Unobserved Factors Research Articles

Growth effects of fiscal policy: an application of the unobserved common factor model

Growth effects of fiscal policy: an application of the unobserved common factor model

How COVID-19 has Impacted American Attitudes Toward China: A Study on Twitter

ABSTRACT Past research has studied social determinants of attitudes toward foreign countries. Confounded by potential endogeneity biases due to unobserved factors or reverse causality, the causal impact of these factors on public opinion is usually difficult to establish. Using social media data, we leverage the suddenness of the COVID-19 pandemic to examine whether a major global event has causally changed Americans’ views of another country. We collate a database of 297 million posts on the social media platform Twitter about China or COVID-19 up to June 2020, and we take tweeting about COVID-19 as a proxy for an individual’s awareness of COVID-19. Using regression discontinuity and difference-in-difference estimation, we reveal that awareness of COVID-19 causes a sharp rise in anti-China attitudes. Our work has implications for understanding how self-interest affects policy preference and how Americans view foreign countries.

THE INFLUENCE OF CREDIT RISK, CAPITAL ADEQUACY AND OPERATIONAL EFFICIENCY ON THE PROFITABILITY OF BANKS LISTED ON THE INDONESIAN STOCK EXCHANGE

The purpose of this research is to determine and analyse the effect of credit risk, capital adequacy level and operational efficiency on the profitability of banking companies listed on the Indonesia Stock Exchange in 2019-2021. This study is a type of quantitative research that uses a descriptive design. In this study, researchers used secondary data obtained from the financial statements of banking companies. Researchers obtained a population of 46 banking companies listed on the Indonesia Stock Exchange in 2019-2021. The sampling technique uses initial public offering (IPO) sampling, which is a purposive method. Based on the sample characteristics, the authors found 31 banking companies with 93 financial data respondents. The data analysis technique is multiple linear regression using SPSS version 25. The results of this study indicate that credit risk has a negative and significant effect on profitability, capital adequacy has a positive and significant effect on profitability, operational efficiency has a negative and significant effect on profitability. Credit risk, capital adequacy and operational efficiency together have a significant effect on profitability. The result of the coefficient of determination (R2) of 0.627 shows that 62.7% of the variation in the adjusted R-squared is explained by variations in the factors Credit Risk (X1), Capital Adequacy (X2), Operational Efficiency (X3). While the remaining 37.3% is influenced by other unobserved factors affecting the profitability variable (Y). Therefore, further research using other variables is needed.

Can farmers’ adoption of conservation tillage achieve sustainable agricultural production targets? Evidence from the Jianghan Plain, Hubei, China

ABSTRACT As a major agricultural country, China is under threat from land degradation because of long-term high-carbon-emission land use patterns. Conservation tillage (CT) technologies are expected to restore soil fertility, transform agricultural production models, and improve sustainability. However, the impacts of farmers’ CT adoption on achieving sustainable agricultural development remain unclear. To investigate the heterogeneous ecological effects resulting from farmers’ adoption of CT technologies, this study estimates the ecological effects of CT and examines the differences in these effects across different technologies, utilizing survey data from 1,449 farmers in the rural Jianghan Plain of China. The results show that farmers’ technology perceptions, previous adoption behaviour, and collective economic conditions significantly affect their CT adoption behaviour. The agricultural eco-efficiency in the Jianghan Plain is only 0.65. After removing bias from self-selection and unobservable factors, the ecological effect of CT adoption was 8.43%. The four different CT technologies evaluated in this study were found to increase agricultural eco-effects and achieve sustainable agriculture. However, the agricultural eco-effects of different CT technologies vary, indicating heterogeneity. Finally, policies that include implementing different combinations of CT models according to local conditions, technical training, and establishing subsidies for CT adoption are highly recommended.

Does Cooperation with Universities and Knowledge Intensive Business Services Matter? Firm-level Evidence from Spain

This manuscript contributes to the literature on firm cooperation with universities and Knowledge Intensive Business Services (KIBS) by framing the analysis according to the literature on causal effects, comparing the effect of each of the agents and exploring which firms benefit the most from cooperation with a specific partner. The results have shown that the bias-adjusted effect is around a 27-30% increase in sales from new products for both types of partners. After covariates and fixed effects are used, it is considered unlikely that this effect is driven by time-varying unobservable factors. Moreover, we have seen that firms that benefit the most from cooperation with universities are different from those firms that benefit the most from cooperation with KIBS.

