Table Of Content
- About the author
- About the book
- This eBook can be cited
- List of Contributors
- Russian Information Warfare: The Case of the White Helmets in Syria: İbrahim Karatas
- On “The Human” and Behavioral Economics: Sidre Gul Bige Gocekli and Necmiye Comertler
- Profitability of Turkish Manufacturing Firms: Efficiency or Market Power?: A. Elif Ay Yalcinkaya and Ramazan Ekinci
- The Relationship Between the Vertical Specialization of Exports and Employment: Burcu Hicyilmaz and Mesut Cakir
- Is Brexit a Transaction Cost Problem?: Yildirim Beyazit Cicen
- Clustering Approach as a Regional Development Tool and Turkish Automotive Sector Clustering Analysis: Mustafa Cem Kirankabes
- The Use of Spatial Models: Examples from Financial Market Applications: Sevgi Eda Tuzcu
- Measures Securing the Collection of Public Receivablesin Turkey: İsmail İsler
- Prioritizing the Problems Confronted by Independent Accountant and Financial Advisors with Best – Worst Method: Engin Cakir
- Culture of Innovation as A Management Function: Kezban Talak
- Weighting Cost Calculation Methods by Healthcare Employees Using the SWARA Method: Cagri Koroglu and Zeynep Aydin
- Current Developments in the Process of Adoption to the IFRS/IAS in Turkey: Mehmet Utku and Yusuf Kaya
- Entrepreneurial Marketing for a Competitive Advantage: Aslihan Yavuzalp Marangoz
- Transition to Multi-sensory Strategies: Sensory Branding: Zuhal Cilingir Uk and Gamze Kayan
- The Antecedents of Customer Loyalty: Nihat Tavsan and Cem Duran
- Do Research and Development Investments and Financial Development Promote Human Development? Empirical Evidence from Developing Countries: Mehmet Levent Erdas and Zeynep Ezanoglu
- Determination of R&D Efficiency Levels of OECD Countries in the Intermediate Innovative Countries Group: Busra Kutlu Karabiyik
- Examining the Relationships Between Emotional Labor Behaviour, Burnout and Work Alienation in the Marine Tourism Businesses: Murat Yorulmaz
- Employees’ Perception of Ethical Leadership: A Research on Tourism Establishments: Melda Akbaba
- A Comparative Analysis of the Health Care Financing Models in the Context of Financing Sources and Health Coverage: Covid-19 Experience: Gulizar Seda Yilmaz
- Environmental Benefits of the COVID-19 Pandemic: Mehmet Metin Dam
- The Challenges of the Turkish Music-Entertainment Industry During the Covid-19 Pandemic: Serhat Karaoglan
- Analysis of Causality Relationships Among COVID-19 and Sector Returns: Evidence from Turkey: Yakup Soylemez
- The Effect of Covid-19 Pandemic on Solidarity Culture in Turkey: Asli Kose
Asst.Prof.Dr., Kilis 7 Aralık University, Kilis, Turkey, firstname.lastname@example.org, ORCID ID: 0000-0001-8701-017X
Aydin Adnan Menderes University, Aydin, Turkey, email@example.com, ORCID ID: 0000-0002-3358-0000
Asst.Prof.Dr., Aydin Adnan Menderes University, Aydin, Turkey, firstname.lastname@example.org, ORCID ID: 0000-0002-5906-4178
Asst.Prof.Dr., Aydin Adnan Menderes University, Aydin, Turkey, email@example.com, ORCID ID: 0000-0003-4980-1047
Yildirim Beyazit Cicen
Asst.Prof.Dr., Gumushane University, Gumushane, Turkey, firstname.lastname@example.org, ORCID ID: 0000-0002-3425-280X
Zuhal Cilingir Uk
Asst.Prof.Dr., Ondokuz Mayıs University, Samsun, Turkey, email@example.com, ORCID ID: 0000-0002-3271-7765
Prof.Dr., Aydin Adnan Menderes University, Aydin, Turkey, firstname.lastname@example.org, ORCID ID: 0000-0002-0370-843X
Mehmet Metin Dam
Asst.Prof.Dr., Aydin Adnan Menderes University, Aydin, Turkey, email@example.com, ORCID ID: 0000-0003-3980-7832
Asst.Prof.Dr., Istinye University, Istanbul, Turkey, firstname.lastname@example.org, ORCID ID: 0000-0001-5171-0270
Asst.Prof.Dr., Bakircay University, İzmir, Turkey, email@example.com, ORCID-ID: 0000-0001-7420-9841←11 | 12→
Mehmet Levent Erdas
Asst.Prof.Dr., Akdeniz University, Antalya, Turkey, firstname.lastname@example.org, ORCID ID: 0000-0001-6594-4262
PhD student, Suleyman Demirel University, Isparta, Turkey, email@example.com, ORCID ID: 0000-0002-4601-7567
Sidre Gul Bige Gocekli
Res.Asst., Aydin Adnan Menderes University, Aydin, Turkey, firstname.lastname@example.org, ORCID ID: 0000-0002-5406-4304
Res.Asst.Dr., Aydin Adnan Menderes University, Aydin, Turkey, email@example.com, ORCID ID: 0000-0003-3501-2012
Asst.Prof.Dr., Pamukkale University, Denizli, Turkey, firstname.lastname@example.org, ORCID ID: 0000-0002-9666-130X
Asst. Prof. Dr., Kirklareli University, Kirklareli, Turkey, email@example.com, ORCID ID: 0000-0002-1028-7135
Busra Kutlu Karabiyik
Res.Asst.Dr., Aydin Adnan Menderes University, Aydin, Turkey, firstname.lastname@example.org, ORCID ID: 0000-0002-6691-2921
Res.Asst.Dr., Kirikkale University, Kirikkale, Turkey, email@example.com, ORCID ID: 0000-0002-4120-4013
Dr., Istinye University, Istanbul, Turkey, firstname.lastname@example.org, ORCID ID: 0000-0002-2125-1840
Asst.Prof.Dr., Pamukkale University, Denizli, Turkey, email@example.com, ORCID ID: 0000-0002-2076-9808
Res.Asst., Ondokuz Mayıs University, Samsun, Turkey, firstname.lastname@example.org, ORCID ID: 0000-0001-8726-5551←12 | 13→
Mustafa Cem Kirankabes
Assoc.Prof.Dr., Balikesir University, Balikesir, Turkey, email@example.com, ORCID ID: 0000-0002-0807-5897
Assoc.Prof.Dr., Aydin Adnan Menderes University, Aydin, Turkey, firstname.lastname@example.org, ORCID ID: 0000-0003-4073-1847
Asst.Prof.Dr., Gumushane University, Gumushane, Turkey, email@example.com, ORCID ID: 0000-0002-8044-6592
Aslihan Yavuzalp Marangoz
Asst. Prof.Dr., Toros University, Mersin, Turkey, firstname.lastname@example.org, ORCID ID: 0000-0002-5119-4330
Asst.Prof.Dr., Zonguldak Bülent Ecevit University, Zonguldak, Turkey, email@example.com, ORCID ID: 0000-0002-6185-3192
Dr., Istanbul Medeniyet University, Istanbul, Turkey, firstname.lastname@example.org, ORCID ID:0000-0002-7837-5852
Dr., Piri Reis University, Istanbul, Turkey, email@example.com, ORCID ID: 0000-0001-7085-0893
Sevgi Eda Tuzcu
Dr., Ankara University, Ankara, Turkey, firstname.lastname@example.org, ORCID ID: 0000-0002-3658-2546
Asst.Prof.Dr., Pamukkale University, Denizli, Turkey, email@example.com, ORCID ID: 0000-0002-7076-6891
A. Elif Ay Yalcinkaya
Asst.Prof.Dr., Dokuz Eylul University, İzmir, Turkey, firstname.lastname@example.org, ORCID-ID: 0000-0003-4417-2341←13 | 14→
Gulizar Seda Yilmaz
Res.Asst.Dr., Aydin Adnan Menderes University, Aydin, Turkey, email@example.com, ORCID ID: 0000-0003-2430-6808
Asst.Prof.Dr., Kocaeli University, Kocaeli, Turkey, firstname.lastname@example.org, ORCID ID: 0000-0002-5736-9146
DR. AHU YAZICI AYYILDIZ
DR. ALİ PETEK
DR. ARZU ORGAN
DR. ASLI YENİPAZARLI
DR. AYNUR UÇKAÇ
DR. AZİZ BOSTAN
DR. BURCU HİÇYILMAZ
DR. BÜLENT YILDIZ
DR. BÜŞRA KUTLU KARABIYIK
DR. ÇAĞRI KÖROĞLU
DR. DİLEK ELVAN ÇOKİŞLER
DR. E.YASEMİN BOZDAĞLIOĞLU
DR. ECE AKSU ARMAĞAN
DR. ENGİN ÇAKIR
DR. ESİN SAYIN
DR. ESMA DURUKAL
DR. FATMA ÇAKIR
DR. FERİŞTAH SÖNMEZ
DR. FUNDA ÇONDUR
DR. GÖKHAN AKEL
DR. GÖNÜL TEZCAN
DR. GÜLİZAR SEDA YILMAZ
DR. GÜLŞAH SEZEN AKAR
DR. HATİCE EROL
DR. HÜSEYİN ŞENKAYAS
DR. İSMAİL İŞLER
DR. İSMET ATEŞ
DR. KIYMET YAVUZASLAN
DR. MEHMET BÖLÜKBAŞ
DR. MEHMET METİN DAM
DR. NECMİYE CÖMERTLER
DR. OSMAN PEKER
DR. SADULLAH ÇELİK
DR. SEDAT ALATAŞ
DR. SEMA OĞLAK
DR. SERCAN YAVAN
DR. ŞABAN ERTEKİN
DR. ŞANSEL ÖZPINAR
DR. TARIK ILİMAN
DR. TUĞBA AKIN
DR. UMUT EVLİMOĞLU
DR. UMUT TOLGA GÜMÜŞ
Abstract Syria’s opposition civil-defense network, namely the White Helmets, has been under the military and information warfare of Russia since the Russian army began to fight alongside the Syrian regime against rebel groups in September 2015. Designating the organization as a terrorist group, Russians attacked the White Helmets’ operation centers and simultaneously waged an information war to smear the organization and perpetuate confusion among the world audience. This study has analyzed Russian media, affiliated news outlets, social media, pro-Russian reports disseminating Russian claims, the White Helmets’ reports and concerning news in the Western media. It also made interviews with top officials of the organization to check the accuracy of Russian accusations. After comparing allegations of both sides, it has concluded that there is massive Russian information warfare on the White Helmets that testifies Russian and the regime crimes on the frontline. The research has also contended that Russian black propaganda has become successful. As a side finding, this study has revealed which tactics the Russian propaganda machine uses during the discrediting campaigns. Methodologically, both quantitative and qualitative researches were used.
Keywords: Information Warfare, Russia, Syria, White Helmets, Security
The White Helmets (officially the Syrian Civil Defence) has been rescuing victims of the war from rubbles in mainly opposition-held areas of Syria since its establishment in 2013. Consisting of former engineers, bakers, tailors, students, shopkeepers, and people with many other professions, the humanitarian organization with over 3.000 volunteers has saved more than 100.000 lives as of January 2020. The White Helmets are funded, trained and supported by foreign governments particularly the US, the UK, Canada, European countries and some Arab states. They have a so dangerous job that some of the group members lost their lives or got injured while rescuing civilians in war-torn regions that were under continuous bombardment. According to the organization’s website, 252 volunteers have been killed and a double of that number was injured (The White Helmets, 2019). Besides deaths emanating from the ongoing Russian and Syrian regime bombardments, they have been ←19 | 20→the private target of jet fighters due to their humanitarian activities and being the witnesses of war crimes. Thus, their buildings, cars and medical centers have been under deliberate attacks of artillery bombardment, missiles or barrel bombs. However, their efforts went viral thanks to Western media, which culminated in a nomination for Nobel Prize. Besides, a documentary film named ‘The White Helmets’ streamed at Netflix won ‘the Best Documentary’ at the 89th Academy Awards.
