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The impact of performance pay in institutions with multiple goals depends on complementarities in the disutility cost of effort and how different tasks interact to achieve each goal. Workers of a mission-oriented nonprofit were randomly assigned to one of two bonus schemes, each incentivizing one of its two main operational goals: the performance of its microcredit program and the strengthening of community institutions of the poor. This study finds that the credit bonus improved credit-related outcomes but it undermined the social outcome. In contrast, the social bonus advanced the social mission as well as the microcredit program, but only for employees working alone, undermining the performance of employees working in teams. These results cannot be explained by a standard multitask principal-agent model featuring only complementarities in the disutility cost of effort. Instead, they suggest that production complementarities are also relevant.

In recent decades it seems that various factors have led to a cultural background change, which although mainly characterized as incremental, in some cases can be sudden. A question therefore arises as to whether the way in which the cultural background has evolved during last decades affects the growth rate of economies. We use an unbalanced panel dataset comprised from 34 OECD countries from 1981 to 2019, and a Least Squares Dummy Variable Correction (LSDVC) method as well as a series of robustness tests including different methods of analysis, adding control variables and breaking the overall period into subperiods. We conclude that the cultural background during the overall period under consideration is characterized as post-materialistic and harms economic growth. Moreover, we highlight both theoretically and empirically the cultural backlash hypothesis since the cultural background of the countries under analysis presents a shift from traditional/materialistic (from 1981 up to 1998) to post-materialist values (from 1999 up to 2019). Doing so, we conclude on a positive effect of cultural background on economic growth when traditional / materialistic values prevail, and a negative effect when post-materialistic values prevail. These results highlight culture as a crucial factor for economic growth and indicate that economic policy makers should take it seriously into account before designing economic policy and in order to explain the effectiveness of economic policies implemented.

The absence of observable innovation data for a firm often leads us to exclude or classify these firms as non-innovators. We assess the reliability of six methods for dealing with unreported innovation using several different counterfactuals for firms without reported R&D or patents. These tests reveal that excluding firms without observable innovation or imputing them as zero innovators and including a dummy variable can lead to biased parameter estimates for observed innovation and other explanatory variables. Excluding firms without patents is especially problematic, leading to false-positive results in empirical tests. Our tests suggest using multiple imputation to handle unreported innovation.

For a given subset of regions, origin–destination flows are decomposed into (1) flows from the subset to its complement, (2) flows from the complement to the subset, (3) flows within the subset, and (4) flows within the complement. With the last flow as the base category, we define three directional dummy variables. We analyze Japan’s inter-municipal migrations by age-sex group using a standard gravity model with directional dummy variables whose regional subsets consist of major cities. Migrations between major cities are more frequent in age groups 30–59, especially for men, than migrations between other municipalities. This observation reflects the fact that many business-related transitions are taking place between major cities. Models with only origin and destination dummies cannot detect such an effect.

I use Chinese panel data to estimate the exogenous effect of economic growth on individuals’ smoking behavior. By instrumenting the endogenous provincial GDP growth rate with a dummy variable indicating whether the province has a new leader, my results show that a higher economic growth rate reduces overall cigarette consumption, but does not reduce the overall smoking participation rate. In addition, a higher economic growth rate reduces cigarette consumption of male but not female. It reduces cigarette consumption of the lower-middle and senior age males, but does not significantly change consumption among the young males. Of these three groups, only the middle-aged males show a decrease in their smoking participation rate with higher economic growth. Overall, the Chinese data show that economic expansion reduces men’s smoking amount. But the overall adjustment is intensive, rather than extensive.

We study identification in nonparametric regression models with a misclassified and endogenous binary regressor when an instrument is correlated with misclassification error. We show that the regression function is nonparametrically identified if one binary instrument variable and one binary covariate satisfy the following conditions. The instrumental variable corrects endogeneity; the instrumental variable must be correlated with the unobserved true underlying binary variable, must be uncorrelated with the error term in the outcome equation, but is allowed to be correlated with the misclassification error. The covariate corrects misclassification; this variable can be one of the regressors in the outcome equation, must be correlated with the unobserved true underlying binary variable, and must be uncorrelated with the misclassification error. We also propose a mixture-based framework for modeling unobserved heterogeneous treatment effects with a misclassified and endogenous binary regressor and show that treatment effects can be identified if the true treatment effect is related to an observed regressor and another observable variable., Comment: 25 pages

Applied economists often use a non-linear function to estimate percentage-change effects of dummy variables in semi-logarithmic models. Delta-method-based inference on these marginal effects is que...

In this paper we present a Bayesian approach to estimate the mean of a binary variable and changes in the mean over time, when the variable is subject to misclassification error. These parameters are partially identified and we derive identified sets under various assumptions about the misclassification rates. We apply our method to estimating the prevalence and trend of prescription opioid misuse, using data from the 2002-2014 National Survey on Drug Use and Health. Using a range of priors, the posterior distribution provides evidence that the prevalence of opioid misuse increases multiple times between 2002 and 2012.

