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The purpose of this paper is to investigate the issue of efficiency in the Brazilian motor carrier industry, which has undergone significant transformations since the economy deregulation in the mid-1990s. The main research objective is to determine whether or not different types of cargoes and geographic regions serviced significantly impact trucking managerial efficiency levels.

Research objectives are accomplished by applying a two-stage data envelopment analysis model with bootstrapped estimates. Based on an unbalanced panel model, secondary data from the annual study published by Transporte Moderno (years 2002-2010) were collected and analyzed.

Results support anecdotal evidence regarding a heterogeneous impact of cargo mix and route mix on efficiency levels.

A major limitation of this work concerns the fact of working with secondary data instead of primary data, especially with respect to the set of inputs and outputs used in the analysis, which may not cover all aspects relevant to building an efficiency frontier. Despite this limitation, the study has made an important contribution in its use of panel data to demonstrate the impacts of different types of cargoes and geographic regions serviced on managerial efficiency levels in the trucking industry.

Managerial impacts in terms of mergers and acquisitions are addressed.

The contribution of this study is twofold. On the theoretical side, a valuable scale for the measurement of managerial efficiency was built and validated, representing an index toward the most productive cargo/route mix. On the other hand, the managerial implication of this possibility of measuring the efficiency levels is that motor carriers can use it as a basis for establishing future action plans.

Brazil, DEA, Longitudinal study, Efficiency drivers, Trucking industry, Unbalanced panel

 Research paper

El estudio investiga el tema de la eficiencia en la industria del transporte Brasileña, la cual ha experimentado cambios significativos desde la desregulación de la economía a mediados de los años noventa. El objetivo principal de la investigación es determinar si los diferentes tipos de cargas y regiones geográficas que han recibido los servicios impactan significativamente o no los niveles de eficiencia administrativa de las empresas de camiones.

Los objetivos de la investigación se lograron usando un modelo DEA de dos etapas con estimaciones bootstrap. Basado en un modelo de panel no balanceado, se obtuvieron y analizaron datos secundarios del estudio anual publicados por Transporte Moderno (años 2002-2010).

Los resultados apoyan la evidencia anécdota en lo relacionado con el impacto heterogéneo de mezcla de cargas y una combinación de rutas en los niveles de eficiencia.

La limitación más grande de este trabajo es el hecho de trabajar con datos secundarios en vez de usar datos primarios, especialmente en relación con el set de inputs y outputs usados en este análisis, que podrían no cubrir todos los aspectos relevantes en la construcción de una frontera eficiente. A pesar de esta limitación, este estudio hace una importante contribución al usar datos de panel para demostrar los impactos de los diferentes tipos de cargas y regiones geográficas atendidas, en los niveles de eficiencia de la administración de la industria de transporte de camiones.

Se discuten los impactos de administración en términos fusiones y adquisiciones.

La contribución de este estudio es doble. Por el lado teórico, una escala relevante para medir la eficiencia de la administración fue construida y validada, que representa un índice hacia la combinación carga/ruta más efectiva. Por otro lado, las implicaciones administrativas de la posibilidad de medir niveles de eficiencia es que las empresas de transportes pueden usarloa como base para establecer planes futuros de acción.

Estudio longitudinal, Industria camionera, Brasil, DEA, Promotores de eficiencia, Panel no balanceado

 Trabajo de investigación

A pesquisa investiga o tema da eficiência na indústria brasileira de transporte rodoviário, a qual tem experimentado mudanças significativas desde a desregulamentação da economia em meados dos anos noventa. O objetivo principal da pesquisa é determinar se os diferentes tipos de cargas e de regiões geográficas atendidas impactam significativamente ou não nos níveis de eficiência na gestão de cargas rodoviárias.

Os objetivos da pesquisa foram atingidos usando um modelo DEA de dois estágios com estimativas bootstrap. Baseados em um modelo de painel, foram recolhidos e analisados dados secundários publicados no estudo anual da publicação Transporte Moderno (anos 2002-2010).

Os resultados apoiam a evidência baseada no senso comum relacionada ao impacto heterogêneo do mix de cargas e do mix de rotas nos níveis de eficiência.