The White Helmets are now sandwiched between asymmetric warfare and information warfare conducted by Russia. The humanitarian aid group had been operating in areas out of the Syrian regime’s control since the civil war erupted. Its efforts got praise from the world governments and audience but things changed when Russia directly got involved in the civil war on the side of the Assad regime. As soon as Russian jets dropped the first bomb on opposition groups on September 30, 2015, Russian media outlets launched a massive anti-White Helmets campaign simultaneously (The Syria Campaign, 2017: 8). Since then, the White Helmets have been hit by bombs and the black propaganda of Russian information warriors. The White Helmets operates in fatal conditions to minimize the number of deaths. Yet, while the group and its volunteers could escape from bombardments, they could not save themselves from the virtual warfare perpetrated by pro-Russian individuals. This study investigates how a humanitarian aid association can be discredited through internet blogs, social media, trolls, and classic media though it saves lives in the combat. To reveal the truth about the White Helmets and the reliability of Russian claims that are regarded as false propaganda by the Western media, accusations of pro-Russian groups and responses of the White Helmets have been analyzed for this study. By this way, this study intends to unveil; (1) how Russian information warfare is influential alongside the real war, (2) how perceptions can be changed negatively even against well-intentioned activities, and (3) why and how information warfare is complementary to ordinary warfare. This research contends from analyzes that Russia uses information as a weapon because Russians want to; (1) create doubt and confusion in the minds of foreign public, (2) do not want to witnesses on the ground that uncover their war crimes, (3) legitimize its attacks and whitewash the Assad regime, and (4) eliminate the White Helmets. In addition, it will also try to reveal how Russian propaganda works by analyzing their methods. In other words, this study plans to unveil Russian information warfare through analyzing its attacks on the White Helmets. The article expects to contribute to the theoretical literature of information warfare, particularly that of Russia.←20 | 21→
Methodologically, pro-Russian media disseminating Russian claims, counter reports and allegations of the White Helmets and concerning news in the Western media that aim to disprove Russian evidence have been examined and compared with each other. Further to documentary analysis, telephone and face-to-face interviews were made with the group leader Raed Saleh and some other officials of the organization. No people from Russian and the Syrian regime side could be accessed. While a section was dedicated to Russian information warfare, the White Helmets group is briefly explained in Introduction (see above) due to space problem.
The White Helmets (officially the Syrian Civil Defence) has been rescuing victims of the war from rubbles in mainly opposition-held areas of Syria since its establishment in 2013. Consisting of former engineers, bakers, tailors, students, shopkeepers, and people with many other professions, the humanitarian organization with over 3.000 volunteers has saved more than 100.000 lives as of January 2020. The White Helmets are funded, trained and supported by foreign governments particularly the US, the UK, Canada, European countries and some Arab states. They have a so dangerous job that some of the group members lost their lives or got injured while rescuing civilians in war-torn regions that were under continuous bombardment. According to the organization’s website, 252 volunteers have been killed and a double of that number was injured (The White Helmets, 2019). Besides deaths emanating from the ongoing Russian and Syrian regime bombardments, they have been the private target of jet fighters due to their humanitarian activities and being the witnesses of war crimes. Thus, their buildings, cars and medical centers have been under deliberate attacks of artillery bombardment, missiles or barrel bombs. However, their efforts went viral thanks to Western media, which culminated in a nomination for Nobel Prize. Besides, a documentary film named ‘The White Helmets’ streamed at Netflix won ‘the Best Documentary’ at the 89th Academy Awards.
The White Helmets are now sandwiched between asymmetric warfare and information warfare conducted by Russia. The humanitarian aid group had been operating in areas out of the Syrian regime’s control since the civil war erupted. Its efforts got praise from the world governments and audience but things changed when Russia directly got involved in the civil war on the side of the Assad regime. As soon as Russian jets dropped the first bomb on opposition groups on September 30, 2015, Russian media outlets launched a massive anti-White Helmets campaign simultaneously (The Syria Campaign, 2017: 8). Since then, the White Helmets have been hit by bombs and the black propaganda of Russian information warriors. The White Helmets operates in fatal conditions ←21 | 22→to minimize the number of deaths. Yet, while the group and its volunteers could escape from bombardments, they could not save themselves from the virtual warfare perpetrated by pro-Russian individuals. This study investigates how a humanitarian aid association can be discredited through internet blogs, social media, trolls, and classic media though it saves lives in the combat. To reveal the truth about the White Helmets and the reliability of Russian claims that are regarded as false propaganda by the Western media, accusations of pro-Russian groups and responses of the White Helmets have been analyzed for this study. By this way, this study intends to unveil; (1) how Russian information warfare is influential alongside the real war, (2) how perceptions can be changed negatively even against well-intentioned activities, and (3) why and how information warfare is complementary to ordinary warfare. This research contends from analyzes that Russia uses information as a weapon because Russians want to; (1) create doubt and confusion in the minds of foreign public, (2) do not want to witnesses on the ground that uncover their war crimes, (3) legitimize its attacks and whitewash the Assad regime, and (4) eliminate the White Helmets. In addition, it will also try to reveal how Russian propaganda works by analyzing their methods. In other words, this study plans to unveil Russian information warfare through analyzing its attacks on the White Helmets. The article expects to contribute to the theoretical literature of information warfare, particularly that of Russia.
Methodologically, pro-Russian media disseminating Russian claims, counter reports and allegations of the White Helmets and concerning news in the Western media that aim to disprove Russian evidence have been examined and compared with each other. Further to documentary analysis, telephone and face-to-face interviews were made with the group leader Raed Saleh and some other officials of the organization. No people from Russian and the Syrian regime side could be accessed. While a section was dedicated to Russian information warfare, the White Helmets group is briefly explained in Introduction (see above) due to space problem.
2 Information Warfare vs. Russia
Information warfare is a concept referring to the use of information and communication technology to obtain a competitive advantage over the adversary. It is secret, bloodless, ambiguous and invisible and there are no doctrines or battlefields for it. Activities include stealing, interdicting, distorting, manipulating or destroying information by using computers, smartphones, internet, social media, classical media, trolls, politicians, celebrities, and so on (Giles, ←22 | 23→2016: 4). It aims to disseminate misinformation to polarize, demoralize, discredit, demonize or delegitimize its target (Porotsky, 2018). Such warfare is sustained through fake news/information generated by state institutions, concerning public servants and hired people whose audience are local and foreign public that are addressed to be deceived, got confused, change their perceptions, support the side trolls want them to do, etc. Information warfare and cyber warfare are not the same. While the former targets mind and perceptions of people through misinformation, the latter aims to steal information by hacking computers or destroy both software and the hardware of an IT system. For example, while fake news is a part of information warfare, sending viruses to a computer system that controls the electricity network is in the cyber warfare category. More clearly, in spite of a blurred line between the two, for instance, a hacker is a cyber-warrior while a troll spreading fake news can be called an information-warrior.
Regarding Russia’s information warfare (informatsionnaya voyna), it is one of the most influential countries wielding information as a weapon in the world. Russia is unique in this area since their understanding of the concept and methods they use are quite different than in other countries. First of all, it is a national (war) policy of the country, which Vladimir Putin expresses as “We must take into account the plans and directions of development of the armed forces of other countries… Our responses must be based on intellectual superiority, they will be asymmetric, and less expensive (Giles, 2016: 3)”. Russian former Chief of Staff General Valery G. Gerasimov says that the ratio of non-military to military activities is 4:1 (for Russia). On the other hand, Aleksander Dvornikov, another top commander being well aware of the effect of non-military measures, says that Russia owes its success in Syria to information operations (Tashev, Purcell & McLaughlin, 2019: 133–134). Russians explain their goals as to obtain psychological and ideological superiority, damaging political systems of hostile countries, particularly those in the Western camp, and affect perceptions of foreign populations, which we all know as public diplomacy. Unlike other countries, for the Russian state, it is an ongoing activity both in peacetime and wartime. Generally, countries begin it just before a war to legitimize their attacks but Russians do it even if there is no state of war. Besides, the Russian state has a holistic approach to this issue that all governmental bodies and even Russian people get involved in information warfare while in other countries, conversely, it is the military staff that undertake this duty. For example, almost all Russian embassies use twitter actively, respond to anti-Russian claims and get into polemics with foreign politicians. According to Ajir and Vailliant (2018: 72–81), the Russian regime maintains its warfare over the ←23 | 24→legacy of the Soviet Union, which began to use information as a weapon from 1919 onwards and dedicated 15,000 personnel and an annual budget of $3–4 billion. Russia utilizes such warfare because it does not have enough resources to confront the United States and its allies. Besides, it is a cheaper way when compared to military solutions and allows gaining superiority in some spheres.
4 The Russian Information Warfare Against the White Helmets
The White Helmets define itself as a neutral organization that focuses only on the humanitarian side of the war. However, they are not impartial politically. Interviewees are particularly furious to Bashar Assad and accuse him of killing his citizens. Therefore, they express that Assad must be held accountable and judged in the court. A top political executive of opposition groups that wanted to remain anonymous argues that not only Assad but his regime formed by his father should also be abolished as the system is the main creator of the current civil war. Yet, despite being anti-Assad, they refuse any allegations that they are working with armed groups or their volunteers are also fighters. On the other hand, the Syrian regime and Russia see the organization as a terrorist group linked with other militant groups (Malsin, 2016). The White Helmets are also accused of lobbying foreign military intervention and working as agents (Samir, 2019).
Before discussing Russia’s information warfare against the group, it should be noted that Russians also wage a real war against them. Since the Assad regime and its main supporter Russia have labeled the group as a terrorist organization, they do not discriminate between armed factions and the White Helmets. Interviewees and reports prepared by the group contend that Russians use a ‘double-tap’ tactic, meaning that they re-bomb a site already bombed a short while before. The aim is to kill the rescue team, harm their equipment and cause more civilian loss (SNHR, 2019). Therefore, while the first strike is conducted to kill a premeditated target, the second is done to kill those running to rescue injured people. In other words, the second strike is exclusive to the White Helmets. In addition, civil-defense centers are specifically pounded by Russian jets to kill volunteers. Another way of eliminating volunteers is assassinations. For example, according to Sanchez (2017), seven members of the White Helmets were assassinated in their operation center located in Salmin, Idlib. Volunteers argue that they encounter such attacks continuously. As a result, 15 % of volunteers have either been killed or injured since the formation of the group.←24 | 25→
Regarding the basic subject, Russia’s information warfare against the White Helmets, it was launched as soon as Russia began to bomb anti-regime groups in September 2015. RT, Sputnik, blogs, trolls, and journalists close to Russia inaugurated a massive campaign to smear the White Helmets. As will be explained below, Russian black propaganda is systematic, coordinated and carried out by a network that feeds each other in terms of information. The aim looks misinforming and misleading people, distorting and covering facts on the ground, gaining the support of the international audience, and showing their attacks as fighting terrorism. According to Solon (2017) of The Guardian, Russians have generally succeeded in changing the perception of the world audience. However, as the claims dominating media are not always correct, this study has selected some basic claims of Russian media and compared it with responses of the White Helmets written in their reports and the Western media.
