the authors), especially in application to CSE data. We conduct a thorough exploratory data analysis (EDA) aiming to spot data anomalies and initiate appropriate data transformations. Given the results of EDA and the nature of the data available, we select the Arellano Bond estimator as the most adequate method for regression analysis. Market Price per share (MPS) termed as the dependent variable, whereas Dividend per Share (DPS) is viewed as the independent variable. The results validated theoretical literature such as signaling effect and bird in hand theory, but questioned some previous empirical studies. The study validated cash dividends as stimulus to investors given the positive relationship between DPS and MPS. Dataset:https://doi.org/10.6084/m9.figshare.13475901

Studies investigating how the number of children in a family affects the parents or the children face problems because the variable of interest is endogenous in the model. The currently accepted solution to this problem is to use instrumental variables (IVs), for example, based on twin births. In this paper, I review and add to the critique of IVs based on twin births and show that that there are so many issues—major and minor—with these IVs that results based on them are not reliable or interpretable. I also review other IVs used in the literature, for example IVs based on the sexes of the firstborn children, and conclude that there are, as of yet, no credible IVs for the number of children. We need to disregard results from studies applying these IVs, reevaluate the current state of knowledge, and develop new, more credible methods.

In this paper, we explore the impact of changes in traffic flow on local air pollution under specific meteorological conditions by integrating hourly traffic flow data, air pollution data and meteorological data, using generalized linear regression models and advanced machine learning algorithms: support vector machines and decision trees. The geographical location is Oslo, the capital of Norway, and the time we selected is from February 2020 to September 2020; We also selected 24-hour data for May 11 and 16 of the same year, representing weekday and holiday traffic flow, respectively, as a subset to further explore. Finally, we selected data from July 2020 for robustness testing, and algorithm performance verification. We found that: the maximum traffic flow on holidays is significantly higher than that on weekdays, but the holidays produce less concentration of {NO}_x throughout the month; the peak arrival time of {NO}_x,\ {NO}_2and NO concentrations is later than the peak arrival time of traffic flow. Among them, {NO}_x has a very significant variation, so we choose {NO}_x concentration as an air pollution indicator to measure the effect of traffic flow variation on air pollution; we also find that {NO}_xconcentration is negatively correlated with hourly precipitation, and the variation trend is like that of minimum air temperature. We used multiple imputation methods to interpolate the missing values. The decision tree results yield that when traffic volumes are high (>81%), low temperatures generate more concentrations of {NO}_x than high temperatures (an increase of 3.1%). Higher concentrations of {NO}_x (2.4%) are also generated when traffic volumes are low (no less than 22%) but there is some precipitation ≥ 0.27%. In the evaluation of the prediction accuracy of the machine learning algorithms, the support vector machine has the best prediction performance with high R-squared and small MAE, MSE and RMSE, indicating that the support vector machine has a better explanation for air pollution caused by traffic flow, while the decision tree is the second best, and the generalized linear regression model is the worst. The selected data for July 2020 obtained results consistent with the overall dataset.

In this paper, we assess the economic benefits of demographic changes in India by employing econometric models and robustness checks based on panel data gathered over a period of more than three decades. Our analysis highlights four key points. First, the contribution of India’s demographic dividend is estimated to be around 1.9 percentage points out of 12% average annual growth rate in per capita income during 1981–2015. Second, India’s demographic window of opportunity began in 2005, significantly improved after 2011, and will continue till 2061. Third, our empirical analysis supports the argument that the realisation of the demographic dividend is conditional on a conducive policy environment with enabling aspects such as quality education, good healthcare, decent employment opportunities, good infrastructure, and gender empowerment. Fourth, the working-age population in India contributes around one-fourth of the inequality in per capita income across states. Thus, to reap the maximum dividends from the available demographic window of opportunity, India needs to work towards enhancing the quality of education and healthcare in addition to providing good infrastructure, gender empowerment, and decent employment opportunities for the growing working-age population.

In this paper, we shed new light on the links between firm-level innovation and growth. We introduce data that capture a difficult-to-observe aspect of firms' innovative activity – new product/service launches – at scale. We show that our novel measures complement existing innovation metrics. We build a simple framework covering firm-level innovation, launches and revenue productivity. Then, we show positive linkages between past patenting and launches and between launches and performance for a large panel of small and medium-sized enterprises (SMEs) in the UK. We go on to explore the roles of age, size, industry and product/service quality in these relationships. A subset of SMEs with high-quality launches explains our results.

This paper examines the relation between personal income and hierarchical power. In the context of a firm hierarchy, I define hierarchical power as the number of subordinates under an individual’s control. Using the available case-study evidence, I find that relative income within firms scales strongly with hierarchical power. I also find that hierarchical power affects income more strongly than any other factor for which data is available. I conclude that this is preliminary evidence for a hierarchical-power theory of personal income distribution.

Recent work from developed parts of the world has documented a positive association between air pollution and criminal activity. We use high-frequency complaints and air pollution data to estimate air pollution’s causal effects on crime in a developing country. In order to establish causality, we exploit plausibly exogenous local variation in wind direction in an instrumental variable setup. We find that a lower number of complaints are received on the days of high air pollution levels. This effect is more pronounced for property crimes than for violent crimes. Our results are robust to a host of robustness checks and falsification checks. Exploring the potential mechanisms, we find that the decline in criminal activity might result from increased costs of indulging in criminal activity.

