essay about distribution of government subsidies and relief operations

Challenges in disaster relief operations: evidence from the 2017 Kermanshah earthquake

Journal of Humanitarian Logistics and Supply Chain Management

ISSN : 2042-6747

Article publication date: 24 December 2020

Issue publication date: 4 February 2021

This paper identifies the challenges during a recent disaster relief operation in a developing country where the humanitarian response is dominated by national actors, with international actors having a minor role.

Design/methodology/approach

A case study design is used; the main data sources are semi-structured interviews with 43 informants involved in the 2017 Kermanshah earthquake relief operation.

The findings suggest that humanitarian practitioners deal with multiple challenges during disaster relief operations. One group of challenges relates to humanitarian logistics (HL) like needs assessment, procurement, warehousing, transportation and distribution, all widely discussed in the literature. Another involves the growing use of social media, legitimacy regulations and the engagement of new humanitarian actors (HAs) like social media activists and celebrities. These factors have not been extensively studied in the literature; given their growing influence, they require more scholarly attention.

Practical implications

The findings will help humanitarian practitioners and policymakers better understand the challenges involved in disaster relief operations conducted by multiple actors and thus help them improve their practices, including the creation of proper regulations, policies and logistics strategies.

Originality/value

The study uses primary data on a recent disaster to assess and extend the findings of previous studies regarding HL challenges. It also elaborates on the critical non-logistical challenges that influence aid delivery in emergency responses, including the growth of social media, regulations and the engagement of new HAs. The results may motivate future empirical and modelling studies to investigate the identified challenges and identify practices to mitigate them.

Humanitarian logistics
Disaster relief operations
Social media
Celebrities
Humanitarian actors
Regulations

Maghsoudi, A. and Moshtari, M. (2021), "Challenges in disaster relief operations: evidence from the 2017 Kermanshah earthquake", Journal of Humanitarian Logistics and Supply Chain Management , Vol. 11 No. 1, pp. 107-134. https://doi.org/10.1108/JHLSCM-08-2019-0054

Emerald Publishing Limited

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

Traditionally, logistics plays a central role in humanitarian assistance as the connecting point between preparedness and response, procurement and distribution, and headquarters and the field ( Thomas, 2008 ). Humanitarian logistics (HL) has been described as “the process of planning, managing, implementing and controlling the efficient, cost-effective flow and storage of relief items as well as related information and funds, from the point of origin (suppliers and donors) to the point of consumption for the purpose of meeting the end beneficiary's requirements” ( Thomas and Mizushima, 2005 , p. 60). HL involves a wide range of activities that includes needs assessments, procurement, resource mobilization, transportation, warehousing and last mile distribution ( Gustavsson, 2003 ). HL activities can account for up to 80% of the total cost of humanitarian assistance ( Van Wassenhove, 2006 ).

The existing HL literature discusses a wide range of practical HL challenges identified during disaster relief operations. These range from specific deficiencies such as a lack of logistical knowledge and trained logisticians, the absence of standards and performance indicators, security problems and inadequate funding and investment in information technology ( Fritz Institute, 2004 ; Maiers et al. , 2005 ; Balcik and Beamon, 2008 ; Kovács and Spens, 2009 ; Sandwell, 2011 ) to general characteristics of the humanitarian environment such as extreme supply and demand uncertainty, the presence of myriad humanitarian actors (HAs), the breakdown of the rule of law and media attention ( Thomas and Kopczak, 2005 ; Van Wassenhove, 2006 ; Kovács and Tatham, 2009 ; Sandwell, 2011 ).

Although previous research has identified many challenges involved in HL, further investigation of and suggestions for understanding challenges and how to mitigate their impact on logistics activities in disaster relief operations can improve humanitarian response and eventually lead to a significant reduction in the level of human suffering. As any disaster relief operation is highly context-specific and dynamic, challenges vary with the type, intensity, location and timing of a given disaster ( Kovács and Moshtari, 2019 ). Over the last decade, humanitarian operations have been affected by new influences like information and communication technology innovations, regulatory changes and the entry of new actors. This study seeks to contribute to this literature by providing additional insights into the both logistical and non-logistical challenges of disaster relief operations through a case study of a recent humanitarian response in a developing county. The findings will help humanitarian practitioners and policymakers better understand the challenges involved in disaster relief operations conducted by multiple actors and thus help them improve their practices, including the creation of proper regulations, policies and logistics strategies. Moreover, the results of the present study can motivate future empirical and modelling studies to investigate the challenges identified and identify practices to mitigate them.

This paper explores a practical approach to identifying the challenges of HL, particularly in cases where established HAs such as government agencies must coordinate with national non-governmental organizations (NGOs), international organizations and new emerging actors like ad hoc groups, celebrities and teams sponsored by religious organizations during disaster relief operations. This paper thus attempts to answer the following research questions: (1) What were the key challenges in disaster relief operations in the 2017 Kermanshah earthquake? and (2) What were the critical challenges in the 2017 Kermanshah earthquake that influenced disaster relief operations but have not been extensively explored in previous studies?

The paper is organized as follows. In section 2 , we provide an overview of the previous HL literature on all aspects of challenges. In section 3 , we describe the case study as a method for this research, while section 4 contains our findings regarding the challenges identified. In section 5 , we provide a discussion of those findings, connecting them back to the HL literature. In section 6 , we present several implications for research and practice, along with study limitations, as a conclusion.

2. Literature review

Bölsche et al. (2013) observe that if the right relief items of the right quality and in the right quantities are distributed and received by the right affected population at the right time and in the right place, HL can contribute to alleviating the suffering of vulnerable people. In the complex environment of a humanitarian setting, logisticians must first aim to meet beneficiaries' requirements. This task is like the supply-demand matching carried out in a business setting. The response will then focus on procuring the required relief items, whether from inside or outside the affected area. Finally, relief items need to be mobilized, stored, transported and distributed over the last mile to affected populations ( Tatham et al. , 2017 ). We have conducted a narrative review of the previous literature and identified a wide range of challenges to logistical activities at the operational level; see Table 1 for a summary.

Discussing needs assessment, Balcik and Beamon (2008) refer to the extreme unpredictability of demand in terms of disaster location, timing, type and size, which can create a bottleneck for logisticians trying to determine accurate numbers and needs in the affected population. L'Hermitte et al. (2015) discuss the cross-border logistical challenges and cross-border refugee movements that complicate needs assessment at the operational level. For instance, to estimate the needs of an affected population in Somalia, HAs had to operate remotely out of Kenya and other parts of Somalia. Furthermore, due to disruptions in communication infrastructure after a disaster, affected populations might not be able to articulate their needs related to culture and language ( Kovács and Spens, 2011 ). Thus, the process of needs assessment or demand capture is vastly more challenging in the HL community than in the everyday business environment ( Tatham et al. , 2017 ).

As to procuring necessary supplies, it is difficult to obtain access to local markets and suppliers in areas with limited or no security, confronting HAs with the late delivery of supplies and time pressure arising from the urgent need for those supplies ( Balcik and Beamon, 2008 ). Scarcity of resources like data and information, supplies, people, technology and transportation, along with inadequate infrastructure, warehouses and funding are but some of the many examples of operational constraints that add to the logistical challenges of the procurement, warehousing and last mile distribution of aid (e.g., Balcik and Beamon, 2008 ; Baporikar and Shangheta, 2018 ; Fritz Institute, 2004 ; Kovács and Spens, 2009 ; Makepeace et al. , 2017 ; Sandwell, 2011 ). In Somalia, HAs lacked basic information relating to crucial items like transport rates, routes and mechanisms to move cargo from the port to their destinations. Because of Kenya's limited port capacity, there was congestion at Mombasa's port and late delivery of aid. Similarly, the movement of affected populations from one place to another can lead to changes in needs and logistics requirements such as supply chain remapping and a plan to increase storage requirements, adding another constraint for HAs that are already faced with limited resources in a highly competitive and vulnerable environment ( L'Hermitte et al. , 2015 ). Kovács and Spens (2009) , studying disasters in Ghana, reveal the HL challenges from HAs' perspective as a lack of resources like funding, transportation infrastructure, early warning systems and warehouses. From a governmental perspective, limited supplies, vehicles and information technology are the primary challenges. Tatham and Houghton (2011) discuss Myanmar in the aftermath of the 2008 Nargis cyclone and report that international HAs had difficulty obtaining access to warehouses. Along the same lines, Kunz and Reiner (2016) refer to transportation regulation and government restrictions as the key HL challenges imposed on international HAs. These challenges can arise due to import barriers and tariffs, travel restrictions, border closures and excessive bureaucracy.

Normally, each of the many HAs responding to a disaster has its own organizational approach, with a distinct mission concept and relief operation structure. Thus, coordinating logistics activities at both the organizational and inter-organizational levels has been repeatedly reported as a factor that can impede the procurement of goods, warehousing and the last mile distribution of aid (e.g., Baporikar and Shangheta, 2018 ; Balcik et al. , 2010 ; Fritz Institute, 2004 ; Kovács and Spens, 2009 ; Makepeace et al. , 2017 ; Tatham et al. , 2017 ). At the organizational level, poor communication and teamwork have been identified as factors that impede the work of program and logistics staff ( Makepeace et al. , 2017 ). The lack of HL standards and guidelines and inadequate training for logisticians have been cited as a factor decreasing the performance of inter-organizational collaboration efforts ( Moshtari and Gonçalves, 2017 ).

The complexity of operations is another HL challenge, particularly with last mile distribution of relief supplies. HAs operate in an extremely complex humanitarian environment, with continuous supply chain disruption in the form of access limitations, capacity constraints and security concerns at the last mile. The nature of distribution is extremely dynamic, with frequent changes in routes and unpredictable road infrastructure. Yet, the last mile process poses the most significant challenges due to the travel ban, border closures, breakdowns and blockages ( Maghfiroh and Hanaoka, 2017 ). In addition, relief supplies can be delivered from different locations, involving myriad local and international HAs. The distribution of scarce supplies with uncertain needs adds to the complexity of delivery processes. Finally, demand fulfilment depends heavily on the nature of a given disaster: type, impact, demographics, and social and economic conditions of the affected areas ( Balcik et al. , 2008 ).

Apart from logistical challenges, non-logistical challenges that are largely external to the focal organizations have been identified in the literature as directly or indirectly hampering the logistics of aid operations, including regulation and the role of social media and new emerging actors (see Table 2 ). First, the HL literature refers to problematic rules and regulations in the affected countries that influence humanitarian aid delivery ( Kunz and Gold, 2017 ; Kunz and Reiner, 2016 ). Kovács and Spens (2009) identify a regulatory environment that could be challenging for HAs due to a lack of reliable governance and the absence of legislation during disaster relief operations. Part of Kovács and Tatham's (2009) study addresses the potential breakdown of the rule of law and national and international scrutiny through multiple forms of public and social media in disasters, all of which hinder the efficient and effective delivery of aid to affected populations. Given the powerful impact of national regulations on international and local HAs' entry to and level of engagement in humanitarian relief activities, this topic has not sufficiently explored.

Second, there is a growing literature on the benefits of social media in terms of its connectivity for rapid information sharing (e.g., Palen et al. , 2009 ) and expressing emotional support for affected communities (e.g., Hughes et al. , 2008 ). Indeed, HAs are becoming more reliant on social media platforms as a means of collecting, sharing and disseminating information before, during and after a disaster. Despite the benefits of social media ( Houston et al. , 2015 ; Wamba et al. , 2017 ), several practitioners and scholars have expressed concerns about the potential challenges of incorporating social media into organizations, particularly HAs. For example, a large volume of content shared via social media might disrupt the supply chain and distract staff from work-related communications, leading to lower productivity ( Barnawal, 2014 ; Leonardi et al. , 2013 ). Likewise, the leakages of an organization's data and its dissemination to external actors via social media platforms could disclose confidential information and thus put the organization's intellectual property rights at risk. Furthermore, the provision of the right type and amount of assistance to beneficiaries affected by the 2010 Haiti earthquake was hampered by how information published on social platforms was used ( Kirac and Milburn, 2018 ). Oh et al. (2013) determined the community-based information processing via Twitter using data from the three disaster situations of the Mumbai terrorist attacks in 2008, the Toyota recall in 2010, and the Seattle café shooting incident in 2012. The result showed that shared data and information with no clear and reliable sources was the most important rumor causing factor on Twitter in disaster relief operations. However, the use of social media in disaster situations is still in its early stages, and there is debate among HAs about whether to accept it as a standard sharing tool during disaster relief operations (e.g., Pender et al. , 2014 ). Further research on the operational benefits and risks of social media will thus also be useful.

The third challenge influencing humanitarian aid delivery is the engagement of a high number of diverse actors, including emerging actors (e.g., Van Wassenhove and Besiou, 2013 ). The number of actors responding to highly mediatized disasters has increased substantially, as was demonstrated as long ago as the 2004 Indian Ocean tsunami relief operations ( Besiou and Van Wassenhove, 2019 ). For instance, there were over a thousand new actors, each with different cultures and structures, operating in Haiti after the 2010 earthquake ( Van Wassenhove and Besiou, 2013 ). While Alexander (2015) has examined the role of celebrities and the impact of celebrity culture on the way people react to disasters, there remains a lack of research to more thoroughly investigate the role of new HAs and how they coordinate with more established HAs in disaster relief operations.

Overall, even though HL challenges have been studied extensively, further research is required to investigate non-logistics challenges at the national level, in particular as to rules and regulations, social media use and the emergence of new actors. Further research is required to understand the HL challenges in different contexts and then to implement tailored mitigation solutions ( Kovács and Moshtari, 2019 ). This paper explores a practical approach to identifying the challenges of HL, particularly in cases where government agencies must coordinate with NGOs and new emerging actors like ad hoc groups and celebrities. This case study identified and discussed HL challenges related to the 2017 Kermanshah earthquake, where the humanitarian response was dominated by national actors, with international actors playing only a minor role, and the disaster affected a relatively small geographical region. In addition, the recency of the earthquake provides an opportunity to explore the operational implications of communication and information technology innovations like social media solutions in response operations, which have not yet been fully explored ( Yan and Pedraza Martinez, 2019 ).

3. Research methodology

The case study approach, which enables the collection of rich data, the in-depth exploration of a complex phenomenon ( Stuart et al. , 2002 ), and the identification of factors explaining that phenomenon ( Voss et al. , 2002 ) is appropriate for answering the present study's research questions. Given their high level of complexity, HL challenges must be investigated in their natural setting, and a case study allows for such an in-context analysis ( Yin, 2009 ). The 2017 Kermanshah earthquake was selected as the case due to the numerous challenges during the disaster relief operations that were reported and mediatized by the organizations involved. The humanitarian response to the Kermanshah earthquake was largely carried out by national actors, with international actors playing only a minor role. Moreover, even though Iran is a disaster-prone area, few studies in humanitarian operations have collected and analyzed empirical data from Iran, meaning that its overall context is not as well understood as it should be, especially given its susceptibility to earthquakes in particular.

The main data sources are semi-structured interviews conducted with HAs involved in Kermanshah earthquake relief operations. In response to the disaster, some of the many groups of actors (government, private sector, local NGOs, international NGOs and social media activists) were directly engaged in HL; others were indirectly engaged through social media activism and community groups that were active in resource mobilization and last mile distribution (see Table 3 ). To obtain a comprehensive view of the response performance, we interviewed multiple informants in each group of actors by means of purposive sampling, covering actors governed by different mandates in various sectors, government officials, practitioners in the private sector and employees of local and international NGOs (see Appendix 1 ). The criteria for inclusion in the study were that respondents were knowledgeable and had been involved in the response to the Kermanshah earthquake. To contribute to the richness and variety of the data ( Heckathorn, 1997 ), respondents with moderate to extensive experience (i.e., 15 years of experience in disasters on average) and in different positions (e.g., logistics officer, deputy head of relief, disaster relief manger and project manager) were selected from different sectors (i.e., provision of food, water, shelter, education and health services) using a snowballing technique.

A total of 43 face-to-face or telephone interviews were conducted. Background information that could identify individual interviewees is not included, as anonymity was a condition of participant involvement. However, information regarding organization type and mission, interviewee position, years of working experience and date of interview for each interviewee appears in Appendix 1 .

The semi-structured interviews lasted an average of 60 minutes and included open-ended questions and probes to encourage detailed responses. Based on the initial literature review, an interview protocol was developed to provide a structure for the data collection process (see Appendix 2 ). The interview protocol was designed to capture HL activities, related logistical and non-logistical challenges, and the performance of the humanitarian response during the Kermanshah earthquake relief operations.

In analyzing the data, we applied an open coding procedure ( Miles and Huberman, 1984 ) to identify and categorize HL challenges. We used a data reduction approach, coding data items that ranged in length from a few words to several paragraphs ( Miles and Huberman, 1994 ). We were careful to code only those challenges related to HL that influenced HAs' performance due to specific actions and interactions rather than the personal views of the respondents. To connect our data to the existing literature ( Eisenhardt and Graebner, 2007 ), the data were coded following a recursive (iterative) process in which data collection, data analysis and coding, and interpretation all occur throughout the study and thus influence one another ( Willis, 2007 ).

The data coding was manually analyzed using a color-coded system through cross tabulations and tables in Microsoft Word. The transcripts were read through several times, with notes taken in tabular form. A set of codes and categories regarding the challenges was then defined and assigned to the text to identify when patterns appeared. The codes were linked to conceptual themes and used as a working template for the other transcripts. Then, the extracted themes were linked to the HL challenges identified in the literature, such as those associated with needs assessments, procurement, warehousing, transportation and last mile distribution. Two researchers analyzed the data. In order to reduce the possibility of bias, in a first step, each researcher separately summarized the challenges in the transcripts and recorded them in the tables. Challenges were highlighted in different colors to follow patterns throughout the transcripts. Then, a set of codes and sub-categories were defined, with similar logistical and non-logistical challenges grouped together. Each researcher applied the coding frame in consistent ways to insure intercoder reliability ( O'Connor and Joffe, 2020 ). In the next step, the two authors compared their results and discussed the differences, overlaps and divergences within their analysis to reach a consensus ( Thomas and Hardens, 2008 ). The codes were then linked to the conceptual or categorical themes identified in the literature review.

The researchers organized a seminar in July 2019, after data collection was complete and preliminary data analysis had been carried out, to present the project's findings. The forum was hosted by the Department of Industrial Engineering at Tarbiat Modarres University in Tehran; it lasted about three and a half hours. There were 25 participants; 12 had been interviewed beforehand, and the rest were academics. The results were presented, after which participants shared their views on the results with the researchers. This event also allowed facilitated discussions among participants. The insights obtained during the seminar enabled the authors to further clarify their findings, ensure the trustworthiness of the qualitative data, and identify any misunderstandings or omissions ( Voss et al. , 2002 ).

3.2 Case description

On Sunday, November 12, 2017, a magnitude 7.3 earthquake occurred along the Iran–Iraq border, with its epicenter near Ezgeleh, Salas-e Babajani County, Kermanshah Province (Iranian Red Crescent Societies [IRCS], 2017). It was the world's deadliest earthquake of 2017; there were at least 630 fatalities and more than 9,000 injuries. A total of 427,266 people were affected in 8 districts of Kermanshah province. The earthquake seriously damaged 30,000 residential units in rural areas, while some cities were partially or completely destroyed ( IRCS, 2017 ). In Sare-Pole-Zahab, some residents blamed the widespread destruction on poor-quality construction. It was noted that older buildings remained standing, while many newer blocks collapsed, including hospital and health clinic structures ( IRCS, 2018 ).

The Iranian government announced that the disaster had caused at least €5 billion in damage. During the response phase, basic needs were tents, blankets, clean water supplies and public sanitation facilities ( International Institute of Earthquake Engineering and Seismology, 2017 ). As people from other provinces entered disaster-stricken areas during the recovery phase, the total population actually increased, which led to a quick shift in urgency from the necessities listed above to sewage overflows and other environmental issues. When the earthquake occurred, local HAs were of course under immense pressure to respond. There was no established mechanism through which the various not-for-profit, private, military and governmental agencies could coordinate their efforts or collectively identify the needs of the affected population ( Ahmadi and Bazargan-Hejazi, 2018 ). Many community groups and new actors knew very little about how to respond to a disaster or how to collaborate to meet beneficiaries' requirements. This resulted in some areas like Sare-Pole-Zahab receiving a great deal of attention, while other affected populations living in remote areas struggled to survive with little support.

A sheltering emergency phase was rapidly completed in affected cities and villages. Emergency water, sanitation and hygiene supplies in affected areas were provided through coordination between the government, the IRCS, municipalities, the private sector and international NGOs. In addition, several other actors attempted to deliver aid directly to the affected populations. These actors preferred to use their own vehicles, travelling not only from neighboring provinces such as Hamadan but also from more distant areas like Tehran, Mashhad, Tabriz and Isfahan. Apart from domestic actors, a few international actors like the Government of Turkey, the European Union and The International Committee of the Red Cross offered and delivered aid ( IRCS, 2017 ).

4. Findings

4.1 logistical challenges experienced during the 2017 kermanshah earthquake relief operations.

Table 4 presents the HL challenges related to needs assessments, procurement, warehousing, transportation and last mile distribution of relief supplies implemented by HAs and their associated impact on supply performance during the 2017 Kermanshah earthquake relief operations. The table also provides representative quotes from the respondents.

The subsections below reveals the findings in detail for each HL challenge.

4.1.1 Needs assessment challenges

Our interview respondents cited several factors that impeded needs assessment procedures during the Kermanshah response. We have categorized them into three groups: inaccurate needs assessment, the lack of shared data on needs and the dynamic nature of the needs themselves. As to inaccurate needs assessment, respondent 10 reported that the humanitarian staff was too tired to complete a rapid needs assessment after being stuck in heavy traffic for an extended time. In addition, respondents 10 and 12 noted that the presence of many actors, some with no experience or expertise, led to the field delivery of large amounts of redundant items and materials because these actors had not carried out a proper needs assessment. As a result, there were reported instances of expired materials like mineral water and food being delivered and of some relief items not being distributed to the population for which they were intended (respondent 33). These actors also failed to capture the overall demand as determined by the cultural, lifestyle, demographic and geographic profiles of the affected populations. For example, there are three main ethnic groups in Kermanshah, and some actors failed to understand the actual needs of each group (respondent 41).

Second, respondents noted a lack of data and information sharing on needs assessment. Some respondents reported difficulty in accessing information on needs from other actors and thus relied on their own needs assessments. Due to disruptions in the communication infrastructure, they could not even share their own information on needs with other actors, although they were able to share the information among their own members. As access to remote areas was difficult, even local and state authorities could not assess the needs of those affected by the disaster in these areas (respondent 19). Some argued a joint needs assessment could help mitigate the challenges caused by the lack of shared data. For instance, respondents 15 and 16 agreed on the importance of executing a joint needs assessment with other HAs in the relief network. This assessment could be done in coordination with NGOs, international NGOs, local community groups, military units and ad hoc groups. However, respondents 24 and 29 cautioned that a lack of trust among these actors could hinder the effectiveness of a joint needs assessment and subsequent information sharing.

We did a joint needs assessment at the field level in coordination with our own team and local communities. We were in fact a source of reliable information for other NGOs and volunteers who aimed to deliver aid at the last mile. We have been working in Kermanshah for 12 years; therefore, we have local experience with and knowledge about the region.