A maior limitação de esta pesquisa concerne ao fato de trabalhar com dados secundários em vez de dados primários, especialmente em relação com a série de inputs e outputs utilizados na análise, que não puderam cobrir todos os aspectos relevantes para definição da fronteira de eficiência. Apesar dessa limitação, esta pesquisa traz uma importante contribuição ao utilizar dados de painel para demonstrar os impactos dos diferentes tipos de cargas e de regiões geográficas atendidas sobre os níveis de eficiência na gestão do transporte de cargas rodoviárias.

Os impactos da gestão em termos de fusão e aquisição são abordados.

A contribuição deste estudo é dupla. Pelo lado teórico, foi construída e validada uma escala relevante para medir a eficiência de gestão de cargas rodoviárias, que indica um índice para a combinação mais efetiva da combinação entre cargas e rotas. Por outro lado, quanto às implicações gerenciais, a possibilidade de medir os níveis de eficiência criam a possibilidade das empresas de transportes utilizá-los como base para futuros planos de ação.

Estudo longitudinal, Setor de transporte rodoviário, Brasil, DEA, Condutores de eficiência, Dados em painel

 Trabalhos de pesquisa

The purpose of this study is to investigate the effect of government expenditure on economic growth in 15 Economic Community of West African States (ECOWAS) countries over the period of 2005–2017. More precisely, this paper investigates whether institutional environment influences the effect of government spending on economic growth.

This study adopts the generalized method of moments-system method of estimation to address the problem of dynamic endogeneity inherent in the relationship. Similarly, unlike previous studies which assume that the disturbances of a panel model are cross-sectionally independent, we account for cross-section dependency and cross-country heterogeneity inherent in empirical modeling using Driscoll and Kraay's nonparametric covariance matrix estimator, adjusted for use with both balanced and unbalanced panels along with Monte Carlo simulations.

The authors find that though, government spending has a positive impact on economic growth but the level of institutional quality adversely affect that positive impact. This suggests that the institutional environment in ECOWAS countries is a drag and not a push factor for government fiscal operations and/policies. Thus, the results provide empirical evidence that there is a conditional relationship between government spending and economic growth in African countries. That is, the effect of government spending on economic growth is dependent on the quality of institutions. Lastly, these findings suggest that in order for government spending to contribute to economic growth, African countries must develop a strong institutional environment.

Unlike previous time series studies for African countries which concentrated on the two variable case, we include institutional quality as a third variable to underline the potential importance of institutional quality for economic growth in ECOWAS countries.

The purpose of this paper is to investigate whether foreign direct investment (FDI) benefitted Thai workers in domestic firms.

By utilizing existing firm-level unbalanced panel data from the survey of the Office of Industrial Economics, Ministry of Industry, Thailand, between 2004 and 2013, this study applies dynamic panel data analysis, using the generalized method of moments proposed by Arellano and Bond (1991), to estimate the wage spillover from multinational enterprises (MNEs) to domestic firms in Thailand.

The study reveals that there is a positive wage spillover from the presence of MNEs in the industry to domestic firms. Furthermore, a wage spillover also exists in the low-technology industry, as well as in firms located in the Metropolitan and Northern regions. These findings confirmed that FDI offers a significant advantage in Thailand’s labor market.

This study is the empirical research to utilize existing firm-level unbalanced panel data in Thailand, applying dynamic panel data analysis to data from 2004 to 2013 to estimate the wage spillover from MNEs to domestic firms.

The purpose of this paper is to evaluate the total-factor energy efficiency (TFEE) in China’s sugar manufacturing industry using firm-level data from 2002/2003 to 2012/2013 crushing seasons, and further explore the determinants of TFEE.

Modified data envelopment analysis is used to measure the TFEE of each sugar mill during the crushing seasons. Then heteroskedastic fractional probit model is applied to estimate the determinants of TFEE because of the bounded nature of TFEE and heteroskedasticity of unbalanced panel.