The local average treatment effect (LATE) estimator has enabled a wide range of empirical studies that use natural experiments to estimate causal effects from observational data. This empirical literature has overlooked a crucial assumption regarding the definition of the treatment, however—the part of the stable unit treatment value assumption referred to as the consistency assumption in epidemiology. The consequence of ignoring this assumption has been that results have been misinterpreted and over-generalized. I illustrate these problems using examples from seminal studies from the natural experiments literature and present how to improve definitions of the treatment in future studies. Correctly interpreted LATEs are substantially more specific than previously acknowledged but reclaim their internal validity. By providing clear and careful definitions of the treatment and substantive interpretations of the results, future studies will increase their usefulness by presenting the causal effect that has been actually estimated.

We build a global bankruptcy prediction model using a support vector machine trained only on firms' endogenous information in the form of financial ratios. The model is tested not only on entirely random unseen data but on samples taken from specific global regions and industries to test for prediction bias, achieving satisfactory prediction performance in all cases. While support vector machines are not easily interpretable, we explore variable importance and find it consistent with economic intuition.

An interaction in a fixed effects (FE) regression is usually specified by demeaning the product term. However, this strategy does not yield a genuine within estimator. Instead, an estimator is produced that reflects unit-level differences of interacted variables whose moderators vary within units. This is desirable if the interaction of one unit-specific and one time-dependent variable is specified in FE, but it may yield problematic results if both interacted variables vary within units. Then, as algebraic transformations show, the FE interaction estimator picks up unit-specific effect heterogeneity of both variables. Accordingly, Monte Carlo experiments reveal that it is biased if one of the interacted variables is correlated with an unobserved unit-specific moderator of the other interacted variable. In light of these insights, we propose that a within interaction of two timedependent variables be estimated by first demeaning each variable and then demeaning the product term. This 'double-demeaned' estimator is not subject to bias caused by unobserved effect heterogeneity. It is, however, less efficient than standard FE and only works with T>2.

The identification of unconditional quantile treatment effects (QTE) has become increasingly popular within social sciences. However, current methods to identify unconditional QTEs of continuous treatment variables are incomplete. Contrary to popular belief, the unconditional quantile regression model introduced by Firpo, Fortin, and Lemieux (2009) does not identify QTE, while the propensity score framework of Firpo (2007) allows for only a binary treatment variable, and the generalized quantile regression model of Powell (2020) is unfeasible with high-dimensional fixed effects. This paper introduces a two-step approach to estimate unconditional QTEs. In the first step, the treatment variable is regressed on the control variables using an OLS. Then, in the second step, the residuals from the OLS are used as the treatment variable in a quantile regression model. The intuition is that the first step decomposes the variance of the treatment variable into a piece explained by the observed control variables and a residual piece independent of the controls. Since the control variables purge the treatment of confounding, they are redundant in the second step. Therefore, our RQR approach circumvents the problem that the inclusion of controls together with the treatment variable in CQR changes the interpretation of the treatment coefficients. Unlike much of the literature on quantile regression, this two-step residualized quantile regression framework is easy to understand, computationally fast, and can include high-dimensional fixed effects.

Recognizing and understanding the roots of poverty, its elements and determinants in Egypt is vital to coming up with policy recommendations that help eradicate poverty and ameliorate welfare. As a developing country, Egypt has been suffering from increasing poverty rates since year 2000. In this study, the three most recent Egyptian Households Income, Expenditure and Consumption Surveys conducted by the Central Agency for Public Mobilization and Statistics for the years 2011, 2013 and 2015 are utilized to analyze and model the determinants of poverty in Egypt. A comprehensive poverty profile is constructed for the three years, as well as a comparison for the changes that have occurred over time on the national, rural and urban levels. Some determinants were selected as the most important factors affecting poverty, such as: demographic characteristics, employment status and educational attainment. Additionally, two econometric techniques are employed to model the different factors affecting households’ consumption as well as probability of falling in poverty in Egypt; namely, Fixed Effects Regression and Logistic Regression. The results of the poverty profile, as well as both models, illustrate that the main variables which help reduce poverty are: having low family size, high number of earners and better educational attainment. On the other hand, factors that worsen poverty status of households are working in agriculture and construction sectors, depending on pension as the main source of income in addition to having high dependency ratio. Furthermore, poverty simulation analyses are conducted to assess the effect of changes in the levels of determinants of poverty on probability of being poor to show the possible consequences and effects of potential poverty lessening policies and plans.

The world made remarkable progress in reducing extreme poverty over the last twenty years. Recent progress has slowed, however, and the economic damage wrought by the COVID-19 pandemic imperils progress towards achieving the Sustainable Development Goals (SDGs) of eradicating extreme poverty and alleviating inequality by the year 2030. To track progress towards the SDGs, we collated—to the best of our knowledge—the largest collection of poverty and inequality related data and developed novel methods to construct comprehensive and comparable estimates of poverty and inequality from 1980 to 2019 in 204 countries and territories, across urban and rural settings, and by age; further, we forecast the effects of the COVID-19 pandemic on poverty out to 2021. We find that over the past four decades, the number of individuals living in extreme poverty declined dramatically, however, extreme poverty counts were rising in Sub-Saharan Africa. The Millennium Development Goal (MDG) era corresponded to the fastest observed reduction in extreme poverty and a period of more equitable growth. Progress made is jeopardized by the economic shock resulting from the COVID-19 pandemic. Estimates of poverty through 2021 highlight the effect of the global economic shock, the effect of governments’ economic responses to the pandemic, and the need to build economies resilient to the next global threat.