The aim of this paper is to investigate the determinants of the movement of non-performing loans in the banking sector of Bosnia and Herzegovina, as well as the impact of the COVID-19 pandemic on them. For this purpose, secondary data from the banking sector of BiH were used, over which a multi-regression analysis was performed. The variables non-performing loans, GDP, loan loss provision, and dummy variable COVID-19 were used in the analysis. The results of the analysis showed a significant influence of all three mentioned variables. The existence of a significant positive link between non-performing loans and the state of the country's economy has been proven. In addition to the above, it has been proven that the variable COVID-19 has a delayed effect on NPLs, due to the current application of the moratorium on loans.

PurposeThis study aims to examine the influence of internal and external factors on the credit risk (represented by nonperforming financing [NPF]) of Indonesian Sharīʿah rural banks (SRBs) – a type of Islamic bank that provides Islamic financial services especially to small and medium businesses in Indonesia. Internal variables comprise capital adequacy ratio (CAR), financing to deposit ratio (FDR), return on assets (ROA), operating expense ratio (OER), financing to value (FTV) and profit and loss sharing (PLS) financing ratio. External variables comprise inflation, economic growth and interest rate.Design/methodology/approachThe study uses the annual reports of SRBs in Indonesia as secondary data for the years 2010–2019. Auto regressive distributed lag (ARDL) is used as the analysis method to examine the short-run and long-run relationships between the variables.FindingsThe findings indicate that four variables experienced a lag in the short run, namely, NPF, inflation, CAR and PLS, with different results recorded for each of the variables. Furthermore, the long-run results show that CAR and ROA influence the NPF of SRBs positively, whereas inflation and PLS have a negative influence on NPF. The rest of the variables – notably economic growth, interest rate, FDR, FTV and OER – do not have an influence on NPF in SRBs.Research limitations/implicationsThe level of NPF in SRBs exceeds the provision of the Central Bank of Indonesia. The findings are expected to have implications for SRBs and the regulator to consider and to manage the factors related to NPF properly due to the important role of SRBs in small and medium businesses’ development.Originality/valueThis study measures the determinants of NPF using internal and external variables, including the addition of a dummy variable, notably FTV. This study also uses ARDL to analyze the financial policies involving data at the present time and lagged time.

We study how banks’ special interests affect the resolution of failed banks. Using a sample of FDIC auctions between 2007 and 2016, we find that bidding banks that lobby regulators have a higher probability of winning an auction. However, the FDIC incurs larger costs in such auctions, amounting to 24.8 percent of the total resolution losses. We also show that lobbying winners match less well with acquired banks and display worse post-acquisition performance than their non-lobbying counterparts, suggesting that lobbying interferes with an efficient allocation of failed banks. Our results provide new insights into the bank resolution process and the role of special interests.

The bioenergy industry is a proven option to replace fossil fuels in the heat supply and partly in the transport sector and generation of electricity. The study aims to investigate the empirical model of whether bioenergy use and institutional quality increase forest destruction in the European Union 28 countries covering the period 1990–2018, using the pooled ordinary least squares (pooled OLS), fixed effect, random effect, and least square dummy variable corrected. The findings indicate that bioenergy use within an institutional framework significantly contributes to a reduction in forest degradation regardless of the development status of countries in the region. This shows that efficient institutions and governments can significantly contribute to reducing forest degradation in the region especially in developed countries. Thus, policy-makers should stimulate the sustainability criteria of bioenergy application end-uses, intensify the quality of institutions, and ensure bioenergy use under effective governance.

The study aims to examine the CO2 emissions by considering the implication of COVID-19 under strict lockdown in India. The nonlinear (asymmetric) relationship is investigated between CO2 emission and COVID-19 with its specific determinants. The positive and negative asymmetries of COVID-19 determinants are also captured by using econometric techniques. The daily data series of CO2 emission, new confirmed cases, confirmed deaths, and lockdown as dummy variables from January 30, 2020, to December 1, 2020, for India is analyzed by employing the nonlinear autoregressive distributed lag model. This research revealed a significant nonlinear relationship between CO2 emission and COVID-19. The bound test and asymmetric coefficients are confirmed by the variables' long- and short-run relationships. The dynamic multiplier graphs present that India's strict lockdown due to the rapid increase in COVID-19 patients significantly reduces toxic gas emissions, especially CO2 emissions. This asymmetric relationship has been proficiently declared that unhealthy public routine, extra traffic, and unhygienic gases released in the air become the reason for environmental destruction. The lockdown is practically imposed for specific periods and reasons, contributing to reducing toxic emissions, but it is not a permanent solution for environmental sustainability. The government of India, policymakers, and environmentalists should make people aware of unhealthy and environmentally envying activities and policies and long-term applicable strategies should be designed to upgrade the environment's quality.

