Subsector Level Research Articles

Market structure, power, and the unfair trading practices directive in the EU food sector: a review of indicators

Abstract Competition and power imbalances in the food chain are under increased scrutiny from policy makers. We assess the competitive conditions in the EU food sector, using firm-level accounting data to examine firm size distributions and market concentration (for 10 countries), and production-function-derived markups (for 7 countries) for food manufacturing, retail, and wholesale industries. Key findings include the following: (i) most firms are small, but larger firms generate most turnover; (ii) concentration is notable in certain subsectors (25% of retail/wholesale and 50% manufacturing subsectors); (iii) the correlation between turnover size, markups, and concentration at subsector level is weak. We discuss the implications for the use of turnover-based classification in the EU policy initiative on unfair trading practices.

World Trends in State Regulation of the Development of Organic Agriculture and the Market of Organic Products: The Experience of the USA, the EU and Russia

The relevance of the topic of research is determined by the need to address the problems of environmental and socioeconomic nature arising against the background of industrialized society, which causes interest in the development of organic agriculture and the formation of a dynamically growing market for organic products. The purpose of the study is to analyze the trends in the development of the global organic market and a comparative analysis of the practices of state regulation of this segment in the United States, the EU and Russia. In the course of the work such scientific methods as synthesis and deduction were used; the information and analytical base was the data of the journal “The World of Organic Agriculture” and publicly available information from the official websites of the Ministries of Agriculture of the United States and the Russian Federation, the European Commission, the Union of Organic Agriculture. The article presents the results of consideration of the dynamics of sales volumes and changes in the average per capita consumption of organic products in the world market, as well as the description of mechanisms of state regulation of organic agriculture and the organic market in the United States, the EU and Russia, highlighting their distinctive characteristics. The study of such issues allows to rationally apply foreign experience, excluding a high degree of risk when making strategic decisions in the formation of directions of development of the agrarian sector of the economy within the framework of national interests. The results of the study can be useful both in the development of programs for the development of organic agriculture at the level of sub-sectors of the economy, and in relation to the subjects of the Russian Federation, striving for sustainable development of the regional market of this type of products.

Economic sanctions and energy efficiency: Evidence from Iranian industrial sub-sectors

Improving energy efficiency is vital for reducing energy consumption and can have substantial impacts on reducing carbon emissions. This study investigates the impact of sanctions on Iran's energy efficiency across different industrial sub-sectors from 2015 to 2019. We compute a sanction intensity index for each industrial sub-sector using principal component analysis. This index measures how much each sub-sector has been affected by sanctions. Additionally, energy efficiency is measured using the directional distance function method, considering the emission impacts as undesirable outputs. We use fixed effects regression analysis, adjusting for various manifestations of cross-sectional (spatial) and temporal dependence and controlling for other sectoral drivers of energy efficiency. We find a significant negative effect of sanction intensity on energy efficiency at the industrial sub-sector level in Iran. The negative effect of sanctions on energy efficiency is significantly larger for industrial units with higher import dependency.

Assessment of methane emissions from oil, gas and coal sectors across inventories and atmospheric inversions

Emissions from fossil fuel exploitation are a leading contributor to global anthropogenic methane emissions, but are highly uncertain. The lack of reliable estimates hinders monitoring of the progress on pledges towards methane reductions. Here we analyze methane emissions from exploitation of coal, oil and gas for major producing nations across a suite of bottom-up inventories and global inversions. Larger disagreement in emissions exists for the oil/gas sector across the inventories compared to coal, arising mostly from disparate data sources for emission factors. Moreover, emissions reported to the United Nations Framework Convention on Climate Change are lower than other bottom-up and inversion estimates, with many countries lacking reporting in the past decades. Finally, comparison with previous global inversions, revealed a strong influence of the prior inventory on the inferred sub-sectoral emissions magnitude. This study highlights the need to improve consensus on the methodological inputs among the bottom-up inventories in order to obtain more consistent inverse modelling results at the sub-sectoral level.