Cite as:Davydenko, A., & Goodwin, P. (2021). Assessing point forecast bias across multiple time series: Measures and visual tools. International Journal of Statistics and Probability, 10(5), 46-69. https://doi.org/10.5539/ijsp.v10n5p46 Note: This is the final version of the paper, which appeared in the International Journal of Statistics and Probability. The first draft of this paper was uploaded to Preprints.org on 11 May, 2021:https://doi.org/10.20944/preprints202105.0261.v1 Abstract Measuring bias is important as it helps identify flaws in quantitative forecasting methods or judgmental forecasts. It can, therefore, potentially help improve forecasts. Despite this, bias tends to be under represented in the literature: many studies focus solely on measuring accuracy. Methods for assessing bias in single series are relatively well known and well researched, but for datasets containing thousands of observations for multiple series, the methodology for measuring and reporting bias is less obvious. We compare alternative approaches against a number of criteria when rolling origin point forecasts are available for different forecasting methods and for multiple horizons over multiple series. We focus on relatively simple, yet interpretable and easy to implement metrics and visualization tools that are likely to be applicable in practice. To study the statistical properties of alternative measures we use theoretical concepts and simulation experiments based on artificial data with predetermined features. We describe the difference between mean and median bias, describe the connection between metrics for accuracy and bias, provide suitable bias measures depending on the loss function used to optimise forecasts, and suggest which measures for accuracy should be used to accompany bias indicators. We propose several new measures and provide our recommendations on how to evaluate forecast bias across multiple series. Summary of Contributions Research on metrics for forecast bias tends to be under-represented in the literature: most studies have focused solely on measuring accuracy. At the same time, measuring and reporting bias is important as it helps detect flaws in forecasting methods and to gain insights into how forecasting performance may be improved. This paper focuses on bias measurement and visualization techniques and on the relationship between bias, accuracy, and the loss function used to optimise forecasts. The paper makes the following contributions to the fields of applied statistical analysis and forecasting. 1) The Point Forecast Evaluation Setup (PFES) was defined where it is needed to evaluate forecast performance for rolling-origin point forecasts across multiple time series and horizons. It is assumed that forecasts were optimised under linear or quadratic loss and it is needed to evaluate accuracy and bias of point forecasts. 2) Regarding the criteria for finding suitable error measures, the following new principles were proposed: construct validity (which reflects the extent to which a metric measures what it is intended to measure), the ease of communication to the participants of the forecasting process (who may not be technical specialists), and the ease of implementation. 3) Special experiments were conducted in order to evaluate well-known bias indicators. In particular, it was demonstrated that the mean percentage error (MPE) is not advisable due to the non-symmetric features of percentage errorsand outliers caused by low actual values. It was also shown that the Overestimation Percentage (OP), the LnQ, the Absolute Mean Scaled Error (AMScE), the Relative Mean Error (RelME) and the Average Relative Absolute Mean Error (AvgRelAME) have their own limitations. The experiments conducted were based on normal and log-normal distributions in order to simulate time series resembling real-world datasets. 4) It was demonstrated that it is important to match bias indicator with the target loss function. In particular, indicators for median bias should be used when evaluating the performance of forecasts optimised under linear loss, while indicators for mean bias should be used when the target loss function is quadratic. 5) In order to detect the presence of median bias, the Overestimation Percentage corrected (OPc) metric was proposed. The OPc metric is calculated as OPc = OP+ZP/2, where OP is the percentage cases when actual was overestimated and ZP is the percentage of zero errors. To visualise the underlying distribution for the Overestimation Percentage corrected (OPc), the OPc-boxplot visual tool was proposed. Additionally, the OPc-diagram was proposed for reporting summary results. This metric was shown to be robust, immune to outliers, and easy to implement and to communicate. The OPc, however, only shows the frequency of cases of overestimation and does not show the magnitude of bias. 6) To indicate the magnitude and the direction of median bias the following additional metrics were proposed based on the use of the geometric mean: the Average Relative Absolute Median Error (AvgRelAMdE) and the Average Relative Median Error (AvgRelMdE). The former measures bias in comparison with a benchmark method, while the latter reports bias in terms of time series median. Enhanced boxplots for the AvgRel-metrics (AvgRel-boxplots)were introduced to aid visual analysis. 7) To report mean bias, the Average Relative Mean Error(AvgRelME) metric was proposed. The AvgRelME was derived based on LnQ, where Q was replaced with(1-RelME) in order to obtain a better proxy for ME compared to the LnQ. 8) The term forecast evaluation workflow (FEW) was defined as a set of sequential activities aiming to ensure a comprehensive, informative, and reliable forecast evaluation. 9) Two alternative forecast evaluation workflows (FEWs) were proposed describing step-by-step instructions for the evaluation and comparison of forecasts depending on the target loss function. The workflows proposed were named FEW-L1 and FEW-L2. The FEW-L1 workflow assumes the linear symmetric target loss function and the FEW-L2 workflow assumes the quadratic symmetric target loss function. The workflows proposed use the new metrics and visual tools and aim to ensure comprehensive forecast evaluation and detailed interpretation of results in the context of the PFES. In order to visually detect outliers and data flaws the workflows proposed rely on the use ofthe pooled prediction realization diagram (PPRD), which is a special variant of the prediction-realization diagramshowing data across series on one plot. Our suggested procedures are applicable both to academic researchers who are developing and evaluating new forecasting methods and practitioners wishing to evaluate the current forecasting performance of their organisation. The framework presented allows the preparation of reports in accordance with principles and methodologies for carrying out data science projects.