The COVID‐19 pandemic led many governments to implement lockdown regulations to curb the spread of the virus. Though lockdowns do minimise the physical damage caused by the virus, there may also be substantial damage to population well‐being. Using a pooled data set, we analyse the relationship between a mandatory lockdown and happiness in three diverse countries: South Africa, New Zealand and Australia. These countries differ amongst others in terms of lockdown regulations and duration. The primary aim is to determine, whether a lockdown is negatively associated with happiness, notwithstanding the characteristics of a country or the strictness of the lockdown regulations. Second, we compare the effect size of the lockdown on happiness between these countries. We use Difference‐in‐Difference estimations to determine the association between lockdown and happiness and a Least Squares Dummy Variable estimation to study the heterogeneity in the effect size of the lockdown by country. Our results show that a lockdown is associated with a decline in happiness, regardless of the characteristics of the country or the type and duration of its lockdown regulations. Furthermore, the effect size differs between countries in the sense that the more stringent the stay‐at‐home regulations are, the greater it seems to be.

Our paper explores the ability of the state-level economic institutions, i.e., economic freedom and its components, to explain the movements of entrepreneurship rate after the Great Recession. We use panel date from 50 U.S. states during the period (2008–2017). This paper employs three estimators, fixed effect, panel-corrected standard errors method with states dummy variables, and generalized method of moments (GMM) to extract robust results. The results show that the effect of economic factors on the entrepreneurship rate persists more than that of the institutional and profit opportunity factors. Economic freedom, economic development, and firms’ death rate have a statistically positive effect on entrepreneurship activities. However, the unemployment rate and tax regulations have a statistically significant negative effect. This finding implies that in the state of a recession such as the Great Recession, the entrepreneurship activities will deteriorate sharply. In such a case, the economy needs a genuine government intervention to support economic growth, reduce the unemployment rate, and thus enhance the entrepreneurial activities.

Microinsurance is one of the fastest growing new markets for the billions at the bottom of the financial pyramid – low-income customers previously precluded from traditional insurance. Will insurers really benefit from serving a larger amount of customers? This paper attempts to provide answers by examining the performance of life insurers in Taiwan. Our results show that microinsurance dummy variables are negatively related to efficiency scores and premium income in both OLS and Tobit models. Providing microinsurance products will increase competition between micro and traditional insurance products and reduce market demand for traditional insurance. The efficiency and premium income of traditional insurers will decrease if the price of microinsurance is regulated at marginal cost.

This study aimed to examine whether government financial assistance influences the financial performance of state-owned enterprises. Commercial state-owned enterprises in South Africa that are listed under the Public Financial Management Act during the post-apartheid era from 1995 to 2017 were sampled. Government guarantees were measured as a dummy variable, while financial performance was measured by accounting measure: return on assets (ROA). Endogeneity issues were addressed, and data analysis was performed on an unbalanced panel using the two-step system GMM. The empirical evidence indicated that support by the government in the form of guarantees and subsidies has a significant negative effect on the financial performance of state-owned enterprises. This is an indication that continued government bailouts to poor performing state-owned enterprises exacerbates their poor financial performance and encourages these enterprises to become too reliant on government assistance, burdening the national fiscus. AcknowledgmentsThe author gratefully acknowledges the National Research Foundation of South Africa for the research grant and Dr Farai Kwenda for his supervision during the study.

This paper employs a high dimensional variable selection technique to select a subset of suicide determinants from 167 potential factors, which are then used to estimate ‘natural’ suicide rates for US states by least squares dummy variables. Over the period 2005-2017, all states are found to have a non-zero and positive natural suicide rate, below their respective actual average rate. Higher actual rates suggest deterioration in socioeconomic conditions; inaccessible and unaffordable mental health care for certain sections of the population and inadequate implementation of measures to identify and reduce suicidal mortality. Evidence-informed policies aiming for zero suicide target could draw inspiration from exemplar states, to direct resources towards states with greater relative differences between actual average and natural suicide rates.

A semiparametric fixed effects model is introduced to describe the nonlinear trending phenomenon in panel data analysis and it allows for the cross-sectional dependence in both the regressors and the residuals. A pooled semiparametric profile likelihood dummy variable approach based on the first-stage local linear fitting is developed to estimate both the parameter vector and the nonlinear time trend function. As both the time series length T and the cross-sectional size N tend to infinity, the resulting estimator of the parameter vector is asymptotically normal with a root- ( N T ) convergence rate. Meanwhile, the asymptotic distribution for the nonparametric estimator of the trend function is also established with a root- ( N T h ) convergence rate. Two simulated examples are provided to illustrate the finite sample performance of the proposed method. In addition, the proposed model and estimation method are applied to a CPI data set as well as an input–output data set.