Finally, as to the dynamic nature of needs, some respondents mentioned the challenge created by the evolving nature of what was required and referred to the merits of an iterative needs assessment process running from initial response to the recovery phase. Respondent 33 said that “needs assessment is dynamic, meaning that it changes over time. For example, we received the information on local needs in the morning with statistics; later, at noon and in the evening or the next day we would receive a different needs assessment report.” Other HA representatives noted difficulties in receiving updated and accurate information on needs.

4.1.2 Procurement, transportation and warehousing challenges

HAs normally pre-position relief items in established warehouses. However, constraining factors such as funding may prohibit the completion of such efforts before a disaster occurs: “Prior to the disaster, local HA warehouses were empty of pre-positioned items in Kermanshah” (respondent 28). Coordination among HAs and ad hoc groups was a major obstacle in post-disaster procurement actions. For instance, respondent 17 noted that the lack of coordination between government water and power organizations, municipalities and the private sector delayed the installation of sanitation facilities for affected populations. In other cases, the direct involvement of civilians and ad hoc groups in the distribution of relief items disrupted the supply chain for unsolicited bilateral donations (UBDs), as these actors were not able to unpack and sort the items to meet the affected populations' needs.

Transportation was another significant HL challenge that HAs faced during the earthquake relief operations. Some roads and bridges had collapsed, and there was tremendous congestion on roads connecting to major centers like Sare-Pole-Zahab. The traffic jams were exacerbated by the presence of multiple actors coming from other cities in their own vehicles, according to respondent 29. One solution to the traffic issues was air transportation, which was costly but enabled quicker delivery of relief items and the transport of injured people to the capital city's hospitals in cases where local hospitals had collapsed.

Another problem was theft. A significant number of items were stored in local warehouses, and some were stolen due to poor security. Shared warehouses had been implemented in coordination with religious teams and ad hoc groups that did not have security teams. Unsecure places such as mosques, schools, residential yards and mobile containers were used as warehouses in disaster-stricken areas (respondents 23 and 28).

4.1.3 Challenges with last mile distribution

Organizations and ad hoc groups gave priority to their own relatives and family members. Then they went for other villages and communities. This led to the presence of those people who had not received aid and came and jumped into the trucks.

In addition, HAs could not control crowds in some instances, and some people jumped on trucks to take tents and blankets, which deprived the elderly population of receiving that aid, according to respondent 27. Furthermore, as the government agencies and NGOs operated in an independent, uncoordinated fashion, many procured items were wasted or oversupplied, while other needs remained unmet in some remote areas. For example, respondents 23 and 39 reported that some areas did not receive stoves for heating because the bulky food items they received filled the limited storage space. While the IRCS was authorized to provide last mile delivery, several unauthorized NGOs and ad hoc groups provided last mile support without coordinating with the IRCS relief team (respondent 5). These groups also distributed items that varied in terms of brand and quality in the same region, creating a problem for the HAs because it raised the beneficiaries' expectations of and requests for high-quality products, as noted by respondent 27.

We did final distribution points inside the military camps and had mixed genders for the final distribution, as within Muslim communities' women prefer to be served by the same gender. We, therefore, received a shared and secure warehouse that saved us the warehousing cost.

4.2 Non-logistical challenges identified during the 2017 Kermanshah earthquake relief operations

The analysis of our data revealed three non-logistics challenges influencing humanitarian response: social media use, regulations and the emergence of new actors (see Table 5 ). These factors have rarely been analyzed by prior studies, but their potential impact on humanitarian operations – and the likelihood that at least social media use and new actors will grow in significance – demands more academic attention.

4.2.1 Growing use of social media

During the Kermanshah earthquake, social media platforms like Telegram and Instagram were beneficial, despite certain inherent drawbacks. The use of social media platforms helped HAs mobilize local, national and international support for the affected populations, facilitating immediate assistance for thousands. Social media also enabled victims, friends and families to share valuable and timely information. This proved to be of immense help in rescue and relief efforts for those affected, as one respondent noted: “We could connect to local communities and identify their needs quickly” (respondent 16). In addition, social media helped some individuals and community groups post links to receive donations that funded assistance for earthquake victims. Social media use was particularly important given that, as respondent 24 indicated, it prompted numerous volunteers, local and non-local civilians, and community groups to participate in delivering aid.

It was rumored around social media that one affected family did not receive shelter in a village. This led to negative feedback and reputation for the responsible HOs providing shelters and the escalation of tent supplies in excess of needs (respondent 19).

4.2.2 Regulations on the roles and involvement of HAs

Our study reveals an absence of regulations to define the roles and involvement of ad hoc groups in disaster relief operations and to ensure coordination with established HAs' supply chains. While such groups have extensive capacity in terms of logistics in areas like transportation, volunteer manpower and funding, the respondents agreed that strict regulation of their involvement is needed. After the Kermanshah earthquake, some actors – government agencies, military teams, smaller NGOs, among – misused the information on needs obtained by other actors like the IRCS. In some cases, HAs shared information with other actors over public or social media, and competitors like NGOs used that information for their own benefit. In one instance, a temporary warehouse used by larger HAs was soon emptied of vital supplies because a large number of new actors descended on the warehouse due to an information disclosure. In another example, respondent 23 stated that if NGOs were to provide transport schedules and reporting containing needs and support, government agencies may cease funding because of suspected fraud and questions about how and from what source the budget and items were procured and for whom they were intended. Furthermore, some local community groups, ad hoc groups and celebrities took advantage of larger NGOs' brands and logos for their own purposes, receiving more volunteers and funding.

Many fundraising campaigns were established immediately after the disaster. Some were unauthorized, as they did not register through Iran's Ministry of Interior and were not under government control. Although these entities could deliver aid to the affected populations, respondents 26 and 32 noted that established HAs such as the IRCS and other NGOs did not trust them to cooperate and jointly mobilize their resources.

A small number of HAs, including international NGOs, were forced to restrict sharing information on their activities, requirements and relief operations to internal channels due to government regulations that prohibited them from publicly sharing such information. In fact, international humanitarian actors (IHAs) were not authorized to participate in disaster relief operations in Iran beyond coordinating with government authorities on matters related to refugee settlement and food security. Respondents 10 and 35 indicated that IHAs are mainly responsible for providing support to refugees living in their home country. These kinds of restrictive policies can suppress the capabilities of IHAs and their logistical capacity to engage with other HAs during disaster relief operations.

Overall, respondents reported a failure on the part of government agencies to exercise control and auditing to establish basic rules and principles for information sharing, logistical procedures and the roles and responsibilities of each actor within the HA network. Other potentially useful policies and protocols exist but were not approved before the Kermanshah earthquake; for example, according to respondent 21, procedures for establishing an integrated logistical databank system and promoting information technology development had been written but were still under review by government bodies before they could be implemented by certain HAs like the IRCS.

4.2.3 Interference of new actors

Some celebrities like Mahnaz Afshar [an Iranian cinema and television actress active since 1998] used Twitter to announce needs, and then we observed large numbers of sanitary items such as diapers and sanitary pads distributed to the disaster-stricken areas, more than was needed and in large volumes exceeding demand.

In some cases, the celebrities did not send the items and money collected to the field or were unable to do so because the government barred their involvement and froze their bank accounts. Often, the pooled donations and mobilized resources went to waste, either in the capital of Tehran or in Kermanshah itself.

There was a lack of coordination between celebrities and the IRCS. For example, Ali Daei, one of the greatest Iranian football players in the world, arranged to import two shipping containers of powdered baby milk from Germany. While it had faced problems due to tariff and custom clearance issues – apart from the sanctions imposed by the US – the IRCS helped them to release the containers and deliver them to Kermanshah; however, that amount of baby milk powder was not required, and the expiration date was about to occur.

Many new actors – ordinary individuals, celebrities and representatives from other communities – visited the site immediately after the quake to deliver aid. This influx of individuals into the province was clearly motivated by a desire to help, but it also led to an increase in temporary dwellers in major villages and cities already burdened by the disaster. The HAs whose operations were observed for this study all confirmed that the massive ingress and engagement of ordinary individuals in the field created operational difficulties due to congestion along the roads connecting the most severely devastated areas and thus had a negative impact on HA performance.

5. Discussion

The results of the qualitative data analysis in this paper lead to several conclusions. First, the HL challenges accord with and, in some cases, expand on previous findings in the HL literature ( Kovács and Spens, 2009 ; Kovács and Tatham, 2009 ; Leiras et al. , 2014 ; Sandwell, 2011 ). Needs assessments, for instance, were identified in this study as an especially critical issue, confirming previous findings (e.g., Balcik and Beamon, 2008 ; L'Hermitte et al. , 2015 ; Tatham et al. , 2017 ). The process of demand capture within the HL community was noted to be considerably more difficult than demand capture in the business context ( Tatham et al. , 2017 ). Indeed, inaccurate and ineffective needs assessments conducted by HAs, particularly by emerging actors, can negatively impact performance by delaying relief item procurement, fostering the distribution of unnecessary items, creating redundancies in resources and increased oversupply costs, occupying warehouse space with items already procured, and causing inequitable aid distribution to affected populations.

The lack of coordination between the network of actors, particularly between established HAs and new actors, coupled with mistrust among HAs and the impact of devastated infrastructure, creates challenges for relief item procurement, warehousing and transportation within disaster-stricken areas. This finding confirms reports in previous studies such as Baporikar and Shangheta (2018) , Balcik et al. (2010) , Fritz Institute (2004) , Kovács and Spens (2009) , Makepeace et al. (2017) , Tatham et al. (2017) , and Kunz and Reiner (2016) . These obstacles delay procurement, increase costs due to oversupplying, cause shortages of some necessary items, raise warehousing costs, and extend lead times. Based on the statements of the respondents, the failure of established HAs to pre-position commodities prior to the Kermanshah earthquake led to stockout costs, increased lead times, unmet demand capture and higher costs of procuring commodities because the HAs were forced to make purchases on the black market. Security problems were another challenging issue that occurred during the 2017 earthquake disaster relief operation; it led to increased costs from the need to replace stolen items. The findings here confirm the importance of ensuring safety and security during a crisis, as established in studies such as L'Hermitte et al. (2015) .

Finally, the lack of an integrated HL logistics databank and standards at the local and national levels made it difficult for HAs to track the delivery of aid, which increased their supply and distribution costs because they were not aware of what actors were present, what capacity these actors had, or which types of aid they were providing. While attempts have been made in the past to resolve the databank issue, many HAs still struggle to achieve the optimal end-to-end visibility for their supply networks in a real-time and open-source format (e.g., Tatham et al. , 2017 ; Makepeace et al. , 2017 ). These findings also complement frequently reported concerns about a lack of standards, training and guidelines in HL ( Fritz Institute, 2004 ; Kovács and Spens, 2009 ; Sandwell, 2011 ).

Besides the HL challenges listed above, our exploratory study revealed another set of issues, i.e., non-logistical challenges, influencing humanitarian response efforts: the growing use of social media, an absence of regulations on the legitimacy and involvement of HAs, and the interference of new actors. There is scant literature providing insight into these factors or elaborating on their operational implications (e.g., Kunz and Reiner, 2016 ; Kovács and Spens, 2009 ; Kirac and Milburn, 2018 ; Besiou and Van Wassenhove, 2019 ; Lewin et al. , 2018 ), and future studies may help understand them and find solutions to lessen their impact on humanitarian response operations.

In addition, our findings reveal that the heavy use of social media by HAs, particularly new actors, resulted in large volumes of unstructured data and content being produced and disseminated through social networks. That content spread virally, encouraging newcomers to become involved. This brought many of these individuals and groups into the field, causing traffic jams and other supply chain bottlenecks. In terms of logistics, social media disrupts supply chains through inaccurate needs assessments that result from the large volume of unstructured data and content, redundancies in unnecessary items due to the presence of many emerging actors, shortages of storage space and greater procurement expenses due to higher prices.

Poor or nonexistent implementation of governmental regulations and policies created challenges in the response to the Kermanshah earthquake. Similar findings have previously been reported in studies of other disasters (e.g., Baporikar and Shangheta, 2018 ; Kovács and Spens, 2009 ). The results of the present study reveal that the organizations most culpable for not enforcing regulations during the Kermanshah relief operations were government agencies. Problems such as a lack of international NGO operational visibility due to government restrictions, the absence of a policy regarding the engagement of new actors, and the lack of external auditing and control over logistics activities were all problems in this category. Furthermore, the extremely lengthy bureaucratic procedures involving international NGOs and government agencies such as the Ministry of Foreign Affairs, NDMO in the Ministry of Interior, and the Bureau for Aliens and Foreign Immigrant Affairs hindered effective coordination and cooperation between these parties and led to reduced amounts of funding and in-kind aid from international donors and agencies.

Finally, the emergence of new actors in the Kermanshah earthquake was overwhelming and challenging, as these actors had poor logistics capabilities, occupied vital storage space, consumed scarce resources, and contributed to the inequitable distribution of relief items. Many respondents reported inefficient UBD use and oversupply costs. Research on the emergence of new actors in disaster relief operations and the management of their engagement remains scant in the HL literature ( Lewin et al. , 2018 ; Van Wassenhove and Besiou, 2013 ).

6. Academic contribution and managerial implications

The purpose of this paper was to extend the understanding of HL challenges with respect to the findings in previous studies by answering the following research questions: (1) What were the key challenges in disaster relief operations in the 2017 Kermanshah earthquake? and (2) What were the critical challenges in the 2017 Kermanshah earthquake that influenced disaster relief operations but have not been extensively explored in previous studies?

The findings suggest that humanitarian logisticians deal with multiple challenges during disaster relief operations. One group of challenges is related to HL (i.e., needs assessment, procurement, warehousing, transportation and distribution) and has been discussed extensively in the literature. Other influences on the performance of HL activities are the growing use of social media, regulations associated with aid delivery, and the engagement of new humanitarian actors such as social media activists and celebrities. These factors have not been extensively examined in the literature and – given their critical influence on HAs' performance – require much more scholarly attention. These issues all contributed to disruptions of the established HAs' supply chains and thus negatively affected the supply performance of actors during disaster relief operations in Kermanshah. These effects included transportation delays, long lead times for last mile distribution, duplication of efforts and redundancies, and the inequitable distribution of relief items to affected populations. In overall, this research contributes to the body of HL literature in terms of categorization of both operational and nonoperational challenges. In terms of logistics, challenges connected to needs assessment were identified (including the sub-categories of inaccurate needs assessment, lack of shared data on needs and the dynamic nature of needs), procurement, warehousing and transportation (including sub-categories of the lack of prepositioning relief items, lack of coordination, UBDs, transportation constraints and lack of security in the field and risk of looting). While prior studies have revealed most of these challenges, the overall research result is fragmented. Therefore, this categorization and sub-categorization denoting the logistics challenges could provide further detailed explanations. Moreover, other challenges including the growing use of social media, regulations on the legitimacy and the use of social media for HAs, and interference of new emerging actors, during disaster relief operations have been explored and identified in this research.

Practitioners suggested strategies to mitigate the risk of poor implementation of regulations. For example, the disaster management authorities at the governmental level felt that there should be a working group that engages key actors like national and international NGOs, community leaders and committed celebrities to establish the rules of engagement for multiple actors in disaster response contexts. In particular, more formal coordination meetings need to occur between government agencies, international NGOs and UN agencies to determine rules and encourage the involvement of international actors to shorten the timeframes of administrative procedures.

The strategies recommended by practitioners to mitigate the risk of social media challenges are varied in terms of identifying the roles and responsibilities of social media players and activists from the preparedness phase prior to disasters throughout the entire disaster management cycle. Additionally, HAs need to increase their basic knowledge of how to incorporate social media into their logistics activities and standard operating procedures around sharing information about needs, procurement, transportation schedules and both intra- and inter-organizational coordination. The development of standard practices on the use of social media could provide additional support to HAs in disaster relief operations; more regular training on how to use social media platforms may be required for disaster management organizations. Finally, government agencies like Iran's NDMO should establish themselves as leaders in coordinating and connecting new and established HAs. Indeed, this study's results suggest that an effective coordination unit should be established to deal with new actors and thus increase the speed of operations.

7. Limitations and further research

This study has several limitations. First, insights from a single case study may not be generalized through abstraction and the attendant loss of context, but they may be applicable to other situations through reflection on similarities and differences between contextual factors ( Greenwood and Levin, 2007 ). While examining a single case study limits the transferability of findings, it offers convincing insights, particularly when the situation (i.e., a disaster response) is deliberately selected to provide certain contributions that alternative cases may not reveal ( Siggelkow, 2007 ). This case study identified and discussed HL challenges related to the 2017 Kermanshah earthquake, and the lessons learned from that experience might apply to other contexts, regions and disaster types. The results could be particularly cogent in contexts where the humanitarian response is effectively dominated by national actors, with international actors playing only a minor role in operations, and in situations where the disaster affects a relatively small geographical region, meaning that a large number of HAs will compete for media visibility and local resources. Future studies should collect their own empirical data to test the generalizability of the challenges discussed here and assess their impact in other contexts.

Second, the data collection in this explorative study was based on a set of semi-structured questions, not a set of measures or statements with which informants could specify a level of agreement; therefore, we were not able to provide the number of respondents who agreed with a particular statement. Future studies may assess these findings though large-scale surveys and compare the perceptions of people associated with HAs.

Third, this paper uses an explorative case study method intended to identify and elaborate on the challenges that HAs faced in a recent natural disaster in a developing country. It did not seek to further develop theory, but future studies may explore and provide theoretical explanations of the role and impact of challenges such as the growing use of social media, the emergence of new actors, and regulating the performance of HAs.

In addition to the findings gleaned from this analysis, some points of interest extracted from the case study merit further research. The first is the inaccurate needs assessment procedures that lead to the distribution of unnecessary items, resource redundancies, and higher costs for HAs' UBDs and warehousing. While the Geneva-based Inter-Agency Standing Committee has developed a complete set of standards for needs assessment procedures for HAs, policy specialists and decision-makers should take note that difficulties in conducting an effective needs assessment translate to a lack of in-country preparedness. Conducting coordinated needs assessments might be one solution to improve the efficiency and effectiveness of logistics services, but the issue of trust among HAs needs to be considered ( Tatham and Kovács, 2010 ), and there must be a clear understanding of the dynamic nature of needs assessments. Considering the importance of joint needs assessments, further research is needed on the topic in the context of HL, along with additional research on policy support for HAs regarding when and how data on needs should be shared during disaster relief operations.

The second point of interest is the lack of logistics coordination, relief item pre-positioning, and supply chain security that hinders effective and efficient delivery of aid to affected populations. Just as these challenges can be exacerbated by the lack of a national integrated logistics databank, these deficiencies result in performance pitfalls such as oversupply, unfair distribution, stockouts, the need to repurchase goods and poor transparency regarding supply chain tracking and tracing. This point reinforces the need for more and better educational programs for local and national humanitarian logisticians on an effective relief-item and services pre-positioning structure and for improved security for staff and goods during relief operations. Indeed, security is a key element of any successful response. Thus, further studies should help develop a framework for security elements across supply chains that connect multiple actors, including the military, government agencies and NGOs.

The third point of interest is the lack of clear regulation or legislation supporting international NGOs and their engagement with local and national HAs. Considering the lengthy bureaucratic procedures between international NGOs and national authorities in Iran, further research is needed on government policy support for national disaster preparedness through coordination mechanisms that involve IHAs.

In addition, and as noted above, the emergence of new actors shows no sign of slowing. Some of these actors have thousands or even millions of followers of their social media presence. While they can support aid delivery through resource mobilization and fundraising, they can also disrupt other HA supply chains by failing to coordinate with the other actors in a network. Therefore, further study is recommended on the involvement of new HAs and policy support to better prepare this emerging group. Further studies are also needed to explore effective and efficient ways for established HAs such as the IRCS to interact and coordinate with new HAs.

Logistics challenges discussed in the literature

Non-logistical challenges identified

Number of interviews per humanitarian actor group

Codes related to logistics challenges

Non-logistical challenges identified during disaster relief operations

Table of interviewee group

Appendix 2 Interview Protocol

Introduction:.

Explain the research objective. Inform the interviewee that (1) he/she can receive the executive summary of study, (2) all the collected information will be used for academic purposes, (3) any personal or organizational information collected that could identify them will remain strictly confidential and the firm's name will be anonymized in the paper.

Organization General Operations:

Obtain the title, experience and responsibilities of interviewee, and basic information. Request contact information of other informants within the organization and its partners.

Describe the services or programs provided by the organization, its number of employees and the geographical distribution of its activities.

Describe the organization's role within the humanitarian supply chain network and their relations with other humanitarian actors such as the government, national Red Crescent, NGOs and local communities.

Describe the role of the organization in delivering aid to beneficiaries, and the factors that distinguish it from other actors.

Challenges of HL during disaster relief operations

Describe the performance outcomes of relief operations in Kermanshah.

Explain whether your organization faced challenges in its operations in the Kermanshah Earthquake (in particular with logistics challenges in terms of needs assessment, procurement, warehousing, transportation and last mile distribution), give examples.

Explain the other challenges and limitations that the focal organization faced during their disaster relief operation in Kermanshah.

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Acknowledgements

The authors gratefully acknowledge Maria Besiou (the associate editor) and two anonymous reviewers for their valuable suggestions and helping to improve the quality of this manuscript. The Academy of Finland (Grant no. 332921) supported this piece of research work. In addition, the authors would like to thank to all respondents whom spent their valuable time with us during the interviews.

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v.2022; 2022

Critical Decision-Making Issues in Disaster Relief Supply Management: A Review

1 Business School, Sichuan University, Chengdu, Sichuan 610017, China

2 School of Management, Zhejiang Shuren University, Hangzhou, Zhejiang 310015, China

3 Institute for Disaster Management and Reconstruction, Sichuan University, Chengdu, Sichuan 610017, China

4 Michael Page (Shanghai) Recruitment Co., Ltd, Shenzhen Branch, Shenzhen, Guangdong, 518000, China

5 Meishan Management Committee of Sichuan Tianfu New Area, Meishan, Sichuan 620000, China

This paper comprehensively reviews the literature related to disaster relief supply management in recent years by taking the perspectives of three critical decision-making issues, i.e., coordination issues, facility location decisions, and inventory decisions. For each decision-making issue discussed, we clarify the barriers of current research papers and identify the major challenges and critical factors that should be considered. In the following, we present the perspectives on the road of coordination between multiple relief actors, characterize the location decisions of relief facilities with a variety of optimization objectives, and emphasize the importance of relief supply varieties and critical factors in the decisions of disaster relief inventories. Future research directions are recommended for further discussions.

1. Introduction

After the occurrence of a disaster, a big population of affected victims needs to live on disaster relief supplies, such as food, water, shelters, and medical treatment, which are required to be delivered in a quick, efficient, and effective way. The outbreak of the COVID-19 pandemic has again emphasized fundamental role of relief supply chains [ 1 ]. But the unpredictable nature of disasters leaves uncertain messages on relief supply and demand regarding timing, location, and impact which largely complicates disaster relief operations. Prepositioning disaster relief supplies are must to serve as buffer for an efficient and effective response to disasters. Nevertheless, managing disaster relief supplies is never an easy task in the face of the following issues.