Labor market theory suggests that a tax cut proportional to earned income will increase labor force participation rates. We formulate our research question: How does receiving the EITC change the number of hours that a single mother will work? We conclude that we have not found evidence that receiving the EITC has an impact on the total number of hours worked per week for a single mother.

This paper aims to analyze the impact of irrigation on crop diversification. Crop diversification index has been employed to study the nature and extent of crop diversification in the study area. Descriptive statistics is used to explain the average CDI and irrigation. We found a mixed correlation between the factors and also observed a fluctuate impact on the crop diversification in the study area.

In this article we investigated various factors affecting crop diversification in tribal areas of Eastern ghats of India. We adopted multiple regression analysis and tried to visualize contribution from different factors to crop diversification. We observe most prominent factor affecting to tribal farming in study area is fertilizers, while least one is irrigation facility. Few policy suggestions are made for their betterment based on this study.

Non‐intersection of appropriately defined Generalized Lorenz (GL) curves is equivalent to a unanimous ranking of distributions of ordinal data by all Cowell and Flachaire (Economica, 2017) indices of inequality and by a new index based on GL curve areas. Comparisons of life satisfaction distributions for six countries reveal a substantial number of unanimous rankings. The GL dominance criteria are compared with other criteria including the dual‐H dominance criteria of Gravel, Magdalou, and Moyes (Economic Theory, 2020).

Despite the market economy to be contemporaneously considered as a complex adaptive system, there are no collective feedback mechanism that provide long range stability and complexity to the system. In this scenario, the logical prediction is a long-term economic collapse by positive loops. In this work, I outline the fundamental idea of a floating taxation system as a feedback system to prevent market collapse by asymmetrical company overgrowth and extreme reduction of system complexity. The paradigm would promote the long-term stability of the economic system. I’ve implemented a generic computational neural network with 5000 virtual companies whose initial states (i.e. capital) and connective weights (trading network) were normally distributed. A negative feedback loop was implemented with different weights. The market complexity was measured in terms of joint entropy in an algorithm to calc neural complexity in networks. Without feedback, some companies had explosive growth annihilating all collateral ones until all system collapses. With feedback loops, the complexity was stable while many companies disappeared (negative selection) and the capital variance substantially increased (from 10 units in initial conditions to 2000 times) as well complexity (increment on order to 104). This data supports a theory about feedback dynamic mechanisms for market self-regulation based on floating taxes, maintaining homeostasis with complexity, capital growth, and competitive balance.

Over 44.7 million Americans carry student loan debt, with the total amount valued at approximately $1.31 trillion (Quarterly Report, 2019). Ergo, consumer spending, a factor of GDP, is stifled and negatively impacts the economy (Frizell, 2014, p. 22). This study examined the relationship between student loan debt and the probability of a recession in the near future, as well as the effects of proposed student loan forgiveness policies through the use of a created model. The Federal Reserve Bank of St. Louis’s website (FRED) was used to extract data regarding total GDP per quarter and student loan debt per quarter ("Federal Reserve Economic Data," 2019). Through the combination of the student loan debt per quarter and total GDP per quarter datasets, the percentage of total GDP composed of student loan debt per quarter was calculated and fitted to a logistic curve. Future quarterly values for total GDP and the percentage of total GDP composed by student loan debt per quarter were found through Long Short Term Models and Euler’s Method, respectively. Through the creation of a probability of recession index, the probability of recession per quarter was compared to the percentage of total GDP composed by student loan debt per quarter to construct an exponential regression model. Utilizing a primarily quantitative method of analysis, the percentage of total GDP composed by student loan debt per quarter was found to be strongly associated[p < 1.26696* 10-8]with the probability of recession per quarter(p(R)), with the p(R) tending to peak as the percentage of total GDP composed of student loan debt per quarter strayed away from the carrying capacity of the logistic curve. Inputting the student loan debt forgiveness policies of potential congressional bills proposed by lawmakers found that eliminating 49.7 % and 36.7% of student loan debt would reduce the recession probabilities to be 1.73545*10-29% and 9.74474*10-25%, respectively.

During his 2020 State of the Union address, did President Trump use a vocabulary that was especially self-aggrandizing, or arising from insecurity or inferiority? Mining the historical records, I conducted a naïve analysis of presidential vocabulary from the founding to the present day. My conclusion is that Trump’s use of first-person pronouns is not notably different from his recent peers, but along the way some possibly meaningful patterns in how presidents speak of themselves and of us emerged.