Application of The Blinder-Oaxaca Decomposition Method to Analyze The Unemployment Gap Between Gender in Riau Province in 2022

Gender equality is one of the goals of SDG, so every country needs to make efforts so that gender equality can be realized. Gender inequality is calculated using the Gender Inequality Index (GII). One of the components in preparing the Gender Inequality Index is labor force participation rate. Within the population included in the labor force, some people are employed and unemployed. In this study, gender inequality in terms of unemployment will be calculated using Blinder-Oaxaca decomposition with three calculation stages. The first is probit regression analysis, the second is marginal effects and the third is Oaxaca blinder decomposition. The results of the probit regression analysis show that all observed independent variables significantly effect on unemployment for both men and women. Meanwhile, the marginal effect results show that this variable’s influence is quite small, only 0.1 - 2 percent. The results of Oaxaca blinder decomposition show that the inequality between male and female unemployment is 1.42%, of which 29.88% is influenced by, observed factors and unobserved factors influence the remaining. There are only three variables that influence the gender gap, namely marital status, number of household members and household income 70.12%.

Strategic configurations and strategic management accounting: a longitudinal case study in the credit mediation industry

Purpose The purpose of this paper is to investigate the relationship between strategy, strategic management accounting (SMA) and performance. Specifically, it aims to explore how SMA alignment is achieved to support both the internal and external fit of organizational configurations and achieve superior performance. Design/methodology/approach This study has adopted a longitudinal case study approach, focusing on a leading company in the credit mediation industry, and uses the configurational theory and a network approach to understand how an alignment between organizational configurations and SMA leads to superior performance. Findings This study shows that the configurational fit involves interactions between environmental, strategic and structural elements and SMA. Moreover, it helps understanding the causal complexity of these interactions by showing how various organizational configurations, along with SMA, may lead to superior performance. Finally, from a longitudinal perspective, the study shows how SMA alignment continuously supports both the external and internal configuration fit. Research limitations/implications Case studies often lack generalizability due to their detailed, context-specific nature. In addition, the study assumes that aligning SMA practices with organizational configurations leads to higher performance, although outcomes may be affected by other unobserved factors. Practical implications This study also has practical implications for managers, as it provides a profound understanding of the role of SMA in supporting both the external and internal alignment of the organizational configuration. Managers should particularly leverage SMA to gather and analyze external environmental data, thereby enabling the organization to ensure the continuous consistency of its strategic priorities, as well as to support and reinforce both existing and emerging strategic imperatives. However, it is essential for managers to perceive SMA not as an isolated instrument, but as an integral component of the broader organizational system. Effective implementation necessitates the integration of SMA techniques with the strategic and structural elements of the organization, which complement their implementation, determining the actual contribution to external and internal fit. Originality/value To the best of the author’s knowledge, this study has been one of the first to adopt a qualitative approach to investigate the relationships between strategy, SMA and performance through the lens of the configurational theory. It elucidates the causal mechanisms underlying the relationships between configurations and SMA from a dynamic, change-oriented perspective, showing how SMA continuously contributes to configurational fit and performance.

Combining Instrumental Variable Estimators for a Panel Data Model with Factors

We address the estimation of factor-augmented panel data models using observed measurements to proxy for unobserved factors or loadings and explore the use of internal instruments to address the resulting endogeneity. The main challenge consists in that economic theory rarely provides insights into which measurements to choose as proxies when several are available. To overcome this problem, we propose a new class of estimators that are linear combinations of instrumental variable estimators and establish large sample results. We also show that an optimal weighting scheme exists, leading to efficiency gains relative to an instrumental variable estimator. Simulations show that the proposed approach performs better than existing methods. We illustrate the new method using data on test scores across US school districts.