1.1. Multiple Relief Actors are Involved with Limited Budgets and Resources

Large-scale disaster relief operations will definitely involve a large number of relief actors, from public sectors like government agencies at all levels, military forces, and humanitarian organizations with official backgrounds (e.g., Red Cross), to private sectors, such as diversified nongovernmental organizations (NGOs), religion-based organizations (e.g., Churches), and private firms (e.g., local retailers and service providers). These organizations either operate self-owned warehouses or contract with their suppliers to acquire relief materials. Supplied with multiple sources after disaster strikes, there would arise operational management problems in material convergence, duplication of effort, and/or potential conflicts in operations, which cause not only huge resources waste but also inefficiency in the relief operations [ 2 ]. Besides, finding an optimal number of available medical staffs is also a problem of the emergency department [ 3 ].

Not all relief actors are capable of prepositioning relief inventories or operating warehouses due to budget constraints [ 4 ]. In fact, the majority of NGOs are poorly supported in finance. Their humanitarian assistances live on postdisaster donations, including cash and material donations, which is hysteretic and would be the main source of unsolicited donations. How to time-efficiently and cost-effectively satisfy the demand of affected populations becomes a critical problem in disaster relief inventory management. On the other hand, when procuring from local/domestic or international suppliers to acquire relief supplies, the availability and accessibility of resources would be a big problem, such as supply shortage of local/domestic manufacturers/retailers and long lead-time due to international transportation and personnel organization. The strategy and tactics of optimizing constrained resources to meet as many needs of the victims as possible are the keys to successful disaster relief operations, which is the strength of OR [ 5 ].

1.2. The Status of the Affected Area is Complicated and Unpredictable

Large-scale natural disasters will destroy the infrastructures in the vicinity, paralyze the transportation network, and impede the delivery of relief supplies. While the warehouses or life-saving roads are devastated, relief materials become inaccessible or unavailable. Relief actors must reorganize their operations and relocate relief supplies to serve the demand of victims. The complicated situations in affected areas trigger accessibility and equity problems in delivering material assistance. Some affected people might be trapped far from the points of distribution (PODs) due to the paralyzed postdisaster transportation. Taking the demographic and socioeconomic characteristics of affected populations into consideration is extremely important but difficult. Moreover, secondary disasters, such as aftershocks and disease outbreaks, dynamically change the status of the affected area. The unpredictable environment in the response process will result in changing demand, supply, and information communication patterns, which upgrades the complexity of managing disaster relief supplies.

1.3. Demand is Extremely Uncertain and Ever-Changing

To ensure an effective and efficient response to affected demand, information about the timing, location, type, and quantity of relief supplies is needed as much as possible. However, in the aftermath of disasters, particularly catastrophes, demand information is limited, rough, inaccurate, and hysteretic. It is very difficult to precisely evaluate the impact of the disaster as the disaster environment is ever-changing. There is always no time for good planning to satisfy the demand of affected populations in a cost-effective and time-efficient way. Consequently, disaster relief supply chains may result in excessive waste and emissions, which may harm the local communities and environment in the long run [ 6 ].

Since disaster relief is about 80% of the logistics it would follow [ 7 ], the critical decision-making issues of disaster relief supplies are worth in-depth understanding and exploring to better support the disaster relief operations. Previous relevant research mostly focuses on the inventory prepositioning (e.g., [ 8 – 13 ]), facility location decisions (e.g., [ 4 , 14 – 16 ]), and inventory planning and control (e.g., [ 17 – 20 ]). Balcik et al. [ 21 ] review papers that determine the capacity, the time, and the location of disaster relief inventories. They categorize the existing studies according to the planning phrases, i.e., predisaster and postdisaster stages, of a disaster management cycle. Behl and Dutta [ 22 ] take a thematic point of view to extensively review the extant literature to reflect the shift in the trend of humanitarian supply chain management. Different from their perspectives, Ye et al. [ 23 ] shift the focus from reviewing the literature to identifying the gaps between research and practice to discuss three critical decision themes of disaster relief inventory management. Given that the above three operational problems we have identified would severely complicate the disaster relief supply management, as a supplement, this survey summarizes the state-of-art academic research from the perspective of three decision-making issues, i.e., coordination decisions, facility location decisions, and inventory decisions. We endeavour to cite the majority of relevant publications including journal articles, book chapters, and academic works, mostly published within the past decade, to discuss their point of view focused on these three critical decisions of disaster relief supply management. We also introduce the humanitarian logistics practice highlighted by the Logistics Operational Guide as only practice-based research with both generality and validity considerations can contribute to the humanitarian operations [ 24 ]. We use databases such as Web of Science, ProQuest, JSTOR, ScienceDirect, Springer, and Emerald along with Google scholar to search keywords such as “disaster relief supply,” “relief material,” “emergency supply,” “emergency material,” “disaster relief supply chain,” and “disaster inventory prepositioning.” We also cross-reference relevant important studies from cited papers without using the keywords we searched. In the rest of the paper, we review the most relevant papers with respect to the three critical decision-making issues in Section 3 , Section 4 , and Section 5 , and summarize the literature with recommendations for future research directions in the final section.

2. Coordination Decisions

Disaster relief supply chains could involve a surprising number of different types of relief actors who undertake material supplying tasks in response to a disaster. These humanitarian organizations include government agencies (e.g., Federal Emergency Management Agency, FEMA) and military forces, international emergency relief organizations (e.g., The [ 25 ] local/regional/national social organizations (e.g, the One Foundation of China), religious organizations (e.g., local churches), and private sectors (e.g. local or national-wide retailers and manufacturers). Besiou and Van Wassenhove [ 26 ] indicate that the changing role and number of stakeholders, particularly regarding the partner relationships and sector collaborations, have been the most frequently discussed topic in Logistics Cluster Practitioner Conference. Because international and local relief organizations operate on different scales, their roles need to be clearly differentiated. Generally, international relief actors stockpile relief supplies in preparation for relative slow-onset disasters and crises and provide long-term humanitarian aid around the world, whereas local relief actors play the main force to deliver the first wave of emergency supplies as they are much closer and more familiar with the terrain, infrastructure, and demographics of the affected areas. Designing coordination mechanisms between multiple relief actors is always the key to effective and efficient disaster relief supply chain management. However, the majority of studies assume a single decision maker in managing relief supplies [ 21 ], whereas the coordination issues lack sufficient understanding from an operational perspective.

2.1. Coordination Barriers

There are hundreds of relief organizations participating in the rescue and relief operations of a disaster, particularly of a catastrophe. For instance, over 700 NGOs from more than 40 countries provided emergency assistance to the victims of the Asia Tsunami in 2004 [ 27 ]. The difficulties of managing the relief supplies in response to disasters are massively upgraded with uncertain demand, limited logistics capacities, poor information feedback, and multiple decision makers [ 28 ]. Therefore, coordination between multiple relief actors meets barriers because of the following reasons:

2.1.1. Limited Local Resources

Apart from inventory prepositioning, relief supplies are acquired from three main sources: global procurement, local procurement, and in-kind donations. Although local procurement has a shorter lead time and lower logistics cost [ 4 ], the local supplying capability is at a high risk of being destroyed or damaged if struck by major disasters [ 29 ]. Besides, local suppliers are very likely to suffer shortages of satisfying the surging demand [ 30 ], which might induce competition between relief actors on the scarce relief resources that consequently raises the price of local supplies [ 27 ]. Moreover, supporting resources are also insufficient, such as the increasing demand for vehicles and staff and the extra financial burden on relief organizations or directly on the people in distress [ 27 ].

2.1.2. Unsolicited Supplies/Donations

Many postdisaster in-kind donations are spontaneous without knowing the actual demand. These unsolicited supplies/donations from well-wishers always cause congestion in the logistics systems [ 31 ]. In the 2004 Indonesia tsunami, 2005 Hurricane Katrina, 2010 Tohoku earthquake, and even in man-made disasters like the 9/11 terrorist attack, it has been highly noticed that unsolicited supplies were donated at the wrong time, in excessive volume, and in unmatchable types to affected areas, especially after sudden-onset disasters [ 27 , 30 , 32 ]. Time and resources are occupied to identify, prioritize, transport, and store these relief supplies, which severely disrupt the priority of material supplying, occupy limited warehouse space, clog the transportation networks, and undermine the relief operations [ 31 ]. Therefore, coordination mechanisms are desired to quickly share and publish information about the relief supplies needed and to organize the deployment and transportation within the whole system to avoid congestion and waste.

2.1.3. Poor Communication and Information Sharing

In the face of highly uncertain demand information about the quantity, type, and location of relief supplies, decision makers are difficult to predict the aggregated demand and available resources to supply, resulting in a significant mismatch of demand and supply. For example, 211 million pounds of ice were ordered by FEMA in one week after the landing of Hurricane Katrina and 60% was unnecessary afterward [ 30 ]. Relief organizations also have coordination problems in observing each other's operations under asymmetric information. As the postdisaster environment is ever-changing, an affected region is possible to have been repeatedly served. This kind of effort duplication worsens the scarcity of relief resources [ 2 ].

Another big challenge the humanitarian decision maker has to continuously deal with is the persistence of coordination-information bubbles. Comes et al. [ 33 ] conduct two case studies on Typhoon Haiyan and the Syria Crisis and emphasize the fragmentation and misalignment of coordination structures and decisions in the emergency, which are created by volatile information and sensemaking response. It is imperative to design methods and approaches to help the decision makers identify the role of information in emergent coordination and make adaptive decisions.

2.1.4. Involvement of Government and Military Operations

A larger proportion of the global affected population are residents of developing countries. In developing countries, governments at all levels are the most reliable forces in disaster relief operations. They also take the leading role in coordinating different social organizations and private firms to provide relief supplies effectively and efficiently. Similar to inter-organizational coordination, the necessity of bridging the intergovernmental distance also became recognized explicitly [ 34 ]. Once the government fails, people would suffer. Meanwhile, the military is a special and critical relief force that is equipped with more advanced logistics capability and expertise than most relief organizations in deploying a large number of relief supplies. However, many relief organizations are reluctant to cooperate with the military force with the concern of triggering conflicts due to different missions, mandates, working disciplines, and/or operating procedures [ 27 , 35 ].

In summary, cooperation procedures and coordination mechanisms should be established to clarify the role of each relief actor, to share information (e.g., logistics capacities, real-time emergency supplies, demand estimation, and operation feedback), to identify resource availability and accessibility, to avoid resource duplications and waste, to manage and deploy relief supplies in a coordinated manner, to reduce inventory-related costs and most importantly, to better serve the beneficiaries and mitigate human sufferings. Therefore, we review the literature from the perspective of macro- and micro-coordination to understand how previous research investigates the coordination issues and the corresponding solutions.

2.2. Coordination Perspective

Academics and practitioners hold a consensus view that effective coordination between multiple relief actors throughout the disaster relief phases creates the basis for improving logistics performance [ 23 , 36 – 38 ]. We discuss the respective macro- and micro-coordination of the disaster relief supply chain in this section.

2.2.1. Macro-Coordination

Macro-coordination refers to establishing a coordination platform on which critical information is centrally gathered and disseminated where the operational standard and guidelines for involved relief actors are set up. Coordination is achieved on three levels: (1) information sharing (e.g., sharing supply and demand information); (2) operational cooperation (e.g., cooperation in transportation or warehousing); (3) organization alliance (e.g., Logistics Clusters). The coordination platform for each level is needed to clarify the partnerships among different relief organizations, integrate collected information on demand and supply, and streamline the utilization of limited resources to avoid duplication of efforts and resource redundancy. In catastrophes, a coordination platform should be designed in a dynamic manner as the complexity of the disaster environment is upgrading. At the national level, the government at all levels usually takes the leading role in the coordination platform.

The coordination platform, for example, the On-site Operation Coordination Centre (OSOCC) established by the UN Disaster Assessment and Coordination Team (UNDAC) and the United Nations Joint Logistics Centre (UNJLC), serves as a focal point for information exchange that facilitates the coordination meetings, demand assessment, and telecommunication, and reports to new arrived relief organizations and coordinates to local authorities [ 39 ]. Similarly, the UNJLC also tracks the movement of relief supplies. Such platforms lay a foundation for disaster relief organizations to cooperate, track the movement of goods, and better solve coordination problems of relief supplies.

In addition to the establishment of coordination platforms, widely accepted standards and guidelines for streamlined relief operations are imperative as well. Disaster relief operations will inevitably involve a number of new and inexperienced relief actors in providing emergency assistance where the quality of relief operations might be impaired [ 36 , 40 ]. Those who (both organizations and individuals) are not capable of following the qualified standard must be rejected. On the other hand, operational guidelines should be provided to encourage the further training of relief organizations and the engagement of private firms. Private firms either contracted with relief organizations in humanitarian logistics or donating relief supplies spontaneously are essential forces in disaster relief operations [ 41 ]. They are required to comply with the material supplying principles, collaborate with professional relief organizations, and get familiar with the operational procedures in a disaster relief environment.

The cluster approach for coordination combines “platform” and “standard” in the relief community. Clusters are made up of humanitarian organizations, including UN agencies, NGOs, the Red Cross and Red Crescent Movement, and other social organizations or even government representatives. They collaborate in addressing the needs of a specific sector (e.g., logistics, camp coordination, health, and protection). Clusters provide a framework for actors to jointly respond to the commonly identified needs, design strategic plans with shared objectives, and effectively coordinate both amongst themselves and with the national authorities [ 42 ]. Each sector should develop a matchable labor division and set up corresponding operational standards and guidelines [ 27 ]. Specifically, the Logistics Cluster is a partner collaboration community aiming at breaking logistics constraints and improving logistics response in the humanitarian environment, which contains four pillars, i.e., partnership base, standards and policy, strengthening response capacity, and operational support [ 42 ]. However, the balance of the horizontal coordination inside clusters and the vertical coordination between clusters still waits for further exploration [ 43 ].

2.2.2. Micro-Coordination

Since it is impossible for any single relief actor to respond to the disaster, micro-coordination activities are needed between and within relief organizations and private sectors to achieve joint goals.

(1) Coordination between relief organizations . Horizontal cooperation is universally observed in practice between multiple relief organizations [ 23 , 27 , 44 ], which contains joint decision-making and collaborative program (e.g., Central Emergency Fund and Consolidated Appeals), cooperation in procurement, transportation, and warehousing, etc. Adida et al. [ 45 ] find that regional hospitals with limited budgets collaborate with each other under mutual aid agreements to make decisions on medical inventory stockpiling (e.g., personal protective equipment, such as masks, gloves, and gowns) in case of medical supply shortage. By establishing a supply resource sharing network, Davis et al. [ 2 ] conclude that warehousing coordination enables relief organizations to access external supplies in neighbouring warehouses for demand fulfilment.

International relief organizations usually cooperate in resource sharing and joint decision making. For example, as an umbrella organization, the United Nations Humanitarian Response Depot (UNHRD) network provides inventory prepositioning, warehousing, and monitoring services using their warehouses located in Italy, Ghana, United Arab Emirates, Malaysia, and Panama, free of charge, for a wide range of authorized relief organizations [ 27 ]. Toyasaki et al. [ 44 ] focus on the horizontal cooperation issues of UNHRD members, such as the motive of becoming a member, the optimal coordination mechanism, and the stock rationing decisions of members, in order to propose the optimal inventory management policy for UNHRD. As for resource sharing, Altay [ 29 ] introduces a database management tool, i.e., National Incident Management System-Incident Resource Inventory System (NIMS-IRIS), used by signatory states in emergency resources (e.g., aircraft, food, and water) requests. Their analytical model is used on the NIMS-IRIS system to optimally and quickly allocate limited resources and minimize the total cost. Ergun et al. [ 46 ] document the use of an IT tool after the 2010 Haiti earthquake to improve last-mile supply distribution and data management in the camp management. They also introduce a cooperative game theory model, which is motivated by practical examples, and develop the conditions under which multiagency coordination is feasible and desirable. Li and Wang [ 47 ] use the scenario construction technology to design the emergency management system of urban flood, which is artificially intelligent in mobilizing and coordinating functional departments to facilitate the establishment of emergency management system and the standardization of operation procedures.

(2) Coordination with private logistics service providers . An increasing number of private firms, particularly those logistics service providers, have participated in disaster relief operations. Examples of such long-term partnerships can be found between WFP and TNT, the American Red Cross and FedEx, and Mercy Crops and DHL [ 27 ]. The long-term partnership is possible to evolve into alliances, such as the collaboration among Quality Medical Donations, the Disaster Resource Network, and the Business Roundtable [ 48 ], which is beneficial to serving ultimate beneficiaries.

The important role of private firms has been highlighted by both literature and practice, whereas the potential threats and challenges of collaborative partnerships cannot be omitted. While relief organizations outsource transportation or food supplying to private firms, there poses a potential risk of breach of contract if private firms lose profits or have security concerns. The partner relief organization might have to bear huge humanitarian losses that cannot be compensated. Egan [ 41 ] proposes three solutions with or without marketing approaches to solving the problems of contracting failure and over-reliance on private sectors.

Table 1 summarizes the coordination mechanisms in disaster relief supply management from respective marco- and micro-coordination perspectives.

Coordination mechanisms in disaster relief supply management.

3. Facility Location Decisions

In the disaster environment, where to preposition the relief supplies before and after the occurrence of disasters significantly affects the performance of disaster relief operations. The location decision metrics between the (potential) disaster sites and the selection of facilities concern: (1) which warehouses or distribution points should be utilized or established; (2) which disaster sites should be served by the selected facilities. This section discusses how the literature selects and establishes facilities to store relief supplies according to the hierarchy of facilities so as to optimally determine the number, the location, and the capacity of facilities.

3.1. Facility Hierarchy

Perspectives from both time and space dimensions are most commonly hired to define the facility types in the facility hierarchy to describe the material flows. From the temporal perspective, warehouses or distribution centers established for long-term stockpiles of relief supplies are defined as permanent facilities, such as FEMA logistics centers and the UNHRD. Temporary distribution points are located much closer to the disaster affected regions, such as State Staging Areas (SSA) of FEMA, local rescue centers, Points of Distribution (PODs) (e.g., local schools, big parking lots, sports centers, and churches), also including supply ships and mobile vehicles [ 16 ]. On the other hand, the spatial perspective categorizes facilities as regional/national and local facilities. Regional/national facilities are usually set up by the government or international relief organizations to cover widespread areas. FEMA logistics centers and UNHRD both fall into this category. Local facilities, by contrast, have much smaller capacities, such as local distribution/rescue centers, regional rescue centers, and Break of Bulk points (BOBs), as they serve relatively smaller regions. Take FEMA's relief network for instance. Seven components are included, which are FEMA logistics centers, Commercial Storage Sites (CSSs), other Federal Agencies Sites (VEN), Mobilization (MOB) Centres, Federal Operational Staging Areas (FOSAs), SSAs, and PODs [ 49 ]. Only FEMA logistics centers and CSSs are permanent and national facilities, whereas the rest are temporary and local ones that are set up or deployed according to the demand requirements after disaster strikes. The Logistics Operational Guide has identified the key points of regional facility decision makings, which are readily available access to a high volume of intermodal international transport, relative location to the area of response, the nature of planned interventions, political climate of the country, economic feasibility, access the correct amenities, and access to sufficient technical support.

Many related papers have explored more than one layer of the facility hierarchy to make their models realistic. For instance, Balcik et al. [ 50 ] propose a three-layer distribution network including the primary hub (seaports or airports), the secondary hub (permanent central warehouses), and the tertiary hub (local distribution centers). Görmez et al. [ 51 ] propose a hierarchical facility location problem and initially locate the temporary facilities. Döyen et al. [ 52 ] build a two-echelon stochastic model to determine the locations of uncapacitated regional rescue centers and capacitated local rescue points. Noyan et al. [ 53 ] include both local distribution centers and PODs in the relief network to preposition and distribute relief items. To facilitate remote victims to get access to relief supplies, Horner and Downs [ 54 ] introduce BOBs which are designed with inferior infrastructure requirements compared to those of the PODs but are chosen closer to remote disaster sites that enable PODs to be located in the vicinity of big population centers. While using scenarios in the model, Mete and Zabinsky [ 55 ] study where to preposition additional medical supplies for potential earthquakes based on the existing network of hospital warehouses in Seattle. Bozkurt and Duran [ 56 ] provide suggestions for CARE international about how to expand the world-wide prepositioning network of relief supplies. Klibi et al. [ 57 ] propose a three-echelon relief network consisting of distribution centers, PODs, and vendors to simulate real-life emergency relief operations and system performance.

3.2. Facility Location

The extremely uncertain and complex disaster environment, which is greatly different from the commercial context, would frustrate the relief supply decision makers. While those facility location models with business context cannot well apply in disaster context [ 58 ], disaster relief inventory prepositioning needs to make decisions on the number (for each layer if considered), the location, and the capacity of facilities by properly evaluating the following critical factors in the optimization models.

3.2.1. Beneficiary Service Level

To alleviate the sufferings of vulnerable people, relief supplies are required to be delivered to the ultimate beneficiaries efficiently, effectively, and impartially. The optimization issues regarding response time, demand coverage, and equity are on the top of the location-decision list. A majority of studies consider the minimization of response time as their major optimization objective. Duran et al. [ 10 ], Bozkurt and Duran [ 56 ] and Rezaei-Malek et al. [ 59 ] seek to minimize the average of the weighted response time, where the weights are chosen according to the proportions of the realized demand flow, to optimally determine the number and the locations of prepositioning warehouses. Renkli and Duran [ 60 ] minimize the response time by minimizing the total weighted delivery distance, and Tofighi et al. [ 61 ] simultaneously minimize the total distribution time and the maximum weighted distribution time of critical items to determine the locations and the inventory levels of both central and local warehouses. To measure the performance, Balcik and Beamon [ 4 ] set up upper and lower bounds of response time in their maximal covering model to choose the qualified number, location, and stocking levels of capacitated distribution centers.

Maximizing the coverage of relief demand is a big challenge in choosing facility locations because as the disaster environment changes overtime, the type, amount, and location of the affected population are difficult to estimate. Unfortunately, there is always a proportion of demand that cannot be well satisfied, which is defined by Jia et al. [ 14 ]; Mete and Zabinsky [ 55 ]; Rawls and Turnquist [ 62 ]; Rezaei-Malek et al. [ 59 ] and Tofighi et al. [ 61 ] as unmet/unfulfilled demand and is measured as penalty cost in the model to minimize. Other related research addresses the demand for covering problems by using different objectives. Tean [ 63 ] maximizes the number of expected disaster survivors. Balcik and Beamon [ 4 ] and Mohammadi et al. [ 13 ] maximize the total expected demand coverage in their models. Afshar and Haghani [ 49 ] and Van Hentenryck et al. [ 64 ] choose the minimization of unsatisfied demand as one of their model objectives in the location decision.

In terms of the equity problem in delivering relief supplies, Noyan et al. [ 53 ] concern about the mobility of affected people which may alter their accessibility to relief supplies, as well as the impartiality issues to achieve high-level equity during emergency response. Equity is also considered by Mohammadi et al. [ 13 ] in their model to provide an equal amount of relief items to all demand nodes in case of discrimination.

3.2.2. Humanitarian Logistics Cost

Controlling the logistics cost is also important to relief sectors because logistics cost can contribute a large proportion, up to 80%, of the total operational cost. [ 65 ]. Given that most NGOs have limited transportation capacity and financial budget (donations are generally launched after disasters), how to control the cost of locating facilities without degrading the service level in emergency response is a vital problem for relief organizations to solve.