Fixed effects (FE) panel models have been used extensively in the past, as those models control for all stable heterogeneity between units. Still, the conventional FE estimator relies on the assumption of parallel trends between treated and untreated groups. It returns biased results in the presence of heterogeneous slopes or growth curves that are related to the parameter of interest (e.g., selection into treatment is based on individual growth of the outcome). In this study, we derive the bias in conventional FE models and show that fixed effects individual slope (FEIS) models can overcome this problem. This is a more general version of the conventional FE model, which accounts for heterogeneous slopes or trends, thereby providing a powerful tool for panel data and other multilevel data in general. We propose two versions of the Hausman test that can be used to identify misspecification in FE models. The performance of the FEIS estimator and the specification tests is evaluated in a series of Monte Carlo experiments. Using the examples of the marital wage premium and returns to preschool education (Head Start), we demonstrate how taking heterogeneous effects into account can seriously change the conclusions drawn from conventional FE models. Thus, we propose to test for bias in FE models in practical applications and to apply FEIS if indicated by the specification tests.

This paper undertakes a comparative study on the topographies of neoliberalism in Latin America (Chile and Mexico) and provides empirical estimates of how the varied geographies of neoliberalism affect functional income inequality between 1980-2011. Our empirical strategy employs a single-equation Unrestricted Error-Correction Model that tests an exploratory baseline specification. We find robust evidence that government consumption is a positive driver of the respective wage shares. Since Chile has entrenched austerity as opposed to Mexico, it is a fundamental explanation for its falling wage share. Private debt is the second most important explanation for why wage shares have fallen in Chile. We find no evidence of this channel in the Mexican case. The article also finds similarly varied effects of globalisation on the respective wage shares. These results demonstrate the importance of country-level studies and how new spatialities of neoliberalism through different state reconfigurations can tell unique distributional stories.

This paper offers a historical analysis on how post-Socialist China’s transition to a globalised mixed-market economy led to class restructuring and estimates the drivers of its inter-decile income shares over the period 1978-2015 using Piketty et al. (2019)’s dataset. The key negative determinants of the bottom 50 percent are government consumption, trade openness and unemployment rate. The stable middle 40 percent is explained by the positive effects of government consumption, financial liberalisation and public indebtedness that compensate for the adverse effects of trade openness. Further, we find that government consumption, trade openness, and unemployment rate are positive determinants of the top 10 percent. More strikingly, trade openness disproportionately benefits the top 10 percent and this suggests that even China’s pragmatic world integration has been partial to business elites. Several policy ideas follow. First, China must overhaul its middle class urban-biased fiscal expenditure and second, the pension system must extend to the entirety of its income distribution. Third, stronger social welfare is required in the context of globalisation.

Volatility asymmetry is a hot topic in high-frequency financial market. In this paper, we propose a new econometric model, which could describe volatility asymmetry based on high-frequency historical data and low-frequency historical data. After providing the quasi-maximum likelihood estimators for the parameters, we establish their asymptotic properties. We also conduct a series of simulation studies to check the finite sample performance and volatility forecasting performance of the proposed methodologies. And an empirical application is demonstrated that the new model has stronger volatility prediction power than GARCH-It\^{o} model in the literature.

The modern study of economic inequality is based on the distribution of entitlements over goods and services. But social commentators at least since Rousseau have been concerned with a different aspect of economic inequality: that it implies that one person is entitled to command another person for their own personal ends. I call this inequality as entitlements over labor. I propose to measure entitlements over labor by calculating the extent to which top income groups can afford to buy the labor of others for the purpose of their personal consumption. Unlike standard inequality measures, this measure is not welfarist, but instead has its normative basis in relations of domination, hierarchy and social status between people. I estimate entitlements over labor in three high-inequality and two low-inequality countries and argue that inequality as entitlements over labor is socially and politically salient, capturing a side of inequality neglected by standard measures.

We demonstrate that relative intergenerational mobility declined sharply for cohorts born in the early 1960s compared to those born around 1950. The former entered the labor market largely after the large rise in inequality that occurred around 1980 while the latter entered the labor market well before this inflection point. We show that the rank-rank slope rose from 0.24 to 0.36 and the IGE increased from 0.21 to 0.50. We find that both the increase in the returns to schooling and the gradient in the likelihood of marriage by parent income can explain some of the increase in persistence. We also find direct evidence of a decline in absolute intergenerational mobility using intergenerational data, consistent with the bounds-based approach of Chetty et al. (2017). (Stone Center on Socio-Economic Inequality Working Paper)

This article describes kmr, a Stata command to estimate a micro compliance function using group’s nonresponse rates (2007, Journal of Econometrics 136: 213-235), which can be used to correct survey weights for unit nonresponse. We illustrate the use of kmr with an empirical example using the Current Population Survey and state-level nonresponse rates. (Stone Center on Socio-Economic Inequality Working Paper)

This paper presents a survey of causes and correction approaches to address the “missing rich” problem in household surveys. “Missing rich” here is a catch-all term for the issues that affect the upper tail of the distribution of income: undercoverage, sparseness, unit and item nonresponse, underreporting and top coding. Upper tail issues can result in serious biases and imprecision of survey- based inequality measures. A number of correction approaches have been proposed. A main distinction is between those that rely on within-survey methods and those that combine survey data with information from external sources such as tax records, National Accounts, rich lists or other external information. Within each category, the methods can correct by replacing top incomes or increasing their weight (reweighting). Correction methods can be nonparametric and parametric. This survey aims to help researchers choose appropriate correction strategies and design robustness tests. (Stone Center on Socio-Economic Inequality Working Paper)