Housing choice in an evolving remote work landscape

We estimate a joint model of housing choice along several dimensions to account for changing valuations of housing outcomes due to the COVID-19 pandemic. We consider housing outcomes including housing type, tenure type, the presence of a patio or yard, the number of bedrooms, neighborhood population density, median housing cost, accessibility of amenities, school quality, crime rate, and commute distance. Data used for this analysis were collected in October and November of 2021 from 24 metropolitan areas across the United States. A Generalized Heterogeneous Data Model (GHDM) is used to estimate these housing outcomes as a function of exogenous household sociodemographic characteristics and latent lifestyle propensities. The GHDM also captures jointness caused by unobserved factors, allowing for the estimation of accurate causal effects between outcomes. The results reveal that lifestyle preferences have significant impacts on housing outcomes. Specifically, individuals with a preference for teleworking are more likely to reside in single-family homes in highly populated areas, experience longer commute distances, and exhibit a higher sensitivity to the presence of amenities in their neighborhoods. Additionally, the analysis of tradeoffs between housing outcomes reveals the relative valuations of various housing outcomes. An increased commute distance is found to lead to an increase in single-family homes, reductions in density, and an increased crime rate. Choosing an apartment in a high-density neighborhood is found to lead to reductions in school quality and significant increases in crime rates. Implications of the results for land-use planning, travel demand analysis, and equity considerations are identified and discussed.

Climate-Driven vs Human-Driven Land Degradation? The Role of Urbanization and Agricultural Intensification in Italy, 1960–2030

Climate warming, agricultural intensity, and urban growth are main forces triggering land degradation in advanced economies. Being active over different spatial and temporal scales, they usually reflect—at least indirectly—the impact of additional factors, such as wellbeing, demographic dynamics, and social development, on land quality. Using descriptive statistics and a multiple regression analysis, we analyzed the impact of these three processes comparatively over a decadal scale from 1960 to 2020 at the provincial level (Nuts-3 sensu Eurostat) in Italy. We enriched the investigation with a short-term forecast for 2030, based on four simplified assumptions grounded on a purely deterministic approach. Land degradation was estimated adopting the Environmental Sensitive Area Index (ESAI) measured at the spatio-temporal scale mentioned above. Computing on multiple observations at nearly 300,000 locations all over Italy, provinces were regarded as representative spatial units of the territorial pattern of land degradation. Between 1960 and 1990, the three predictors (climate, agriculture, and urbanization) explained a relatively high proportion of variance, suggesting a modest role for any other (unobserved) factor. All of these factors were found to be highly significant predictors of land degradation intensity across provinces, the most impactful being farming intensity. The highest adjusted-R2 coefficient was observed in both 1990 and 2000, and suggests that the three predictors still reflect the most powerful drivers of land degradation in Italy at those times, with a marginal role for additional (unobserved) factors. The impact of farming intensity remained high, with the role of urbanization increasing moderately, and the role of climate aridity declining weakly between 2000 and 2010. In more recent times (2010 and 2020), and in future (2030) scenarios, the adjusted R2 diminished moderately, suggesting a non-negligible importance of external (unobserved) factors and the rising role of spatial heterogeneity. The climate factor became progressively insignificant over time, while increasing the role of urbanization systematically. The impact of farming intensity remained high and significant. These results underlie a latent shift in the spatial distribution of the level of land vulnerability in Italy toward a spatially polarized model, influenced primarily by human pressure and socioeconomic drivers and less intensively shaped by biophysical factors. Climate aridity was revealed to be more effective in the explanation of land degradation patterns in the 1960s rather than in recent observation times.

Moment-Based Estimation of Linear Panel Data Models with Factor-Augmented Errors

Abstract I compare two popular methods of estimation for linear panel data models with unobserved factors: the first eliminates the factors with a parameterized quasi-long-differencing (QLD) transformation. The other, referred to as common correlated effects (CCE), uses cross-sectional averages of the data to proxy for the factor space. I show that the CCE assumptions imply unused moment conditions that can be exploited by the QLD transformation. I also derive new linear estimators that weaken identifying assumptions and have desirable theoretical properties. Unlike CCE, these estimators do not require the number of covariates to be less than the number of time periods. I provide the first proof of a fixed-T consistent mean group estimator for heterogeneous linear models with interactive fixed effects. I investigate the effects of per-student expenditure on standardized test performance using data from the state of Michigan.