Balcik and Beamon [ 4 ] consider the cost of establishing distribution centers as well as acquiring and prepositioning relief supplies under budget limits in the predisaster phase. Manopiniwes et al. [ 66 ] determine the location of warehouses by jointly considering time and capacity constraints to minimize the total logistics cost, including the opening cost of warehouses, in the flood relief operation in Thailand. Mohammadi et al. [ 13 ] determine the optimal number and location of distribution centers (DCs) used for prepositioning relief supplies against earthquakes with the minimization objective of the total cost of establishing DCs, acquiring, storage, and transportation. Rezaei-Malek et al. [ 59 ] determine the locations of warehouses to minimize the total operational cost together with the response time in their model. More broadly, the logistics cost is minimized in other ways. Van Hentenryck et al. [ 64 ] examine the cost of prepositioning relief items in selected warehouses, and Horner and Downs [ 54 ] minimize the distribution cost of developing a relief network, while Mete and Zabinsky [ 55 ] put the total operating cost of medical warehouses into the objective function of their stochastic programming model. Both Tofighi et al. [ 61 ] and Rezaei-Malek et al. [ 59 ] consider the additional cost of unused relief inventories in the preparation of disaster relief operations. Chu and Chen [ 67 ] design a novel effective IFA-GA algorithm to solve a multiobjective optimization function which contains the deprivation cost, the unsatisfied demand cost, and the logistics cost.

3.2.3. Infrastructure and Transportation Network

Infrastructure issues focus on the facility conditions of warehouses and distribution centers, roads, and transportation lanes, which are at the risk of being destroyed by a disaster. Therefore, location decisions must involve the potential disruptions of infrastructure and transportation networks which undermines the disaster relief operations. Ukkusuri and Yushimito [ 68 ] preposition relief supplies by considering the reliability of transportation networks in disasters. They choose supply holding locations to deliver relief supplies to demand points with maximal probability. Likewise, Hong et al. [ 69 ] define “network reliability” as the probability of a possible flow of relief supplies in the postdisaster phase. They introduce global and local probabilistic constraints to realize high reliability of the relief network. Renkli and Duran [ 60 ] examine the survivability of infrastructure to formulate uncapacitated locations of relief facilities. Paul and MacDonald [ 70 ] take the possible damage of earthquakes into account when they determine the initial capacity of DCs in the preestablished network.

Another stream of research sets up selection criteria for facilities. Kapucu et al. [ 71 ] categorize different types of the staging area. They list 10 general criteria to guide emergency government agencies to select staging areas from candidate sites, including location (e.g., at a safe distance from disaster sites), operation center location, highway/road access, helicopter access, safety and security, demobilization, hardstand, equipment, storage, and utilities. Roh et al. [ 72 ] identify five groups of selection criteria in facility location decisions, including location, logistics, national stability, cost, and cooperation. They show that the subattributes “political stability” and “economic stability” from the national stability group are the most significant factors for the location selection of relief warehouses.

All the critical factors and the corresponding measurement of each group for the location decisions of disaster relief facilities are summarized in Table 2 .

Critical factors in the location decision of disaster relief facilities.

4. Inventory Decisions

Critical inventory decisions for disaster relief supplies are made on the inventory level, order quantity, reorder point, etc. (see [ 17 , 73 , 74 ]. Because both demand and supply are highly unpredictable and ever-changing during disaster relief operations, inventory decisions of relief supplies are also dynamic, posing a great challenge to relief actors.

4.1. Relief Supply Variety

We first characterize a variety of different types of disaster relief supplies as the inventory decisions must be made specifically for a group of relief items. Living necessities of affected populations cover a wide range of relief items such as bottled water, instant or can food, clothes, sanitation equipment, medicine, shelters, and tents [ 58 , 75 ]. Among these, food is a focused type that must be well managed and properly replenished due to its perishability [ 76 , 77 ]. Salas et al. [ 78 ] investigate the inventory problem of perishable food by using a stochastic programming model to minimize all related costs including ordering, shortage, disposal, and penalty cost. Natarajan and Swaminathan [ 79 ] consider UNICEF's financial constraints in procuring ready-to-use therapeutic food (RUTF) to maintain their relief inventory level in Africa. Kunz et al. [ 80 ] analyze the delivery of RUTF during the postdisaster response phase to figure out the impact of disaster management capability (DMC) on lead time. Besides, medical supplies are also an imperative type of relief supplies for life-saving. Mete and Zabinsky [ 55 ] focus on how to preposition medical supplies that need to be distributed to hospitals following a disaster. Tofighi et al. [ 61 ] put medical first-aid kits into the critical relief items to be kept in both central warehouses and local distribution centers. Moreover, given the poor sanitation conditions of the disaster sites, the demand for some typical medicine raises up in case of a secondary outbreak of diseases, such as Artemisinin Combination Therapy (ATC) for malaria [ 81 ].

Relief inventory items also contain various durable products. Such types of items are identified by UNHRD, which consist of electrical devices, individual kits, office and living accommodation, radio and telecommunication, shelters and housings, and water supply systems. [ 82 ]. Taskin and Lodree [ 18 ] study manufacturers' inventory control responding to generator orders placed by relief organizations and government agencies, revealing the objective conflicts between the manufacturers and public sectors during the Hurricane season. Lodree [ 83 ] categorizes two types of relief supplies: (1) seasonal products like flashlights and generators; (2) consumable products like long shelf-life food and bottled water. Some papers also look into other disaster-related inventory types, such as spare parts inventory of trucks that carry relief commodities to disaster sites [ 84 ] and maintenance components (e.g., generators, transformers, and capacitors) used to recover the electronic power system destroyed by the Hurricane [ 85 ]. De Leeuw et al. [ 86 ] investigate the stockpile problem of the Water Sanitation and Hygiene (WASH) cluster (identified by UN) and list a bunch of materials and equipment that support the disaster relief operations in this cluster, including bladder tanks, pipes, pumps, and water purification items, latrine slabs, and potties.

4.2. Critical Inventory Decision Issues

As the disaster environment changes over time, relief inventory decisions are affected or restricted by many highly unpredictable issues.

4.2.1. Disaster-Related Uncertainties

Lodree and Taskin [ 87 ] specifically consider the initial responses of all relevant disaster relief actors and characterize two types of uncertainties in the optimization model, i.e., the occurrence probability of a disaster and the demand surge for relief supplies, facilities, and human resources. Garrido et al. [ 88 ] choose the flood's intensity level and the occurrence probability of a disaster as random input variables of their spatiotemporal stochastic model to determine the optimal inventory level of relief supplies with the objective of maximizing demand satisfaction. Saputra et al. [ 89 ] develop a trade-off model and use it in a spreadsheet-based platform to study how the mean time between two disasters affects the strategies for inventory prepositioning. Roni et al. [ 90 ] consider both regular and surge demand in the disaster response stage and formulate a new mixed-integer programming model based on the level crossing theory to develop a hybrid policy for disaster relief inventories.

4.2.2. Forecast Information

Some disasters are relatively predictable, such as hurricanes. Then, forecast information has a powerful impact on choosing proper inventory levels of relief supplies. Lodree and Taskin [ 91 ] formulate an optimal stopping problem with a Bayesian update framework for manufacturing and retail firms to confront the surging demand for emergency supplies. They refer to the Hurricane predictions to decide when to postpone the emergency inventories in case that tropical depression or disturbance evolves into a severe Hurricane. A stochastic programming model is introduced by Taskin and Lodree [ 18 ] to design a proactive inventory policy (on optimal ordering/replenishing points) for the manufacturers and retailers during the prehurricane season, by using historical data to predict seasonal demand.

4.2.3. Financial Constraints

McCoy and Brandeau [ 92 ] investigate the relationship between the stockpile size of relief items and the benefit to disaster victims. They give advice to UNHCR on how to choose the optimal inventory level and allocate financial resources with a limited budget. Natarajan and Swaminathan [ 79 ] find that the uncertainties in funding timing and funding level of donors have an influence on the relief organizations' operational costs and fill rates. They formulate a multiperiod stochastic inventory model to decide the optimal replenishment policy and to show that avoiding funding delays is critical in a fully funded system, where the front-loaded funding at 75% level supports the disaster relief operations equally as the back-loaded full funding.

4.2.4. Unforeseen Disruption

Disaster relief life cycle is generally divided into two phases in the existing literature, i.e., the preparedness and the response phases [ 93 ]. When responding to a disaster, many unforeseen disruptions, such as the outbreak of epidemic, aftershocks, or overlapping disasters (e.g., Hurricane Tomas arisen by the Haiti earthquake), cause additional surging demand within the affected region [ 81 , 93 ]. The supply shortage is always blamed on the damage to roads, destroyed warehouses, fire, or theft [ 93 , 94 ]. All of these unforeseen disruptions must be dynamically considered in the inventory plans to satisfy the overall demand of victims. Thereafter, a majority of research focuses on the relocation or transshipment of relief supplies from other functioning warehouses or supply holding ships in the vicinity to reduce the replenishment lead time [ 94 , 95 ]. Ozguven and Ozbay [ 96 ] develop a pLEPs algorithm to solve their inventory control model (MC-SHIC) by determining the inventory levels of relief items in shelters. They collect information about transportation tracking, relief commodity flow, and inventory level fluctuation by RFID technology. Ozguven and Ozbay [ 97 ] also improve their previous model by integrally considering the closed-loop feedback-based inventory control that uses RFID devices.

Regarding inventory mobility, Rottkemper et al. [ 81 ] propose a mixed-integer programming model to relocate the inventory with the objective of minimizing unsatisfied demand and operating costs. Sarder and Iqbal [ 94 ] focus on the relocation of medical relief items for healthcare organizations and propose a three-layer model to minimize the unmet demand and the transportation cost of the system. Mulyono and Ishida [ 98 ] format shelter clusters in disaster sites that use the stable roommate (SR) algorithm to build interconnections between shelters. Stock transshipment is implemented in their model in case of a potential shortage of relief items. Kessentini et al. [ 99 ] identify the urgent request from some relief centers and provide transshipment options to minimize lead time. They develop an agent-based model and a simulator to solve this problem. Richter [ 16 ] proposes dynamic relocation models to give insights on how to relocate the mobile relief inventories carried on a supply ship (e.g., Floating Doctors, Project Hope) in response to the changing demand.

5. Conclusions and Future Directions

This paper comprehensively reviews the literature related to disaster relief supply management in recent years, by taking the perspectives of three critical decision-making issues, i.e., coordination issues, facility location decisions, and inventory decisions. For each decision-making issue we discussed, we summarize the current research papers and identify the major challenges and critical factors to be considered. We first clarify the barriers to the road of coordination between multiple relief actors and take both macro- and micro-perspectives on the coordination issues. Then, we emphasize the location decisions of different types of relief facilities with a variety of optimization objectives. After that, we characterize the relief supply variety and discuss the critical factors, such as disaster-related uncertainties, forecast information, financial budget, and unforeseen disruptions, which would have a remarkable influence on the disaster relief inventory control.

Although the importance of coordinating multiple relief actors is realized and highlighted while many cases and coordination mechanisms are mentioned, relief organizations collaborate and cooperate with each other in benefit sharing, and risk-taking is not well addressed in the literature. Besides, criteria to evaluate the performance of relief operations coordination are not well established. In future research, the evaluation criteria must be set up to clearly understand the purposes and consequences of coordination in disaster relief inventory management, as well as to provide guidance and principles for the relief actors to follow to enhance the overall performance.

Another question to be explored is how the suppliers of relief supplies develop their inventory policies in disaster relief operations. Most research focuses on the inventory prepositioning of relief actors in response to disasters. In fact, the suppliers of these relief actors, whose inventory capacities are assumed to be sufficient and assessable as backup resources, must build close partnerships with relief actors to guarantee the material supply. However, they are also at the risk of being destroyed or breaching a contract. Some papers have considered the inventory policies designed for suppliers (see [ 18 , 91 ]), but the interaction between the suppliers and the relief actors requires in-depth discussions to achieve better-integrated performance and control the potential risks.

Acknowledgments

This work was supported by Science and Technology Support Program in Sichuan Province (grant no. 2021JDRC0116); Fundamental Research Funds for Central Universities (grant no. 2022 IDMR self-set).

Conflicts of Interest

The authors declare that they have no conflicts of interest.

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Do Subsidies Help the Economy?

Effect of subsidies on supply, tax credits.

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Government & Policy

How Do Government Subsidies Help an Industry?

essay about distribution of government subsidies and relief operations

Katrina Ávila Munichiello is an experienced editor, writer, fact-checker, and proofreader with more than fourteen years of experience working with print and online publications.

Government subsidies help an industry by paying for part of the cost of the production of a good or service by offering tax credits or reimbursements or by paying for part of the cost a consumer would pay to purchase a good or service.

A subsidy is often granted by a government to support critical parts of the economy that are thought to be vulnerable to external forces.

Key Takeaways

Subsidies are payments, tax breaks, or other forms of economic support given by governments to certain industries or economic sectors.
The goal of subsidies is to aid or support what are deemed to be key parts of the economy or national infrastructure.
While subsidies may have a direct positive impact on the particular industry or companies involved, economists argue that subsidies work against free trade and create market inefficiencies.

Governments seek to implement subsidies to encourage production and consumption in specific industries. When government subsidies are implemented to the supplier, an industry is able to allow its producers to produce more goods and services. This increases the overall supply of that good or service, which increases the quantity demanded of that good or service and lowers the overall price of the good or service.

In this sense, when the government gives subsidies to the supplier, what results is a win-win situation for both the supplier and the consumer. Essentially, the supplier is benefitting as if the good were selling at a higher price and is able to produce more of the product. Meanwhile, consumers get to enjoy the product for what would be a comparatively cheaper price, since suppliers do not need to charge exorbitant rates to break even on production.

Since the government helps suppliers through tax credits or reimbursements , the lower overall price of their goods and services is more than offset by the savings they receive.

On the consumer side, government subsidies can help potential consumers with the cost of a good or service, usually through tax credits. For example, a great example of this is the transition to more renewable sources of energy. With still nascent models of green economics, the current demand to purchase new energy-saving technology is low. In order to sway consumer interest, government subsidies or tax credits can help with this high cost of adoption. When consumers refit their houses with solar panels, the government will provide a tax credit to individuals and families to offset the high price of purchasing the new solar panels.

In this sense, consumer-targeted subsidies will not necessarily increase supply, since producers aren't being motivated or compensated to produce more. However, tax credits will offset higher prices for consumers so that the margin still goes back to producers.

In the same vein, some states also provide a tax credit or subsidy for buying an electric or hybrid vehicle. This helps the renewable energy industry by allowing more consumers to purchase the products associated with that industry, without having to absorb the entire cost.

What Are Some Critiques of Government Subsidies?

Critics of subsidies claim that they interfere with free markets, and therefore can cause anomalies or inefficiencies. Critics argue that subsidies create unfair conditions that favor one set of companies over others, reducing competition. These companies can take advantage of subsidies to engage in rent-seeking, ultimately at the harm of consumers.

What Are Direct vs. Indirect Subsidies?

Direct subsidies involve cash transfers or tax breaks that immediately impact a company or industry. Indirect subsidies do not have a specific cash value or involve payments of cash. These can instead include making it easier to obtain inputs or reducing costs in other ways.

Which Industries Do the U.S. Government Subsidize?

The U.S. government heavily subsidizes the domestic agricultural sector. It also subsidizes oil and energy producers, some housing, automakers, and some healthcare (e.g. Medicare).

Government subsidies can help an industry on both the supplier side and the consumer side, no matter on which end they are implemented. To implement subsidies, governments need to raise taxes or reallocate taxes from existing budgets. There is also an argument that incentives in the form of subsidies actually reduce the incentives of firms to cut costs. However, whether it's by increasing supply through supplier-side subsidies, or helping consumers with high costs of adoption through tax credits, it's clear that government intervention in market economics has real-life impacts on both parties alike.

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1 0000000404811396 https://isni.org/isni/0000000404811396 International Monetary Fund

This paper addresses the problems of defining and measuring government subsidies, examines why and how government subsidies are used as a fiscal policy tool, assesses their economic effects, appraises international empirical evidence on government subsidies, and offers options for their reform. Recent international trends in government subsidy expenditure are analyzed for the 16-year period from 1975 to 1990, using general government subsidy data for 60 countries from the System of National Accounts (SNA) and central government expenditure on subsidies and other current transfers for 68 countries from Government Finance Statistics (GFS). The paper reviews major policy options for subsidy reform, focusing on ways to improve the cost-effectiveness of subsidy programs.

I. Introduction

This paper examines government subsidies in 68 countries during 1975-1990, with the objective of providing a description of patterns and trends. Evidence from recent studies suggests that government expenditures on subsidies remain high in many countries, often amounting to several percentage points of GDP. Subsidization on such a scale implies substantial opportunity costs.

There are at least three compelling reasons for studying government subsidy behavior. First, subsidies are a major instrument of government expenditure policy. Second, on a domestic level, subsidies affect domestic resource allocation, income distribution, and expenditure productivity, and may affect structural and sectoral adjustment by reducing the flexibility of the economy. Third, on an international level, increased international integration—through trade and the proliferation of multilateral and bilateral arrangements—brings about questions regarding the extent to which subsidies cause distortions in international resource allocation by affecting competitiveness.

This paper is structured as follows. Section II addresses issues concerning the definition and measurement of subsidies. Section III examines the use of government subsidies as a fiscal policy tool, and focuses on why and how subsidies are provided, as well as their economic effects. Section IV reviews empirical research on subsidies and discusses international trends and patterns in available subsidy data. Section V provides some thoughts on assessing the economic costs of subsidies and options for policy reform. Section VI concludes. Three appendices discuss data sources and their limitations, provide a detailed review of the results of previous research, and present some disaggregated subsidy data.

II. Subsidies: Definition and Measurement

1. What is a subsidy?

It has sometimes been argued that “the concept of a subsidy is just too elusive” to even attempt to define ( Houthakker (1972) ), and that the “definition of a subsidy, like that of beauty, varies with the beholder whose eye is focused on the object under scrutiny” ( U.S. Congress, House Committee on Agriculture (1972) ). Much in the same vein, Break (1972) has suggested that “whereas for most government spending programs it is only the benefits that are elusive and difficult to quantify, for subsidy programs it is frequently both benefits and costs.”

As a result, the fairly large body of research on government subsidies uses a variety of concepts to define a subsidy. In the most general terms, a subsidy can be defined as any government assistance, in cash or in kind, to private sector producers or consumers for which “the government receives no equivalent compensation in return, but conditions the assistance on a particular performance by the recipient” ( U.S. Congress, Joint Economic Committee (1972) ). It includes government operations that result in producers receiving higher returns than suggested by competitive market outcomes (“producer subsidies”), and consumers obtaining goods or services below their economic cost (“consumer subsidies”). This broad definition extends beyond the more narrow subsidy concepts that are employed in government budgets or national accounts, and it leaves room for a wide range of government activities to be defined as subsidies. 2/ However, such a broad definition is necessary to capture both explicit and implicit subsidy elements that are contained in different forms of government intervention.

While a wide array of government activity may contain subsidy elements, subsidies may be classified on the basis of the following seven categories:

direct government payments to producers or consumers ( cash subsidies or cash grants);

government guarantees, interest subsidies to enterprises, or soft loans (i.e., low-interest government loans) ( credit subsidies ):

reductions of specific tax liabilities ( tax subsidies );

government equity participation ( equity subsidies );

government provision of goods and services at below-market prices ( in-kind subsidies );

government purchases of goods and services at above-market prices ( procurement subsidies );

implicit payments through government regulatory actions that alter market prices or access ( regulatory subsidies ).

At least four caveats should be kept in mind with respect to this classification. First, the types of subsidies contained within each of the seven categories are not really homogeneous. Tax subsidies, for example, may take on different forms, including those obtained through tax exemptions, tax credits, tax allowances, special rate reliefs, tax deferrals, or the accumulation of tax arrears.

Second, some subsidies may, at least a priori, belong to several different categories. For example, consignment subsidies, that is, grants given to projects that are only repayable should the project turn out to be commercially successful, may, if the project is unsuccessful, be a cash grant, or, when the project is successful, become a credit subsidy when the interest rate is below the market rate.

Third, it leaves ample room for ambiguities and measurement problems. For example, overvalued exchange rates affect market prices and access, and, while they contain subsidy elements (e.g., to those who purchase imported goods), they also entail costs or negative subsidies (e.g., to exporters); the full extent of the subsidy element of overvalued exchange rates may be difficult to establish, even on a gross basis. Similarly, even rather simple things, like, for example, the subsidy element of granting specific companies or sectors the right for accelerated depreciation, may easily become cumbersome to calculate.

Fourth, the categories do not capture well intergenerational or multi-period aspects of subsidies, such as the subsidy elements that may be contained in immature social security systems.

To identify a subsidy, it is usually necessary to identify its beneficiaries, or to establish, in principle, who the beneficiaries are. For pure public goods, such as military defense, street lighting, or other goods that are characterized by nonexclusive consumption, it is not possible to identify specific beneficiaries. Regardless of how many beneficiaries there may be, it is impossible to consider something to be a subsidy as long as it is not possible to identify who the beneficiaries are. 3/

Still, there are few goods that are pure public goods, but even when they are, it may sometimes be possible to identify subsidies that arise in the process of producing a pure public good. For example, military defense is a pure public good and hence no subsidy element can be established; but government procurement of defense goods is not a public good, and this may give rise to procurement subsidies.

2. How to measure subsidies

There are different ways to measure subsidies, each of which has its own shortcomings. Examples of popular ways to measure subsidies are producer or consumer subsidy equivalents (PSEs and CSEs), budgetary cost (which can be measured either on a gross or net basis), and grant equivalents. 4/

Probably the conceptually most simple way to measure subsidies is to look at their budgetary cost. For example, the net budgetary cost of subsidies could simply be defined as gross budgetary outlays on subsidies minus any cost recovery, for example, through user charges or fees. However, government budgets only provide an incomplete picture of the full extent of subsidy outlays, as they may show subsidies either under the budget category “subsidies,” under various other budget categories, or not at all. More specifically, using government budgets for assessing subsidies has three main shortcomings.

First, the budget category “subsidies” does not contain all budgetary subsidies. In government budgets, only cash subsidies are classified as subsidies; other types of subsidies (i.e., credit, tax, equity, in-kind, procurement, and regulatory subsidies) are classified elsewhere in government budgets. For example, tax subsidies show up implicitly as reduced tax revenue, but not explicitly in the budget category “subsidies;” loans to state enterprises are frequently classified as “net lending” rather than subsidies, even when they have little prospect to be repaid, or are used to cover operating deficits of these enterprises.