Black carbon emissions inventory and scenario analysis for Pakistan

Black carbon (BC) emissions, resulting from the incomplete combustion of carbonaceous fuels, have been extensively linked to adverse impacts on air quality, climate change, and public health. Nevertheless, there is currently a lack of a comprehensive analysis that integrates activity-based BC emissions inventory and scenario analysis at the national/regional, sectoral, and sub-sectoral levels in Pakistan. This study aims to fill this gap by conducting a comprehensive evaluation of Pakistan's BC emissions inventory for 2021 along projecting emissions until 2050 under the reference emission scenario (RES) and the accelerated reduction scenario (ARS) using the GAINS modeling framework to assess the potential impact of mitigation measures. This study takes a unique approach by considering commonly overlooked sources of BC emissions, such as kerosene lighting, brick kilns, diesel generator sets, and natural gas flaring, which are not typically included in conventional analyses. National BC emissions in 2021 were estimated at 181 kt, with residential combustion being the major contributor, accounting for more than half (108 kt) of the total emissions. The transport, industry, waste, agriculture, power plants, and fuel conversion sectors contributed 26.1 kt, 20.1 kt, 10.7 kt, 8.9 kt, 6.0 kt, and 0.9 kt, respectively. We anticipate that the total BC emissions in Pakistan will reach 201 kt under the RES and 41 kt under the ARS scenario by the year 2050. The ARS achieves substantial BC reductions by the adoption of cleaner fuels, improved biomass stoves, end-of-pipe emission control technologies with higher removal efficiencies, and implementing a ban on the open burning of waste and crop residues. This study underscores the considerable potential for reducing BC emissions across various sectors in Pakistan over the next three decades.

Credit Financing and Agricultural Development in Nigeria

This study investigates the short-run dynamics and long-run link between credit financing offered by Deposit Money Banks (DMBs) and agricultural development in Nigeria for a period of 1981 to 2020. A quantitative ex-post facto research design was employed to analyze bank credit impact on agricultural advancement at both aggregate and sub-sectoral agricultural production. The research strategy utilized allows the use of economic series obtained from Central Bank of Nigeria Statistical Bulletin (2021) and World Bank Development Indicators (2021) to be analyzed by Autoregressive Distributed Lag (ARDL) estimation technique. From ARDL estimations, it is indicated that DMBs loans and advances have significantly negative short-run impacts on agricultural development in Nigeria at both aggregate and sub-sector levels. However, Bounds Testing analysis shows that the impacts of DMBs financing on Nigeria agriculture sector and labour employment in the sector become positively significant over a long period at aggregate level and for crop production only as a sub-sector. But the observed positive long-run impact of DMBs lending to agriculture was not significant for advancement of other sub-sectors (livestock, fishing and forestry). Hence, the study affirms that in the short run DMBs lending significantly slow down agricultural development in Nigeria but increases it significantly in the long run. The study recommends that there is need for agricultural policy makers and governments in Nigeria to develop appropriate specific agricultural financing mechanism that can aid performance of agricultural sector in the short-run and maintain long-run positive gains in the sector.

What is the Effect of VIX and (un)Expected Illiquidity on Sectoral Herding in US REITs during (Non)Crises? Evidence from a Markov Switching Model (2014 – 2022)

The study investigates the impact of sector and market-wide illiquidity shocks on herding within US Real Estate Investment Trusts (REITs), on a sub-sector level, including health, hotel, mortgage, residential, retail and Warehouse REITs. Using daily data from January 2014 to February 2022, and consistent with noise trader risk theory, the research confirms the existence of herding behavior within US REITs on a sub-sector level, along with identifying that herding effects are intense on days with negative market returns as compared to days with positive market returns. Assessing the impact of investor sentiments via the VIX index, we find that herding behavior rises with a hike in investor uncertainty and fear. Motivated by the presence of a structural break within our data set corresponding to the Covid-19 outbreak, we use a Markov Switching approach and find significant evidence of sub-sector herding being more intense during the crash regime/covid-19 phase, relative to the expansionary phase. When assessing illiquidity, our results confirm that i) during the expansionary phase only expected illiquidity (market and sector-wide) enhances sub-sector herding within US REITs while ii) during the crash phase only unexpected illiquidity (market and sector-wide) enhances sub-sector herding within US REITs.