The results show that throughout the crushing seasons, the average TFEE is 0.57; there are spatial differences of TFEE in Guangxi sugar industry, highest in southern area; the TFEE of foreign-owned sugar mills is larger than that of private-owned and state-owned sugar mills; the larger the enterprise size, the higher the TFEE; private ownership, large size, raw material, safe productivity, total recovery rate as well as technical progress can improve TFEE significantly.

This paper analyzes TFEE using a rich data set at firm level, allowing the existence of firm heterogeneity, as well as being complementary to the study of energy efficiency in China’s sugar industry. Moreover, ownership structure is involved in the determinants of TFEE, which is rarely done in literature. Lastly, heteroskedastic fractional probit model is employed to recognize the bounded nature of TFEE as well as selection bias of unbalanced panel to study the determinants of TFEE.

This paper aims to explore the effects of bank diversification (i.e. diversification of income and diversification of assets) on Bangladeshi banks’ profitability.

Using a dynamic panel data model with system generalized methods of moments, the authors examine an unbalanced panel data from 32 banks spanning 318 bank-year observations from 2007 to 2016.

The findings indicate a significant positive association of income diversification and asset diversification on bank profitability. Therefore, the results show that banks can generate profit from diversification of income and diversification of assets.

One of the rare attempts to investigate the relationship between diversification and profitability in Bangladesh’s banking sector is this report. The authors anticipate the results to have major consequences for Bangladeshi bank regulators and other related economies.

The purpose of this paper is to identify key determinants of foreign direct investment (FDI) inflows in developing countries by using unbalanced panel data set pertaining to the years 1990-2012. This study considers 20 developing countries from the whole of South, East and South-East Asia.

Using seven explanatory variables (market size, trade openness, infrastructure, inflation, interest rate, research and development and human capital), the authors have tried to find the best fit model from the two models considered (fixed effect model and random effect model) with the help of Hausman test.

Fixed effect estimation indicates that market size, trade openness, interest rate and human capital yield significant coefficients in relation to FDI inflow for the panel of developing countries under study. The findings reveal that market size is the most significant determinant of FDI inflow.

Like any other study, this work also has some limitations. Lack of data on key determinants such as labor cost, exchange rate, corruption, natural resources, effectiveness of rule of law and political risk may be considered one such limitation. Further, controlling for variables such as exchange rate, corruption, labor cost and political risk could make significant improvements to this study.

This study has significant implications for policy makers, mangers and investors. Policy makers would be able to understand the importance of the major determinants of FDI mentioned in the paper, and take steps to formulate policies that encourage FDI. Such measures could include developing market size, making regulations more international trade friendly and investing in the nation’s human capital. Further, steps could be taken to keep interest rates and inflation rates under control as these factors have been found to influence FDI.

The sample of 20 developing nations chosen for this study has not been considered by any study earlier. This is a unique contribution to existing body of research, and highlights the originality value of this paper.

The purpose of this paper is discussing on cross-city empirical economic growth, by estimating an unbalanced dynamic panel for the most vulnerable region of Brazil.

The authors propose including additional and specific sources of cross-city variation, enabling them to capture the essence and reality of this region. The sample selection is given by the solution of a trade-off on the number of cities and the available explanatory variables. Considering the final choice, the analysis is based on 6,452 observations extracted from a sample of 925 cities between 2009 and 2015. Reconciling the regional growth literature and this availability of observable data, the authors decide to explain cross-city real gross domestic product per capita in log, controlling for its lagged value besides 15 explanatory variables on human capital, financial system, business environment and social infrastructure.

This study uses growth drivers on human capital, financial system, business environment and social infrastructure. Considering 6,452 observations for the period from 2009 to 2015, this study finds a significant role played by the levels of education of formal workers, rural financing, real estate financing and FIRJAN indices (health and employment).

A more comprehensive and complete understanding of cross-city variation, whether in the Northeast, in the North of the country or in other regions, involves the expansion of growth drivers in the model. Certainly, the impact of the industrial sector (not captured by the FIRJAN employment/income index), or programs and initiatives geared to technology, must be significant and positive. Despite the low market share, the insertion of microcredit data for informal, small business owners and more underserved families, can bring insights not measured in this article.