Second, government budgets do not contain many types of operations that create subsidies. Hence, a significant part of subsidy operations is carried out “off budget.” Regulatory subsidies, for example, usually benefit one population group at the expense of another group. For instance, controlled consumer prices for basic consumer goods may benefit consumers at the expense of producers, without necessarily having a direct and immediate budgetary impact. Also, some subsidy operations, such as payments to cover operational losses of state enterprises, have often been kept “off budget.” Finally, national government budgets do not contain subsidization operations carried out through international organizations. For example, subsidy expenditures of the European Union that are carried out in the context of the common agricultural policy and are paid from the common budget are not included in the national budgets of European Union member countries. These operations are substantial: between 1973 and 1986, for example, expenditure by the European Agricultural Guidance and Guarantee Fund (EAGGF), which is the financial arm of the Common Agricultural Policy of the European Union, rose from ECU 4.1 billion to ECU 22.5 billion (0.7 percent of aggregate GDP of European Union member countries) ( Rosenblatt et al. (1988) ). 5/

Third, budgets do not show the full economic impact of current subsidy practices. For example, controlled consumer prices may have an immediate budgetary impact or not, depending on whether producers are reimbursed by the government for the difference between the free market price and the controlled consumer price. In many cases the budgetary impact may be delayed, even though, eventually, it will occur. For example, utility companies may be forced to sell electricity at artificially low prices, but then, at some point in time, may need “loans” from the government to cover their operating losses. In countries where the banking system is subject to considerable government interference, such loans to state enterprises are often given through banks. These directed lending operations to state enterprises usually have an adverse effect on bank profitability. It may be possible to roll over these loans, and thereby avoid giving explicit budgetary subsidies for some time. However, such policies are not sustainable, and usually leave behind a trail of bank restructuring and bad debt consolidation, and enterprise restructuring and reform.

Even a single implicit subsidy may assume significant quantitative proportions. For example, around 1993, tax arrears (a tax subsidy) amounted to between 5 and 10 percent of GDP in the Visegràd countries—the Czech and Slovak Republics, Hungary, and Poland ( Schaffer (1995) ). As the payment of tax arrears may not always be immediately enforced (in part because governments are usually reluctant to pursue firms into bankruptcy), and since inflation erodes the real value of taxes that are paid late, the cost of tax arrears to the budget is difficult to measure and can easily mushroom.

Observed subsidies, particularly when they largely rely on government budgets, typically measure but a fraction of the full extent of subsidization that exists in an economy at any point in time. But since it is almost impossible to know the full extent of subsidization, the available subsidy data have usually been confined to what can readily be observed and quantified. This approach is also used to compile subsidy data for the two major cross-country sources for data on subsidies, the IMF’s Government Finance Statistics (GFS), and the United Nations’ System of National Accounts (SNA).

Both GFS and SNA define subsidies as unrequited government payments to producers for current operations, plus the losses on sales of departmental enterprises, that is, government units that are engaged in commercial activities, such as a government printing office. GFS and SNA data on subsidies have three main shortcomings.

First, they only report cash subsidies. Hence, all other types of subsidies (i.e., those that fall in the six other categories that were established above) are excluded. For instance, free public education, a classical example of an in-kind subsidy, or tax holidays for investors, a classical example of a tax subsidy, are not recorded as subsidies.

Second, they only provide information on subsidy recipients, not beneficiaries. However, the subsidy recipients are often not the ultimate beneficiaries. For example, state enterprises that incur losses on account of controlled output prices may receive government subsidies, but the ultimate beneficiaries may be consumers (who pay lower prices), not the enterprises (which can only maintain low prices because the government covers their losses).

Third, they only provide information on payments to producers, and exclude from subsidies all payments to consumers that allow these consumers to obtain goods and services at prices below cost, like, for example, food stamps. All payments to consumers are lumped together under transfers to households, regardless of whether they constitute a subsidy or not. For example, pension payments, which are not a consumer subsidy, are classified as “transfers to households,” as well as expenditures on food stamp programs, which constitute a consumer subsidy.

There are some small differences between GFS and SNA data that are explained in some detail in Appendix I. For example, the GFS reports subsidies on the basis of budget execution data, while the SNA uses national income accounts data. 6/ In theory, GFS and SNA subsidy data could be used interchangeably, once corrected for the slight differences in definition. In practice, however, differences arise because (i) GFS data are reported on a cash basis, whereas SNA data are reported on an accrual basis; (ii) SNA data reflect the general government, whereas GFS data are largely confined to the central government since few countries report general government data on government subsidies. In addition, it should be noted that GFS data for subsidies per se are available for relatively few countries; much more common is for countries to report combined subsidy and transfer payments (including pensions), which accounts for the large difference between the SNA subsidies and GFS subsidies and transfers figures.

III. Subsidies as a Fiscal Policy Tool

1. Why subsidize? 7/

There are a large number of explanations as to why governments use subsidies as a policy tool. Houthakker (1972) , for example, has argued that at least some of it may have to do with logrolling, or vote trading. While pointing out that this is unlikely to result in an efficient allocation of resources, he suggests that it may nevertheless have political benefits since, “as we all know from birthdays and Christmas Eves, the exchange of gifts, even of rather useless gifts, frequently helps to stimulate good fellowship and a sense of community” ( Houthakker (1972) ).

From an economic perspective, the main purpose of subsidies is to reallocate resources, that is, to alter economic activity to achieve an outcome that is “more desirable” from what would occur otherwise. Hence, arguments for subsidies are often based on some concept of efficiency or economic justice. But even when subsidies generate a more desirable outcome, it does not mean that the entire value of the subsidy is corrective in nature, or that the particular type of subsidy used for a given purpose is best among the available policy alternatives.

Economic arguments for using government subsidies generally fall into three main categories:

offsetting various market imperfections;

exploiting economies of scale in production;

meeting social policy objectives, including, for example, protecting the poor, changing the distribution of income, and increasing or retaining employment.

The case for using government subsidies to offset various market imperfections is straightforward, as it is geared toward increasing efficiency. The argument applies to a case where markets do not allocate resources to their most efficient use, usually because the owners of these resources cannot reap their full return. In theory, a second-best policy tool, such as subsidies, may offset market imperfections. For example, free rider problems usually lead firms to underinvest in research and development (R&D) activities; subsidizing firms to undertake R&D activities would be one way to overcome this problem. Similarly, informational asymmetries can be viewed as an example of a market imperfection. Informational asymmetries, for example between borrowers and lenders of funds, can lead to market interest rates above the social rate of return. This would imply that socially profitable undertakings will not be implemented. A possible remedy is credit subsidies, provided, for example, through subsidized interest rates.

Similar arguments can be used to subsidize enterprises in order to obtain economies of scale in production. For example, when foreign-owned firms have a cost advantage because of their larger size, a government subsidy could allow a domestic firm to expand and overcome its initial competitive disadvantage, and, therefore, compete successfully in the long run. In theory, this could shift enough profits to the domestic firm (that can, in turn, be taxed) to justify the cost of the subsidy.

In both of the cases described above (offsetting market failures and obtaining economies of scale), successful subsidization means that the government is able to “pick winners.” Picking winners requires good analytical capacities, an in-depth knowledge of different industries and activities, and accurate foresight. In the case of R&D, for example, this would require knowing the likely future rates of return to different research projects. In the case of increasing international competitiveness by exploiting economies of scale, the government would need to evaluate the long-run costs and benefits of subsidizing different industries, and assess the long-term prospects for each competing activity. 8/ Furthermore, the analysis must consider the possibility that other countries retaliate; such “beggar-thy-neighbor” trade policies can exacerbate international trade tensions and lead to a counterproductive spiral of offsetting subsidies between trading partners.

Social policy objectives , such as a more equal distribution of consumption, provide important reasons for subsidies. Often, however, these goals are not accomplished or at least not accomplished at minimum cost. For example, many economies maintain generalized food subsidies in the form of fixed prices for essential staple goods as a social safety net device. Generalized food subsidies have the advantage of not generating “exclusion” errors, since nobody is excluded from receiving the benefit. At the same time, they generate “inclusion” errors, and therefore substantial waste, as many unintended beneficiaries (those who do not need the subsidy, or, more generally, the nonpoor) also benefit from the policy. In addition, they may easily generate a whole range of adverse supply effects.

In general, to have a chance of being successful, it is necessary (but not sufficient) that subsidy policies avoid generating rent-seeking behavior and be driven by economic, not political, considerations. Frequently, however, subsidies may benefit well-placed groups and distort incentives, which puts the desired resource allocation effects into doubt.

2. How countries subsidize

A given policy objective can usually be pursued through many different policy tools. Subsidization objectives are no different. Subsidies are intended to benefit specific groups of beneficiaries, but the extent to which they do frequently depends on how the subsidy is provided.

Bread subsidies, which exist in many countries, may be used to illustrate these points. The intended beneficiaries of bread subsidies are consumers, but the subsidy may be paid to either consumers or producers, and if it is given to producers, it may either be directed at inputs or outputs, or be given in the form of general operating support.

For example, consumers may receive coupons that they can apply toward bread purchases; bakeries which receive these coupons would submit them to the government for reimbursement. The size of the reimbursement would have to be close to the market value of the bread the coupons purchase in order for bakeries to continue to accept the coupons and supply bread in exchange. Alternatively, the government may fix the market price of bread at an artificially low level. To avoid undesired side effects, like, for example, a supply crunch or a black market for bread, the government must provide subsidies to bread producers, for example in the form of cash subsidies to bakeries that incur losses on account of controlled output prices. Alternatively, the government may provide support to these bakeries by requiring banks to provide loans to them. This would shift the burden of subsidization to the banking system, which is unlikely to be sustainable.

The government may also provide bread subsidies by subsidizing the inputs for producing bread (e.g., wheat or wheat flour). This may translate into lower consumer prices or higher profit margins for bread producers or both, and the extent to which the subsidy reaches the intended beneficiaries will depend on supply and demand conditions, and the market structure. Of course, input subsidies can be provided in different ways. A transparent way would be to pay a cash subsidy to producers for each unit of input purchased. Two examples of less transparent ways to subsidize are to sell, through a state enterprise, wheat to flour mills at a price below cost, or to give flour mills access to foreign exchange at a preferential exchange rate (e.g., via the central bank) to import wheat themselves. In these two cases, the subsidies are unlikely to show up in budget, but they contribute to the government’s quasi-fiscal deficit and have deleterious effects on the balance sheets of the state institutions involved. Finally, bread can be subsidized through producer price controls and export quotas on wheat and wheat products. In this case, an implicit tax is imposed on producers to match the implicit subsidy enjoyed by consumers. 9/

3. Economic effects of subsidies

The short-term economic effects of subsidies are closely linked to how they are provided. In the short run, subsidies may not be borne immediately by the government budget, bypassing any immediate burden on taxpayers or households. Ultimately, however, subsidies must be paid for. Therefore, it is important that subsidies are effective (i.e., reach their intended target group) and achieve a given objective at minimum cost (in terms of budgetary outlays and any economic distortions the subsides may cause).

In practice, subsidies are often ineffective and costly, regardless of whether they directly affect public expenditures (for example, cash subsidies or implicit subsidies that are hidden in other expenditure categories or provided through quasi-fiscal operations) or not (as in the case of tax or regulatory subsidies).

The economic effects of subsidies usually go beyond their explicit or immediately visible budgetary or quasi-fiscal cost. By severing the link between consumer prices and production costs, subsidies result in an inefficient allocation of resources if they are imposed on a competitive market where market imperfections or the opportunity of exploiting economies of scale do not justify their existence. 10/ These inefficiencies in resource allocation can also result in lower growth, as economic resources, such as capital and labor, are diverted from areas where their marginal productivity is highest.

Subsidies often have effects that are unintended by policymakers. Two examples may illustrate this point. Price subsidies generally affect the quantities demanded. For instance, introducing subsidies for imported foodstuffs that lower the consumer price for these goods may require a large increase in imports to avoid shortages; this, in turn, will also affect the availability of foreign exchange. Generalized subsidies for normal goods waste resources because they are not targeted, but they may also have distributive effects that are quite different from those intended by policymakers. For instance, price controls on agricultural products that lower the price below the competitive market equilibrium, will, in all likelihood, result in shortages if imports are not allowed to fill this gap. The shortage will provide opportunities to earn economic rents to well-placed groups that: have privileged access to the product at its controlled price. The poor—presumably the group that the price control seeks to protect—may frequently not be in a position of having privileged access to the subsidized product at its controlled price. The net result may be that, on average, consumers end up paying a price that is higher than the competitive market price, with the benefit of the price control policy accruing to traders. 11/

But even when economic rents are not present, subsidies may have unintended distributional effects. For example, if the supply of local housing is totally inelastic, housing subsidies may just increase land prices, without providing any benefit to home buyers ( Ford (1990) ).

IV. Empirical Evidence On Subsidies 12/

1. Previous research

Previous research on government subsidies has often originated in national administrations, and mostly been driven by concerns that subsidies and other special benefit programs were spinning out of control; For example, Break (1972) , in a study prepared for the Joint Economic Committee of the U.S. Congress, noted that “subsidy advocates have both a natural propensity and a remarkable ability to disguise the amounts of money involved in their programs.” Similarly, Houthakker (1972) , also writing for the Joint Economic Committee of the U.S. Congress, argued that subsidy programs need attention because political inertia and vested interest created by the subsidy programs tend to preserve such programs long after their initial justification (if indeed there was one) has disappeared. Putting it more bluntly, and probably echoing public sentiment, a recent article concluded that “where there are subsidies, there will be fraud” ( The Economist (1994) ).

Indeed, government subsidy practices have been an important public concern in many countries, developing and industrial countries alike. In the U.S., for example, efforts to reevaluate and control subsidy programs on a broad basis date back to the early 1970s, as evidenced by the studies commissioned by the U.S. Congress ( U.S. Congress, Joint Economic Committee (1972) ; U.S. Congress, House Committee on Agriculture (1972) ). Similarly, the German government is required to publish detailed periodic assessments of government subsidy practices in Germany ( Bundesministerium der Finanzen (1991 , 1989 , 1987 , 1985 , and nine earlier reports)). However, subsidy programs have also been a matter of concern in many developing countries, as evidenced, for example, by the many detailed analyses on government subsidy practices in India. 13/

These studies on national subsidy practices have been supplemented by cross-country studies by national administrations, such as the studies by Webb, Lopez, and Penn (1990) , and Roberts and Trapido (1991) for the United States Department of Agriculture (USDA), and the various background papers that have accompanied these works (e.g., Roningen and Dixit (1989) ).

Beginning in the 1980s, a number of international institutions turned their attention to the subsidy practices of their member countries. At least to some extent, this was the result of having noted that the gradual elimination of trade barriers could result in increased direct government support to their domestic industries (Gönenç (1990), Snape (1991) ). Examples of recent comparative works are the extensive surveys by the Commission of the European Communities (CEE) (1989 , 1990 , 1992 ), the European Free Trade Association (EFTA) (1986, 1990 ), and the OECD (1983 , 1990 ), but there has also been much research activity in other institutions, notably the World Bank and the IMF. 14/ This research activity has been accompanied by various background papers, such as those of Bruce (1990) , Grossman (1990) , and Winters (1990) for the OECD, or the study by Hufbauer (1989) for EFTA. It also resulted in a number of papers that use the data generated in international organizations, examples being the recent studies by Eales (1989) , Tigner (1989) , and Peraldi (1990) that have come in addition to a growing body of independent comparative research ( Hufbauer and Shelton Erb (1984) , Pinstrup-Andersen (1988) , Goldsworth (1989) , and Gerritse (1990) ).

2. International patterns and trends

Using data availability over 1975-90 as the only criterion for country selection, 60 countries were chosen from the SNA database and 68 countries from the GFS database. 15/ Table 1 provides data on SNA general government subsidies and GFS central government subsidies and other current transfers as a share of GDP, for different categories of countries. 16/ The SNA data suggest that subsidy expenditure differed sharply across country groups. The socialist economies of Eastern Europe had the highest subsidy outlays, and spent, on average, 9.4 percent of GDP on subsidies during 1975-90, compared to a worldwide average of subsidy expenditure of 2.5 percent of national output. Industrial countries averaged higher subsidy expenditure than developing countries, with the nations of the European Union (EU) spending more than other industrialized nations.

SNA Subsidies and GFS Subsidies and Other Current Transfers as Percent of GDP, 1975-90 1/

1/ GFS data comprise both subsidies and other current transfers, which, among others, includes social security spending. SNA data only comprise subsidies.

2/ The aggregate category “Developing countries” does not include Israel and South Africa, although these two countries are included in their respective geographical country groups.

Within the group of developing countries, Middle Eastern and North African countries, on average, had more than double the subsidy outlays relative to GDP of Asian, African, and Western Hemisphere countries. Table 1 reveals a number of interesting trends in subsidy expenditures during 1975-90. 17/ The pattern experienced in many regions was rising subsidy expenditure until the early 1980s, and a downward trend thereafter. Especially sharp declines in subsidy spending were experienced during 1988-90, particularly in Eastern Europe. For the country sample as a whole, the subsidy/GDP ratio declined from a peak of 3.0 percent in 1981 to 2.1 percent in 1990.

Trends in subsidy expenditure varied substantially across country groups. For the industrial nations as a whole, the SNA subsidy/GDP ratio reached its peak in 1983 at 3.2 percent, after having risen from 2.9 percent in 1975; by 1990, however, this had declined to just 2.7 percent of GDP. The changes in spending in the EU were more dramatic, as the subsidy/GDP ratio rose by almost a full percentage point from 1975 through 1984, but with a decline in this ratio from 1985 through 1989. In the developing economies, the subsidy/GDP ratio reached its peak in 1980, and fell erratically afterwards.

Movements in the subsidy/GDP ratio, however, were quite heterogenous across developing countries. In Africa, for instance, subsidies appear to have hit their nadir in the early 1980s, and have been rising since then—precisely the time period during which other developing countries (especially in the Middle East and North Africa) were reducing outlays. Both small, low-income economies and heavily indebted countries reduced subsidies substantially during 1983-90, although subsidy expenditure in these countries were significantly below the averages for developing economies at the onset of the debt crisis. Given these divergent trends among country groups, at first glance it does not appear that world economic conditions are the dominant factor in explaining trends in subsidy expenditures as a share of GDP.

The GFS data in Table 1 reveal somewhat different patterns and trends for government subsidies and transfers than just described for the SNA subsidy data. The data give evidence of the large share of GDP and central government outlays devoted to transfers in industrialized economies. 18/ The industrialized countries spent more than twice as much of GDP on subsidies and transfers than any other country group, except Eastern Europe.

The GFS data indicate that subsidy and transfer spending in the industrial countries was some 1.8 percent of GDP higher in 1983-90 than 1975-82. This stands in sharp contrast to the SNA data on subsidies, which show a slight decline in industrial country subsidy outlays in 1983-90 compared with 1975-82. 19/ Nevertheless, both the SNA and GFS data appear to share a common long-run trend, with expenditures peaking in the early 1980s and declining slightly thereafter. In the developing economies, a similar pattern is evident, with subsidy and transfer expenditure as a share of GDP hitting its highest levels in the early 1980s but tailing off throughout most of the remaining years in the decade. In Eastern Europe, GFS-measured outlays on transfers plus subsidies declined during 1981-90, but not as much as the fall in subsidies alone, which suggests that transfers rose over the period as a fraction of GDP.

Unlike the SNA data on subsidies, the GFS data indicate that spending on combined subsidies and transfers, for many country groups, are somewhat sensitive to the business cycle. The GFS series shows sharp upward spikes in spending during the economic downturn in 1982 and the slowdown of growth in 1990 in the industrial countries. This most likely reflects the countercyclical nature of transfer payments. While this is to be expected in the industrial countries, it is surprising to see a similar effect in the developing countries as well.

Table 2 provides data on subsidies and transfers spending as a share of central government expenditure, and reveals that these outlays have tended to increase as a share of central government spending during 1975-90. The sharpest increases were experienced in the industrial countries, where the share of spending devoted to subsidies and transfers rose by over 5 1/2 percentage points between 1975 and 1990. In the developing economies, the share of spending devoted to subsidies and transfers has fallen slightly since the mid-1980s, as it has in Eastern Europe.

GFS Subsidies and Transfers as Percent of Central Government Expenditures and Net Lending, 1975-90

1/ The aggregate category “Developing countries” does not include Israel and South Africa, although these two countries are included in their respective geographical country groups.

Table 3 provides information on individual country expenditure on subsidies (SNA definition) and subsidies and transfers (GFS definition), all relative to GDP. Also, the table shows the ranking of each country, as well as the standard deviation divided by the mean, which gives an indication of how much subsidy spending has tended to fluctuate within countries relative to mean values from year to year.

Country Rankings for Average SNA Subsidy and GFS Subsidy and Transfer Expenditures as a Share of GDP, 1975-90 1/

1/ Data for the years 1975-80 are not available for the Eastern European countries included in this study (Poland, Hungary, and Yugoslavia). The reported averages reflect the 1980-90 period.

The share of GDP absorbed by subsidies varied widely across countries; according to the SNA data, spending ranged from a high of 17.2 percent of GDP in Hungary to a low of less than 0.1 percent of GDP in Nicaragua and Paraguay. Table 3 indicates that 8 of the 10 countries devoting the highest share of GDP to subsidies, according to the SNA data, are in Europe and Eastern Europe (Ireland, Norway, Sweden, Greece, Luxembourg, Belgium, Hungary, and Poland), with the two exceptions being Israel and Egypt.

The GFS data present a fairly similar picture: 6 of the 10 countries with the highest subsidy/GDP ratio, on the basis of the SNA data, are also among the 10 biggest subsidizers according to the GFS data. The country ranking shows that some countries are ranked rather similarly in both data sets, while others are ranked rather differently. For example, Norway is ranked 6th in both sets; and Hungary is ranked first in the SNA data set and second in the GFS data set. But rankings can also be very different. For example, the Netherlands are ranked first in the GFS data set, but only 20th in the SNA data set; this may be due to high expenditures on social security compared to other countries, as well as a relatively smaller degree of intervention via cash subsidies. The reverse case also exists. Egypt, for example, is ranked number 5 in the SNA data set, but is number 19 in the GFS data set; this is the result of extensive subsidization (relative to other countries) and rather small social expenditures. 20/ Nevertheless, GFS and SNA data are highly correlated, with an overall correlation coefficient of 0.65 for 1975-90, which would suggest that subsidies and transfers are not, generally, close substitutes.

Changes in SNA subsidy payments from year to year are not necessarily correlated with movements in the GFS data for subsidies and transfers. For those countries that are contained in both data sets (56 countries), the coefficient of correlation between the SNA and GFS time series ranged from -0.81 (South Korea) to 0.96 (Poland). For 26 countries the coefficient of correlation is positive and statistically significant; for 8 countries a negative and statistically significant relationship holds, implying that there were some offsetting movements between other current transfers and subsidies. For the other 22 countries, there was no statistically significant relationship between the SNA and GFS data for the 1975-90 period. A negative coefficient of correlation would, for example, result when cash subsidies to enterprises (included in both SNA and GFS data) decrease, while social payments to households (only included in the GFS data) increase to more than offset the decrease in subsidies to enterprises.

The data on the standard deviation/mean ratio indicate a wide divergence across countries ( Table 3 ). For the entire SNA country sample, the average value of this measure of volatility is 0.36. Developing countries show more variation in subsidy spending (0.49) than the industrial countries (0.20), reflecting the greater progress of the developing countries in reducing subsidies from their peak levels of the early 1980s. GFS data on subsidies and transfers tend to show less variability relative to their mean values over the 1975-90 time period than the SNA data, although the absolute change in this spending (as measured by standard deviations) is greater.