An analysis of sub-sectoral level carbon budget allocation for India's iron and steel sector

The global carbon budget, defined as the cumulative amount of permissible carbon emissions to meet the temperature targets, is widely used to study climate change. This carbon budget is rapidly decreasing, with a high probability of it getting exhausted in the coming few decades. The extensive use of non-renewable resources has resulted in a rapid depletion of the global carbon budget. Currently, there is a lack of studies assessing the long-term impact of various sectors on the rapidly decreasing global carbon budget. The recent budget estimates have further reduced by 200 GtCO2 in the past decade, creating conflicts among the nations towards getting a higher share of the remaining budget.This study focuses on allocating the global carbon budget to a sub-sectoral level for India's iron and steel sector. First, the global carbon budget is allocated using different approaches, giving India a range of carbon budgets. Further, the allocation is done to the sectoral and sub-sectoral levels to obtain the benchmark targets between 2020 and 2050 from the national carbon budget. Projections have been made for the production capacity and the expected emissions till 2050 for India's iron and steel sector using three scenarios based on specific CO2 emission reduction. The results indicate that the carbon emissions for the “specific CO2 emission stagnation” scenario for 2 °C temperature target are 66% to 726% above the benchmark carbon budget allocated to the iron and steel sector using different allocation methods. The study also shows the benchmark carbon budget of the iron and steel sector for the 2 °C, 1.7 °C, and 1.5 °C temperature targets will deplete between 2023 and 2046 for the different emission scenarios.

К ВОПРОСУ О РАЗРАБОТКЕ СТРАТЕГИЧЕСКИХ НАПРАВЛЕНИЙ ИННОВАЦИОННОГО РАЗВИТИЯ АПК В СОВРЕМЕННЫХ УСЛОВИЯХ

The development of strategic directions for the innovative development of the agro-industrial complex in the conditions of the formation of a new technological order can become a significant impetus for agricultural production. The article examines the legislative framework of strategic planning in Russia, including in the agro-industrial complex. And also to achieve the goals and objectives of long-term development of Russia, the main strategic directions of innovative development of the agro-industrial complex are highlighted. The need for a prompt response to external threats and challenges dictates the inevitability of applying new approaches both to the development and implementation of innovative development mechanisms, with the minimization of bureaucracy. Thus, the set of strategic directions of innovative development of the agro-industrial complex should be an integral, unified system containing an exhaustive list of measures and mechanisms to achieve the targets. Effective formation of strategic directions of innovative development depends on theoretical approaches to the concept of strategic directions. As numerous studies under the leadership of agricultural scientists E. S. Ogloblin, I. S. Sandu, V. I. Nechaev have shown, if it is necessary to form a new vision for determining strategic directions of innovative development, it is necessary to provide for the implementation of separate stages, depending on the life cycle of the development of innovative processes at the level of branches and sub-sectors of the agro-industrial complex. In this connection, the authors present the steps that are advisable to implement at the initial stage of innovative development of the agricultural sector of the economy. Also listed are the economic initiatives designated by the President of Russia BB. Putin in his address to the Federal Assembly as part of the formation of a paradigm for the further development of the country's economy. In conclusion, it is noted that a comprehensive solution of these areas can provide both acceleration of the implementation of scientific and technological progress in various sectors of the agro-industrial complex, as well as significant organizational, technical and technological renewal of agro-industrial production and increase its competitiveness.

Winners and losers from Pfizer and Biontech's vaccine announcement: Evidence from S&P 500 (Sub)sector indices.

This study explores how the US stock market reacted to the news of a successful development of vaccine by Pfizer and Biontech on November 9, 2020. In particular, the study analyses the effect of the vaccine announcement on 11 sector indices and 79 subsector indices. A key contribution of the present study is to provide a deeper subsector level of analysis lacking in existing literature. An event study approach is applied in identifying abnormal returns due to the November 9th vaccine announcement. Several event periods (-1, 0, 1, 2, 3, 0-1, 0-3) are analysed to provide a more complete picture of the effects. Based on analysis, it is established that there are considerable inter and intra sectoral variations in the impact of the vaccine news. The results show that the impact follows a clear pattern. The sectors that were hit hardest by the pandemic such as energy, financials, as well as subsectors like hotels and casinos, benefited the most from positive vaccine news. Subsectors that gained from the pandemic such as airfreight, household appliances and computers and electronics retail were depressed the most by the news. These findings suggest that while the availability of vaccines is expected to help steer economies gradually to normalcy, the re-adjustment is likely to be asymmetric across subsectors. While some subsectors expect to expand as these industries recover from the contraction inflicted by the COVID-19 environment, other subsectors expect adjustment losses as these industries shed off the above average gains driven by the COVID-19 environment.