International audience; This paper investigates the relationship between competition and innovation at the firm level. Recent papers (Aghion et al., 2005; Askenazy et al., 2013; Rafique Hashmi, 2013) advocate a non-linear relationship, conciliating the Schumpeterian and Arrowian views. In this paper, the effect of competition on innovation is studied at the firm level using a large dataset for France coming from the annual surveys on R&D, covering the period 2000-2013, where a dummy variable is available for product or process innovation. The econometric results do not confirm the inverted-U shape relationship between the Lerner index and innovation, found by Aghion et al. (2005). This may be due to the use of firm-level data rather than industry-level data. However, for most firms, there is a negative effect of competition on innovation, meaning that more competition in the industry or a small market share has a negative effect on the propensity to innovate, either in products or processes, and the effect seems to be slightly stronger for product than for process innovation.

Purpose This paper aims to analyse the risk and excess returns of the Spanish real estate investment trusts (S-REITs) using various methods, though focusing primarily on the Fama-French three-factor (FF3) model, over the period from 2007Q3 to 2017Q2. Design/methodology/approach The autoregressive distributed lag model is used for the empirical analysis to test long-term stable relationships between variables. Findings The findings indicate that the FF3 model is suitable for the S-REITs market, better explaining the S-REITs’ returns variation than the traditional single-index capital asset pricing model (CAPM) and the Carhart four-factor model. The empirical evidence is reasonably consistent with the FF3 model; the values for the market, size and value are highly statistically significant over the analysis period, with 68.7% variation in S-REITs’ returns explained by the model. In the long run, the market factor has less explanatory power than the size and value factors; the positive long-term multiplier of the size factor indicates that small S-REIT companies have higher returns, along with higher risk, while the negative multiplier of the value indicator suggests that S-REITs portfolios prefer to allocate growth REITs with low book-to-market ratios. The empirical findings from a modified FF3 model, which additionally incorporates Spain’s gross domestic product (GDP) growth rate, two consumer price index (CPI) macro-factors and three dummy variables, indicates that GDP growth rate and CPI also affect S-REITs’ yields, while investment funds with capital calls have a small influence on S-REITs’ returns. Practical implications The regression results of the standard and extended FF3 model can help researchers understand S-REITs’ risk and return through a general stock pattern. Potential investors are given more information to consider the new Spanish investment vehicle before making a decision. Originality/value The paper uses standard techniques but applies them for the first time to the S-REIT market.

In this paper, we consider the signals approach as an early-warning-system to detect crises. Crisis detection from a signals approach involves Type I and II errors which are handled through a utility function. We provide a Bayesian model and we test the effectiveness of the signals approach in three data sets: (1) Currency and banking crises for 76 currency and 26 banking crises in 15 developing and 5 industrial countries between 1970 and 1995, (2) costly asset price booms using quarterly data ranging from 1970 to 2007, and (3) public debt crises in Europe in 11 countries in the European Monetary Union from the introduction of the Euro until November 2011. The Bayesian model relies on a vector autoregression for indicator variables, and incorporates dynamic factors, time-varying weights in the latent composite indicator and special priors to avoid the proliferation of parameters. The Bayesian vector autoregressions are extended to a semi-parametric context to capture non-linearities. Our evidence reveals that our approach is successful as an early-warning mechanism after allowing for breaks and nonlinearities and, perhaps more importantly, the composite indicator is better represented as a flexible nonlinear function of the underlying indicators.

This research empirically analyzes the impact of various instruments of economic sanctions on official exchange rate volatility by employing data from a panel of 23 target countries covering the period 1996–2015 and using the Least Squares Dummy Variable Corrected (LSDVC) model. Our findings suggest that economic sanctions do significantly influence the target countries’ exchange rate volatility. Specifically, we are able to see different sanction present its different effects on exchange rate volatility. Furthermore, the robustness evidence of the eliminating country as Iran, eliminating variable of political ideology, intercepting time period, cross-sectional regression analysis, using real exchange rate volatility as proxy variable and a new sanctions database, are basically consistent with the previous finding. Overall, our empirical findings offer implications for those sanctioned countries about how to stabilize their exchange rate when facing sanctions.

This paper investigates the impact of international sanctions on energy efficiency by employing the panel fixed effect as well as average marginal effect from the Tobit model via data on 30 sanctioned states over the period 1996–2015 with international sanctions including unilateral, plurilateral, U.S., EU, UN, economic, and non-economic cases. Overall, we find that the imposition of unilateral sanctions leads to a 0.067% decrease in energy efficiency, but that of plurilateral sanctions positively contribute to energy efficiency in the case of the full sample of countries. Moreover, the imposition of UN sanctions has a greater decrease on energy efficiency in the target states than the 0.042% reduction of energy efficiency when the sanctionist is the U.S. For robustness, empirical results indicate that the imposition of plurilateral sanctions results in a drop of energy efficiency in Islamic countries, but an increase in non-Islamic countries, while there are also negative shocks induced by the imposition of EU sanctions on energy efficiency in Asian countries, but not for non-Asian countries. We also consider endogenous problems and dynamic specification by using indicator variables and System GMM. In summary, our empirical findings provide policy suggestions for those sanctioned countries about how to maintain energy efficiency when facing international sanctions.