The first claim is that the White Helmets are an Al-Qaeda-linked terrorist organization and a tool of Western powers used for a regime change in Syria since it receives financial support from Western governments (Lester, 2018: 33). In addition, the group is accused of false flag chemical attacks aiming to force the US-led coalition to attack the regime forces (Nocetti, 2019: 7). Besides, it is blamed for assisting the executions of rebel groups. Operating only in rebel-held regions is also a matter of criticism. Further to the Assad regime, the YPG, Kurdish armed group controlling some parts of northern and eastern Syria, is also a critique of the White Helmets, arguing that it is an organization affiliated with armed groups based in Idlib. In line with such accusations, Russian media and journalists have published many reports claiming that the volunteers of the White Helmets are both rescuers and militants. They also publish and televise testimonies and photos of allegedly former members of the group. For example, Bartlett (2018) argues on RT’s website that offices of the White Helmets are always close to Al-Qaeda and other terrorist groups including ISIS. She also claims that the volunteers of the group got involved in executions, cheered killings and disposed remains of Assad’s soldiers. To support her arguments, she gives some links of websites containing videos allegedly proving the involvement of the White Helmets in murders. When websites (mintprsessnews.com, threadreaderapp.com, 21stcdenturywire.com, etc.) are analyzed, it can be seen that they are full of Russian propaganda content. What is more, the editor of 21stcdenturywire.com is Vanessa Beeley, another staunch supporter of Russian policies with British origin writing anti-White Helmets articles at Russian media and her blog. Canadian journalist Eva Bartlett introduces herself as a rights activist and an independent writer. Yet, she has photos with a wristband ←25 | 26→‘I love Bashar’ (The Syria Campaign, 2017: 31). She also made a speech at the UN to support the Assad government in 2016.
Maxim Grigoriev’s allegations are also worth mentioning. Mr. Grigoriev is the Director of The Foundation for the Study of Democracy. He wrote a book together with his colleague S. Maizel named ‘The White Helmets: Fact-Checking By Eyewitnesses and Former Volunteers’. The book is allegedly based on interviews with the current and former White Helmets volunteers and residents living in areas where the organization operates. According to the book, volunteers confessed that they were doing rescue operations due to fear of armed groups, helping executions, getting regular payment, fighting, and so on. Authors also share photos of some volunteers with rifles (papes 9, 16, 19 and 22). They conclude from the interviews that; (1) most of the White Helmets are not volunteers but permanents workers with a monthly salary, (2) armed groups forced them to join the group as an alternative to being imprisoned or fighting, (3) the White Helmets offices were working closely with armed groups, (4) they got combat-training from Al-Nusra, and (5) most of volunteers continued to fight for various terrorist organizations (Grigoriev & Maizel, 2019: 7–10). The aforementioned book can be found in pdf format in almost all pro-Russian websites. In addition to Russians, Kurdish YPG and their supporters claim that the White Helmets are affiliated with Al-Qaeda. Salmo (2020), one of the interviewees, says he was working as the head of the organization in Afrin until 2015 but detained by YPG that accused the White Helmets of having ties with Al-Nusra.
The above accusations were asked to the White Helmets by phone and in face-to-face meetings. Raed Saleh (2020) denies terror links and says that they are an impartial organization with no terror links. He says they offer help to any group regardless of their ideologies. He argues that these are all the regime and Russian propaganda to discredit their organizations. In addition, he adds, had the White Helmets collaborated with terrorist groups, their Western donors would not support them. On the other hand, Salmo (2020) says when a person wants to be a volunteer, he/she has to leave other groups and forget politics. The White Helmets has a ‘volunteer agreement’, which is compulsory to sign. Besides, volunteers must have no double-profession and a clean past with no crimes. Grigoriev and Maizel’s book was shown to three interviewees during the interview. They identified some of their friends but rejected accusations. Salmo said that “Those photos with guns were taken before volunteers joined the organization. Russians find photos and present them as if they are still fighting”. He added that he had a gun when the uprising erupted in 2011 to protect himself from the paramilitary forces of Assad. Yet, he left the gun ←26 | 27→after joining the rescue group. Abdullah (2020) told the story of Abdul Hadi Kamil, a volunteer arrested by the regime forces. While Kamil was accompanying a civilian convoy, the regime’s soldiers captured and imprisoned him. Then, he appeared at a Russian TV and said the White Helmets was working with Al-Qaeda. Thus, interviewees claim that the regime gets fake confessions by torture. Salmo (2020) mentioned about another method of Russians to get untrue confessions. He said that when a city is surrendered by regime forces, many volunteers leave the city but some of them stay there since it is their hometown. For example, when the Syrian Arab Army (SAA) entered Aleppo, 30 out of 150 volunteers left there. Hundred volunteers in Homs, 200 out of 500 in Ghouta and 400 in Daraa stayed instead of leaving. When soldiers asked civilians who were working for the organization, volunteers were fingered out by their scared neighbors. Hence, they were arrested and threatened to talk against the White Helmets. Moreover, interviewees claimed that some photos in the book are fake. For example, there are two photos of a volunteer (one with a gun and one with the uniform of the organization) on page 16. They said that photos belong to two different people, claiming that the authors inserted a photo of another person looking like a volunteer. Regarding YPG’s hostility to the White Helmets, the Kurdish group criticizes the organization to operate in Afrin currently controlled by Turkey-backed Free Syrian Army but no attacks on volunteers have been reported. Yet, YPG refused the White Helmets’ help for extinguishing fires burning wheat and barley crops in the YPG-held area in 2019.
Besides the White Helmets, Western media also found some discrepancies in Russian propaganda. For example, Qui (2017) of The New York Times argues that they have found no evidence that the White Helmets are linked to terrorist organizations. On the other hand, France 24 (2018) analyzed a few allegations shared by pro-Russian groups alleging that the White Helmets are undercover Islamist terrorists. The French media outlet, for example, uncovered that a person confessing at a pro-Assad TV was arrested and forced to read a pre-prepared script. In addition, it revealed that the posted video alongside the volunteer’s testimony was actually taken in Bahrain in 2011. Moreover, France 24 posts an example of how a photo of suffocating children with no wounds was claimed to be fake propaganda of the White Helmets pretending to treat unwounded children. The photo was spread on Twitter by a person through a tweet tagging pro-Russian bloggers Eva Bartlett and Vanessa Beeley. The tweet was retweeted 12.500 times and liked 13.600 times. Yet, the person twitting the allegation admitted in another tweet that the photo was real, and apologized. That twit was retweeted 45 times and liked 56 times. Furthermore, Bellingcat ←27 | 28→website proved that the photos Rossiya 1 TV broadcasted in a news report supposedly indicating that the White Helmets stage videos for fake evidence are actually from a film called ‘Revolution Man’ (Bellingcat, 2016). Furthermore, Worrall (2016) of Channel 24 analyzed Eva Bartlett’s claims about Syrian children. Bartlett claimed in her blog that ‘Al Qaeda/White Helmets rescue the same girl three times in three months’ and showed the three photos of allegedly the same girl, arguing that the White Helmets are featuring the same girl in their videos to deceive people. Yet, after contacting photographers and examining photos, Worrall concludes that photos were taken by international news agencies, for example Reuters, and there are three different girls in photos. In fact, the White Helmets indeed staged their ‘Mannequin Challenge’ video. Yet, as soon as it went viral, Russian trolls used it as evidence that the White Helmets is producing fake videos. The organization accepted its mistake and apologized for it but it had already been misused by Russian media1.
Another allegation is that the White Helmets use chemical weapons to provoke Western countries to strike the regime. According to Schneider and Lütkefend (2019: 4), there have been 336 chemical weapons attacks since the Syrian Civil war started, of which 98 % are attributed to the regime and 2 % to ISIS. The most conspicuous attacks are the one in Ghouta that killed 1400 civilians on August 21, 2013, Khan Shaykhun attack killing 91 people on April 4, 2017, and Douma attack killing around 50 people in April 7, 2018. Gas attacks are regarded as a collective way of punishing rebel groups and populations in rebel-held areas. While foreign governments and international organizations accused the Assad regime, and the US defined such attacks as its red line that led it to fire tomahawk missiles to punish the Syrian government, Russian information warriors deny that SAA forces used chemical weapons. According to them, it is the White Helmets that spread such fake news.
Russians also blamed the organization for using chemical weapons. For example, right after the Douma attack, stories shared on social media and TVs claimed that there was no chemical attack, testimonies were fake, no patients in hospitals and it was fabricated by the White Helmets. Nocetti (2019: 1) believes that the Russian strategy was to cause confusion in the Western public, divide foreign leaders and sow doubt on the truth. On the contrary, McKeigue et al. (2019: 3–17) cast doubt on the White Helmets’ claims about chemical attacks. They claim, for example, that during the sarin attack in Khan Shaykhun, ←28 | 29→100 affected patients came to the hospital before the attack. In addition, they argue that gas cylinders were highly likely placed at locations manually rather than dropped from an aircraft. Finally, the authors claim that no regime or Russian aircraft flew over the area. On the other hand, Wikileaks published an email of a member of the investigation team appointed by the Organization for the Prohibition of Chemical Weapons (OPCW). The undisclosed inspector expressed his suspicion about the possibility of the chemical attack made via dropping barrels from an aircraft. As expected, Russians used this email to whitewash the regime. However, Fernando Arias, the head of OPCW, stood by the report contending that the attack was carried out by a regime aircraft. In addition, OPCW inspectors said they were not allowed to enter the site for inspection. Since OPCW insisted on the correctness of its report, Russia threatened not to approve the organization’s next year’s budget. On the other hand, CBS News (2019) reports that it is not OPCW’s duty to state whether cylinders were placed manually or dropped from the air.
Moreover, Schneider and Lütkefend look allegations about chemical attacks from a different but more scientific perspective. First, they argue that munitions used in all chemical attacks in Syria are always the same. The regime uses either Soviet-era munitions or homemade ones produced in Latakia (Schneider & Lütkefend, 2019: 11). Therefore, it is hardly acceptable to blame White Helmets for munitions of the regime. Second, chemical barrels are dropped by SAA’s Mi-8/17 transport helicopters fleet. These helicopters are under the command of the Syrian army’s elite troops called ‘Tiger Forces’, whom OPCW accuses of conducting chemical attacks. For example, when they were first deployed in Aleppo in 2015, chemical attacks were begun to be witnessed. When they moved to northern Hama in 2017, this time regime helicopters dropped chemical barrels to that place. Finally, when they were redeployed to Damascus in February 2018, helicopters conducted another gas attack in Eastern Ghouta (Ibid: 19–20). Regarding the reason for why the regime uses chemical gas, the White Helmets attribute it to merciless of Russians and the regime as well as forcing civilians to push militants to surrender. Indeed, civilians protested armed groups after attacks and eventually, militants had to accept an evacuation plan with the Assad forces.
Meanwhile, the Russian side has many other minor allegations about the White Helmets. Among them is whether people of the group are volunteers or paid workers. According to Di Giovanni (2018), full-time working volunteers are paid $150 per month. Yet, Russians refuse them to be called volunteers. Abdussamed (2020), a volunteer, insists that they are volunteers. He says that he worked for one year without a stipend. In the second year, he got 10$ per ←29 | 30→month and then got paid $150 a few years later. According to his explanation, the amount volunteers get paid depends on how much donation the organization receives. The more donations are, the more their salaries are high. If there is no donation, there will be no salary. Russian information warriors also claim that the White Helmets are in the organ trafficking business. When the allegation is tracked, it is seen that Beeley (2019) disseminated it via her article at rt.com. Yet, Beeley’s source is a testimony, which the White Helmets argue taken under torture, published in Grigoriev and Maizel’s above book. On the other hand, the organization’s relations with James Le Mesurier, the late head of Mayday Foundation, are presented as a clue proving that the White Helmets is an agent working for Western states. Le Mesurier, a former British Army officer, was found dead in Istanbul in November 2019. According to the Turkish police, he committed suicide due to psychological problems (Daily Sabah, 2019). While British authorities saw him as a true hero, Russians propagated that he was a spy (MacMillan, 2019; RT, 2019). Regarding his connection with the White Helmets, Salmo (2020) says “We found Le Mesurier before he found us while we were looking for donors”. Moreover, the White Helmets point to Russian black propaganda a few days before Le Mesurier died, implying without insisting that Russians might have been behind his death if the reason is not suicide. In one of twits of Russian Foreign Ministry dated November 8, 2019, three days before Le Mesurier’s death, ministry’s spokesperson Maria Zakharova accuses Le Mesurier of being an agent of Britain’s MI6 (Russian MFA, 2019).