On financial system and development: The results on the significant and positive coefficient of rural and real estate financing are fundamental in conducting public policies aimed at granting credit. On human capital: The expected and intuitive relevant role of education suggests that good policies that are implementable need to be looked for and replicated to other northeastern cities. The state of Ceará seems to be that benchmark to be followed by the other states.

Another public policy that needs to be strengthened so that the most vulnerable cities can grow is related to the partnership with the private sector in the expansion and maintenance of basic sanitation. In this context, the new Legal Framework for Basic Sanitation is an important step. Its main objective is to universalize and qualify the provision of services in the sector. Theoretically, it seems to be an important advance and this also unlocks the first big wave of investments.

The analysis aims to contribute to the recent studies on regional growth applied to Brazil. To the best of the authors’ knowledge, this is an innovative contribution, and the main differences between this paper and the others are the sample of cities, the period, the growth model and the estimation technique. For instance, Da Mata et al. (2005, 2007) explore population growth and its implications for economic dynamics and income generation among 123 urban agglomerations between 1970 and 2000. Alves (2021) studied slum growth in contemporary urbanization of households in 272 Brazilian cities from 1991 to 2010.

The upsurge of Chinese and Indian outward foreign direct investment (FDI) raises an unanswered question about locational determinants of direct investment from the two countries. Using an unbalanced bilateral FDI database, we find that Chinese and Indian FDI are attracted to countries with large market size, low GDP growth, high volumes of imports from China or India, and low corporate tax rates. We also find important differences between China and India. While Chinese FDI is drawn to countries with open economic regimes, depreciated host currencies, better institutional environments, and English speaking status, none of these factors are important for Indian FDI. Chinese FDI is also deterred by geographic distance and OCED membership. However, neither of these has any impact on Indian FDI.

The purpose of this study is to explain the adjustment rate toward the target capital structure of Chinese nonfinancial listed firms and to investigate the impacts of the split-share reforms (2005–2006) on the capital structure adjustment rate.

The authors control for the unobserved heterogeneity and the fractional nature of the adjustment rate by applying an unbiased dynamic panel fractional estimator on the unbalanced panel data of 27,545 firm-year observations of Chinese nonfinancial firms listed during 1998–2015.

The authors find that Chinese firms adjust at an annual rate of 19–27% to reach their capital structure targets. The authors also find a positive impact of the split-share reforms on the adjustment rates of Chinese nonfinancial firms toward their target capital structure. Split-share reforms also helped Chinese firms to increase the use of equity financing in their capital structure.

The authors argue that the government should strengthen capital markets to enable easy access to more financing options so that Chinese firms can acquire cheaper external financing.

To the best of authors' knowledge, this is the first study that applies an unbiased dynamic panel fractional estimator on an extended data set of 27,545 firm-year observations of Chinese nonfinancial firms listed during 1998–2015.

The purpose of this paper is to assess the extent to which financial and market performance of companies in the oil and gas sector can be attributed to the value of their intangibles.

The research utilized publicly available data on global oil and gas companies from 2000 to 2015. Panel data analysis was used to assess the relationship between intangibles (measured by Calculated Intangible Value (CIV)) and financial and market performance of these companies.

Results show that intangibles had a significant impact on firm performance in multiple financial measures. Firms’ intangibles also influence their market capitalization, indicating that the financial markets discount such information in their pricing.

Although the impact of intangibles on corporate performance is found to be significant, the size of that impact is small, suggesting that significant increase in the size of intangibles would only lead to a modest increase in corporate performance. Additionally, the research sample was limited to the top oil and gas firms listed in the Fortune 2000 global list and limits the generalization of the findings. Despite these limitations, the research provides greater confidence in using CIV to assess intangibles in organizations.

This research highlights the importance and ways of measurement of intangibles for managers in oil and gas companies and its significance for their firms’ performance.

The paper fills the gap in the literature in the assessment of intangibles in the oil and gas sector, as well as in the assessment of using CIV to measure the impact of intangibles on company performance.