***This article has been accepted in Environmental Research Letters. Please refer to the DOI for the accepted manuscript: https://doi.org/10.1088/1748-9326/abc279.*** Environmental and social problems triggered by rapid palm oil expansion in the tropics have spurred the proliferation of sustainability certification systems such as the Roundtable on Sustainable Palm Oil (RSPO). While the RSPO aims to improve the impact of oil palm production on people and environments, its effect on local development, environmental quality, and, especially, potential trade-offs between these outcomes remain unclear. Here, we evaluate whether RSPO certification of large-scale industrial concessions has promoted village development and supported environmental quality in Indonesia, the top global palm oil producer. Using a panel dataset with observations from 11,000 villages in Kalimantan and Sumatra from 2003-2014, we apply rigorous quasi-experimental methods to quantify the RSPO's impacts on village development and environmental outcomes. In the short-run, RSPO contributed to environmental conservation, but had limited development outcomes. On average, relative to villages with non-certified concessions, RSPO certification reduced deforestation and protected primary forests in Sumatra and lowered the incidence of village-reported land pollution in Kalimantan. RSPO certification also increased the number of private educational facilities in Kalimantan, but had no statistically significant impacts on other development indicators. However, the trade-offs and complementarities between conservation and development vary by slope, a proxy for ecosystem fragility and oil palm profitability. On gentler slopes, we generally find complementarities between conservation and development outcomes. In Kalimantan, certification increased the number of private educational facilities and reduced deforestation and the incidence of land pollution on slopes

Outdoor and nature-based recreation provides countless social benefits, yet public land managers often lack information on the spatial and temporal extent of recreation activities. Social media is a promising source of data to fill information gaps because the amount of recreational use is positively correlated with social media activity. However, despite the implication that these correlations could be employed to accurately estimate visitation, there are no known transferable models parameterized for use with multiple social media data sources. This study tackles these issues by examining the relative value of multiple sources of social media in models that estimate visitation at unmonitored sites and times across multiple destinations. Using a novel dataset of over 30,000 social media posts and 286,000 observed visits from two regions in the United States, we compare multiple competing statistical models for estimating visitation. We find social media data substantially improve visitor estimates at unmonitored sites, even when a model is parameterized with data from another region. Visitation estimates are further improved when models are parameterized with on-site counts. These findings indicate that while social media do not fully substitute for on-site data, they are a powerful component of recreation research and visitor management.

The article introduces a new approach to the problem of political spectrum polarization. Political science has introduced a multitude of spectrograms based on different factors, dimensions, axes, and cardinal points. Most often the graphics do not complement each other, and it seems that each of them describes a completely different reality. There was an urgent need to conduct an objective analysis of political philosophies and find the factors that influence the political spectrum polarization. For these purposes, the Qualitative Comparative Analysis (QCA) was used, as it allows to introduce a fraction of objectivity due to the use of a formalized mathematical apparatus of the theory of sets. Thirteen main political doctrines were analyzed and obtained that spectrum polarization depends on three conditions: attitude to private property, individual freedom, and wealth redistribution. As the factors that influence the political spectrum polarization were firmly determined, it became possible to build a spectrogram unambiguously.

Replication crisis and debates about p-values have raised doubts about what we can statistically infer from research findings, both in experimental and observational studies. The goal of this paper is to provide an adequate differentiation of experimental studies that enables researchers to better understand which inferences can and – perhaps more important – cannot be made from particular designs.

Mixed-effects multilevel models are often used to investigate cross-level interactions, a specific type of context effect that may be understood as an upper-level variable moderating the association between a lower-level predictor and the outcome. We argue that multilevel models involving cross-level interactions should always include random slopes on the lower-level components of those interactions. Failure to do so will usually result in severely anti-conservative statistical inference. We illustrate the problem with extensive Monte Carlo simulations and examine its practical relevance by studying 30 prototypical cross-level interactions with European Social Survey data for 28 countries. In these empirical applications, introducing a random slope term reduces the absolute t-ratio of the cross-level interaction term by 31 per cent or more in three quarters of cases, with an average reduction of 42 per cent. Many practitioners seem to be unaware of these issues. Roughly half of the cross-level interaction estimates published in the European Sociological Review between 2011 and 2016 are based on models that omit the crucial random slope term. Detailed analysis of the associated test statistics suggests that many of the estimates would not reach conventional thresholds for statistical significance in correctly specified models that include the random slope. This raises the question how much robust evidence of cross-level interactions sociology has actually produced over the past decades.

As the rental housing market moves online, the internet offers divergent possible futures: either the promise of more-equal access to information for previously marginalized homeseekers, or a reproduction of longstanding information inequalities. Biases in online listings’ representativeness could impact different communities’ access to housing search information, reinforcing traditional information segregation patterns through a digital divide. They could also circumscribe housing practitioners’ and researchers’ ability to draw broad market insights from listings to understand rental supply and affordability. This study examines millions of Craigslist rental listings across the USA and finds that they spatially concentrate and overrepresent whiter, wealthier, and better-educated communities. Other significant demographic differences exist in age, language, college enrollment, rent, poverty rate, and household size. Most cities’ online housing markets are digitally segregated by race and class, and we discuss various implications for residential mobility, community legibility, gentrification, housing voucher utilization, and automated monitoring and analytics in the smart cities paradigm. While Craigslist contains valuable crowdsourced data to better understand affordability and available rental supply in real time, it does not evenly represent all market segments. The internet promises information democratization, and online listings can reduce housing search costs and increase choice sets. However, technology access/preferences and information channel segregation can concentrate such information-broadcasting benefits in already-advantaged communities, reproducing traditional inequalities and reinforcing residential sorting and segregation dynamics. Technology platforms like Craigslist construct new institutions with the power to shape spatial economies, human interactions, and planners’ ability to monitor and respond to urban challenges.