Purpose The purpose of this paper is to examine the level of income inequality among fisher households in the four segments of the Volta Basin in Ghana and the determinants of small-scale fisher household welfare. Design/methodology/approach The paper investigates income inequality and the determinants of household consumption expenditure of fishers in the Volta Basin of Ghana using the Lorenz curve, Gini coefficient decomposition and the multiple linear regression technique. Findings The findings confirm the empirical evidence that households in the general downstream communities and their compatriots in the upstream communities were statistically different in terms of household characteristics and physical and social capital. Income inequality was highest among households in upstream communities but least amongst downstream communities. Livelihoods of the fishery households largely and strictly favored male households. Also, educational status was associated with higher levels of income which enormously help reduce food insecurity. Research limitations/implications The reliance on dummy variables might lead to omissions of revealed understated differences between households. Practical implications The study recommends that effort should be made to help increase fish production in the upstream communities. Furthermore, alternative sources of income should be introduced to households in the upstream so they do not become more disadvantaged by the construction of the dam. Originality/value The uniqueness of the paper is that it tries to estimate the impact of the construction of the dam on the Volta Basin by taking into consideration the effect of the construction of the dam on the upstream and downstream separately.

A new autoregressive model with seasonal dummy variables in which coefficients of seasonal dummies vary smoothly and deterministically over time is introduced. The error variance of the model is seasonally heteroskedastic and multiplicatively decomposed as in ARCH models. This variance is also allowed to be smoothly and deterministically time-varying. Under regularity conditions, consistency and asymptotic normality of the maximum likelihood estimators of parameters of this model is proved. The purpose of the model is to find out how the average monthly temperatures in the well-known central England temperature series have been varying during the period of more than 240 years. The main result is that warming has occurred but that there are notable differences between months. In particular, no warming is found for February, April, May and June.

The cooperative landscape in Ethiopia is very heterogeneous with a mixture of remains of the pre-1991 government-controlled system and new post-1991 bottom-up collective action initiatives. This heterogeneity, coupled with a large growth in the number of cooperatives in the country, offers an interesting perspective to study the determinants of the (in)efficiency of cooperatives. In this paper, we analyse the performance of Ethiopian agricultural cooperatives, focusing on the degree of technical (in)efficiency and its determinants. We use the stochastic frontier approach in which we account for heteroskedasticity and the monotonicity of production functions, presenting a methodological improvement with respect to previous technical efficiency studies. The results show that NGO- and government-initiated cooperatives are less efficient than community-initiated ones, implying that governments and NGOs should not interfere too strongly in cooperative formation. Cooperatives with a high degree of heterogeneity in members' participation are found to be about 98% less efficient, while cooperatives that have paid employees are 33% more efficient. Besides, results show that cooperatives in Ethiopia function more efficiently if they incentivize committee members through monetary compensation.

Hedonic regression and repeat sales are commonly used methods in real estate analysis. While the merits of combining these models when constructing house price indices are well documented, research on the utility of adopting the same approach for residential property valuation has not been conducted to date. Specifically, house value estimates were obtained by combining predictions from repeat sales and various hedonic regression specifications, which were enhanced to account for spatial effects. Three of these enhancements—regression kriging, mixed regressive-spatial autoregressive, and geographically weighted regression—are widely utilized spatial econometric models. However, a fourth augmentation, which addresses systematic residual patterns in regressions with district indicator variables and the presence of outliers in housing data, was also proposed. The resulting models were applied to a dataset containing 16,417 real estate transactions in Oslo, Norway, revealing that when the repeat sales approach is included, it reduces the median absolute percentage error of solely hedonic models by 6.8–9.5%, where greater improvements are associated with less accurate spatial enhancements. These improvements can be attributed to the inclusion of both spatial and non-spatial information inherent in previous sales prices. While the former has limited utility for well-specified spatial models, the non-spatial information that is implicit in previous sales prices likely captures otherwise difficult to observe phenomena, potentially making its contribution highly valuable in automated valuation models. © 2019. This is the authors' manuscript to the article.

Summary Gender is prohibited from use in decision making in many countries. This does not necessarily benefit females in situations of automated algorithmic decisions, e.g. when a credit scoring model is used as a decision tool for loan granting. By analysing a unique proprietary data set on car loans from a European bank, the paper shows that gender as a variable in a credit scoring model is statistically significant. Its removal does not alter the predictive accuracy of the model, yet the proportions of accepted women/men depend on whether gender is included. The paper explores the association between predictors in the model with gender, to demonstrate the omitted variable bias and how other variables proxy for gender. It points to inconsistencies of the existing regulations in the context of automated decision making.