Socioeconomic impacts of seafood sectors in the European Union through a multi-regional input output model

Fisheries, aquaculture, and seafood transformation sectors are components of the Green Deal roadmap promoting the transformation of the European Union into a resource-efficient, climate-friendly, and competitive economy. Whilst several studies have shown these sectors' socioeconomic national contributions, the method developed in this article aimed at demonstrating their importance at the European level and at showing the interlinkages that exist between countries, at the sub sectorial level. This article presents the development of a seafood European multi-regional input-output model methodology and its potentialities. The seafood sectors were disaggregated for each country from national input-output tables through a double disaggregation algorithm thanks to proxy input-output tables and STECF data, which highlighted the purchase of intermediate products at the STECF subsector level. The resulting disaggregated economic data for the seafood sectors were then linked to the multiregional turnover, gross value added, and jobs multipliers inverted from the FIGARO multiregional input-output table to obtain the indirect and induced socioeconomic impacts of the seafood sectors at the European level. The results showed economic leakages illustrated by the gross value-added multiplier. While shellfish farming, passive gear fishing, and active gear fishing, were the sectors in which the generated economic wealth remained the most in the European Union territory, a third of generated economic wealth by saltwater finfish farming and seafood transformations was captured outside the EU borders. Moreover, although France, the UK, and Spain are the main turnover contributors across sectors (except for micro and small seafood transformation companies), the impacts generated by the seafood industries greatly benefit the German economy that capture more economic wealth than it contributes across all sectors. This method provides a baseline scenario for further studies on the evaluation of socio-economic benefits of changes in practices such as circular economy emphasizing the interdependencies of supply chains at the European level.

Asymmetric effects of non-ferrous metal price shocks on clean energy stocks: Evidence from a quantile-on-quantile method

Combining advanced quantile-on-quantile (QQ) regression and causality-in-quantiles (QC) methods, we examine the asymmetric effects of non-ferrous metal price shocks on clean energy stocks at aggregate and sub-sector levels. From the aggregate perspective, the impact of non-ferrous metal price shocks is strongly negative for bull clean energy stock markets but is positive under bear circumstances. According to QC analysis, non-ferrous metal price shocks can effectively predict returns on clean energy stocks in some quantiles. Sub-sectors of clean energy stocks react differently, proving the heterogeneity of different industries. Synergistic movements between non-ferrous metal price shocks and some clean energy sectors in bear markets are detected, indicating that non-ferrous metals are not safe havens for clean energy stock markets under extreme market conditions. Furthermore, non-ferrous metals have a significantly stronger negative impact on clean energy stocks during the epidemic, demonstrating the structural changes effect of COVID-19.

PREDICTION OF SOCIAL LAG IN MEXICO: A MACHINE LEARNING APPROACH FROM ECONOMIC UNIT DATA

Social lag in Mexico is officially calculated at the municipal level every five years, based on data from the population and housing census, by the National Council for the Evaluation of Social Development Policy (Consejo Nacional para la Evaluación de la Política de Desarrollo Social, CONEVAL). However, it is advisable to have annual forecasts of social lag for the follow-up of public policies. This study presents a machine learning approach to predict the classes or degrees of social lag (high, medium, low) at the municipal level in Mexico, based on information on economic units from the 2015 National Statistical Directory of Economic Units (Directorio Estadístico Nacional de Unidades Económicas, DENUE). Three supervised machine learning classifiers were implemented: logistic regression, support vector machine and random forests; and they were trained and tested in prediction based on information from counts of economic units, in their different categories, population and geographic coordinates of the municipalities; likewise, objective social lag classes were used at the municipal level, calculated with 2015 census information, reported in the literature. The criteria for evaluating the performance of the classifiers were the F-macro value, the overall accuracy and the area under the curve (ROC). The results indicate that the best overall performance was obtained with the random forest classifier with an F-macro value of 0.713 and an overall classification accuracy of 0.716; and F1-macro values for the high, medium, and low social lag classes of 0.596, 0.730, and 0.822, respectively. This classifier was used to predict social lag for 2016 and 2017. The results showed that there is a relationship between social lag and economic units aggregated at the subsector level, and that the proposed approach represents a viable and low-cost alternative for predicting social lag when census information is lacking.