The market economy is contemporaneously considered as a complex adaptive system, chaotic and far from equilibrium. However, there are no feedback mechanisms that provide stability to the system. In this preliminary essay, I outline the fundamental idea of a floating taxation system to compensate for the market oscillations of goods, growth and profit of companies and their socio-environmental impact, promoting the long-term stability of the economic system.

This article analyzes the relation between GDP per capita (CPIBpc) and access to tertiary education, seen from the perspective of growth rate of the number of enrollments (TCMes) in higher education in Colombia for the period (1971-2016). By using a VEC model and assuming everything else constant, it is concluded that TCMes Granger caused the Colombian GDP per capita and vice-versa, therefore, the existence of a long run relation between both variables is verified. This result helps to explain the dynamics of Colombian economic growth per capita of the last forty-five years and the impact of the accumulation of human capital on it., {"references":["Barro, R. J. (2001). Education and economic growth. The contribution of human and social capital to sustained economic growth and well-being, 14-41.","Enders, W. (2003). Applied Econometric Time Series. John Wiley & Sons.","EViews 10 Help Topics. (2018). Retrieved from: http://www.eviews.com/help/helpintro.html","Gobierno de Colombia., (2018)., Descripción del programa 'De cero a siempre' . Recuperado el 24 de agosto de 2018 de: http://www.deceroasiempre.gov.co/QuienesSomos/Paginas/QuienesSomos.aspx","Hamilton, J. D. (1994). Time series analysis (Vol. 2). Princeton: Princeton university press.","Hanushek, E. A. (2013). Economic growth in developing countries: The role of human capital. Economics of Education Review, 37, 204-212.","Hanushek, E. A. (2016). Will more higher education improve economic growth?. Oxford Review of Economic Policy, 32(4), 538-552.","Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts.","Lin, J. (2008). Notes on testing causality. Institute of Economics, Academia Sinica, Department of Economics, National Chengchi University.","Lütkepohl, H. (2005). New introduction to multiple time series analysis. Springer Science & Business Media.","Mercan, M., & Sezer, S. (2014). The effect of education expenditure on economic growth: The case of Turkey. Procedia-Social and Behavioral Sciences, 109(0), 925-930.","Novales, A. (2014). Modelos vectoriales autoregresivos (VAR).","Romer, D. (2012). Advanced macroeconomics. Fourth edition. Mac Graw Hill.","Sosa, W. (2006). Vectores autoregresivos (VAR), curso del Banco central de Chile: Vectores Autorregresivos para Datos en Paneles. Retrieved on November 21 of 2016 from: http://faculty.udesa.edu.ar/WalterSosa/PVAR/bcchile2.pdf","Stock, J. H., & Watson, M. W. (2001). Vector autoregressions. The Journal of Economic Perspectives, 15(4), 101-115.","The World Bank. (2018). Database: World Development Indicators. Retrieved from http://databank.worldbank.org/data/reports.aspx?source=2&country=COL#","Woschnagg, E & Cipan, J. (2004). Evaluating forecast accuracy. University of Vienna, Department of Economics."]}

This paper discusses the dimension problem in economic aggregation, as it relates to ecological and biophysical economics. The dimension problem consists of a simple dilemma: when we aggregate, the observer must choose the dimension of analysis. The dilemma is that this choice affects the resulting measurement. This means that aggregate measurements are dependent on one's goals, and on underlying theory. I explore the consequences of this predicament for ecological and biophysical economics. I discuss the many problems of using 'real' monetary value as the dimension of analysis. And I highlight how the dimension problem undermines the practice of seeking 'optimal' policy. Although there are no solutions, I discuss ways that ecological and biophysical economists can deal with the dimension problem.

This longitudinal study examined the labor market of South Dakota, a state with an exceptionally low unemployment rate, over the ten years spanning 2006 to 2015. Analysis focused on the questions, what proportion of the market is made up of low wage jobs? and are the numbers of low wage workers increasing or decreasing over time? Data collected by the U. S. Census Bureau and U. S. Department of Commerce’s Bureau of Labor Statistics (BLS) was dissected on a micro level to determine which occupations held disproportionate numbers of low paying jobs. A threshold for households to live free of subsidies was set, then compared to workers’ incomes. Detailed data mining revealed information that contributes to the literature of employment and labor fields, strategic economic development, and the role of public policy in helping citizens get and stay independent of subsidies.

The purpose of this study is to investigate the position and effect of the Chamber of Commerce, Industries, Mining and Agriculture on the economy of Kashan city. In this research, we use descriptive and survey methods to investigate and describe the general characteristics of respondents and inference that is calculated using sample values of the statistics and then, through estimating or testing the statistical hypothesis, generalizes the statistics to the parameters of society. The statistical population of this study was all real and legal members of the Chamber of Commerce in Kashan city (500 subjects) from among which the sample size is calculated to 220 using the Morgan table. Validity of the questionnaire was assessed through face validity and based on the opinion of some university professors and Cronbach's alpha test was used for the reliability of the questions in the questionnaire where the obtained coefficient is equal to 0.941 which indicates a very good relationship between the questionnaire's questions.Three hypotheses were also suggested in this research, that there is a meaningful relationship between the activities of the Chamber of Commerce, the participation of the real and legal members of this organization and the satisfaction of its members with the activities of the Chamber of Commerce and Economic Development of the city. The obtained results indicated that all three factors affect the economic development of the city and have a meaningful relationship with each other. At first, the activities of the chamber of commerce, then the satisfaction of the real and legal members of the Chamber of Commerce of Kashan, and finally the participation of the real and legal members of the chamber of commerce, respectively, have the highest impact on the city economic development variable. (Eisenberg, 2009)