In general, total cash subsidy expenditures do not seem to be influenced by trends in international commodity prices. This may suggest that the range of cash subsidy programs is probably too broad to be influenced by a single price. For example, using SNA data for 56 countries for 1975-90, econometric tests revealed that only 7 countries (Brazil, Canada, Cyprus, Egypt, Poland, Nicaragua, and Turkey) had a positive and statistically significant relationship (at the 0.10 confidence level) between oil prices and the subsidy/GDP ratio. Similarly, only 3 countries (Brazil, Cyprus, and Iceland) showed a positive and statistically significant relationship between wheat prices and the subsidy/GDP ratio. 21/

V. Reform Options

From an economic perspective, subsidies can only be justified under very specific circumstances; in most cases where subsidies have been used, they would be difficult to justify on purely economic grounds. In practice, subsidy programs are often costly in terms of their fiscal and quasi-fiscal burdens and the distortions they cause in resource allocation, and not very effective in reaching their intended target group of beneficiaries. It is usually difficult to measure the overall burden on the economy of government subsidies, and to exercise effective control over subsidy programs, as subsidies are provided in a variety of forms. Direct payments to consumers and producers are only a small fraction of overall subsidies provided by governments.

In assessing the economic burden of subsidies and options for reform, attention should be focused on the following five areas:

Increasing transparency . Transparency is desirable both from public and private perspectives to identify the benefits, beneficiaries, and costs of individual subsidy programs. When these cannot be readily identified, subsidy control and reform will be hampered. Often, there exists a tendency to provide subsidies through extra-budgetary instruments, such as government marketing boards, parastatal agencies, and specific extra-budgetary funds. Sometimes, governments may reduce transparency unintentionally. For example, in transition economies, such as Poland, governments have made drastic reductions in cash subsidy payments to state enterprises, also to impose hard budget constraints; but subsidies consequently resurfaced in an implicit form as tax arrears ( Schwartz (1994) ).

To increase transparency, subsidies should preferably be provided in the form of cash, and directly by the government budget. When subsidies are provided in any other form than cash or by institutions outside of the central government, transparency usually suffers. For example, consider the case of housing subsidies that are provided through state-owned financial Institutions in the form of subsidized interest rates for housing loans. Those who stand to gain or lose under such a system are hard to identify, because these programs are ultimately financed by a combination of budgetary transfers or net lending to the institutions that provide the low-interest loans, higher interest rates in other sectors of the economy, and reduced profit margins for financial institutions. In addition, the recipients of a subsidized housing loan often may not readily recognize the benefits of the program. A better alternative would be to use cash subsidies, that is, lump-sum payments that cover a fraction of the housing cost, and are provided directly from the budget to the beneficiary. This would provide a clear and explicit picture of the amounts involved, which, in turn, provides a basis for judging the affordability and desirability of the subsidy.

However, increasing transparency can only be a first step in reforming subsidies, as transparency in itself is not a remedy, even though it may have beneficial side effects when it brings to the open costs and benefits of subsidy programs. In the housing loan example just used, the ultimate beneficiaries of the program may actually be the landowners who benefit from higher land prices. If the housing loan initially creates an excess demand for land, landowners would almost certainly siphon off part of the subsidy provided to home buyers. Therefore, enhanced transparency would be just a first step for being able to identify beneficiaries and analyze the fiscal costs of subsidies.

Enhancing cost effectiveness . Not all subsidies are bad subsidies. However, to be “good,” subsidies have to be effective (that is, reach their intended target group), and achieve their objective at minimum cost in terms of their fiscal burden and efficiency losses. For example, generalized subsidy programs for normal goods, which promise to supply unlimited amounts of the subsidized goods to anyone who wishes to buy them, usually meet the first criterion but not the second and the third, that is, they are effective in that they reach their target group, but they are often highly distortive and come at a considerably greater cost than necessary. A common feature of such schemes it that the nonpoor receive a greater absolute subsidy per capita than the poor, although, relative to income, the subsidy amount received by the nonpoor is smaller than that received by the poor (World Bank (1990)).

It is often possible to reduce the cost of existing subsidy programs while still attaining the same policy objectives. 22/ Take, for example, the case of a generalized subsidy that is intended as a social safety net device. In most cases, generalized subsidies can be replaced with targeted cash transfers to reach vulnerable groups such as pensioners, the unemployed, and families supporting a large number of children. This will not only reduce the budgetary cost of social protection, but also reduce the distortions associated with subsidies. If cash transfer instruments are not available, then food coupons, allowing a limited quantity of consumption per person, may be a viable option for reducing social protection costs. Even when subsidies are not targeted by income or categorical group, savings may be obtained by setting consumption quantities for the coupon program equal to the consumption level of poor groups. This will avoid the regressive incidence of benefits (in absolute terms) often associated with generalized subsidies. 23/ The distortionary effects of food subsidies can also be minimized if coupons are denominated at the full market value of the commodities in question, rather than offering the right to purchase the commodities at a subsidized price. If generalized subsidies are to be maintained as a social protection instrument, targeting can be achieved by subsidizing inferior goods.

Experience has shown that making use of means-testing, self-targeting, or categorical targeting of recipients usually does not significantly reduce the effectiveness of subsidy programs (for example, by creating exclusion errors), but increases efficiency and reduces the fiscal burden compared to generalized subsidies. Means-testing on the basis of income or wealth, or any other individual assessment mechanism, may often be impractical, particularly when it requires significant organizational, administrative, and logistical capacities. However, simple means testing, for example on the basis of self-reporting and without systematic verification of income, has often been shown not to be overly inaccurate, particularly when it can be combined with elements of self-targeting and low benefit levels ( Grosh (1994) ). Sophisticated means testing is usually only advisable when benefit levels are high, the potential applicants literate and in the formal sector, and the basic administrative and organizational apparatus already in place. Social worker evaluations and proxy means tests, which calculate eligibility on the basis of a series of variables that may include housing characteristics and location, family structure, occupation, education, gender of household head, and ownership of durable goods, may often be practical alternatives to simple or sophisticated means testing.

In general, self-targeting mechanisms, which essentially rely on the opportunity cost of time used for obtaining benefits, social stigma, and subsidization of products that only the poor are likely to want, should be used as part of any subsidy program, whenever feasible. Still, it has to be kept in mind that self-targeting can at least potentially discourage participation among the poor and lower the net benefit that a subsidy program bestows ( Grosh (1994) ).

Limiting duration . Concerns for the duration of any particular subsidy program arise because economic agents alter their behavior in order to capture the benefits of subsidy programs. Beneficiaries may also resist exclusion from subsidy programs when their circumstances change. It is this behavior that, over time, renders many subsidy programs ineffective and excessively costly. 24/ Therefore, effective subsidization over time requires periodic reassessments of the rationale for the subsidy, and, if needed, revision, retargeting, or elimination. For example, some countries have used subsidies to increase the use of underutilized production inputs, such as fertilizers in agricultural production. An increased demand for the subsidized input would indicate that it is time to reduce or eliminate the subsidy. For some subsidy programs, duration should clearly be limited from the outset. Subsidies to encourage infant industries or to cushion the undesirable effects of a price shock, for example, should be declared temporary from the very beginning. Some countries have begun to limit from the outset not only individual subsidy programs, but also the life of institutions that provide these subsidies.

Strengthening cost control and cost recovery . To improve cost control, it is imperative first to know exactly what these costs are. When subsidies are provided directly from the government budget, it is easier to gauge these costs. Once the costs of individual subsidy programs are known, in a second step then, cost control could be enhanced. Often, this may be accomplished by frequent program reviews and improved targeting. However, it may sometimes also be possible to control costs by improving pricing policies, for example by introducing cost recovery measures. For example, some countries provide irrigation services to farmers for free or at below-cost. Irrigation services are often provided in the form of a generalized subsidy, implying that they are available to both poor and rich farmers alike. Introducing full-cost user charges for all farmers would raise revenues and reduce costs. While the demand for irrigation services by rich farmers is unlikely to decline much, the demand for irrigation services by poor farmers could be kept up by transferring part of the revenues from user charges back to the poor farmers. Cost-recovery policies can, of course, also be implemented for other subsidies (e.g., government-provided agricultural fertilizers), and for various types of social spending, for example in health and education.

Selecting a pragmatic approach . Subsidy programs must be consistent with the institutional and administrative capabilities of the government in question. Implicit subsidies are usually more difficult to administer and control than explicit subsidies, because their fiscal burden is not as readily apparent to policymakers. Hence, to improve administration and control, subsidy programs should be made as explicit as possible. In some cases it may only be possible to phase out or reform subsidy programs over a number years. For example, some countries heavily subsidize university education while paying less attention to primary education. Furthermore, they subsidize institutions, rather than students. A pragmatic approach to reform could seek gradually to shift away from subsidizing universities as institutions and toward subsidizing primary school students via direct student loans.

VI. Conclusions

Governments provide subsidies to achieve different policy objectives, including offsetting market imperfections, exploiting economies of scale, and meeting various social policy objectives. Subsidies can take many forms, including direct; government payments to producers or consumers (cash or explicit subsidies), low-interest government loans (credit subsidies), various types of reductions in tax liabilities (tax subsidies), government equity participation (equity subsidies), government provision of goods and services at subsidized prices (in-kind subsidies), government purchases of goods and services at above-market prices (procurement subsidies), and different types of regulatory actions that alter market prices or access (regulatory subsidies).

Measuring subsidies is complicated, as each of the various available options has its own shortcomings. A popular way of measuring subsidies is to look at their budgetary cost. However, many subsidies do not result in explicit and contemporaneous budgetary costs. This occurs for two main reasons. First, subsidies may be provided implicitly, as in the case of tax relief for certain producers. Second, the budgetary impact of these subsidies is delayed, as in the case of a below-cost energy tariff that may eventually (but not necessarily immediately) necessitate a budgetary payment to the energy company to cover operating losses.

Measuring subsidies is particularly problematic in a cross-country context, simply because what can readily be observed or inferred may only be a small fraction of what is actually spent, and this fraction may differ from country to country. More by necessity than by choice, empirical work has often relied on rather pragmatic subsidy definitions that allow for ready quantification, as in the case of cash subsidies. This is also the case for two popular data sources on subsidies, the IMF’s GFS and United Nations’ SNA, which define subsidies rather narrowly as cash payments to producers for current operations.

The SNA data on subsidies (to producers) reveal that the subsidy/GDP ratio rose in most regions during the late 1970s, with a declining trend starting in the mid-1980s. Movements in the subsidy/GDP ratio were quite heterogenous among developing countries during 1975-90; in Africa, for instance, subsidies appear to have hit their nadir in the early 1980s, and have been rising since then—precisely the time period during which other developing countries (especially in the Middle East and North Africa) were reducing subsidy outlays. Both small low-income economies and heavily indebted countries reduced subsidies substantially during 1982-1990.

GFS data, which cover both subsidies and other current transfers, indicate that these outlays in the industrial countries reached their peak in the early 1980s, and declined slightly thereafter. In developing countries, the GFS data track the changes in the SNA data, with subsidies and transfers rising until the early 1980s, but tailing off throughout most of the remaining years of the decade.

Subsidies impose substantial burdens on the economy, both in terms of fiscal costs and adverse effects on efficiency. In assessing the fiscal burden of subsidies and options for reform, attention should be focused on increasing transparency, enhancing cost effectiveness, limiting duration, strengthening cost control, and selecting a pragmatic approach to subsidy policies.

APPENDIX I The Data: Sources and Limitations

The data used in this study were taken from two main sources: The IMF’s Government Finance Statistics (GFS) database and the United Nation’s System National Accounts Statistics (SNA) database. IMF staff estimates were used in some years to reduce the number of missing observations.

Conceptually, there are no substantial differences between the GFS and SNA definition of subsidies. The GFS defines government subsidies as all unrequited, nonrepayable government transfers on current account to private industries and public enterprises (GFS (1986)), and, among others, also include the cash operating deficits of departmental enterprise sales to the public. More precisely, but essentially not very differently, the SNA defines government subsidies as:

“current unrequited payments that government units, including nonresident government units, make to enterprises on the basis of the levels of their production activities or the quantities or values of the goods and services which they produce, sell or import. They are receivable by resident producers or importers. In the case of resident producers they may be designed to influence their levels of production, the prices at which their outputs are sold or the remuneration of the institutional units engaged in production. Subsidies are equivalent to negative taxes on production in so far as their impact on the operating surplus is in the opposite direction to that of taxes on production. Subsidies are not payable to final consumers, and current transfers that governments make directly to households as consumers are treated as social benefits. Subsidies also do not include grants that governments make to enterprises in order to finance their capital formation, or compensate them for damage to their capital assets, such grants being treated as capital transfers” ( Inter-Secretariat Working Group on National Accounts (1993) ) .

In practice, that is gauging the data that are available, there exist significant differences between the GFS and SNA. These result from three main differences that are shown in Table 4 .

Definition and Coverage of GFS and SNA Data Used

First, while the SNA data only include payments to private and public enterprises and exclude payments to households, the available GFS data, for most countries, do not distinguish between subsidies and other current transfers. For example, in the GFS database, only 7 out of 68 sample countries provided a disaggregation of subsidies and other current transfers during 1985-90. Hence, compared to SNA subsidy data, GFS subsidy data also include current transfers, that is, nonrepayable and unrequited payments to households for current purposes (social benefits). It should be noted that GFS data at the central government level—those utilized for this study—also include transfers to other levels of government (e.g., state and local government).

Second, there is a difference in the way data are recorded in the GFS and SNA. While GFS data are recorded on a cash basis, SNA data are recorded on an accrual basis and also include imputed transactions that involve in-kind payments pertaining to (but not necessarily taking place in) the current period.

Third, GFS and SNA data have a different institutional coverage. The GFS data are only available for the consolidated central government; subsidy data for the rest of the general government are only available for a few countries. The consolidated central government covers central government units that are part of the general central budget, and central government units with their own budgets, for example, social security funds. In contrast, the SNA data are for the general government and also include transactions with supranational organizations (like, for example, agricultural subsidies received from the Commission of the European Union).

For the purpose of this study, SNA data are preferable to GFS data. Roughly speaking, the available SNA data are more representative of the extent of subsidization than the available GFS data, which, as it aggregates subsidies and other current transfers, are more an indicator of the influence of the central government over social and economic matters. Generally, there is a difference in the rationale behind subsidies and other current transfers: given that other current transfers also reflect social benefits given to households, they are more part of the general process of redistributing income, whereas subsidies reflect the government’s policy objectives with regards to different economic activities. Also, as the GFS data are confined to the consolidated central government, they are influenced by the degree of centralization and fiscal responsibilities assigned to different levels of government.

However, while they include all sectors of the economy, both SNA and GFS data sets only cover cash subsidies. This is problematic, particularly since various subsidization tools are often close substitutes. Hence, a comparison of cash subsidies alone does not provide a good picture of the overall degree of subsidization in the economy. Recent studies that have used a broader definition of subsidies (albeit for a smaller set of countries and sectors) have shown that non-cash (implicit) subsidies account for a large share of total subsidies (CEE (1990)): for example, while, on average during 1981-86, Greece and Denmark had similar levels of overall subsidies (an annual average of about ECU 1 billion during 1981-86), Greece provided 95 percent in the form of cash grants, while in Denmark this was only 44 percent. 25/

In sum, both SNA and GFS data sets have their advantages and disadvantages relative to the data sets used in other studies. The advantage of both data sets is that, in principle, they cover all sectors of the economy in all countries. The disadvantage of both data sets is that they only cover explicit (cash) subsidies, and, hence, exclude all implicit (noncash) subsidies. In addition, the GFS data set has the disadvantage that it cannot distinguish between subsidies and other current transfers, which implies that various large categories of transfers, such as transfers to households (social benefits), cannot be separated from subsidies.

APPENDIX II Previous Research: Review and Comparison

A. Alternative sources of information and their general comparability

Starting in the early 1980s, various multilateral organizations began to launch major surveys on government subsidies in order to increase transparency and identify national practices that were of likely interest to their member countries. The outcome of these efforts was a number of major surveys, including three surveys on “state aids” by the Commission of the European Communities (CEE (1989 , 1990 , 1992 )), periodic surveys on “government aids” by European Free Trade Association (EFTA (1986, 1990 )), two surveys on “industrial support policies” by the Organization for Economic Cooperation and Development ( OECD (1990 , 1992) ). In the case of the OECD, these surveys have been accompanied by a number of studies by OECD staff that use some of the same data. 26/

The surveys by major multilateral institutions are complemented by various reports by national administrations. In most cases these reports were largely intended to analyze the country’s own subsidization practices, but they sometimes contain comparisons of own practices with those of other countries, like for example the periodic reports by the German Ministry of Finance (1991, 1989, 1987, 1985, and 9 previous reports). In some cases, the reports by national administrations were produced with the intention of providing a cross-country comparison of national subsidization practices, such as the various reports originating in the USDA. 27/

In general, all these surveys and studies provide useful alternative sources of information (ASIs), and the results may be compared to those contained in the SNA and GFS databases. Table 5 presents an overview on the coverage of the main ASIs. A comparison between these and the SNA and GFS data used in this study is complicated by several differences, particularly in the types of transactions that are covered, sectoral coverage, measurement basis, time periods covered, and country coverage. In addition, some ASIs present information in a way that does not allow for inter-country comparisons.

As regards the transactions covered . most alternative sources of information rely on a broader definition than the “cash” definition of subsidies used by the SNA and GFS. For example, the CEE (1989, 1990, 1992), EFTA (1986, 1990 ), and OECD (1990 , 1992) all include, in addition to cash subsidies, subsidies arising from soft loans, government guarantees, and equity subsidies. In addition, tax subsidies are included in the CEE (1989, 1990, 1992) and OECD (1990 , 1992) studies.

Summary of Coverage in Various Databases and Recent Studies

1/ For the purpose of this paper.

2/ Level of government taken into account varies depending on the country.

However, while the set of transactions covered in the GFS and SNA is narrower than in the ASIs, the subsidy levels shown in some ASIs is surprisingly low. In the EFTA study, for example, for the 6 EFTA countries and for the 1981-86 time period, subsidies reported by the SNA exceed those in the EFTA study by between 4 times (Austria) and 35 times (Switzerland) ( Table 6 ). 28/

As regards the sectoral coverage , most ASIs include in their definition of subsidies only payments to producers, and exclude all transfer payments, such as transfer payments to households (which, for practical purposes, were included in the GFS data used in this study). An exception are the consumer subsidy equivalent (CSE) calculations in the various studies on agricultural subsidies originating in the USDA (e.g., Webb, Lopez, and Penn (1990) , and Roberts and Trapido (1990) ). Given these similarities of restrictions, the ASIs only cover specific sectors (e.g., industry, agriculture), whereas the GFS and SNA data cover all sectors.

The more narrow sectoral coverage (compared to GFS and SNA) may explain at least part of the surprisingly low subsidy levels that are found in some ASIs. For example, the EFTA study which was just mentioned above, only considers subsidies to industry (manufacturing, energy, fisheries, mining), but excludes subsidies to agriculture. The CEE surveys (1989, 1990, 1992) only contain national agricultural subsidies, but exclude those awarded to individual member countries under the common agricultural policy (CAP). CAP subsidies are provided through the common European Union budget, which, in turn, is financed by member states. 29/ The CEE surveys also exclude other subsidies that could be potentially large in magnitude, like, for example, subsidies to infrastructure, procurement subsidies, all subsidies to energy (except for coal, which is included), subsidies to transport (except for railways and inland waterways, which are included) (CEE (1992)).

As regards the measurement basis , most ASIs, just like GFS and SNA, focus on the recipients of subsidies rather than the ultimate beneficiaries. Nevertheless, the various ASIs try to address the inherent problems of the gross expenditure concept used in the GFS and SNA. In practice, it was not always possible to apply one and the same measurement concept to all subsidy programs, and, hence, a number of compromises had to be made.

The EFTA surveys (1986, 1990 ), for example, use the concept of “net cost to the government,” as compared to the gross data of the SNA. EFTA’s “net cost” concept differs from the GFS and SNA concept in that it takes into account all repayments, gains, or results of recovery operations. For example, under a gross concept, the full amount of government spending on equity participation could be considered a subsidy. In contrast, under the net concept, equity subsidies are calculated as the difference between the cost of government borrowing and any dividends received; reductions in the value of equity capital (e.g., write-offs) are added to cost; losses or gains on sales of shares are taken into account. 30/

The subsidy definition used in the CEE surveys is similar to EFTA’s definition: in theory, the concept of “net grant equivalent” is used. In practice, this results in a mixed bag of assessment methods. For example, the subsidy element of loans awarded under an exchange rate guarantee scheme was calculated as the benefit of the scheme to the recipient. However, in cases where this information could not be calculated it was substituted by the financial losses to the government under the scheme; for simple export financing schemes, the net cost of the scheme was used.

The various studies originating in the USDA (e.g., Webb, Lopez, and Penn (1990) ) are based on producer and consumer subsidy equivalents (PSEs and CSEs) for individual commodities in the agricultural sector. The PSE (CSE) measures the value of transfers from government policies to producers (consumers). In practice, the PSE of a given subsidy scheme is just the sum of all subsidies resulting from price support schemes, direct income transfers, and all other budgetary support (net). 31/ An advantage of the PSE over alternative measures is that is captures both the transfers from government expenditure and the transfers from price distortions. Still, by equating expenditures with benefits, it also falls short of providing a beneficiary-based evaluation of subsidies. 32/

As regards time periods and countries covered , the ASIs focus on a shorter time period and fewer countries than what is available in the GFS and SNA databases. However, since there is full overlap with GFS and SNA data regarding the time periods and a significant overlap regarding the country coverage, it is fairly easy to extract from the GFS and SNA those time periods and countries needed for comparison.

Still, the information contained in some ASIs does not lend itself to cross-country comparisons. Particularly the two OECD (1990 , 1992) surveys report their findings in a way that makes it impossible to compare national support levels: levels of subsidy expenditures in individual OECD member countries are not shown, and, instead, only growth rates of real subsidy expenditures and the weights of specific subsidy components in the total subsidies of a specific country are provided. Similarly, the various studies by OECD staff (e.g., Ford (1990) , Ford and Suyker (1990) , and Gonenc (1990) ) also present data in a way that does not allow one to identify levels of subsidization. When studies that originate in the OECD provide information of the levels of subsidization, they are entirely based on OECD national accounts data which, in principle, are the same as the SNA data.

Compared to the OECD surveys that intentionally refrain from comparing levels of subsidization, the various EFTA and CEE surveys were prepared with the explicit intention to compare subsidization practices across countries. Still, there are ample warnings with regard to the comparability of results across countries in both the CEE and EFTA surveys. As pointed out in EFTA’s 1986 survey, “differences in budgetary practices imply that comparisons between countries are not necessarily straightforward” (EFTA (1986)). Still, while recognizing these flaws, both the EFTA and CEE surveys suggest that the data presented should give a reasonable basis for comparing the amount of government subsidies.

The main shortcoming of all available data sources, not only the data contained in the SNA and GFS databases, is their focus on specific sectors and types of subsidies. However, given the task at hand, this is unavoidable.

Nevertheless, the results of some of the ASIs are surprising. For example, the low levels of subsidization in the EFTA survey are difficult to understand, particularly when compared to the SNA data which are already based on a rather narrow definition of subsidies ( Table 6 ). Given existing definitional differences, the low subsidization levels in the EFTA surveys may either be interpreted as implying that the majority of subsidies are outside of the industrial sector (for example, in agriculture) or that the net cost to government is small compared to the gross cost, which is unlikely.