This article analyzes the impact of demonetization on the volatility of the Indian stock market and assesses the investment certainty in India after demonetization. A symmetric generalized autoregressive conditional heteroskedasticity (1, 1) model has been applied by incorporating a dummy variable for the event of demonetization in the conditional variance equation. This article also looks for percentage change in unconditional variance after demonetization. The results show that after demonetization, the risk related to market price decreased and the certainty of investment increased in the Indian stock market. In addition, the degree of volatility shocks declined in the post-demonetization period in comparison to the pre-demonetization period. TOPICS:Security analysis and valuation, statistical methods, emerging markets, risk management

People have long been trading in a “monetary” way. This persistence of monetary trading suggests that it might be an efficient trading mechanism. We formalize this intuition in a random-matching, absence-of-double-coincidence-of-wants environment. The record-keeping technology is operated by an information-processing center which summarizes and updates individuals’ past trading behavior in a binary variable. A trading mechanism consists of an updating rule and an individual trading behavior rule. To capture the difficulties in collecting and processing information about others’ past behavior, we assume that the center faces costs, both fixed and variable, to operate. We show that (1) any non-autarkic equilibrium trading mechanism is, in terms of aggregate variables (such as consumption and frequency of trade), observationally equivalent to a monetary trading mechanism and (2) any non-autarkic optimal equilibrium trading mechanism is a monetary trading mechanism.

This paper analyses the overall trend of key macroeconomic variables of BRICS nations impacted by the US financial crises (2008–2009) through structural break analysis for period 2000–2013. Dummy v...

Purpose By disentangling the impact of positive and negative shocks of GDP, FDI and oil consumption on electricity consumption, the purpose of this paper is to investigate whether this bifurcation significantly determines the level of electricity consumption in the short run and long run. Design/methodology/approach Using the recently developed nonlinear autoregressive distributed lag (NARDL) bounds approach, the study investigates the changes in the level of electricity consumption over a period of 1980–2015. The inclusion of a dummy variable for the possible structural break makes the electricity demand function more reliable. After checking the stationarity of data series, the study has employed the bounds test, which confirms the existence of the long run stability in the system. Further, using the VECM, the causality among the comprised variables has also been examined. Findings The findings confirm that not only the positive shocks but also the negative shocks in GDP have a positive and significant impact on electricity consumption in the long run. Similarly, the increased FDI has widened the scope of electricity consumption in the region, whereas the negative shocks’ impact is found negative in the long run. In comparison to GDP and FDI, the influence of the increased oil consumption on electricity demand is found negative and significant in India, which reveals that electricity acts as a significant substitute for oil consumption in the long run. Originality/value To the best of the literature evidences available, none of the studies in the past has examined electricity demand in an NARDL framework. The study may help in estimating the demand for electricity consumption comprehensively, as this approach captures the separate influence of favourable and unfavourable changes while determining the level of electricity consumption. This approach may be crucial for policy makers, especially in an energy importer country, such as India.

The aim of this paper is to offer an empirical insight into the spatial effects of growth of regional income and disparities across EU regions (NUTS 2). Since regions are spatial units and there are interrelated standard linear regression is not sufficient to evidence the convergence process. Two models (Spatial Lag Model – SLM and Spatial Error model – SEM), derived from spatial econometrics, have been used to identify and explain spatial effects in convergence clubs—all EU countries (EU-28), countries that entered the EU in 2004 (EU-13) and countries that were in EU prior to 2004 (EU-15). Unconditional and conditional β-convergence has been examined in the period 2000-2015 thus covering two financial perspectives (including n + 2 rule3). Dummy variables have been also applied to catch the country-specific effects, such as national policies, legislation, technology progress, etc.

The focus is on cross-sectional dependence in panel trade flow models. We propose alternative specifications for modeling time-invariant factors such as sociocultural indicator variables, e.g., common language and currency. These are typically treated as a source of heterogeneity that is eliminated using fixed effects transformations, but we find evidence of cross-sectional dependence after eliminating country-specific and time-specific effects. These findings suggest use of alternative simultaneous dependence model specifications that accommodate cross-sectional dependence, which we set forth along with Bayesian estimation methods. Ignoring cross-sectional dependence implies biased estimates from panel trade flow models that rely on fixed effects.

This study extends the literature on modeling the volatility of housing returns to the case of condominium returns for five major U.S. metropolitan areas (Boston, Chicago, Los Angeles, New York, and San Francisco). Through the estimation of ARMA models for the respective condominium returns, we find volatility clustering of the residuals. The results from an ARMA‐TGARCH‐M model reveal the absence of asymmetry in the conditional variance. Dummy variables associated with the housing market collapse unique to each metropolitan area were statistically insignificant in the conditional variance equation, but negative and statistically significant in the mean equation. Condominium markets in Los Angeles and San Francisco exhibit the greatest persistence to volatility shocks.