In association with information warfare, how Russians disseminate their claims in media is an important issue that needs to be analyzed. In fact, the organization already got a social media report prepared to Graphika Intelligence firm and published the details of the report in another report called ‘Killing The Truth’ (The Syria Campaign, 2017). According to the 48-page report, Russian bots and trolls reached 56 million people with twits that insulted the White Helmets during ten key incidents taking place in 2016 and 2017, particularly to cover up sarin gas attack to Khan Shaykhoun. Besides misinformation, twits also targeted the organization. For example, Vanessa Beeley, a key person in the Russian campaign, went far to say that “White Helmets are not getting it. We know they are terrorists. Makes them a legit target” in a twit dated October 30, 2015. While she deleted her twit later, it can still be found on many websites. Graphika found out that 50 % of those sharing anti-White Helmets twits have been involved in other Russian social media campaigns. The intelligence firm argues that they are well organized, functioning in the same network and act ←30 | 31→together. For example, on September 28, 2017, 21 accounts shared a video of Russian ‘In The Now’ website claiming that Khan Shaykhoun was a false flag within 42 seconds. In addition, on the same day, #SyriaHoax hashtag became the number one topic on Twitter with the help of a troll army. Simultaneously, prominent people such as American radio host Alex Jones, Senator Richard Black, and Democrat Congresswoman Tulsi Gabbard stated their doubts about the attack, confusing people’s minds.
As cited from Western media above, Russia employs hundreds of trolls, tens of blogs and a few influential state-sponsored media outlets. To test how influential they are in social media, twits with #whitehelmets hashtag shared on January, 30 and 31, 2020 were analyzed for this study and seen that while 32 twits were anti-White Helmets, 18 were supporting the organization. Those twitting against the White Helmets do not use real names and share twits of Eva Bartlett, Vanessa Beeley and Sputnik News. While some accounts twit about the White Helmets twice a day, their main arguments are the organization’s being a terrorist organization and staged chemical attacks.
Finally, based on the above analysis, why the Russian government wages information warfare on the White Helmets is a bit clear now. According to the interviewees, since the White Helmets operate frontline, they can supply first-hand evidence showing the real perpetrators of attacks. While Russia and the Assad regime do not want testimonies of their attacks, the White Helmets do not give up operating under the bombardments. Therefore, they target volunteers and affiliate them with terrorist groups to make the organization’s evidence null. In addition, the White Helmets are an anti-regime group that wants Assad to be held accountable for his crimes. Thus, even if not physically, the organization is hostile to the regime, a reason that provokes the Syrian regime to attack the volunteers. Moreover, the White Helmets is funded by Western governments, which do not prefer to see Assad in power. In other words, from Assad’s point of view, the organization is on the wrong side, thereby deserves attacking. Meanwhile, Jain (2018: 3) criticizes Western states’ attitude which he calls ‘selective humanitarianism’. He uses this term because 400 volunteers evacuated by the Israeli army on July 21, 2018, were accepted by Western governments to resettle in their countries. Jain argues that these governments remove the most qualified people from the frontline and leave rescue operations to unqualified people. Yet, resettlement of volunteers happened once and there are still thousands of volunteers in Syria. Overall, the White Helmets are struggling to survive from Russian military and information warfare while rescuing civilians from rubbles.←31 | 32→
Russia’s information warfare alongside military warfare has been more conspicuous in the course of the Syrian civil war. While using the information as a weapon during wartime is usual, speeches of top Russian officials disclose that Russia has such activities during peacetime as well. This study analyzed how Russian black propaganda hit the White Helmets, a civil-defense network operating in Syria since the civil war erupted. Reports, news, analysis of social media and interviews have shown that Russian media and affiliated people deliberately attack the White Helmets as it is the only organization that can catch the Syrian regime and Russian army while committing crimes. Russia’s vast information army attack the organization through an organized and collective work to distort facts, misinform people and get confusion among the world audience. One aspect this study discovered is that the people got involved in smear campaigns are both evidence producers and disseminators. One can hardly find a source not affiliated to the huge network. Yet, such tactics work, and thanks to them, the White Helmets have been supposed as a terrorist organization by a considerable number of people. Even volunteers of the White Helmets admit that their propaganda has become successful. While the Western media counter-attempt to disclose fake evidence through fact-checking, they do not seem to balance Russian propaganda. However, despite the harming propaganda, the organization continues its humanitarian operations.
Abdullah, I. (2020). Interview, by Author, Istanbul.
Abdussamed, A. (2020). Interview, by Author, Istanbul.
Ajir, M., & Vailliant, B. (2018). Russian Information Warfare: Implications for Deterrence Theory. Strategic Studies Quarterly, 2018(Fall), 70–89.
Bartlett, E. (2018, August 10). Decision to Bring White Helmets to Canada Dangerous and Criminal. Russia Today. https://www.rt.com/op-ed/435670-white-helmets-canada-syria/, Accessed 02.01.2020.
Beeley, V. (2019, January 22). The White Helmets, Alleged Organ Traders & Child Kidnappers, Should Be Condemned Not Condoned. Russia Today. https://www.rt.com/op-ed/449431-syria-white-helmets-organ-traders/, Accessed 10.01.2020.
Bellingcat (2016, November 30). There’s No Such Thing as a Good Fake – When Publicity Stunts Go Wrong. https://www.bellingcat.com/resources/articles/2016/11/30/theres-no-thing-good-fake-publicity-stunts-go-wrong/, Accessed 21.12.2019.←32 | 33→
CBS News (2019, November 25). Chemical Weapons Watchdog OPCW Defends Syria Report as Whistleblower Claims Bias. https://www.cbsnews.com/news/opcw-chemical-weapons-watchdog-douma-chlorine-gas-wikileaks-russia-syria-claim-bias-today-2019-11-25/, Accessed December 30, 2019.
Daily Sabah (2019, November 20). Officials Reveal White Helmets Founder’s Cause of Death. https://www.dailysabah.com/politics/2019/11/20/turkish-officials-reveal-white-helmets-founder-le-mesuriers-cause-of-death, Accessed December 25, 2019.
France 24 (2018, May 14). White Helmets ‘Staging Fake Attacks’ In Syria? We Sort Fact from Fiction. https://observers.france24.com/en/20180514-white-helmets-allegations-fact-fiction, Accessed December 30, 2019.
Giles, K. (2016). Handbook of Russian Information Warfare. Rome: NATO.
Di Giovanni, J. (2018, October 16). Why Assad and Russia Target the White Helmets. TheNew York Review Daily. https://www.nybooks.com/daily/2018/10/16/why-assad-and-russia-target-the-white-helmets/, Accessed 10.01.2020.
Grigoriev, M., & Maizel, S. (2019). The White Helmets: Fact-Checking. Moscow: The Foundation for the Study of Democracy.
Jain, V. (2018, August 2). Resettling the White Helmets: A Dangerous Foreign Policy Precedent for Health in Syria. Journal of International Affairs. https://jia.sipa.columbia.edu/online-articles/resettling-white-helmets-dangerous-foreign-policy-precedent-health-syria, Accessed January 21, 2020.
Lester, N. (2018). Introducing a Trauma-Informed Practice Framework to Provide Support in Conflict-Affected Countries: The Case of the Syrian White Helmets. The Rusi Journal, 163(6), 28–41.
MacMillan, A. (2019, November 11). Former British Officer Who Helped White Helmets Hailed As ‘True Hero’. The National. https://www.thenational.ae/world/mena/former-british-officer-who-helped-white-helmets-hailed-as-true-hero-1.936165, Accessed 10.01.2020.
Mahmoud, I. (2020). Interview, by Author, Istanbul.
Malsin, J. (2016, October 6). As the War Worsens, Rescue Workers Risk Their Lives on The Front Lines. Time. https://time.com/syria-white-helmets/, Accessed 17.01.2020.
McKeigue, P., Miller, D., Mason, J., & Robinson, P. (2019). How the OPCW’s Investigation of the Douma Incident Was Nobbled. Research Gate. https://www.researchgate.net/publication/334733143_How_the_OPCW%27s_investigation_of_the_Douma_incident_was_nobbled?channel=doi&linkId=5d3dfa7e299bf1995b53c730&showFulltext=true, Accessed 21.01.2020. doi:10.13140/RG.2.2.17223. 85927.←33 | 34→
Nocetti, J. (2019). Dazed and Confused: Russian “Information Warfare” and the Middle East – The Syria Lessons. Report No. 93. Barcelona: Euro Mesco.
Porotsky, S. (2018, February 8). Analyzing Russian Information Warfare and Influence Operations. Global Security Review. https://globalsecurityreview.com/cold-war-2-0-russian-information-warfare/, Accessed 03.01.2020.
Qui, L. (2017, April 10). Syria Conspiracy Theories Flourish, at Both Ends of the Spectrum. The New York Times. https://www.nytimes.com/2017/04/10/us/politics/factcheck-syria-strike-conspiracy-theories.html, Accessed December 23, 2019.
RT (2019, November 15). Assad Likens ‘Suicide’ of White Helmets Founder to Epstein & Other High-Profile Mystery Deaths. https://www.rt.com/news/473579-assad-epstein-white-helmets-deaths/, Accessed 03.01.2020.
Russian MFA (@mfa_russia) (2019, November). Zakharova’a Tweet. Twitter. https://twitter.com/mfa_russia/status/1192763676878610432?ref_src=twsrc%5Etfw%7Ctwcamp%5Etweetembed%7Ctwterm%5E1192763676878610432&ref_url=https%3A%2F%2Fwww.thenational.ae%2Fworld%2Fmena%2Fformer-british-officer-who-helped-white-helmets-hailed-as-true-hero-1.936165, Accessed 10.01. 2020.
Saleh, R. (2020). Interview, by Author, Idlib (via phone).
Salmo, A. (2020). Interview, by Author, Istanbul.
Samir, A. (2019, May 28). Who Really Are the Syrian White Helmets?. Insideover. https://www.insideover.com/war/who-really-are-the-syrian-white-helmets.html, Accessed 02.01.2020.
Sanchez, R. (2017, August 12). Seven Members of Syria’s White Helmets Shot Dead By Unknown Gunmen. The Telegragh. https://www.telegraph.co.uk/news/2017/08/12/seven-members-syrias-white-helmets-shot-dead-unknown-gunmen/, Accessed December 17, 2019.
Schneider, T., & Lütkefend, T. (2019). Nowhere to Hide: The Logic of Chemical Weapons Use in Syria. Berlin: Global Public Policy Institute.
SNHR (2019). Syrian-Russian Alliance Forces Target 31 Civil Defense Vital Facilities in The Fourth De-Escalation Zone in 11 Weeks. Relief Web. https://reliefweb.int/sites/reliefweb.int/files/resources/The_Syrian_Russian_alliance_forces_target_31_vital_centers_of_the_Civil_Defense_Organization_en.pdf, Accessed December 28, 2019.
Solon, O. (2017, December 18). How Syria’s White Helmets Became Victims of an Online Propaganda Machine. The Guardian. https://www.theguardian.com/world/2017/dec/18/syria-white-helmets-conspiracy-theories, Accessed 05.01.2020.←34 | 35→
Tashev, B., Purcell, M., & McLaughlin, B. (2019). Russia’s Information Warfare Exploring the Cognitive Dimension. MCU Journal, 10(2), 129–147.
The Syria Campaign (2017). Killing the Truth. https://thesyriacampaign.org/wp-content/uploads/2017/12/KillingtheTruth.pdf, Accessed December 27, 2019.
The White Helmets (2019). Main Page. https://whitehelmets.org/en/, Accessed 23.01.2020.
Worrall, P. (2016, December 20). Eva Bartlett’s Claims About Syrian Children. Channel 4. https://www.channel4.com/news/factcheck/factcheck-eva-bartletts-claims-about-syrian-children, Accessed 03.01.2020.←35 | 36→
1 More analyzes about dubious photos and videos made viral by Russian accounts can be found at above media outlets and others.