Foreign Ownership and Productivity of Food Industries in Indonesia

Abstract The Indonesian government issued a policy that restricts foreign ownership in several industries including the food industry, with the objective of protecting the domestically owned firm. This research analyzed the productivity differences between foreign-owned and domestically owned plants in the Indonesian food industry and whether the percentage of foreign ownership is related to productivity. Micro data on the food industry were obtained from the Annual Survey of Large and Medium scale Industries (IBS) in 2015 conducted by Statistics Indonesia. The results showed that there were productivity differences between the foreign-owned and domestically owned plants. The foreign-owned plants had higher productivity than the domestic plants. The productivity of plants with minority foreign ownership (less than 50 percent) was not significantly different from that of domestically owned plants. Meanwhile, the productivity of plants with foreign ownership more than 50 percent was significantly different from that of plants with domestic ownership. Wholly foreignowned plants (100 percent) did not demonstrate the highest productivity difference. When the data were disaggregated at the sub-sector level, the results varied among industries.

Effects of Energy Efficiency on Firm Productivity in Kenya’s Manufacturing Sector

There is concern about probable energy efficiency and economic performance trade-off, particularly in developing countries which often require more energy consumption to spur their economies. This study assesses the relation between energy efficiency and total factor productivity in Kenya’s manufacturing sector by applying a sample of firms in the World Bank Enterprise Survey. Energy intensity is used as a proxy for energy efficiency while total factor productivity is estimated using the Levinsohn-Petrin Algorithm. A dynamic panel data model is applied in the analysis of the energy efficiency and total factor productivity relationship which is at the sub-sector and firm size levels. The sub-sectors of concern are: chemicals, pharmaceuticals and plastics, food, textile and garments and paper and other manufacturing sub-sectors. Firm sizes of interest are: small, medium and large. The findings show heterogeneity in energy intensity across sub-sectors. Total factor productivity is also found to be heterogeneous across sub-sectors and firms of different sizes. The estimates show that in general, energy efficiency significantly promotes total factor productivity. Other factors that promote total factor productivity include capital intensity, age, size, top manager’s years of experience, foreign ownership and exporting status. However, the effect of these variables varies across the sub-sectors and firm sizes. The study findings suggest that policies to improve energy efficiency should be accorded additional emphasis jointly with improvements in total factor productivity.

How do dynamic jumps in global crude oil prices impact China's industrial sector?

Given that sharply rising external risks pose great challenges to the stable development of China's industrial sector, extreme risks to the global oil market deserve more attention. This study investigated dynamic jumps in global oil prices and their impacts on China's industrial sector at the aggregate and subsector levels. We applied the asymmetric ARMA-EGARCH-ARJI model to analyze the characteristics of global oil price fluctuations and introduced the jump component into the ARMA-EGARCH-X model to examine the impacts of dynamic jumps on China's industrial sector. We distinguished oil price jumps under different oil market conditions to assess the asymmetric effect of jump shocks. We found that global oil price fluctuations contain both volatility clustering with asymmetry and dynamic jumps. At the aggregate level, oil price jumps have negative shocks on China's industrial sector returns and positive spillovers on its volatility. The impacts show hysteresis and will be reversely adjusted afterward, which implies an overreaction of the industrial market to current oil price jumps. Moreover, jump shocks display an asymmetric effect under different global oil market conditions, which is embodied by the fact that they will reduce industrial sector returns more when the oil price decreases than when it increases. The five industrial subsectors respond differently to oil price jump shocks. Our findings deserve particular attention from both market participants and policy makers.