OECD national accounts data, which were substituted in Table 6 for the OECD survey data since the latter do not give subsidization levels, coincide almost exactly with SNA data, as should be expected. The only country for which there is a substantial (and inexplicable) difference is Belgium. Similarly, the data reported by the German Finance Ministry (BMF), which are in turn based on OECD national accounts data, largely coincide with the SNA data, with the major exceptions being Belgium, France, Italy, and the Netherlands, for which the BMF shows substantially lower subsidization levels than either the SNA or the OECD.

Subsidy Levels in Six Recent Studies and Surveys, 1981-86

(Averages for 1981-86, in percent of GDP)

1/ The data reported here refer to OECD national accounts statistics; OECD subsidy data using the concepts of gross government budget expenditures (GGBE) and net cost to government (NCG) have not been published in a way that would allow identifying levels of subsidization relative to GDP.

2/ Based on OECD data.

The CEE surveys produce results that are also somewhat compatible with the SNA data, notwithstanding the broader subsidy definition used in the CEE surveys and the narrower sectoral coverage. For example, for 1981-86, 3 of the 4 EU countries with the highest level of subsidies relative to GDP, according to the CEE (1990) survey (Belgium, Luxembourg, and Ireland) are also among the top 4 in the SNA database. Similarly, 3 of the 4 EU countries with the lowest level of subsidies relative to GDP during 1981-86 (the U.K., the Netherlands, and Germany) are also among the bottom 4 EU countries in the SNA database ( Table 7 ). Both the rank order and the subsidy levels reported in the CEE (1990, 1992) surveys and the SNA database are, generally, of a similar order of magnitude. For 1981-86, subsidy to GDP ratios in the SNA database range from 0.8 times the level reported in the CEE (1990) study (Germany and Luxembourg) to twice the level (the Netherlands and Greece); for 1986-88 the range is 0.8 times (Germany) to 3 times (Ireland and Denmark); for 1988-90 it is 0.9 times (Germany) to 3.5 times (Ireland).

Subsidies in the European Union: Comparison of CEE and SNA Data

(Averages for 1981-86, 1986-88, and 1988-90, In Percent of GDP)

The direction of change in subsidization levels is more often the same in the two sources of information than it is not. For example, during 1981-90, the direction of change in the level of subsidies (relative to GDP) is the same in 7 of 10 countries. Only for Denmark, Germany, and the Netherlands do the CEE survey data suggest a slight decrease in the level of subsidies relative to GDP during 1981-90, whereas the SNA database records a slight increase.

B. Subsidization objectives

While the SNA and GFS data provide little information on the policy objectives for government subsidies, various studies and surveys have tried to categorize the different objectives. While, again, categories and measurement differ, there are 5 broad policy objectives that are generally considered: support for research and development activities (R&D), support to small and medium enterprises (SME), trade-related subsidies, sectoral support (usually to declining industries, e.g., steel, coal mining, and shipbuilding), and regional development.

Tables 8 and 9 show the policy objectives of government subsidies according to the CEE (1990, 1992), EFTA (1990) , and OECD (1992 ) surveys.

Policy Objectives of Government Subsidies in the European Union

(In Percent of Total Subsidies)

According to the CEE survey, 6 of the 12 EU countries maintained the same main policy objective during 1981-90 (Germany, Ireland, Italy, Luxembourg, Spain, and the U.K.), while the other 6 countries switched their major policy objectives at least once. For 5 of the 6 countries that maintained their major policy objective throughout the 10-year time period, regional support was the dominant objective of government subsidies.

However, while regional purposes were the overall dominant policy objective of providing subsidies in EU member countries, sectoral objectives became increasingly important during the 1980s. While, during 1981-86, subsidy practices in no EU member country were dominated by sectoral objectives, during 1988-90, sectoral objectives dominated subsidization practices in three countries (Denmark, Portugal, and Spain), and, by the end of the decade, it was clearly the second-most important subsidy policy objective behind regional objectives. Hence, it is difficult to follow the CEE (1992) conclusion that there has been “a shift away from sector-specific interventions to more horizontal and regional support,” which is furthermore celebrated as a welcome trend: the CEE’s own data does not seem to allow for this conclusion.

The OECD survey shows a rather similar pattern. Apart from “other objectives” which in the OECD study combines crisis aid, general investment aid, and employment training support, and was the dominant subsidization objective in 8 OECD member countries during 1986-89, regional support was the main objective for providing government subsidies in 5 OECD member countries (Canada, Finland, Germany, Ireland, and Italy). This was followed by R&D support, which dominated the subsidy agenda in 4 countries (Denmark, Iceland, Japan, and the Netherlands), and sectoral objectives, which dominated in three countries (Australia, France, and Spain).

Policy Objectives of Government Subsidies in the OECD and EFTA

The EFTA survey is fairly consistent with the other two surveys, in that “other objectives” (which, in the case of EFTA, means general subsidies, employment subsidies, enterprise-specific subsidies, and structural adjustment and rescue operations) and regional objectives also dominate the subsidization agenda.

In comparing the three surveys, it seems that it is in the “other” objectives where the differences are particularly large. In all three surveys, these other objectives include general subsidies, which are probably the least targeted ones. While in the OECD and EFTA surveys these other subsidies loom large, averaging 45 and 51 percent of total subsidies respectively, the CEE survey shows these subsidies as being relatively small, that is, no more than 16 percent of the total during 1981-86, and declining.

Another interesting aspect of the three surveys are the results regarding the classical economic objectives of subsidization, that is, market failure, and the existence of economies of scale in production, that is, a situation where unit production costs fall as the volume of output rises. 33/ R&D expenditures and support for SMEs to overcome initial competitive disadvantages, particularly versus foreign-owned firms, are probably most closely associated with these so-called economic objectives of subsidies. Other types of subsidies, particularly regional and sectoral support policies, may often contain a large number of elements that cannot readily be associated with efficiency objectives.

While the CEE survey suggests that there has been a slight increase in the share of total subsidies devoted to these economic objectives, from 15 percent of the total during 1981-86 to 20 percent during 1988-90, all three surveys agree that they are still relatively minor compared to the other objectives, amounting to no more that 10 and 9 percent of the total in the OECD and EFTA studies, respectively. Nevertheless, the three surveys suggest that in Denmark, Iceland, the Netherlands, Japan, Switzerland, R&D received the major share of subsidies, even though in Denmark sectoral objectives did overtake R&D as the major policy objectives in the late 1980s.

C. Subsidization tools

Cash subsidies, the only type of subsidy considered in the SNA database, are the most important component of total subsidies in the CEE, EFTA, and OECD surveys. According to the CEE surveys (CEE (1990)), on average during 1981-88, EU countries gave 58 percent of total subsidies to manufacturing industries in the form of cash grants (CEE (1990)). According to the EFTA surveys, 52 percent of all subsidies by EFTA member countries during 1984-87 were made In the form of cash grants ( Ford and Suyker (1990) ). Finally, the OECD survey ( OECD (1992) ) suggests that, during 1986-89, 54 percent of all subsidies by OECD member countries were given in the form of cash grants.

All surveys showed significant differences in the use of cash subsidies across countries. For example, for 1986-88, the CEE survey (CEE (1990)) suggests that Greece provided 88 percent of all subsidies to manufacturing in the form of cash subsidies, while Portugal provided only 26 percent of all subsidies in that form ( Table 10 ). Similarly, during 1984-87, the EFTA (1990) suggests that Switzerland and Finland each provided over 70 percent of all subsidies in the form of cash grants, while Austria and Iceland provided less than 30 percent of all subsidies in the form of cash grants ( Table 11 ). Finally, according to the OECD (1992) , Canada, Iceland, Italy and the Netherlands gave over 90 percent of all subsidies in the form of cash grants, while Belgium, Spain, and the U.S. provided less than 10 percent of all subsidies in the form of cash grants.

The CEE surveys suggests that, during 1981-86, cash subsidies were the preferred subsidization tool in 7 out of 10 EU countries (Belgium, Greece, Ireland, Italy, Luxembourg, the Netherlands, and the U.K.); tax reductions were the preferred subsidization tool in Germany, and soft loans were the preferred tool in Denmark and France ( Table 10 ). In addition, government guarantees were used heavily in Belgium, and equity participation was relied on heavily in Italy. No data are available for Spain and Portugal, which were not EU members at the time. During 1986-88, cash subsidies became the preferred subsidization tool in 10 out of 12 EU countries. The other two countries, Germany and Portugal, provided subsidies mostly in the form of tax reductions.

Subsidization Tools Applied to the Manufacturing Industries in European Union Countries 1/

(Period Averages. In Percent of Total Subsidies)

The EFTA surveys provide a rather similar picture of the preferred tools of subsidization. While subsidy definitions and the scope of EFTA surveys are different from the CEE surveys, 34/ during 1984-87, 4 of the 6 EFTA countries (Finland, Norway, Sweden, and Switzerland) provided government subsidies largely in the form of cash subsidies; the remaining 2 countries provided subsidies either largely in the form of equity participation (Austria) or in the form of guarantees (Iceland) ( Table 11 ).

Subsidization Tools Applied in EFTA and OECD Member Countries

The OECD surveys show that the use of different subsidization tools varies significantly across its member countries. Using the more recent survey ( OECD (1992) ), that estimates subsidies in terms of their net cost to government, 13 of 22 OECD member countries (Australia, Canada, France, Iceland, Ireland, Italy, Japan, the Netherlands, New Zealand, Norway, Portugal, Sweden, and the U.K.) preferred cash subsidies as the dominant subsidization tools during 1986-89; in 4 countries (Finland, Germany, Turkey, and the U.S.) tax subsidies were used as the preferred tool for providing subsidies, in one country (Austria) equity subsidies were the preferred tool, and in 3 countries (Belgium, Denmark, and Spain) so-called mixed instruments dominated among the various subsidization tools ( Table 11 ). The OECD study also suggests that countries may rapidly switch between different subsidization tools. For example, in Belgium during 1986-88, tax subsidies amounted to less than 1 percent of all subsidies, while in 1989 they amounted to 100 percent. A priori, however, such drastic shifts in individual magnitudes would seem implausible, and, barring the lack of details in the OECD study, it is not clear whether this reflects a shift in policy or data inadequacies.

While the surveys by the CEE, EFTA, and the OECD suggest that cash subsidies are the preferred subsidization tool, and that, overall, the relative magnitude is similar in EU, EFTA, and OECD member countries, for individual countries the surveys often provide a quite different picture of the use of individual instruments. For example, the EFTA (1990) survey suggests that during 1984-87, on average, Switzerland provided 77 percent of all subsidies in the form of cash grants, while Iceland only provided 27 percent of all subsidies in the form of cash grants. In contrast, the OECD (1992) suggests that during 1986-89, on average, Switzerland provided only 14 percent of all subsidies in the form of cash grants, while Iceland provided 100 percent in the form of cash grants. Similarly, the CEE (1990) suggests that during 1986-88, on average, Spain provided 78 percent of all subsidies in the form of cash grants, while the OECD (1992) suggests that during 1986-89, on average, Spain provided only 7 percent of all subsidies in the form of cash grants.

How accurate and comprehensive are the available data? Clearly, different data sources often provide a wide range of estimates for subsidies, depending on measurement and coverage. A single best source of data does not exist. Ford and Suyker (1990) report that in Germany in 1986, for example, estimates on the extent of subsidization ranged from 2.2 percent of GDP (national accounts) to 6.1 percent of GDP (estimate by economic research institutes), with the Government’s own periodic reports suggesting an overall total of 3.7 percent of GDP (BMF (1989)). Similarly, in India, national accounts show that total subsidies in 1987 amounted to about 3.5 percent of GDP, while an alternative estimate ( Mundle and Rao (1991) ) suggests that, in the same year, subsidies to the rural sector alone amounted to a minimum of 6.0 percent of GDP. Other available estimates ( Asha (1986) or Gulati (1989) ) also show levels of subsidization that significantly exceed the national account estimates.

Certainly, the SNA or GFS definitions of subsidies, with their focus on cash grants, do not capture the large range of implicit subsidies, which, as the two examples show, may be significant. Neither, however, do the CEE, EFTA, or OECD surveys present a complete picture, be it because of their focus on certain sectors, short time periods, or because of their exclusion of certain types of subsidies that may be quantitatively important. Also, all the surveys and studies considered here suffer from the fact that subsidies are measured on a country by country basis, which overlooks the fact that producers may also obtain subsidies directly from multilateral institutions. These payments may be substantial, as in the case of transfers to agricultural producers in the European Union, which in 1987 alone amounted to ECU 54 billion ( Webb, Lopez, and Penn (1990) ) 35/ , more than the annual GDP of Greece.

Clearly, to the extent that countries use various subsidy tools as close substitutes, the inclusion or exclusion of certain instruments could have a significant effect on the measurement of subsidies. For SNA-based studies, substitution between subsidy tools is probably a more serious problem than it is for more broad-based studies. For example, to the extent that governments consider cash grants and implicit subsidies to be substitutes, SNA based studies not only underestimate the full extent of subsidization, but are also bound to provide a distorted picture of trends in subsidization policy when.governments can readily switch back and forth between direct and indirect instruments.

APPENDIX III

Countries and Country Categories Considered in this Study

(G) indicates that data are only available under the GFS definition of subsidies and transfers.

(S) indicates that data are only available under the SNA definition of subsidies.

SNA Subsidies as Percent of GDP, 1975-1990

GFS Subsidies and Transfers in Percent of GDP, 1975-1990

1/ The aggregate category “Developing countries” does not include Israel and South Africa, although these countries are included in their respective geographical country groups.

GFS Subsidies and Transfers as Percent of Central Government Expenditures and Net Lending, 1975-1990

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Ms. Hugounenq was assigned to the Fiscal Affairs Department under the 1993 summer internship program. She is a graduate student at DELTA/ENS in Paris. We would like to thank Ke-young Chu and Peter Heller for helpful comments, and Tarja Papavassiliou for assistance with the data used in this study. The views and opinions expressed here are the authors’ and do not necessarily reflect those of the International Monetary Fund.

For example, this definition does not allow one to make the distinction between subsidies and transfers that is used in government budgets and national accounts.

Even it were possible to identify fully the beneficiaries of pure public goods, it would be difficult to establish the subsidy element or the total benefits derived. In theory, benefits should be measured on the basis of a beneficiary’s marginal rate of substitution between the public good and the composite consumption of all other goods. This approach would imply that one would first need to assume that public goods are provided optimally, then decide on utility functions, and finally allocate benefits on the basis of these utility functions. This procedure is, of course, fraught with largely arbitrary assumptions, particularly with respect to the assumed utility functions.

For a discussion of these different concepts, see Appendix II.

It is unclear whether subsidies provided through the European Union were a substitute for national subsidies, as overall budgetary subsidies of European Union member countries also increased during that period (see Appendix III, Table 13 ).

Recall that in national income accounts, GDP at market prices equals GDP at factor cost minus subsidies plus indirect taxes.

This section draws heavily on Ford (1990) , and Ford and Suyker (1990) .

For an exposition on the difficulties in applying strategic trade policy, see, for example, Krugman (1994) .

These price controls are not optimal from the consumer’s standpoint, given their Impact on production, and therefore consumption, and the foregone consumer surplus associated with lower production and consumption.

For an elaboration on the standard microeconomic analysis of the effects of subsidies on resource allocation, see, for example, Hyman (1993) .

With sufficient competition among traders —provided that traders can directly (albeit illegally) purchase the controlled product from producers—the resulting equilibrium price may approximate the free market price.

See Appendix II for a more elaborate discussion of major recent studies.

See, for example, Asha (1986) , Gulati (1989) , Mundle and Rao (1991) , and Jha (1991) .

See Myers and Brondolo (1986) , Jimenez (1989) , and Mayo and Gross (1989) , for examples of recent cross-country studies originating in the World Bank, and Tait and Heller (1982) , Heller and Diamond (1990) , Holzmann (1991) , and Mackenzie (1991) ) for example’s of recent studies originating in the IMF.

Data from national authorities were also used to supplement the GFS and SNA data. See Appendix I for a further description of the data.

These GFS and SNA data should be interpreted with due caution, as differences in measured subsidy outlays across different countries may reflect differences in how subsidies are provided (e.g., explicitly through the budget or implicitly through noncash means), rather than actual disparities in the level of subsidization.

Also see Table 13 for individual years.

Available GFS data for the time period 1985-90, for example, indicate that industrial countries spent on average 12.14 percent of GDP on transfers to nonprofit institutions and households, compared to an average of 0.96 percent of GDP in Africa; 1.69 percent of GDP in Asia; 7.23 percent of GDP in the Middle East and Africa; and 2.70 percent of GDP in the Western Hemisphere.

Some caution should be used in directly comparing the GFS and SNA data, given differences in country coverage and the fact that the GFS data only cover central government outlays.

For further analysis of subsidy expenditure in Egypt see, for example, Fouad (1991) .

These results are based on simple OLS regressions of the subsidy/GDP ratio on a constant and the price, in U.S. dollars, of petroleum and wheat, respectively, separately for each country, with the appropriate corrections made to address serial correlation. The results of these regressions should be interpreted with caution, due to possible bias caused by omitted variables, and the fact that calendar year data on commodity prices may not always correspond to the fiscal year data used for subsidies.

For further elaboration, see, for example, Chu and Gupta (1993) , Grosh (1994) , and Expenditure Policy Division Staff (1995) .

See Grosh (1994) for a review of several studies on the incidence of food subsidies by income group.

If economic agents respond instantaneously, then the effectiveness of a subsidy is reduced in the initial period as well.

In the CEE study (CEE (1990)), which is largely confined to the manufacturing sector but employs a broad definition of subsidies, subsidies in Greece and Denmark during 1981-86 averaged 2.5 and 1.3 percent of GDP, respectively. In the SNA data set, which includes all sectors but is confined to cash subsidies, subsidies in Greece and Denmark, during the same time period, amounted to 5.0 and 3.1 percent of GDP, respectively (see Table 7 ).

See, for example, Ford (1990) , Ford and Suyker (1990) , and Gönenç (1990).

See, for example, Webb, Lopez, and Perm (1990) , or Roberts and Trapido (1990) .

The years 1981-86 were selected because data for these years were available in all studies. Choosing an earlier or later time period would not lead to substantially different conclusions.

In national budgets, these contributions are usually recorded as transfers to international organizations, not as subsidies. For a further discussion on the subsidization mechanisms under the CAP, refer to Rosenblatt et al. (1988) .

For further detail see EFTA (1986), pp. 16-17.

In general, the PSE for a specific good can be calculated as Q* (P d - P w )+D+I , where Q denotes the output volume produced, P d and P w are domestic and world market prices (expressed in domestic currency), respectively, D denotes direct government subsidy payments (cash subsidies), I denotes other budgetary support (e.g., indirect transfers through marketing support and other non-cash subsidies, net of any fees or levies paid). Sometimes, the PSE is measured as a “Percentage PSE,” which simply implies dividing the above expression by Q* P d +D and shows the degree of government support relative to the total cash value of production. This approach was chosen in the publications originating in the USDA.

In addition, the “percentage PSE” calculations presented in the studies originating in the USDA do not lend themselves to a comparison with other ASIs, mainly because they are limited to agriculture, carried out on an individual commodity basis, and because they measure the degree of subsidization only relative to the overall value of the specific commodity.

See, for example, Ford (1990) for a discussion.

For example, the EFTA study is based on net costs instead of gross costs, and it excludes tax concessions.

About 96 percent of these transfers to producers are the result of price intervention schemes, 3 percent result from income support payments, and 1 percent from infrastructure support and marketing assistance.

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Government Subsidies

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Opening the box of subsidies: which is more effective for innovation?

Regular Article
Published: 30 January 2021
Volume 11 , pages 421–449, ( 2021 )

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Shiyuan Liu 1 ,
Jiang Du 1 ,
Weike Zhang ORCID: orcid.org/0000-0002-5176-4460 2 &
Xiaoli Tian 3

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Government research and development (R&D) subsidy is one of the main policy instruments to deal with market failure, and its effectiveness has attracted attention increasingly. This study investigates the impact of two types of government R&D subsidies on innovation using the data of Chinese listed enterprises from 2010 to 2016. We find that compared with ex-post rewards, ex-ante grants have a better effect on innovation performance by stimulating private R&D investment. Additionally, the effectiveness of government R&D subsidies is weakened in enterprises engaging in rent-seeking and political connections. This study provides a new perspective for understanding the effect of government R&D subsidies, and the research conclusions are the relevant reference for the government to improve the efficiency of allocating public funds.

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Can government subsidy strategies and strategy combinations effectively stimulate enterprise innovation? Theory and evidence

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Data source: National Bureau of Statistics of China http://data.stats.gov.cn

The industry distribution of the sampled enterprises is shown in Appendix A. In this study, 79.1% of the sampled GEM enterprises belong to high-tech industries defined by the China National Bureau of Statistics.

CSMAR database: http://www.gtarsc.com/ .

RESSET database: http://www.resset.cn/ .

The larger the index ( Mkt ) is, the higher the level of marketization will be. Since the indexes for 2015 and 2016 have not been announced, this study adopts data from 2008 to 2014 to estimate the marketization index in the former two years using the exponential smoothing forecasting method.

National Bureau of Statistics of China: http://data.stats.gov.cn

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This work was supported by the National Natural Science Foundation of China (Grant No.: 72004150) and the Fundamental Research Funds for the Central Universities of Sichuan University (Grant No.: skbsh2020-06).

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Appendix A: Industry distribution of the sampled enterprises

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Liu, S., Du, J., Zhang, W. et al. Opening the box of subsidies: which is more effective for innovation?. Eurasian Bus Rev 11 , 421–449 (2021). https://doi.org/10.1007/s40821-020-00178-2

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Received : 16 May 2020

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Accepted : 08 December 2020

Published : 30 January 2021

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DOI : https://doi.org/10.1007/s40821-020-00178-2

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The Definition of Subsidy and State Aid: WTO and EC Law in Comparative Perspective

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3 Introduction: Governmental Intervention in the Economy and Subsidies

Published: December 2009
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This chapter focuses on the forms of public intervention in the economy that may in general give an economic advantage and may thus in principle be covered by subsidy or State aid rules. In doing so, and in particular in distinguishing those cases that are commonly held to be subsidies (those of ‘financial assistance’) from those that are more controversial (those of ‘regulation’), an attempt is made to provide a conceptual explanation of this different treatment. This is followed by the examination of the legal techniques that can be used to determine the legal relevance of governmental action in the context of a definition of subsidy. A final section concentrates on the significance of the objectives of the measures, and maps their position within the issue of the definition of subsidy.

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Home > Graduate School > ETD > DISSERTATIONS > 1243

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A three part essay on the role of government subsidies on firm level innovative capacity a synthesis of effect, productivity and profitability..

ASSYAD Al-Wreikat , Southern Illinois University Carbondale Follow

Date of Award

Degree name.