Sidre Gul Bige Gocekli and Necmiye Comertler
Abstract Economics is a social science which has humans as its subjects. Economics investigates human behavior on economic decisions and tries to arrive at a generalized theory about economic situations. However, in the process of time, economics lost its humanity. The human has been left out of the equations of the economic theories. This absurd situation was caused by the endeavor of economics to be accepted as a science. Nevertheless, some economists fortunately realized the peril economics is in and proposed some ways which are now called “behavioral economics” to let the human in economics again. The human should be brought back to economics. Humans in economics can gain “their humanity” only by including the individual and social characteristics that make human beings human to economic models. After this is made possible then economics will be what it should be, and behavioral economics and economics will become the same thing. In this study we first discuss about what the human is and his/her place in economics, and then deliberate on what behavioral economics is and its history shortly.
Keywords: Human, Homo-economicus, Rationality, Economics, Behavioral Economics
Individuals come together to form the society. On the other hand, they are heavily influenced by the community they create, that is, society in general. While traditional economics examines the individual and society as the mathematical sum of individuals, it ignores the individual-society interaction. There will be those who would argue that examining individuals one by one and studying society as a community is different. The individuals who forming societies determine the general characteristics of society and affect society. In other respects, individuals living in society too are affected by the properties of the society they are in, such as religion, language, culture, etc. while being evolved with the rules and norms of the society. Therefore, it is impossible to dissociate the individual and society. Researching the individual abstracted ←37 | 38→from society or the society abstracted from the individual will lead to incomplete and incorrect results.
When individuals create communities, they first come together with other individuals who are similar to themselves but also have the characteristics that can complement their shortcomings. Then, within the community that is formed, individuals begin to influence and be influenced by others’ characteristics. Thus, a society with common grounds arise. Those born in this society will have similar characteristics to the individuals who raise them. The individual and society are in constant interaction. Therefore, examining only the individual or only the society independent of each other will lead to the failure of the established theories over time in sciences such as economics, the main subject of which is the human and human decisions. What is worse than severing the relationship of man with society in Traditional Economics Doctrine is the expulsion of the human from economics, which is a social science that claims to examine the human being.
Economic theories use mathematical models to make economic analysis, interpret the past, and make predictions. Using mathematics as a tool gives an objective perspective to economics. However, these mathematical models, which were created over time, ceased to be a tool for predicting economic behavior and turned into a goal; and mathematical and economic assumptions were made to give consistency to the results obtained from these models. For instance, the “ceteris paribus” assumption (when everything else is fixed) is one of them. Although there is nothing constant in real life and all the variables change continuously and simultaneously, this is a necessary assumption to simplify the mathematical model to be able to make economic predictions. With this assumption, the mathematical model becomes mostly a linear model with few unknowns and is relatively easier to solve. Otherwise, the model will become nonlinear equations with many unknowns which is hard to get out of. Therefore, “the ceteris paribus” assumption is a productive one to make simple comments. But it is not essential. Models without this assumption should be developed to make more up-to-date and more accurate determinations. Although it was difficult to analyze models without assumptions until the development of supercomputer technologies and technologies that can perform very complex calculations, it has become possible today. Removing the ceteris paribus conjecture over time may be beneficial for developing economic theories that model more realistic economic behavior thanks to these technologies.
How successful can ordinary people be in solving complex economic problems that even their experts have difficulty solving? At this point, the biggest mistake made by traditional economics to facilitate theoretical ←38 | 39→modeling is “homo economicus” (economic-man) or rationality assumption. Economic-man or rational human assumption, which is one of the most fundamental assumptions of traditional economics, helps economic models to become completely mathematically consistent within themselves. Because the rational individual is a selfish creature, such as an android-robot, who minds his cost-benefit calculations, can make these calculations from the mind, his economic decisions are always consistent within, and his decisions are always the same without an external factor that can mislead him.
Thaler calls economic man “an econ” and describes him as an unusual alien creature. (Thaler & Sunstein, 2009: 7–9; Thaler, 2016: 1577–1578). Since this creature is a constant element and his decisions are unchanging, the creature himself is treated as given and excluded from the model, thus the model works without any problems. As can be seen, economics, whose subject is human, tries to interpret the results caused by the decisions of the subject while leaving its subject out of the model. This situation is similar to holding the wedding of a person without his knowledge and then describing how the wedding went to the person himself.
The human should be brought back to economics. Humans in economics can gain “their humanity” only by including the individual and social characteristics that make human beings human to economic models. For this purpose, economics should benefit from other disciplines of science such as psychology, sociology, anthropology, biology, and neurology. In this context, behavioral economics tries to correct the erroneous rationality assumption of traditional economics by using these science disciplines and to establish models that include real people in economics.
2 The Return of the Human to Economics: The Emergence and Development of Behavioral Economics
Behavioral economics is about developing economic analysis for real people in the real world, making economic models more robust, more accurate, and more practical. Behavioral economics, as in traditional economics, deals with incentives, costs and benefits, impulse-response, and economic efficiency. Behavioral economics enriches the toolbox of traditional economics by utilizing psychology, neurology, sociology, politics, and law (Altman, 2012: 9). Empirical findings from detailed research show that making the model of the economic man at the center of behavioral economics more accurate and realistic will improve the economic understanding and thus make the discipline of economics more functional (Diamond & Vartiainen, 2007).←39 | 40→
To explain how people make decisions in real life and to make economic analyzes based on more realistic assumptions about the conditions that affect these decisions are among the goals of behavioral economics. In behavioral economics, people are not calculators. Rather, they are decision-makers who act with passion and reason. This enriched economic theory provides us with a better understanding of the economic behavior of individuals and societies (Altman, 2012: 9).
Throughout the 18th century, philosophers such as David Hume wrote articles about how the mind works. Hume’s (1739) “A Treatise of Human Nature” is accepted as the founding document of cognitive science by philosophy historians. This study influenced scientists in many disciplines, especially psychology. I also affected economists of that period like Adam Smith. Scrutinization of the operation of the human mind has led economists to begin to notice the discrepancies between what “rational man” should do and what he does. During the 1800s, economists such as William Stanley Jevons has done studies that would form the basis of traditional economics. These economics was based on hedonic psychology, and therefore they viewed the utility as pleasure and pain. (Angner & Loewenstein, 2006: 7–8).
It is difficult to say exactly when behavioral economics started. However, according to Cartwright (2018a: 5), Adam Smith can be pointed as its founder. In his book “The Theory of Moral Sentiments” published in 1759, Smith mentions that people are not motivated only by their self-interests, but they also have a natural sympathy for others and have a natural sense of virtue. In his book “Wealth of Nations” published in 1776, Adam Smith claimes that economic behavior is motivated by personal interests. However, in 1759, 17 years before this work, he proposed a theory of human behavior that resembles anything but self-interest. In “The Theory of Moral Sentiments”, Adam Smith claimes that behavior was determined as a result of the conflict between the terms which he calls “passions” and “impartial spectators”. Passions include impulses such as hunger and sexuality, emotions such as fear and anger, and motivational states such as pain. Smith saw behavior as directly under the control of passions but believed that people could suppress their passion-driven behavior by evaluating their behavior as an impartial bystander from an outsider’s perspective (Ashraf, Camerer & Loewenstein, 2005: 131).
In the first centuries after Smith, economists focused a lot on emotions, impulses, incentives, morality, and the like. However, as the 20th century began, economists turned away from psychology and behavioral economics. This situation was caused by Vilfredo Pareto. In a letter that dates to 1897, Pareto stated that it would be beneficial for a pure political economy to rely as little as possible ←40 | 41→on the field of psychology. In his article where he then described a new approach to choice theory, Pareto claimed that the greatest achievement of this approach was that it eliminated all psychological analysis. Psychology can be removed from economics by focusing on choice rather than desire. Inferences can be made on just what people do instead of why they do what. Pareto stated that he is not concerned with why one is indifferent between two things but is concerned with the pure and naked truth. Pareto questioned what happens when people act rationally. Likewise, Adam Smith made the invisible hand argument by asking the question of what would happen when people behave selfishly. Assuming that humans are rational and selfish for mathematical convenience does not mean that they are truly rational and selfish. Smith made this distinction. Even Pareto distinguished when what people do could be represented by rational choice or not (Cartwright, 2018a: 5).
In 1948, Edward Chamberlin published his article “An Experimental Imperfect Market”. This article is considered to be the first experimental paper published in the field of economics. In this study, Chamberlin reached the key finding that market results can deviate from the market equilibrium. He emphasized that the findings obtained by conducting experiments in the laboratory of society can be appreciated as scientific results. This approach was a pioneering idea for that period (Cartwright, 2018b).
In the 1950s, Herbert Simon published articles on the concept of “bounded rationality”. These studies are particularly important for behavioral economics as they are a direct attack on the belief that “man is rational” which is the dominant view of the time. (Hosseini, 2003).
Considering that behavioral economics is divided into old and new, it can be said that the old behavioral economics started in the 1950s and early 1960s with the studies of George Katona and Herbert Simon. Simon’s contributions are well known because of the popularity of the “bounded rationality” and “satisficing2” theories and because he won the Nobel Prize in Economics. However, economists do not know the contributions of Katona that well (Hosseini, 2011).
Katona is known as the founding father of the old behavioral economics discipline, which pioneered modern behavioral economics. Katona (1980) mentions three main features of behavioral economics in his work “Essays ←41 | 42→on Behavioral Economics”. First of these consists of empirical studies of the behavior of firms and consumers in a particular country at a given time. Generalizations about economic behavior emerge gradually from comparing observed behavior in different situations. Second, behavioral economics focuses on studying the decision-making process of these behaviors rather than analyzing the amounts spent, saved, and invested as a result of spending, saving, investment, and similar behaviors. Third, the study of the human factor has a significant place in behavioral economics. Katona tried to bring realism to the economic analysis with the help of psychological concepts (Hosseini, 2011). For these reasons, Katona and Simon are regarded as the founding fathers of behavioral economics.
While traditional economists assume that the principles of economic behavior, which they infer from the characteristics of human nature, are the same in all times and cultures, Katona and Simon reject this and try to explain the actual behavior of economic actors (Yang & Lester, 1995; Hosseini, 2003; Hosseini, 2011: 978). They wanted to replace the mechanical psychology of traditional economics with its real counterpart to create economics, which “tries to find out what happens when people make decisions as consumers, producers, or policymakers” as Katona (1975: 71) states.
Katona was misunderstood and criticized by the economists of his time for being a psychologist. However, his views should not be denied due to his contributions to the emergence and development of behavioral economics, although he misjudged some economic theories due to his insufficient knowledge in economics.
Simon questioned the rationale for representing people with the Homo-economicus. Simon (1955) first states how the rational person should behave, then expresses there is no evidence that people did or could do these calculations in any of the real choice situations. Therefore Simon suggests examining people’s knowledge and computational capacities and that this should be the beginning of economic models. The limitations people face lead Simon to the concept of “bounded rationality” (Cartwright, 2018a: 6–7). Publishing “Models of Man” in 1957, Simon created a model of human concept that is more compatible with the bounded rationality theory (Hosseini, 2003). Simon argued that behavioral economics emerged because it was necessary to enrich the existing traditional economic theory to reach a more realistic picture of the economic process. According to Simon, economists as social scientists should be prepared to describe key characteristics of people. This can only be possible by using behavioral economics (Gilad & Kaish, 1986: xvi). Simon tried to add the bounded rationality of human beings into various economic theories by creating heavy ←42 | 43→mathematical models and was criticized by the economists of his time. Simon won the Nobel Prize in Economics in 1978 for his pioneering research on the decision-making process within economic institutions. However, the call to replace the Economic Man with a more realistic human being has been ignored (Cartwright, 2018a: 7).