Advancing bioeconomy monitorings: A case for considering bioplastics

Substituting fossil-based with bio-based products is a promising approach for addressing environmental problems, but assessing potential trade-offs is challenging. Bioplastics, for example, are considered both a potential solution as well as a new source of harmful environmental impacts, mainly arising from their use of agricultural biomass. Yet, bioeconomy monitoring systems do not currently capture such net effects of substitution at the sub-sectoral level. This paper explores how such monitoring could be enhanced to support sustainable transition by analysing plastics substitution and related economic and environmental effects using an extended input-output model that includes environmental data and disaggregates the plastics and bioplastics sectors. The ongoing transition in the German plastics sector – measured by combining information from expert interviews with secondary sources – is characterized by low domestic demand, high dependence on imported inputs, and relatively low fossil-resource savings. Results for selected indicators reveal few trade-offs regarding fossil resource use, water use, value added, and employer compensation. Process greenhouse gas emissions are significantly higher when substituting bioplastics for plastics, if credits for bio-based carbon are neglected. This study proposes that sustainable transition in the German plastics sector would benefit from more specific monitoring and clearly justified policy targets, based upon reconsideration of stated objectives in bioeconomy strategies. Methodological recommendations include accounting for carbon cycles in greenhouse gas emissions modelling, using further indicators related to possibly harmful impacts on the environment, and distinguishing between domestic and foreign effects by applying the proposed input-output model to the most relevant countries in bioplastics value chains.

Driving forces and mitigating strategies of CO2 emissions in China: A decomposition analysis based on 38 industrial sub-sectors

Due to the substantial industry heterogeneity in China, CO2 emissions and the major forces driving these emissions may vary in different sub-sectors. However, this topic has rarely been discussed at the industrial sub-sector level. To fill this gap, using an generalized Divisia index model (GDIM) incorporating investment factors, and considering 38 industrial sub-sectors, this study investigated the trajectories of China's industrial CO2 emissions and their driving forces, both at the overall industrial sector and its sub-sector levels. The results showed, (1) during 2000–2017, 97.2% of the increases in China's total industrial CO2 emissions was attributable to four sub-sectors. (2) Investment scale was the largest driver of increases in CO2 emissions, followed by output scale and energy consumption. (3) Carbon intensity of investment, energy intensity, and investment efficiency were main drivers of reductions in CO2 emissions, but their effects remained limited. (4) The roles of different drivers varied across sub-sectors, resulting in great heterogeneity in emission trajectories between different sub-sectors. The findings indicate the necessity of designing emission-reduction strategies at the sub-sector level, and more efforts should focus on the electricity, heat, metallurgy, mining, and chemical industries.

Analysing Growth Paths and Employment Performance of Indian Food Processing Industry: Uncovering Dynamics at the Sub-sectoral Level

Against the backdrop of a potential productivity-employment trade-off, the performance of the Indian Food Processing Industry (FPI) and its sub-sectors on key employment-related indicators was assessed over two decades (1998-99 to 2017-18). Based on ASI's 4-digit level dataset on FPI's 18 sub-sectors, the patterns of output growth were analysed and categorised into extensive, intensive, stagnant and virtuous growth paths. Pooled panel regression-based employment elasticities were estimated across subsectors to determine prospects of employment generation. Despite declining employment elasticities for aggregate FPI, prospects for future employment creation were found in the rapidly growing subsectors, which exhibited relatively higher employment elasticity of growth. The exploration of sub-sectoral dynamics highlights the need for adequately reflecting on the heterogeneity in employment-related parameters across growth paths and suitably inform policy interventions ing at a juncture when the Government of India has renewed emphasis on boosting employment in the organised manufacturing segment.

The effects of Mandatory Energy Efficiency Policy on resource allocation efficiency: Evidence from Chinese industrial sector

The extensive theoretical literature emphasizes that the improvement of resource allocation efficiency plays a critical role in the economic efficiency effect of energy and environmental policies. However, few empirical studies focus on the assessment of this effect of energy policies on resource allocation efficiency. Thus, this article treats the implementation of China’s Mandatory Energy Efficiency Policy (MEEP) during the 11th Five-Year Plan, as a quasi-natural experiment to testify the impact of MEEP on resource allocation efficiency of industries. To this end, an annual data panel including a total of 2,382,263 observations from 625,478 micro enterprises at the city sub-sector level is constructed for 280 cities from 1998 to 2008. The Difference-in-Difference (DID) model and the DDD model are adopted to estimate the effect of MEEP on the resource allocation efficiency of industries in China and its heterogeneity across sub-industries, respectively. The results show that China’s MEEP enforced during the 11th Five-Year Plan indeed contributes significantly to the efficiency improvement of resource allocation in the industrial sector. The conclusion is still robust by replacing the different indicators or changing time intervals. Further, this effect for the high-energy-consuming or high-polluting industries is significantly lower than that in others. It may be attributed to the differences in the response rates to MEEP caused by the ownerships among firms and industries.