Doctor of Philosophy

First Advisor

SYLWESTER, KEVIN

Second Advisor

BURNETT, ROYCE

Our analysis contains three essay that look to the effect of innovations given a diverse set of measures. The purpose of the first chapter, which is divided into two levels, is two-fold. First, via a Probit model, we assess the effect subsidies have on firm level innovation and determine if these effects impound in firm level performance given these subsidies. Second, we seek to determine the extent subsidies promote learning and hence positively impact outcomes associated with innovation. We do so by employing the theory of absorptive capacity to guide efforts to isolate the effects that the combination or interaction of investments in research and development and foreign technology have on firm level performance. We adopt Ordinary Least Squares in this endeavor. In the second chapter, we measure, evaluate, and assess whether external infrastructures allow firms to exploit their resources in order to gain maximum efficiency and effectiveness. In this regard, we review the impact infrastructures have on the capacity of a firm to innovate when those infrastructures are viewed as key to the operations of a firm. In doing so, our goal is to assess the impact of these infrastructures components on firm performance. We do so through a Probt model. The purpose of third chapter is to determine the role innovation has in firm performance. Our approach, in essence, seeks to assess the role innovation and subsidies, individually and collectively, have on firm performance. Our study makes use of ordinary least squares regression to investigate this query.

Since October 26, 2016

This dissertation is only available for download to the SIUC community. Current SIUC affiliates may also access this paper off campus by searching Dissertations & Theses @ Southern Illinois University Carbondale from ProQuest. Others should contact the interlibrary loan department of your local library or contact ProQuest's Dissertation Express service.

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Governments today are facing serious, seemingly intractable public management issues in the aftermath of COVID-19 that go to the core of effective governance and leadership, testing the very form, structure, and capacity required to meet these problems head-on. Leaders have found it necessary to go beyond established parameters and institutional structures, working across organizational boundaries in pursuit of multilayered, networked approaches that better respond to system and societal shocks brought by the pandemic.

In fall 2020, the IBM Center for The Business of Government initiated a Challenge Grant competition soliciting essays from academics and practitioners describing how government can best transform the way it works, operates, and delivers services to the public in light of the impact of the COVID-19 pandemic. Edited by Center Leadership Fellow Michael J. Keegan, COVID-19 and its Impact: Seven Essays on Reframing Government Management, features selected commentary on sustaining transformation and increasing resilience. ICMA's Tad McGalliard, director of research and development, and Laura Goddeeris, director of survey research, are among the contributing authors. Their essay draws upon ICMA survey research in exploring which pandemic-driven innovations and operational changes might prevail in a post-pandemic environment.

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"The key to transformation is not to lose momentum and fall back on the old ways, when potentially innovative practices and programs are still evolving from the crisis." -- Tad McGalliard, ICMA director of research and development

Key takeaways from this report include:

The pandemic accelerated changes in the way government works and delivers services that were already underway. This change has unlocked opportunities to build a new civic future.
Local leaders will need to address numerous policy issues raised by these changes in work environments and service delivery. Fostering a more flexible and outcome-driven culture will contribute to a new model of success for government.
Expectations of individuals and communities will focus on access to continued online services even after conditions merit reopening of government facilities. Building a hybrid operating model to engage with citizens that adopts consistent standards for customer experience will be necessary for successful government performance.
Cities and counties across the country are leading the way in understanding how to deliver COVID and other services to communities in need, who suffer disproportionately during the pandemic.
Governments must anticipate risks and develop data-driven programs to mitigate risks, respond to events, and be resilient in the aftermath of inevitable threats—physical and cyber—that face agencies at all levels.
Unprecedented demand on public procurement in response to the COVID-19 pandemic reveal significant vulnerabilities in government supply chains and procurement processes. The pandemic offers the opportunity to consider how governments can make contracting more resilient going forward.

Essays featured in this compendium:

Five Ways COVID-19 Changes How Local Governments Do Business, by Richard Feiock
The Future of Work in Local Governments Beyond COVID-19, by Sherri Greenberg
Transforming Local Government Service Delivery in the Wake of COVID-19, by Tad McGalliard and Laura Goddeeris
Community Driven Government—Reimagining Systems in a Pandemic, by Maya McKenzie and Gurdeep Gill
COVID-19 and the Resilience Imperative in Public Procurement: Building Back Better, by Zach Huitink
Achieving Supply Chain Immunity: Planning, Preparation, and Coordination in National Emergency Response, by Rob Handfield
Trust and Resilience: How Public Service Principles Encouraged Compliance with COVID-19 Public Health Guidelines in New Zealand, by Rodney Scott and Eleanor Merton

You may be interested in related resources from ICMA survey research:

COVID-19 Impacts on Local Governments (complete survey summary), July 2020
New ICMA Survey Shows Depth of Economic Downturn for Cities and Counties, July 2020
New Data Estimates Local Governments Will Spend Up to $20 Billion On COVID-19 Actions, March 2020
Government Technology Solutions Survey (complete survey summary), 2017

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Relief operations refer to logistical or material assistance that is provided to people in reaction to humanitarian catastrophe instigated by both manmade and natural factors. NGOs are voluntary organizations that are, usually, non-profit and independent from government that engages them in societal and developmental issues (Cahill, 2003). They play various roles in disaster management, for example, by offering rescue and material aids to affected people. Another area of focus of NGOs is fund raising and establishing temporary camps for the affected population. Besides, they help in building resilience for a community so as to enable it to prepare for future shocks and stresses. NGOs play a major role in preparing a community and mitigating the calamity risks. On the other hand, the government offers rescue help to the affected population through military services (Auarberch, menon&.Noris, 2010). In case of a disaster, the government deploys its military forces to rescue people. The government also offers financial and material aid such as provision of food to the affected population.

The extent to which NGOs and government relief operations should consider the influence of their relief on settlements

The NGOs and the government should greatly consider the effects of their relief operations on settlement so as to avert further injuries and deaths, because the quality of shelter greatly influences health and well-being of a person. In most cases, relief operations result into higher density of population (Bennet, 2014). This, in turn, leads to poor sanitation and inadequate water supply, and diseases outbreak. Therefore, the government has a responsibility of ensuring that the place settled by the affected population has good sanitation so as to prevent death cases brought about by health hazards. The settlement area should have good access to clean drinking water and good refuse disposal mechanisms. Moreover, the place should be secure so as to prevent the population from internal and external attacks that would lead to deaths and injuries. The place should be free from climate, water-related hazards and attacks in order to ensure that the people are safe.

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In addition, the place should be accessible so that there can be ample supply of food and other necessities to the affected population. This will reduce death cases that may occur due to starving and also lack of health care facilities. If the place is not accessible, the government should undertake the necessary actions so as to upgrade the emergency settlement.

Do relief operators have a responsibility of helping the government to encourage the dispersion of the affected people?

Relief operators have a responsibility of helping the affected government to encourage sheltering the people to be relocated since the government may not be able to offer all the relief services alone. Moreover, the relief operators should help the government in identification of the best place to settle and relocate the affected population (United Nations, 2002). Although, government departments are the ones that coordinate distribution and relocation of work, the relief operators also should play a major role in assisting the affected people.

It is also important to note that the relief operators have a responsibility of identifying the best storage area for the donated materials. They should also provide the government with help in choosing the best transport systems for transportation of both materials and people.

Was the relief organization involved in the Katrina relief operations effective in managing their influence to settlement?

Relief organizations involved in the Hurricane Katrina relief operations were efficient in managing their influence to settlement; the organizations relocated the affected population to all the fifty states, hence, reducing overpopulation. As the affected population was relocated to other states, a massive displacement was felt all over the country (Charteoff, 2008). Although, relocation led to the largest housing crisis, many organizations, like the Housing and Urban Development Department, offered housing units nationally. By distributing the affected population to fifty states, the organizations prevented spread of diseases due to poor sanitation.

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To common knowledge, this act of distributing the affected population to fifty states was a consideration to enable the organization to prevent high density of population. Since the relief organization was able to avoid high population density in some areas, hence, it succeeded in prevention of all health hazards associated with high density of population.

The importance and limitation of using military resources for foreign disaster relief operations

Significance of using military resources for foreign disaster relief operations is hard to overestimate. One of the main reasons is that through coordination and monitoring, the resources can rescue many people in large scale disasters. The military resources, usually, have capabilities that make them very efficient in rescuing and maintaining lives in extreme disasters. They also have the necessary equipment and discipline needed in saving and maintaining life processes.

Further, the military resources also help in maintaining security during the relief operations. In areas where people are affected by natural or other kinds of disasters such as terrorism, the military resources become of great help since they secure the place, hence, enable other relief organizations such as NGOs to perform rescue operations (Chandra et al, 2009).

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The use of military resources also minimizes the costs connected with the outcomes of natural disaster for population (Davidson, 1996). The military resources aim at rescuing as many people and property as possible which ensures minimization of the losses incurred; hence the human cost of disaster is also reduced.

Nevertheless, there are limitations that are attributed to foreign military resources deployed to provide humanitarian relief to another country. One of them is that the foreign military must be monitored at all times in order to ensure that they do not use illegal substances that may put the lives of the affected population in danger. Such include: drugs and dangerous weapons. Moreover, such operations require sufficient funding; the affected country spends a lot of money in order to hire the foreign military forces and their resources.

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All relief operations must be LGU-certified – DILG

Reference : https://www.dilg.gov.ph/news/All-relief-operations-must-be-LGU-certified-DILG/NC-2020-1133

All operations on the distribution of relief goods from any private citizen and other entities must be coordinated to the local government units (LGUs) to ensure that no enhanced or general community quarantine (ECQ) protocols are violated according to the Department of the Interior and Local Government (DILG), DILG Secretary Eduardo M. Año said that all efforts and assistance for the people are welcome so long as it complies with all ECQ measures so that it doesn’t cause the spread of COVID-19 in communities. He says that “there is no problem in wanting to help just ensure that LGUs are notified.” “Helping is a beautiful deed, lalo na ngayong tayo ay nasa krisis. The ECQ measures are enforced not to curtail the people’s rights, especially the ones who are generous to share and help their fellow Filipinos, but to ensure that coronavirus doesn’t spread, at itong pagtulong sa pamamagitan ng pamimigay ng relief goods ay hinihikayat, dapat lang ay ipaalam sa mga LGU,” Año said. According to the DILG Chief, in some cases relief operations of private entities are conducted without a certification from the LGU and that the people distributing and handing the relief do not have quarantine passes. He said that one of the basic guidelines imposed by the Inter-agency Task Force on the Emerging Infectious Diseases (IATF) and the DILG under the ECQ is that no individual is allowed to go out of the house without a quarantine pass. “Yung ibang mga nagdi-distribute ay walang quarantine pass, hence, they are not authorized to get out of their residence. Nakita ko din na may mga paglabag sa physical distancing at mayroon na din mga bata at senior na lumabas ng bahay,” Año explained referring to violations committed in a relief operation green-lighted without the authorization of the LGU. Año said that the conduct of relief distribution without the LGUs’ knowledge heightens the risk of further infection and community transmission, defeating the purpose of restricting people’s movement to contain the spread of COVID-19. He also said that some groups disguise their illegal activities under the cover of relief operations in order to sell illegal drugs, hoard medical items, and conduct propaganda activities. He also assured the public that LGUs will be responsive in processing the certification of relief organizations and their volunteers amid the COVID-19 pandemic. “Nakakasigurado po kayo na mabilis na aaksyunan ng mga LGUs ang mga bagay na ito. Hindi naman sila hahadlang na mabigyan ng karagdagan pang tulong ang ating mga kababayan,” he said. He also said that Secretary Año has already instructed the Philippine National Police to strictly implement this policy to ensure that only authorized individuals are outside of their residence.Facebook

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Electricity tariff increase will reduce subsidies by N1.14 trillion in 2024 – NERC

”all other customers under band b to e service category and representing 85 per cent of customers population will not be affected...".

The Nigerian Electricity Regulatory Commission (NERC) says the newly approved tariff is expected to reduce subsidies for the 2024 fiscal year by about N1.14 trillion. The Vice Chairman of NERC, Musliu Oseni, said this in a statement in Abuja on Wednesday.

”With the newly approved tariffs, subsidies for the 2024 fiscal year are expected to reduce by about NGN1.14 trillion in furtherance of the Federal Government’s realignment of the subsidy regime,” he said.

Mr Oseni said that the federal government had indicated a transition in policy direction towards introducing a more targeted subsidy regime aimed at mitigating the impact of changes in macroeconomic parameters “while largely protecting vulnerable customers and fostering investments targeted at providing efficient service delivery in the Nigerian Electricity Supply Industry (NESI).”

According to him, the commission conducted a thorough review of the tariff applications submitted by the 11 Electricity Distribution Companies (DisCos) in line with the processes established in its regulations and business rules.

He said that the review process was preceded by an analysis of the performance improvement plans of the licensees and included a public hearing during which interested stakeholders and intervenors examined the rate filing submitted by the public utilities.

”The overarching objective of the commission in the consideration of the tariff application is the creation of a financially sustainable electricity market providing adequate and reliable power supply to drive the Nigerian economy.

”The commission, upon due consideration of the tariff applications, has approved revised rates affecting only customers classified under Band A category which is about 15 per cent of the customer population,” he said.

Mr Oseni said that empirical service data had confirmed that this class of customers had truly received the committed level of service.

He said that under the revised tariff order issued by the NERC, DisCos were under an obligation to provide customers classified under the Band A service category a minimum average supply of 20 hours a day measured over one week.

”All other customers under Band B to E service category and representing 85 per cent of customers population will not be affected by the current review of end-users tariffs.

ALSO READ: UPDATED: Nigerian govt announces hike in electricity tariffs

”All DisCos have been provided with mandatory targets for investments and migration of more customers to Band A.

”The commission has established a robust monitoring framework leveraging on technology to ensure that the public has visibility of the service covenant with their service providers,” he said.

Mr Oseni said that an enforcement and compensation mechanism had also been established in the event of service failure.

”We wish to assure all Nigerians that the commission working in collaboration with the policymakers remains committed towards providing adequate and reliable electricity to all citizens.

”This is as we work diligently with state governments to deliver on the gains of the Electricity Act 2023,” he said.

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Government subsidies Free Essay Example
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(PDF) Government Subsidies
1. Introduction. This paper addresses the problems of defining and measuring government. subsidies, examines why and how government subsidies are used as a fiscal policy. tool, discusses their ...
PDF Government Subsidy Policy and its Impact on Efficiency and Economic Growth
The policy of subsidy and price control show how prices are the opportunity cost of goods and how government will not remove them since people like it. It will be worse if government remove this policy and people protest. In Venezuela the government is short of cash and people make money. Low price keep people alive and this is very important.
Challenges in disaster relief operations: evidence from the 2017
1. Introduction. Traditionally, logistics plays a central role in humanitarian assistance as the connecting point between preparedness and response, procurement and distribution, and headquarters and the field (Thomas, 2008).Humanitarian logistics (HL) has been described as "the process of planning, managing, implementing and controlling the efficient, cost-effective flow and storage of ...
Critical Decision-Making Issues in Disaster Relief Supply Management: A
1.1. Multiple Relief Actors are Involved with Limited Budgets and Resources. Large-scale disaster relief operations will definitely involve a large number of relief actors, from public sectors like government agencies at all levels, military forces, and humanitarian organizations with official backgrounds (e.g., Red Cross), to private sectors, such as diversified nongovernmental organizations ...
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Government subsidies help an industry by paying for part of the cost of the production of a good or service by offering tax credits or reimbursements or by paying for part of the cost a consumer ...
PDF The Economics of Subsidies for Community Development: A Primer
This essay provides a primer on the economics of subsidies, with special application to the role of subsidies in community development. The overall goal is to outline the appropriate role of subsidies for community development, with an eye toward using subsidies to improve program design and enhance cost-effectiveness.
PDF A Subsidy Primer
subsidies. Specific subsidies go to particular groups of beneficiaries, as opposed to the population as a whole. A subsidy that is available only to cotton farmers is specific. A subsidy to supply flu vaccine for anybody who needs one is consid-ered (by trade lawyers, at least) to be non-specific, because almost anybody can benefit from it.
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This paper addresses the problems of defining and measuring government subsidies, examines why and how government subsidies are used as a fiscal policy tool, assesses their economic effects, appraises international empirical evidence on government subsidies, and offers options for their reform. Recent international trends in government subsidy expenditure are analyzed for the 16-year period ...
PDF Capital Structure: Debt, Equity, and Government Subsidies
I find government subsidies are negatively associated with all five measures of firm leverage, consistent with government subsidies affecting firm capital structure. Firms in the top decile of government subsidy receipts have book (market) leverage ratios 3.2 (2.5) percentage points lower than those in the lowest decile.
Relief Distributions Networks: A Systematic Review
Quantity seems to be the prime aspect when addressing relief distribution and its success. Review papers have ... government subsidies have a positive effect on third-party logistics provider's ...
Summary of American Rescue Plan Act of 2021 and Provisions ...
President Biden March 11 signed into law the American Rescue Plan Act of 2021, a $1.9 trillion COVID-19 relief package, which includes a number of provisions that affect hospitals and health systems.. The legislation includes additional new funding for rural hospitals and health care providers for COVID-19 relief; increased federal subsidies for COBRA coverage; and changes to the Medicare wage ...
Disaster relief operations: past, present and future
Galindo and Batta ( 2013) further explain disaster relief operations as the set of activities performed before, during and after a disaster to reduce its impact on human lives and properties (c.f. Altay and Green 2006 ). However, many of these activities often require operations research (OR)/management science (MS) skills (Altay and Green 2006 ...
Can government subsidy strategies and strategy combinations ...
There are many ways for the government to intervene in enterprise innovation. From the perspective of "supply-side" intervention, it mainly includes direct subsidies, tax incentives Footnote 2 (indirect subsidies), guarantee funds, Footnote 3 etc. (Gomez-Valenzuela et al. 2020).From the perspective of "demand side", it mainly includes intellectual property Footnote 4 and government ...
Government Subsidies in: IMF Working Papers Volume 1995 Issue 091 (1995)
This paper addresses the problems of defining and measuring government subsidies, examines why and how government subsidies are used as a fiscal policy tool, assesses their economic effects, appraises international empirical evidence on government subsidies, and offers options for their reform. Recent international trends in government subsidy expenditure are analyzed for the 16-year period ...
Government subsidies Free Essay Example
423. An agricultural subsidy can be defined as a grant offered to farmers for their products. These subsidies are provided in order to add-on to farmers incomes, to control the costs of agricultural products in the market and to regulate supply of these products. The US government is required by the law to provide farm subsidies and is required ...
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Government research and development (R&D) subsidy is one of the main policy instruments to deal with market failure, and its effectiveness has attracted attention increasingly. This study investigates the impact of two types of government R&D subsidies on innovation using the data of Chinese listed enterprises from 2010 to 2016. We find that compared with ex-post rewards, ex-ante grants have a ...
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This is followed by the examination of the legal techniques that can be used to determine the legal relevance of governmental action in the context of a definition of subsidy. A final section concentrates on the significance of the objectives of the measures, and maps their position within the issue of the definition of subsidy.
Purposes of government subsidy and firm performance
Furthermore, we analyse the relationship between subsidy and performance in SOEs and non-SOEs. For SOEs, government subsidy significantly reduces the firm's accounting performance. When the government subsidy to the state-owned enterprises grows by 1%, the accounting performance will significantly decrease by 0.777%.
Sustainability
Government subsidy is a common practice in poverty alleviation. We used game theory and the mathematical model of operations management to investigate the efficiency of subsidy with different poverty scales when the firm owns the decision power of the wholesale price. Comparative analysis of the equilibrium solutions demonstrated the following results: Exclusive subsidy has a significant ...
"A Three Part Essay on The Role of Government Subsidies on Firm Level I
Our analysis contains three essay that look to the effect of innovations given a diverse set of measures. The purpose of the first chapter, which is divided into two levels, is two-fold. First, via a Probit model, we assess the effect subsidies have on firm level innovation and determine if these effects impound in firm level performance given these subsidies. Second, we seek to determine the ...
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A special report, COVID19 and its Impact: Seven Essays on Reframing Government Management and Operations with essays from academic and government experts around the country. Governments today are facing serious, seemingly intractable public management issues in the aftermath of COVID-19 that go to the core of effective governance and leadership ...
NGO and Government Relief Operations Essay Sample
The NGOs and the government should greatly consider the effects of their relief operations on settlement so as to avert further injuries and deaths, because the quality of shelter greatly influences health and well-being of a person. In most cases, relief operations result into higher density of population (Bennet, 2014).
All relief operations must be LGU-certified
All operations on the distribution of relief goods from any private citizen and other entities must be coordinated to the local government units (LGUs) to ensure that no enhanced or general community quarantine (ECQ) protocols are violated according to the Department of the Interior and Local Government (DILG), ...
Electricity tariff increase will reduce subsidies by N1.14 trillion in
"With the newly approved tariffs, subsidies for the 2024 fiscal year are expected to reduce by about NGN1.14 trillion in furtherance of the Federal Government's realignment of the subsidy ...

Challenges in disaster relief operations: evidence from the 2017 Kermanshah earthquake

Design/methodology/approach

Practical implications

Originality/value

1. Introduction

2. Literature review

3. Research methodology

3.2 Case description

4. Findings

4.1.1 Needs assessment challenges

4.1.2 Procurement, transportation and warehousing challenges

4.1.3 Challenges with last mile distribution

4.2 Non-logistical challenges identified during the 2017 Kermanshah earthquake relief operations

4.2.1 Growing use of social media

4.2.2 Regulations on the roles and involvement of HAs

4.2.3 Interference of new actors

5. Discussion

6. Academic contribution and managerial implications

7. Limitations and further research

Appendix 2 Interview Protocol

Organization General Operations:

Challenges of HL during disaster relief operations

Further reading

Acknowledgements

Corresponding author

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Questions & More Information

Critical Decision-Making Issues in Disaster Relief Supply Management: A Review

1. Introduction

1.1. Multiple Relief Actors are Involved with Limited Budgets and Resources

1.2. The Status of the Affected Area is Complicated and Unpredictable

1.3. Demand is Extremely Uncertain and Ever-Changing

2. Coordination Decisions

2.1. Coordination Barriers

2.1.1. Limited Local Resources

2.1.2. Unsolicited Supplies/Donations

2.1.3. Poor Communication and Information Sharing

2.1.4. Involvement of Government and Military Operations

2.2. Coordination Perspective

2.2.1. Macro-Coordination

2.2.2. Micro-Coordination

3. Facility Location Decisions

3.1. Facility Hierarchy

3.2. Facility Location

3.2.1. Beneficiary Service Level

3.2.2. Humanitarian Logistics Cost

3.2.3. Infrastructure and Transportation Network

4. Inventory Decisions

4.1. Relief Supply Variety

4.2. Critical Inventory Decision Issues

4.2.1. Disaster-Related Uncertainties

4.2.2. Forecast Information

4.2.3. Financial Constraints

4.2.4. Unforeseen Disruption

5. Conclusions and Future Directions

Acknowledgments

Conflicts of Interest

Do Subsidies Help the Economy?

The Bottom Line

How Do Government Subsidies Help an Industry?

Key Takeaways

What Are Some Critiques of Government Subsidies?

What Are Direct vs. Indirect Subsidies?

Which Industries Do the U.S. Government Subsidize?

II. Subsidies: Definition and Measurement

III. Subsidies as a Fiscal Policy Tool

IV. Empirical Evidence On Subsidies 12/

VI. Conclusions

APPENDIX II Previous Research: Review and Comparison

APPENDIX III

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Nordic Council of Ministers

The World Bank

Table of Contents

Opening the box of subsidies: which is more effective for innovation?

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