In 1962, Vernon Smith reports on a series of market experiments carried out between 1955 and 1961. These experiments were some of the first experiments carried out in economics. Smith (1962: 134) demonstrated that there are strong tendencies in achieving competitive equilibrium of supply and demand, as long as individuals can prohibit alliances and collusion and maintain absolute openness of all auction bids and transactions, although the numbers are small. Smith won the Nobel Prize in Economics in 2002 for establishing laboratory experiments “as a tool in empirical economic analysis, especially in studies of alternative market mechanisms” (Cartwright, 2018a: 8) and for his contribution which is stated by Nobel Prize Committee (2002) as developing “methods for laboratory experiments in economics, which has helped our understanding of economic behavior”. Smith’s experiment showed that in some cases, traditional economics gave correct results and which raised the question of the necessity of behavioral economics. However, Smith’s study showed that the accuracy, errors, and deficiencies of the economic models can be determined via experiments. The experiments of Smith gave new tools to economists and paved the way for new behavioral economics. Smith’s works helped the behavioral economics but he is considered not as a behavioral economist but an experimental economist. New behavioral economics is considered to began in 1970s with the works of Kahneman, Tversky and Thaler.
In economics, the market is understood to direct the behavior toward a competitive equilibrium where all economic actors behave optimally and social welfare is maximized. However, many economists have seen this ideal market picture’s shortcomings in limited information, few buyers and sellers, adverse selection, moral hazard, and the like. What psychologists Daniel Kahneman and Amos Tversky brought to economics in the 1980s was the idea that market imperfections could also result from erroneous human behavior (Heukelom, 2014: 1). Kahneman and Tversky published the article “Judgment under Uncertainty: Heuristics and Biases” in 1974 and in 1979 they put forward the “Prospect Theory”. Kahneman and Tversky (1979) realized that the Expected Utility Theory did not work in many situations and developed Prospect Theory to show how people perceive the utility. According to this, people perceive utility differently in cases of gain and loss and make different choices than rational economic people.←43 | 44→
Tversky and Kahneman (1981: 453) stated that the definition of rationality is controversial, but the consensus is that rational choices must meet some basic requirements of integrity and consistency. Kahneman and Tversky defined decision problems in their studies where people systematically violate the requirements of integrity and consistency. They realized these violations can be explained with the psychological principles of the evaluation options and perception of decision problems. Kahneman won the Nobel Prize in Economics in 2002 for introducing a new approach to economics, particularly in human judgment and decision making under uncertainty (Since Tversky passed away before this date, the award was given only to Kahneman) (Cartwright, 2018a: 8).
Robert Aumann and Thomas Schelling won the Nobel Prize in Economics in 2005 for their contribution to cooperation and conflict. In particular, Schelling’s work focused on how people behave rather than how they should behave. This is an important step towards behavioral game theory (Cartwright, 2018b).
Richard Thaler (1985) used the concept of mental accounting in his work “Mental Accounting and Consumer Choice”. In his study, he stated that losses and gains pass through hedonic coding in mind. He stated that the decisions of individuals differ in cases such as lottery tickets being free of charge, being a gift, or incurring a cost beforehand. He has also shown that the resulting pleasure is different when the reward is obtained at once or in parts. Richard Thaler and Cass Sunstein (2009) highlighted the importance of framing and context effects in their book “Nudge”. They also argued that the targeted results could be achieved more easily by using these, in other words, by nudging people, by policymakers. Thaler won the Nobel Prize in Economics in 2017 for his contributions to the field of behavioral economics and for building a bridge between economics and psychology.
Behavioral economics today is classified under microeconomics because it mostly deals with the economic behavior of the individual. The starting point of behavioral economics is to take the human out of the mold that traditional economics has put him. However, this goal makes it difficult to define behavioral economics. Because all studies that serve this purpose can be evaluated within the framework of behavioral economics. That is, any theory that replaces any of the core features of traditional economics with alternatives that have a better empirical basis in human behavior is a potential member of the class of behavioral economic theories (Dhami, 2016: 2).←44 | 45→
Although it is difficult to define behavioral economics precisely, it can be said that it is not a hostile theory that aims at the destruction of traditional economics, but rather a discipline of economics that aims to restore the human element that has been forgotten in time and to renew and improve existing economic theories.
Using various psychological factors to better understand the world and make more accurate predictions about economic behavior is no different from using a new data source. Therefore, behavioral economics is not a revolution, but rather a return to the open-minded and intuitively motivated discipline that Adam Smith found. The increasing interest in studies on “humans” instead of econs has started a wave that will make new theoretical developments possible. If the developments in economics continue in this line and everyone adds all the factors that determine economic behavior to the analysis, the term “behavioral economics” will gradually disappear from the dictionaries. Because all economics will be behavioral to the extent the subjects allow and an approach that increases the explanatory power of economic models will be acquired (Thaler, 2016: 1597). In short, economics will be what it should be, and behavioral economics and economics will become the same thing.
Altman, M. (2012). Behavioral Economics for Dummies. John Wiley & Sons Canada Ltd., Mississauga.
Angner, E., & Loewenstein, G. (2006). Behavioral Economics, To appear vol. 5. Elsevier’s Handbook of the Philosophy of Science.
Ashraf, N., Camerer, C. F., & Loewenstein, G. (2005). Adam Smith, behavioral economist. Journal of Economic Perspectives, 19(2005), 131–145.
Cartwright, E. (2018a). Behavioral Economics Third Edition. Routledge Advanced Texts in Economics and Finance. Routledge, New York.
Cartwright, E. (2018b). Behavioral Economics Third Edition Web Material, Timeline for Behavioral Economics. Access date December 2019, https://routledgetextbooks.com/textbooks/9781138097124/students.php
Chamberlin, E. H. (1948). An experimental imperfect market. The Journal of Political Economy, 56(2), 95–108.
Dhami, S. (2016). The Foundations of Behavioral Economic Analysis. Oxford University Press, Oxford.
Diamond, P., & Vartiainen, H. (Eds.). (2007). Behavioral Economics and Its Applications. Princeton University Press, New Jersey.←45 | 46→
Geiger, N. (2014). The rise of behavioural economics: A quantitative assessment, Schriftenreihe des Promotionsschwerpunkts Globalisierung und Beschäftigung, No. 44/2015, Universität Hohenheim, Stuttgart, http://nbn-resolving.de/urn:nbn:de:bsz:100-opus-10845
Gilad, B., & Kaish, S. (Eds.) (1986). Handbook of Behavioral Economics, Vols. A and B. JAI Press, London.
Heukelom, F. (2014). Behavioral Economics: A History. Cambridge University Press, New York.
Hosseini, H. (2003). The arrival of behavioral economics: From Michigan, or the Carnegie School in the 1950s and the early 1960s. Journal of Socio-Economics, 23(2003), 391–409.
Hosseini, H. (2011). George Katona: A founding father of old behavioral economics. The Journal of Socio-Economics, 40(2011), 977–984.
Hume, D. (1739). İnsanın Doğası Üzerine Bir İnceleme. Çeviren Aziz Yardımlı, 2016. İdea Yayınevi, İstanbul.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291.
Katona, G. (1975). Psychological Economics. Elsevier, London.
Katona, G. (1980). Essays on Behavioral Economics. University of Michigan Press, Ann Arbor, MI.
Nobel Prize Committee. (2002). Vernon L. Smith Facts. The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 2002. https://www.nobelprize.org/prizes/economic-sciences/2002/smith/facts/
Simon, H. A. (1955). A behavioral model of rational choice. Quarterly Journal of Economics, 69(1), 99–118.
Smith, A. (1759). Ahlaki Duygular Kuramı. Çeviren Derman Kızılay, 2018. Pinhan Yayıncılık, İstanbul.
Smith, A. (1776). Milletlerin Zenginliği. Çeviren Haldun Derin, 8. Basım, 2014. Türkiye İş Bankası Kültür Yayınları, İstanbul.
Smith, V. L. (1962). An experimental study of competitive market behavior. Journal of Political Economy, 70(2), 322–322.
Thaler, R. H. (1985). Mental accounting and consumer choice. Marketing Science, 4(3), 199–214.
Thaler, R. H. (2016). Behavioral economics: Past, present, and future. American Economic Review, 106(7), 1577–1600.
Thaler, R. H., & Sunstein, C. R. (2009). Nudge: Improving Decisions About Health, Wealth and Happiness, Penguin Books, London.←46 | 47→
Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science, 211(4481), 453–458.
Yang, B., & Lester, P. (1995). New directions for economics. Journal of Socio-economics, 24(1995), 443–456.←47 | 48→
1 This paper has been produced from the unpublished ongoing thesis “Gocekli, S. G. B. (2021). Davranışsal İktisat ve Beklenti Teorisi Üzerine 3 Makale. (Unpublished Ph.D. Thesis). Aydin Adnan Menderes University Institute of Social Sciences, Aydin.”
2 The word satisficing, a combination of the words satisfy and suffice was created by Simon. He explains that bounded rational people make their choices as soon as they find the first option that satisfies their desire levels among various options instead of optimization (Geiger, 2014: 3).
A. Elif Ay Yalcinkaya and Ramazan Ekinci
Abstract The relationship between efficiency, profitability and market power in the literature has developed on the basis of the efficient-structure (ES) hypothesis by Demsetz (1973) and the structure-conduct-performance (SCP or market power) hypothesis by Bain (1951) and Baumol (1982). This paper tests the different hypotheses explaining profitability in the generic framework of hypotheses of collusion versus efficiency, presenting as its main contribution the use of a direct measurement of efficiency. The analysis is performed on 115 firms operating in the Turkish manufacturing industry during the 2010–2019 period. The data is extracted from the Public Disclosure Platform (KAP) and firms balance sheet items. In order to test the hypotheses, the profit efficiency measures within the method of Bayesian Stochastic Frontier Analysis were used. In the empirical section, we perform a static and dynamic panel data approach to estimate the regression equations. The static and dynamic results show that there is a negative and significant relation between concentration and profitability for all industry, while a positive and significant relation between large firms’ concentration ratio and profitability. We also find a positive and significant effect of profit efficiency on firm profitability. This supports the ES hypothesis in the Turkish manufacturing industry.
Keywords: Efficient-Structure Hypothesis, Structure-Conduct-Performance Hypothesis, Turkish Manufacturing Industry, Bayesian Stochastic Frontier Analysis, Profitability
One of the basic assumptions of industrial organization theory is that firms act for profit maximization. To achieve this goal, firms can maximize their profits by maximizing their revenues or minimizing their costs. In general, there are two different channels for companies to increase their profitability. The first of these channels is the power of firms to increase their prices above the market price depending on the market power, and the second is to increase the efficiency level by reducing production costs and thus reach high profitability levels. The condition of profit maximization based on these two conditions is valid under imperfect competition conditions. Under imperfect competition, average costs stay above the market price. Therefore, the firm cannot obtain enough output to minimize its average costs. As a result, there is a loss of welfare in terms of ←49 | 50→imperfect competition conditions. Therefore, profit rates are considered as a result of the market power that firms can gain due to imperfect competition. However, under perfect competition conditions, profit maximization becomes equivalent to cost minimization and market power disappears. In the long run, the firm can only make normal profit.
The relationship between efficiency, profitability and market power in the literature has developed on the basis of the efficient-structure (ES) hypothesis by Demsetz (1973) and the structure-conduct-performance (SCP or market power) hypothesis by Bain (1951) and Baumol (1982). According to the SCP hypothesis, price determination power of a firm depends on the market structure. For this reason, it is stated that firms’ higher market power with collusion between them results in higher price policy and profitability. Therefore, in a market where concentration is high, it is stated that the inefficiency created by low level of competition leads to monopoly pricing and excessive (monopoly) profitability. On the other hand, according to the efficient structure hypothesis that is put forward by Demsetz (1973), competitive market structure creates pressure on firms and forces firms to be effective. As a result, as companies increase their efficiency levels, they gain competitive power. Thus, it is stated that they gradually grow in the market and dominate a larger market share. As a result, it is claimed that the increase in competition created by efficiency increases the profit of the firm.