We propose a new estimation method of the Lorenz curve based on dummy variable regression for granting clear economic connotations to parameters in the estimation of the Lorenz curve. The dummy variable regression model of the Lorenz curve is established by introducing dummy variables to group data about income level. On this basis, it is proved that the regression function is endowed with the convexity that the Lorenz curve should have. To verify the effectiveness of the new method, empirical study is carried out taking the income data of urban and rural residents in the Chinese Household Income Project Survey (CHIP) database as examples. In the empirical study, the proposed model is compared with several existing classical parameter models. The results indicate that the slope parameter of the model represents the cumulative income–population resilience of corresponding residents, and reflects the income structure of residents to some extent. Meanwhile, compared with classical parameter models, the newly proposed method has advantages of higher fitting precision and stronger adaptability.

The female labour force participation decision in the UK is a well-researched topic in empirical economics and econometrics. In this paper, using data from the UK Labour Force Survey in 2007, we contribute to the rich body of the literature by estimating a model using the kernel mixed-data-type conditional-probability (KMDTCP) estimator. We extend the analysis by comparing the predicted probabilities of the KMDTCP estimator with the estimates of a multinomial logit (MNL) model. Additionally, kernel smoothing specification tests are applied in order to compare different functional forms for the independent variables in the MNL model against the KMDTCP estimates. Our results demonstrate that the difference between the predictions from the KMDTCP estimator and the MNL is reduced when the functional form of continuous independent variables in the MNL model is relaxed to a semi-parametric form using dummy variables and interaction terms. The properties that are captured by the KMDTCP estimator show clearly that the propensity to work decreases with the increase in the number of dependent children. Furthermore, our analyses show that females older than 40 years of age have a high propensity to work irrespective of the number of dependent children.

Stability of inflation expectations is a necessary part of inflation targeting. Among many factors that may affect the dynamics of inflation expectations, one of the most important is the communication policy of the central bank and representatives of the government. This paper measures the effectiveness of verbal interventions by the Government and the Bank of Russia on the high-frequency indicator of inflation expectations from the stock market for the period July 2015 - December 2016. Dummy variables are used to characterize verbal interventions in terms of the degree of regularity, the source and the information contained. One of the main features of this paper is the analysis of the verbal interventions from individual representatives of monetary and fiscal policies. By the assessment of the model of conditional heteroscedasticity, we conclude that verbal interventions by the Bank of Russia and members of the Government of the Russian Federation accompanied by decrease of inflation expectations: key verbal interventions were statements about state budget deficit and future inflation. The results obtained can be used to develop the communication policy tools in future.

We extend the distributional event response model (DERM) of Rucker, Thurman, and Yoder (2005) in two ways. First, we develop a mixed event response model (MERM) to allow for possible asymmetric effects, and second, we examine how volatility, in addition to return, changes surrounding an event. We apply our model to the crude oil futures market using 25 years of daily data. Our results show that among the 10 events considered, the 2008 global financial crisis had the largest impact in magnitude on both return and volatility. The location and duration of response patterns are also found to vary across different events, with the financial crises having long-lasting impacts, while truly unanticipated events, such as the September 11 terrorist attacks, having short-lived impacts. Results suggest that simply using an event-day dummy variable would hinder discovering overall market responses to slowly evolving information events.

The political-economic cycle can be caused as the consequence of the wrong political decisions, made with the aim of re-election and maintaining the political power. These decisions influence the macroeconomic indicators of the country and their presence is problematic in the advanced economies. The main objective of this study is to verify the existence of the political-economic cycle model in the case of Greece and to identify the type of this cycle. The basement is given by the approach of Alesina and Roubini (1992), which observes the relationship between the political dummy variables and the selected macroeconomic variables such as gross domestic product, unemployment rate and inflation rate. The eight linear regression models are developed in the R software environment, while the three of them are opportunistic and the five are ideological. These models are identified as statistically significant and according to the methodology, tested for the presence of serial correlation, heteroscedasticity and residual normality. As the models do not confirm the presence of an opportunistic or ideological political-economic cycle, according to the data, the influence of the political parties on changes in the macroeconomic variables before the election is not proved for the case of Greece.

The main aim of the current study is to explore the relationship between working capital (WC) and firm performance. We chose a sample of 326 Czech firms, including 20 certified firms from the EFQM (European Foundation for Quality Management) Excellence Model from the Albertina database. The sample of the Czech firms was taken from three sectors: manufacturing, automobile, and construction. We employed a two-step system generalized method of moment (GMM) technique to determine the results. The study results revealed a negative impact of WC on firm performance; moreover, the firms having a quality certificate from the EFQM Excellence Model perform better. The findings of previous research, which were held globally, and the current study results will encourage the directors, managers, and leaders of the Czech firms to participate in the quality award.