Affect heuristics and peer effect on substance use among adolescents in Brazil

Significant changes are observed during adolescence with respect to health-related habits and risky behaviors. Among the behavioral changes, the consumption of substances such as alcoholic beverages, cigarettes, and illicit drugs, and social interactions are relevant factors for the intake of such substances. This study aims to analyze the relationship between the joint decisions of substance use by adolescents and their peers from the perspective of the affect heuristics. To this end, a multivariate probit model was estimated, based on microdata from the 2019 National School Health Survey. The results showed that substance use by adolescents was positively related to substance use by their peers; therefore, having friends who consume alcohol, illicit drugs, and cigarettes or having parents who consume alcohol or cigarettes increases the likelihood of an adolescent consuming these substances. The study also identified that the substance consumption decision was governed by an unobserved common factor compatible with the affect heuristic.

HIGH-DIMENSIONAL FORECASTING WITH KNOWN KNOWNS AND KNOWN UNKNOWNS

Abstract Forecasts play a central role in decision-making under uncertainty. After a brief review of the general issues, this article considers ways of using high-dimensional data in forecasting. We consider selecting variables from a known active set, known knowns, using Lasso and One Covariate at a time Multiple Testing, and approximating unobserved latent factors, known unknowns, by various means. This combines both sparse and dense approaches to forecasting. We demonstrate the various issues involved in variable selection in a high-dimensional setting with an application to forecasting UK inflation at different horizons over the period 2020q1–2023q1. This application shows both the power of parsimonious models and the importance of allowing for global variables.

Emissions effect of financial development in the GCC: is the effect asymmetric?

AbstractObviously, financial development is one of the factors to consider in designing climate policies. We investigated the effects of financial development on co2 emissions alongside income, total factor productivity, and international trade in Gulf Cooperation Council (GCC) countries. Ignoring common factors can lead to erroneous findings and misleading policy recommendations. The same consequences occur if the nature of a factor’s effects is incorrectly considered. Hence, the Asymmetric Pooled Mean Group augmented with common unobserved factors—a cutting-edge method allowing for the discovery of not only the features of the pooled panel but also the characteristics of each country—was applied to data from 1992 to 2021. Additionally, we accounted for key properties of the panel time series data—cross-sectional dependence, non-stationarity and heterogeneity. To our knowledge, there is no such application for GCC countries, and only one internationally. In designing climate policy measures, a few key findings of our research are worth considering. (i) policies should account for factors common to GCC countries, as ignoring them makes co2 effects of financial development misleading. (ii) an upturn in financial development leads to less emissions than a downturn in it. This asymmetric effect implies that policies should boost financial development. (iii) co2 in GCC countries may converge to an identical relationship in the long run implying that there are common climate initiatives and projects that GCC authorities should work on jointly.

An approach for constructing spatially paired pseudo repeat-sales housing price indices in China

AbstractConstructing a reliable housing price index is crucial for accurately reflecting housing price dynamics and enhancing transparency in the housing market. However, existing methods for constructing housing price indices often confront many challenges, such as omitted variable issues in the hedonic model and constrained samples used in the repeat-sales model. To address these challenges, this study identifies a spatially paired relationship between complexes and develops a Spatially Paired Pseudo Repeat-Sales model to construct the housing price index. This approach offers two significant advantages: first, it enlarges the sample size used in the repeat-sales model at least 1.8 times; second, it effectively estimates the effects of spatial dependency and physical housing factors on prices while mitigating the impacts of unobservable factors through differentiation. The findings of this paper suggest that using the spatially paired pseudo repeat-sales model can significantly improve the estimation of housing price volatility, by approximately 13%. Moreover, the constructed housing price index model demonstrates significant robustness, even when the spatial weight settings are altered. This research provides convincing evidence of improved housing market transparency for stakeholders, including governments, institutional developers, and individual investors. Additionally, the constructed model can inform other similar research in housing price index construction by providing a spatially pairing perspective on housing complexes.

Observable, but Not Unobservable Health Numbers are Associated With Self-Reported Health: NHANES 2017-2020.

Present study explored the influence of self-reported health on diff between observable and unobservable health factors. Data obtained through the 2017-2020 pre-pandemic dataset of the National Health and Nutrition Examination Survey (NHANES). A series of analyses of covariance were conducted with self-reported health as the independent variable, and measures of blood pressure, waist-to-hip ratio, fasting glucose and total cholesterol as dependent variables. Family income served as a covariate. Results revealed significant differences in self-reported health and blood pressure, waist-to-hip ratio, and fasting glucose. Individuals who rated their health excellent or very good tended to have better health numbers than those who rated their health as fair or poor. No statistically significant difference was found with self-reported health and cholesterol. It is possible that health numbers that can be observed (or assessed) at home, without a health care professional, may be more aligned with how individuals perceive their own health. Health care professionals may want to further help patients recognize their own health status. The use of wearable technologies may provide added health information resulting in more accurate perception of health. A more accurate perception of a patient's own health may aid health care professionals in further improving patient care.