In this paper is analyzed the relation between GDP growth and External balance in Colombia for the study period (1963-2016) by using a VECM. Supposing everything else unchanged, we conclude that Colombian external balance granger caused GDP growth and there was indeed a long run relation between both variables. This outcome helps to explain the Colombian GDP growth dynamics over the last fifty years and the impact of trade policy on economic growth., JEL codes: F00, F14, F17, F43, F47., {"references":["Casas, E. A. M., & Guzmán, J. F. (2015). El deterioro de los términos de intercambio: Teoría y evidencia empírica para Colombia 1980-2010. Revista Mundo Económico y Empresarial, (10). Enders, W. (2003). Applied Econometric Time Series. John Wiley & Sons. EViews 10 Help Topics. (2018). Retrieved from: http://www.eviews.com/help/helpintro.html Fleming, J. M. (1962). Domestic financial policies under fixed and under floating exchange rates. Staff Papers, 9(3), 369-380. González, J. T. (2014). COLOMBIA: LOGROS EN DOS DECADAS DE MODELO DE DESARROLLO APERTURISTA-ANALISIS SEGÚN RESULTADOS DE BALANZA COMERCIAL. Análisis Político, 27(82), 212. Hamilton, J. D. (1994). Time series analysis (Vol. 2). Princeton: Princeton university press. Lin, J. (2008). Notes on testing causality. Institute of Economics, Academia Sinica, Department of Economics, National Chengchi University. Montoya-Uribe, D. A., Gonzalez-Parias, C. H., & Duarte-Herrera, L. K. (2016). Situación y retos de Colombia en materia de comercio exterior, en el marco de la Alianza del Pacífico. Entramado, 12(1), 50-65. Novales, A. (2014). Modelos vectoriales autoregresivos (VAR). Oliveros, H., & Huertas-Campos, C. A. (2015). Desequilibrios nominales y reales del tipo de cambio en Colombia. Artículos de revista. Sosa, W. (2006). Vectores autoregresivos (VAR), curso del Banco central de Chile: Vectores Autorregresivos para Datos en Paneles. Retrieved on November 21 of 2016 from: http://faculty.udesa.edu.ar/WalterSosa/PVAR/bcchile2.pdf Stock, J. H., & Watson, M. W. (2001). Vector autoregressions. The Journal of Economic Perspectives, 15(4), 101-115. The World Bank. (2018). Database: World Development Indicators. Retrieved from http://databank.worldbank.org/data/reports.aspx?source=2&country=COL# Urquijo, P. (2015). Análisis de la implementación del Tratado de Libre Comercio entre Estados Unidos y Colombia (Master's thesis, Universidad Militar Nueva Granada). Villar, L., Romero, J. V., & Pabón, C. (2015). Política cambiaria, monetaria y fiscal: ¿ha aprendido Colombia a mitigar los efectos de las crisis? Woschnagg, E & Cipan, J. (2004). Evaluating forecast accuracy. University of Vienna, Department of Economics."]}

Twin births are a well-known and widespread example of a so-called “natural experiment”. Instrumental variables based on twin births have been used in many studies to estimate the causal effect of the number of children on the parents or siblings. I use the potential outcomes framework to show that these instrumental variables do not work as intended. They are fundamentally flawed and will always lead to severely biased estimates without any meaningful interpretation. This has been overlooked in previous research because too little attention has been paid to defining the treatment in this natural experiment. I analyze three different possible interpretations of the treatment and show that they all lead to inherent violations of the necessary assumptions. The effect of the number of on the parents or siblings is a policy relevant and theoretically important issue. The scientific record should therefore be corrected to not lead to misguided decisions.

The study focuses quantitatively on ways to assess school education, currently under severe criticism, in terms the first of its three major components, students, teachers and curriculum,. We developed a novel method to assess long term retention as a measure of true learning in tests that do not involve any cramming and found to our dismay that it falls progressively more towards recent years. Our initial work on poverty showed that consumption on commodities is hyperbolic (as in catalysis) and hierarchic and, that the total income is not fractionally distributed among the commodities, as economists assume; the leftover (residual) income after the more essential items is spent on the commodities of lesser preference. Education is also a naturally ordered commodity and parental income quantitatively determined school dropouts/attendance obeying the same hyperbolic relationship, as we could show. We could thereby affirm the first-ever mechanism for Engels’ three curves of consumption, based on the time constants associated with the corresponding hyperbolic functions. The disturbing observation was that long term retention as a true measure of learning decreases rapidly with years and seems to be independent of socioeconomic circumstances and gender but depends on subjects. The test also revealed that those who are not good at cramming could exhibit good retention, though the conventional tests fail to identify them. Causes of the progressive loss of retention for the recent years, either as limits to learning or as limitations in imparting learning, need to be assessed by further studies. This would hopefully begin an era of inclusive education for a just society for which methodology beyond pious intentions never existed.