The question of whether higher concentration is due to market power or efficiency is one of the most controversial issues in the literature. The purpose of this study, in the framework of market power and efficient structure hypothesis, is to analyze the activities that determine the profitability of the manufacturing industry in Turkey. First of all, the relationship between profitability and concentration is estimated within the framework of the SCP hypothesis and the coefficients that explain the basic hypothesis are obtained. Then, within the framework of the ES hypothesis, the relationship between profitability and efficiency is estimated and the coefficients of the efficiency parameters are obtained. Finally, by evaluating the results of these two hypotheses together, it is tried to determine the basic variables explaining the profitability level of the sector and their parameters (market power or efficiency). Market share is generally used as an indicator of efficiency in the literature. In this study, unlike other studies, efficiency scores estimated on profit function are given to. Thus, different from stochastic frontier analysis, the Bayesian stochastic frontier analysis approach is used as a prediction technique. Depending on the technique used, the validity of the hypotheses is demonstrated by static or dynamic panel data analysis with an approach different from the literature. As a result, ←50 | 51→it is tried to bring an innovation to the literature with the estimation tools and techniques used.
There are many studies in the literature on financial and non-financial sectors analyzing the effect of market concentration on profitability. In theoretical and empirical studies, there is still no consensus on how the relationship between these two variables emerges, and its consequences (Lee & Mahmood, 2009). As a matter of fact, in theoretical studies, it is seen that concentration has positive and negative significant effects as well as no significant effect. The positive effect of concentration on profitability is explained by two different hypotheses that contradict with each other. The first of them is the SCP hypothesis, caused by market concentration and a positive and significant effect on profitability exists (Bain, 1951). The second is the efficiency hypothesis, which suggests that the relationship between profitability and market concentration is determined by the efficiency of dominant firms (Demsetz, 1973). Apart from these, there are also opinions arguing that market concentration has a negative effect on profitability (Leibenstein, 1966; Keil, 2017). Another view states that there is no significant relationship between profitability and market concentration (Brozen, 1971).
The results of empirical studies as well as theoretical studies reveal inconsistent results. Most of the results obtained indicate a positive effect. On the other hand, there are few studies showing that market concentration has a negative effect on profitability (Anderson, Fok, & Scott, 2000; Alhassan Tetteh & Brobbey, 2016; Mukhopadhyay & Chakraborty, 2017). In addition, there are some studies showing that there is no statistically significant relationship between profitability and market concentration after controlling other variables that affect profitability (Clarke, Davies & Waterson, 1984; Khan & Hanif, 2019; Keil, 2018).
The first empirical studies investigating the effect of market concentration on profitability were generally conducted on developed countries. Here, mostly cross section and ordinary least squares methods were used. In most of these studies, it was demonstrated that there is a positive relationship between profitability and market concentration (Collins & Preston, 1969; Strickland & Weiss, 1976). However, the results of the study, conducted by Clarke, Davies and Waterson (1984), show that there is no statistically significant relationship between profitability and market concentration.
On the other hand, as the data becomes accessible more easily and we access to a wider data set, the number of studies on the manufacturing industry increased ←51 | 52→in both developed and developing countries in recent years. Depending on the increase in the data set, the analysis methods differed. Accordingly, in recent years, there has been an increasing number of studies using panel data methodology and producing mixed results. In this context, McDonald’s study (1999) is the first study to reveal the correlation between market concentration and profitability using the dynamic panel data method. McDonald found a positive relationship between profitability and market concentration in Australian manufacturing industries. Gallagher Ignatieva and McCulloch (2015) reached a positive relationship on the Australian example too. Similarly, Bhandari (2010) showed that there is a positive relationship between profitability and market concentration using the static panel data method. In another study, Setiawan and Effendi (2016) revealed a positive relationship between profitability and market concentration in Indonesian manufacturing industry. In the literature, there are also studies showing that there is a negative relationship aside from empirical studies that reveal the positive relationship between profitability and market concentration. For example, Mukhopadhyay and Chakraborty (2017) showed that there is a negative relationship between market concentration and profitability using the dynamic panel data method. In addition to the positive and negative relationships, there are also studies showing that there is no statistically significant relationship between profitability and market concentration. For example, Mishra (2008) could not reach a meaningful relationship between profitability and market concentration on manufacturing industries in Indonesia, by using dynamic panel data method. Similarly, according to Keil (2018), there is no statistically significant relationship between profitability and market concentration in the manufacturing industry in the United States.
In order to analyze the impact of concentration on profitability, the model is formed as follows:
Here is the profit and is the concentration ratio of the firm i in the industry j for the period t. Control variables in the model are represented by the Z vector. The fact that the estimated coefficient is greater than zero () means that the market structure resulting in higher concentration leads to higher firm profitability. is the error term, that changes across the individual firms and time periods. Therefore, it is shown as below:←52 | 53→
In the equation, shows the firm effects which is the time invariant component, while is the time effect which is the firm invariant component. is normally distributed random error. In this study, Equation (1) is estimated using two different approaches: static and dynamic models. Although Equation (1) is a static model by its nature, it transforms into a dynamic model by using the lagged value of the rate of profitability as an independent variable. Both models control unobserved factors that differ from one firm to the other, but are constant over time.
The second hypothesis we tried to test in our study is the efficient market hypothesis and is defined as follows:
where shows the profit rate of firms as in Equation (1). is the efficiency level of the firm i for the period t, and it is estimated by stochastic frontier analysis. In the equation, Z is the vector of control variables and is the error term. If the coefficient is positive () and statistically significant, it can be said that the efficient market hypothesis is valid. On the other hand, when Equations (1) and (2) are evaluated together, if both and coefficients are significant and positive , it can be said that the source of market power that increases the profitability of firms is efficiency. However, if is positive () and significant but is either insignificant or negative, then it can be said that the higher profits are because of market power. In the first case, the efficient-structure (ES) hypothesis, in the second case, the structure-conduct-performance (SCP) hypothesis is valid.
Firm-specific and macroeconomic variables that are thought to have an effect on profitability in the model are as follows:
• age of the firm, which is calculated as the years of the firms in the sector and represents the experience of the firms.
• liquidity ratio, which is current assets divided by short-term liabilities;
• leverage ratio, which is total equity divided by total assets;
• capital intensity, which is fixed assets divided by total assets;
• export intensity, which is export income divided by sales;
• labor cost, which is labor expenses divided by sales;
• annual inflation rate,
• annual growth rate on GDP.←53 | 54→
4.1 The Stochastic Profit Frontier
The stochastic frontier analysis (SFA) was proposed by Meeusen, Van den Broeck (1977) and Aigner, Lovell, Schmidt (1977). SFA allows the composite error, namely a firm may deviate from its optimal frontier because of its own inefficiency and some other changes.
The profit function used in the study is expressed as (Berger and Mester, 1997):
firms; time periods,
represents ln of firm i’s profit during period t. is the scalar intercept and is the vector of parameters to be estimated. is the vector of output and input variables. is identically and independently distributed random error. Last but not least, is profit inefficiency. It follows one-sided distribution, confirming that the inefficiency is not negative. We use one output – net sales (y) – and three inputs – financial debt (), labor () and physical capital () – as in the studies by Purwanto, Manongga and Pakereng (2014); Kotey and O’Donnell (2002); Assaf and Cvelbar (2011); Pérez-Rodríguez and Acosta-González (2007). The prices of inputs are as follows: financial debt price (), labor price () and physical capital price (). All nominal variables are further deflated by the consumer price index with the base year 2010.
The translog profit function to estimate profit efficiency for the panel data is:
For the linear homogeneity constraint, the dependent variable and input prices in Equation (1) are normalized with the price of capital, . In order to consider the effect of technological change, linear and quadratic time period ←54 | 55→variables are included in the model. In addition, the equity ratio (equity/total assets, Eq) is added to the model as a semi-fixed input to control the differences in risk preferences between firms.
, is the profit efficiency of the firm i during the period t. The closer the profit efficiency score of the firms is to 1, the more efficient firms are considered to be with respect to profits.
4.2 Bayesian Estimation
The Bayesian inference in stochastic frontier models are used by Van den Broeck, Koop, Osiewalski and Steel (1994). This includes the prior information about the vector of parameters through the probability density function . The Bayes’ theorem combines two types of information: . Here means that “it is proportional to” and is the posterior probability density function. Also, the information is summarized in the likelihood function . Therefore, is proportional to the likelihood function and the prior probability density. In other words, the posterior distribution includes all information about the vector of parameters . Inferences about the unknown parameters are made by the posterior distribution (Koop, 1994). With different distributions of the error term, the likelihood function of a stochastic frontier model has been provided in various studies (Kumbhakar & Lovell, 2003).
The inefficiency follows a one-sided distribution and truncated normal, half normal, gamma and exponential distributions are widely used in the literature. Determining the appropriate distribution for is a critical problem to obtain inefficiency (Ehlers, 2011). Because of this, in our study, we compare four models with different distributions and the most appropriate distribution for the term is determined. The use of Bayesian techniques involves the evaluation of complex integrals. We estimate by using the Markov Chain Monte Carlo method and the Gibbs sampling algorithm, which is introduced by Koop, Steel and Osiewalski (1995) and widely used in the literature (Tsionas, 2002; Huang, 2004; Kumbhakar & Tsionas, 2005; Griffin & Steel, 2007).
In our study, the firm-level data are extracted from the financial statements of 115 firms which operated in Turkey from 2010 to 2019. Since the industry-level data such as the industrial concentration are not available as an official statistic, ←55 | 56→the industry level data are aggregated from the firm level data. Namely, for each of the 16 three-digit industries concentration ratio and the Herfindahl–Hirschman index (HHI) are computed separately. All the firm level data are sourced from the Public Disclosure Platform (PDP). Macroeconomic variables are extracted from the Central Bank of the Republic of Turkey (CBRT).
Tab. 3.1: Descriptive Statistics of Variables Used in the Efficiency Estimation
Defined as earnings before interest and taxes
Net sales measured by net revenue from the sale of the company’s core products.
(input price 1)
Financial capital price (interest expenses/total debt)
(input price 2)
Labor price (personnel expenses/number of employees)
(input price 3)
Physical capital price (depreciation of both tangible and intangible assets/total fixed assets)
Eq (Equity to total assets)
Capital ratio (total equity/total assets)
Note: Standard deviation in parenthesis.
The variables in this study are selected by considering the previous literature and availability of data. Regarding the selected variables, the relationship between input prices, output quantities and profit is represented by the profit function. The dependent variable of the stochastic frontier is profit (π), measured by earnings before interest and taxes. The input prices are estimated through proxy variables, since they are not directly observable. Tab. 3.1 shows the descriptive statistics for all these variables.
Determining the most suitable distribution of the inefficiency term is necessary to estimate the stochastic profit frontier. The Lewis and Raftery values, which are obtained for truncated normal, half normal, exponential and gamma distributions, are compared in Tab. 3.2:
The Lewis and Raftery approximation of the log-marginal likelihood is -812.52, which is larger than what the half normal, truncated normal and gamma models produced. Also, the posterior model probability is considerably ←56 | 57→higher for the exponential model. Therefore, the data clearly favor the exponential stochastic-frontier model.
Tab. 3.2: Model Comparison for Different Distributions of the Term Inefficiency
Tab. 3.3 gives the results of estimating the translog profit function. After the stochastic frontier is determined, Gibbs sampling are used to estimate posterior means and posterior standard deviations. A total of 50,000 interactions are created to achieve convergence and avoid sensitivity of initial values (5,000 of these interactions are dropped). The standard deviations of most of the parameters are low and this supports the model convergence (Assaf & Magnini, 2012). Except for labor, the sign on posterior expected value of other input elasticities assessed in the sample mean is positive. This means that, an increase in the use of each input would increase output, ceteris paribus, The coefficient of labor is unexpectedly negative, which provides a reflection of the capital-intensive nature of manufacturing industry in Turkey.
Tab. 3.4 shows descriptive statistics of profit efficiency scores estimated by Bayesian Stochastic Frontier Model (BSFA) with exponential distribution. Profit efficiency changes from 78.04 % in 2010 to 74.09 % in 2019, with a mean efficiency of 76.38 %. The real sector is therefore wasting 23.62 % of its potential profits.