Given the incessant increase in OPEC energy intensity, policymakers need a good understanding of the underlying factors and the environmental impacts when considering future energy policies. An index decomposition analysis is used to decompose OPEC energy intensity for 1971-2013. The link between the decomposed energy indices and CO2 emissions is examined using structural time series and least square dummy variable corrected models. Both models also estimate the underlying carbon emission trend (UCET) which arguably reflects the impact of noneconomic factors. For OPEC as a group, increases in energy intensity are linked to both energy inefficiency and structural shifts toward energy-intensive activities. About 56% of the increases are attributed to the former and the remaining 44% is due to the later. The country-level results also show major contributions from both components to energy intensity. The econometric results show that shifts toward energy-intensive activities and, notably, deteriorating energy efficiency generally go in tandem with substantial increases in CO2 emissions. The estimated UCET is upward sloping indicating carbon-emitting behaviour and lifestyle. Therefore policies aimed at conserving energy and limiting the concentration of energy-intensive activities in the oil-exporting countries should be considered alongside other policies that attempt to influence behaviours and lifestyles.Keywords: Energy intensity, Index decomposition analysis; Energy (in)efficiency; Structural composition; Structural time series model; Least square dummy variable corrected model; CO2 emissions, OPECJEL codes: C22, Q41, Q43

In the setting of the Stochastic Frontier Production Models, the productive efficiency of the 28 countries belonging to the European Union is analysed. To this end, panel data encompassing a broad period of time is selected, which facilitates the study into whether there is greater efficiency in the years of crisis as a consequence of the adjustment measures. A translog specification of a Cobb-Douglas model is considered, in which the output is measured through the GDP of the countries and two productive factors (capital and labour). The model also includes a trend component that addresses the possible presence of technological change, and dummy variables for each country in order to separate unobserved heterogeneity from productive inefficiency. In relation to the perturbation that models the inefficiency, a model of the type Battese and Coelli (1995) is considered with a trend component and a variable related to economic growth. Finally, the growth of productivity is decomposed into the sum of the changes in technology, in economies of scale, and in inefficiency.

PurposeThe purpose of this study is to highlight the determinants of market rents and to build a hedonic market rent index for each urban area and rental sector in France for the period 1970–2013. The authors also analyse the market rent dynamics over this period, with a special attention to the turning points in the French housing policy.Design/methodology/approachFor this purpose, the authors implement a hedonic model, called stratified time dummy variable, using the Box–Cox transformation as a functional form.FindingsThe contribution of this study to the housing research is threefold: First, the study improves our understanding of the French’s rental submarket specificities and their valuation. It sheds new light on the determinants of rents. Second, this study builds a hedonic market rent index over the period 1970–2013 for each geographical and sectoral segment (Paris urban area, urban areas of more and less than 100,000 inhabitants and private and public rental sectors). Third, this study explains rent dynamics focusing on the turning points in the French housing policy.Originality/valueFinally, the authors provide the first long-term market rent index in France by submarket (geographical and sectoral). In the case of the French market, no long-term market rent exists. The only long series available is an indexed rent.

We show through extensive Monte Carlo simulations that structural breaks in volatility (volatility shifts) across two independently generated return series cause spurious volatility transmission when estimated with popular bivariate GARCH models. However, using a dummy variable for the induced volatility shift virtually eliminates this bias. We also show that structural breaks in volatility have a substantial impact on the estimated hedge ratios. We confirm our simulation findings using the US stock market data.

Applied work often studies the effect of a binary variable (“treatment”) using linear models with additive effects. I study the interpretation of the OLS estimands in such models when treatment effects are heterogeneous. I show that the treatment coefficient is a convex combination of two parameters, which under certain conditions can be interpreted as the average treatment effects on the treated and untreated. The weights on these parameters are inversely related to the proportion of observations in each group. Reliance on these implicit weights can have serious consequences for applied work, as I illustrate with two well-known applications. I develop simple diagnostic tools that empirical researchers can use to avoid potential biases. Software for implementing these methods is available in R and Stata. In an important special case, my diagnostics require only the knowledge of the proportion of treated units.

PurposeThis paper aims to examine the housing market in Greece after the Global Financial Crisis focussing on regional analysis and urban markets in Athens and Thessaloniki.Design/methodology/approachThe paper uses a data set of over 70,750 property values from 2007 until 2014 incorporating characteristics variables upon which hedonic models are estimated. These form the bases for calculating value indices for mix adjusted houses/apartments by year and region. The indices are used in a panel model in which regional and economic variables are included as independent variables. Using advances in dynamic panel data modelling, a bias-corrected least squares dummy variable corrected (LSDVC) model is applied.FindingsResults indicate the importance of macroeconomic variables in terms of the role of disposable income and significantly different regional effects. Examining the major urban markets, results indicate significant differences in the response of house values to exogenous demand side influences, consistent with the finding of significant regional differences in the LSDVC.Research limitations/implicationsWhile data on valuations are used that may contain smoothing, the data set covers a large sample of residential properties. As regional economic differences are significant and persistent, housing markets will also behave differently, and hence national policies, unless targeted, will have regionally differentiated effects.Practical implicationsRegional heterogeneity needs to be considered in model estimation.Social implicationsPolicymakers should consider regional differences to improve policy effectiveness.Originality/valueThis is the first paper to use a large sample of residential properties in Greece and apply the LSDVC model to overcome estimation biases.