Land Access Modes and Agricultural Productivity in Benin

Improving productivity is an important channel for satisfying household food needs through food availability. Increasing the productivity of small-scale farmers is linked to a number of factors, including land access, labor, chemicals, fertilizers and so on. Most farmers resort to expanding their cultivated areas to increase production because of insufficient financial resources available for purchasing inputs during agricultural seasons. This situation, combined with increasing pressure on land, does not favor agricultural production and, by extension, food security. This study therefore assesses the impact of different modes of access to land on agricultural productivity. Regime-switching multinomial regression techniques were used to account for endogeneity bias due to observed and unobserved factors. The results of the study show that access through free loans, tenancy and sharecropping negatively affect agricultural yields. The counterfactual analysis reveals a positive gain estimated at 509.9 kg/ha from land access by landowners. If the lenders were landowners, their gain would be 396.6 kg/ha, whereas the farmers would gain 485.1 kg/ha if they were landowners, and similarly the sharecroppers would gain 389.8 kg/ha if they were landowners. It is clear from these results that improving agricultural yields depends on securing land and requires the establishment of an effective land ownership system. This research impacts land ownership policies, which need to be revised to address customary rights and reduce inequalities in access to secure land. It highlights the way land tenure security drives agricultural advancements and offers actionable recommendations for policy improvements on food security.

Will the real carbon pricing please stand up?

AbstractThis paper aims at extracting a common factor from major carbon pricing which is interpreted as an effective carbon pricing or a shadow pricing. For this purpose, we use a dynamic factor model for which the unobserved common factors and idiosyncratic noises are potentially nonstationary processes. The two‐step Kalman smoother procedure is used to estimate the model. We found (i) that the extracted common factor (i.e., the effective carbon pricing) exhibits very low values which range from 2.05 to 16.1 USD per ton, and more importantly, (ii) this factor does not cointegrate with the market prices. This last result highlights a total lack of integration of carbon markets.

The Total Effect of Social Origins on Educational Attainment: Meta-analysis of Sibling Correlations From 18 Countries

The sibling correlation (SC), which estimates the total effect of family background (i.e., social origins), can be interpreted as measuring a society's inequality of opportunity. Its sensitivity to observed and unobserved factors makes the SC an all-encompassing measure and an attractive choice for comparative research. We gather and summarize all available estimates of SCs in educational attainment (M = .46, SD = .09) and employ meta-regression to explore variability in these estimates. First, we find significantly lower SCs in Sweden, Norway, Finland, and Denmark than in the United States, with U.S. correlations roughly .10 (i.e., 25%) higher. Most other (primarily European) countries in our study are estimated to fall in between these countries and the United States. Second, we find a novel Great Gatsby Curve–type positive association between income inequality in childhood and the SC, both cross-nationally and within countries over time. This finding supports theoretical accounts of the Great Gatsby Curve that emphasize the role of educational inequality as a link between economic inequality and social immobility. It implies that greater equality of educational opportunity likely requires reduced economic inequality. Additionally, correlations between sisters are modestly higher, on average, than those between brothers or all siblings, and we find no overall differences between cohorts.

The gender productivity gap in Biochemical and Biophysical sciences. Some evidence to solve the productivity puzzle

This paper studies the gender productivity gap in biochemical and biophysical sciences in Argentina for the period 2011–2018. Women publish less papers than men due to academic and non-academic differences. We define the former as the individual characteristics related to research activity, that are developed in the public sphere, and non-academic as the ones related to the private sphere (such as housework and breeding). The later tend to negatively impact on the academic characteristics of female researchers, even when they are not supposed to be related. We test whether the gender productivity gap persists even when academic characteristics between men and women are alike, and to what extent these characteristics contribute to reduce the gap. The methodological approached is based on an extension of the Oaxaca-Blinder decomposition, named multivariate decomposition for nonlinear response models. Results show that both observable (academic) and unobservable factors (both academic and non-academic) explain the gender productivity gap and that the former contribute to the reduction but not the elimination.