Tab. 3.5 represents the estimation results of the profitability function. Columns (A), (B) and (C) present fixed-effects (FE), random effect (RE) and system GMM estimations of Equation (1), respectively. In Equation (1), the main variable is concentration rate which is calculated separately for each year by Herfindahl-Hirschman index (HHI). Since the concentration ratio is an industry measure, it changes between years and industries.
Then the regression model specified in Equation (2) is estimated, which incorporates profit efficiency as an explanatory variable. Fixed effect, random effect and GMM results are reported in Columns (D), (E) and (F), respectively.←57 | 58→
Tab. 3.3: The Stochastic Frontier Model’s Posterior Statistics
←58 | 59→
Tab. 3.4: Mean Profit Efficiency
According to the dynamic model, a low value of the lagged (ROA (-1)) profitability coefficient (0.28 and 0.23) indicates that the Turkish manufacturing industry is relatively competitive. Many empirical industrial organization studies found a positive relationship between concentration and profits. In this study, the coefficient of the industry concentration has statistically significant negative sign.
Because of this, concentration variable is separated for small and large firms to control different effect of industry concentration on profits of small and large firms and two variables are created. The first variable is created that takes the HHI value for large firm and zero for small firms. Similarly, the second variable is created that takes the HHI value for small firms and zero for large firms. The coefficients of these variables show different effects of industry concentration of small and large firms on their profitability. In our study, the result shows that the coefficient is positively significant for large firm and positive but not statistically significant for small firms. It is indicated that not all firms can benefit and gain superior profits in a concentrated industry. In the concentrated industries, the large firms get benefit and make more profits, while the small firms will lose to these large firms.
The results reported in (D), (E) and (F) of Tab. 3.5 incorporate efficiency variable and control variables. It is observed that efficiency coefficient is positive and significant in all estimated models (FE, RE, GMM). Since there is a significant positive relationship between the profitability indicator and efficiency scores, this supports the efficient structure hypothesis in Turkish real sector.
This study examines the determinants of firm profitability within SCP and ES hypotheses. We use the annual data on 115 firm between the years 2010 and 2019. In this study, HHI values are estimated as proxies for the SCP hypothesis, while profit efficiency scores are estimated as proxies for the ES hypothesis from Bayesian stochastic frontier analysis (BSFA).←59 | 60→
Tab. 3.5: Estimation Results of Profitability Function
←60 | 61→
From the efficiency estimates of the BSFA, we find the average profit efficiency of 76.38 %. This suggests that an estimated 23.62 % of the profit is lost due to a combination of both technical and allocative inefficiency in production.
The results of static (Fixed Effect, Random Effect) and dynamic (GMM) estimations suggest that there is a negative and significant relation between concentration ratio and profitability, while a positive and significant relation between large firms’ concentration ratio and profitability. Additionally, we find insignificant relationship between small firms’ concentration ratio and profitability. Hence, the SCP hypothesis are rejected. This suggests the absence of collusive behavior in all industries, which reflects a competitive behavior among small firms. In other words, the ability of large firms to harness the market power can be limited by competition of small firms. Hence it is possible for these small firms to exit from the industry and concentrate the industry. As a result, large firms will grow even more and become industry leaders and have the power to set their products’ prices above their marginal cost and make super normal profits. We also find a positive and significant effect of profit efficiency on firm profitability. This supports the ES hypothesis in the Turkish manufacturing industry.
Aigner, D. J., Lovell, C. A. K., & Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 6, 21–37.←61 | 62→
Alhassan, A. L., Tetteh, M. L., & Brobbey, F. O. (2016). Market power, efficiency and bank profitability: Evidence from Ghana. Econ Change Restruct, 49, 71–93.
Anderson, R. I., Fok, R., & Scott, J. (2000). Hotel industry efficiency: An advanced linear programming examination. American Business Review, 18, 40–48.
Assaf, A. G., & Cvelbar, K. L. (2011). Privatization, market competition, international attractiveness, management tenure and hotel performance: Evidence from Slovenia. International Journal of Hospitality Management, 30(2), 391–397.
Assaf, A. G., & Magnini, V. (2012). Accounting for customer satisfaction in measuring hotel efficiency: Evidence from the US hotel industry. International Journal of Hospitality Management, 31, 642–647.
Bain, J. S. (1951). Relation of profit rate to industry concentration: American manufacturing 1936–1940. The Quarterly Journal of Economics, 65(3), 293–324.
Baumol, W. J. (1982). Contestable markets: An uprising in the theory of industry structure. American Economic Review, 72(1), 1–15.
Berger, A. N., & Mester, L. J. (1997). Inside the black box: What explains differences in the efficiencies of financial institutions?. Journal of Banking & Finance, 21, 895–947.
Bhandari, A. K. (2010). Concentration, entry barriers and profitability in the Indian industries: An empirical analysis. Journal of Quantitative Economics, 8(2), 61–80.
Brozen, Y. (1971). Concentration and structural and market disequilibria. Antitrust Bulletin, 16, 241–248.
Clarke, R., Davies, S., & Waterson, M. (1984). The profitability-concentration relation: Market power or efficiency? The Journal of Industrial Economics, 32(4), 435–450.
Coelli, T. J., Rao, D. S. P., O’Donnell, C. J., & Battese, G. E. (2005). An introduction to efficiency and productivity analysis. Boston, MA: Springer.
Collins, N. R., & Preston, L. E. (1969). Price-cost margins and industry structure. The Review of Economics and Statistics, 51(3), 271–286.
Demsetz, H. (1973). Industry structure, market rivalry and public policy. The Journal of Law and Economics, 16(1), 1–9.
Ehlers, R. S. (2011). Comparison of Bayesian models for production efficiency. Journal of Applied Statistics, 38, 2433–2443.←62 | 63→
Gallagher, D. R., Ignatieva, K., & McCulloch, J. (2015). Industry concentration, excess returns and innovation in Australia. Accounting and Finance, 55(2), 443–466.
Griffin, J. E., & Steel, M. F. (2007). Bayesian stochastic frontier analysis using WinBUGS. Journal of Productivity Analysis, 27, 163–176.
Huang, H. C. (2004). Estimation of technical inefficiencies with heterogeneous technologies. Journal of Productivity Analysis, 21, 277–296.
Keil, J. (2017). Explaining the concentration-profitability paradox. Review of Political Economy, 29(2), 209–231.
Keil, J. (2018). Is there a causal effect of concentration on persistent profitability differentials? Industrial and Corporate Change, 28(2), 241–257.
Khan, M. H., & Hanif, M. N. (2019). Empirical evaluation of ‘structure-conduct-performance’ and ‘efficient-structure’ paradigms in banking sector of Pakistan. International Review of Applied Economics, 33(5), 682–696.
Koop, G. (1994). Recent progress in applied Bayesian econometrics. Journal of Economic Surveys, 8(1), 1–34.
Koop, G., Steel, M., & Osiewalski, J. (1995). Posterior analysis of stochastic frontier models using Gibbs sampling. Computational Statistics, 10, 353–373.
Kotey, B., & O’Donnell, C. J., (2002). Data envelopment analysis in small and medium enterprises: A study of the Australian food, beverages and tobacco manufacturing Industry. Small Enterprise Research, 10(2), 3–22.
Kumbhakar, S. C., & Lovell, C. K. (2003). Stochastic frontier analysis. Cambridge, UK: Cambridge University Press.
Kumbhakar, S. C., & Tsionas, E. G. (2005). Measuring technical and allocative inefficiency in the translog cost system: A Bayesian approach. Journal of Econometrics, 126, 355–384.
Lee, C. Y., & Mahmood, I. P. (2009). Inter-industry differences in profitability: The legacy of the structure-efficiency debate revisited. Industrial and Corporate Change, 18(3), 351–380.
Leibenstein, H. (1966). Allocative efficiency vs. X-efficiency. The American Economic Review, 56(3), 392–415.
Meeusen, W., & Van Den Broeck, J. (1977). Efficiency estimation from Cobb-Douglas production functions with composed error. International Economic Review, 18(2), 435–444.
McDonald, J. T. (1999). The determinants of firm profitability in Australian manufacturing. The Economic Record, 75(229), 115–26.
Mishra, P. (2008). Concentration-markup relationship in Indian manufacturing sector. Economic and Political Weekly, 43(39), 75–81.←63 | 64→
Mukhopadhyay, J., & Chakraborty, I. (2017). Competition and industry performance: A panel VAR analysis in Indian manufacturing sector. Journal of Quantitative Economics, 15, 343–366.
Pérez-Rodríguez, J. V., & Acosta-González, E. (2007). Cost efficiency of the lodging industry in the tourist destination of Gran Canaria (Spain). Tourism Management, 28(4), 993–1005.
Purwanto, N., Manongga, D., & Pakereng, M. A. I. (2014). Efficiency of small, and medium-sized Tofu enterprises (SME) in Salatiga using data envelopment analysis (DEA). International Journal of Computer Applications, 91(12), 44–50.
Setiawan, M., & Effendi, N. (2016). Survey of the industrial concentration and price-cost margin of the Indonesian manufacturing industry. International Economic Journal, 30(1), 123–146.
Strickland, A. D., & Weiss, L. W. (1976). Advertising, concentration, and price-cost margins. Journal of Political Economy, 84(5), 1109–1121.
Tsionas, E. G. (2002). Stochastic frontier models with random coefficients. Journal of Applied Econometrics, 17, 127–147.
Van den Broeck, J., Koop, G., Osiewalski, J., & Steel, M. F. (1994). Stochastic frontier models: A Bayesian perspective. Journal of Econometrics, 61, 273–303.
Burcu Hicyilmaz and Mesut Cakir
Abstract In the globalization process, as well as the increasing trade of final goods and services, the intermediate goods trade used in the production of the goods subject to trade increased. So trade has become characterized by global value chains increasingly. It caused great changes in the production and trade process and required different calculations and approaches to analyze the structure and effects of international trade. The aim of this study is to investigate the effect of vertical specialization rate of exports of intermediate goods and services on employment in seven countries consisting of Turkey and the most important trade partners of Turkey which is Germany, England, France, Italy, USA, and Russia. The data of ten sectors for seven countries are used in the study and analysis is carried out for the five-year periods of 2000–2004, 2005–2009 and 2010–2014 with the Two-Stage OLS (2SLS) method. According to the results of analysis, while vertical specialization has no effect on employment in 2000–2004 period, it has a statistically significant and negative effect in 2005–2009 and 2010–2014 periods. This effect of the vertical specialization rate, which is the indicator of the backward participation in the global value chain, has shown that this new fact creates an employment-reducing structure in the countries discussed and also creates a process that shifts the employment potential towards foreign countries.
Keywords: Global Value Chains, Vertical Specialization, Employment, Two-Stage OLS
The developments in globalization and the increasingly intertwined structure of production among economies have led to the emergence of new concepts and the change of existing ones in to explain the current situations. According to Chang, Bayhaqi &Yuhua (2012: 2–3), the main concepts that are considered based on globalization in production can be grouped under three terms: Global supply chains (GSC), global value chain (GVC) and global production chain (GPC). GSC refers to the system of people, information, organization, activities and resources responsible for moving a product or service from the supplier to ←65 | 66→the buyers. GVC refers to all the value added activities such as raw materials and intermediate inputs, design, marketing and distribution that are required to ensure that a product or service is supplied to end consumers. GPC refers to the links between a group of firms in a certain global value chain to produce a particular product.
In the globalization process, as well as the increasing trade of final goods and services, the intermediate goods trade used in the production of the goods subject to trade increased. So trade has become increasingly characterized by global value chains. It caused great changes in the production and trade process and required different calculations and approaches to analyze the structure and effects of international trade.
The global value chain has changed international trade by affecting countries’ interdependence and the structure of competition among countries. Horizontal competition enables firms to compete in the same sector for the same consumer-base; vertical competition, on the other hand, means that firms in the same value chain compete to perform specific and specialized tasks or steps in the manufacturing process (The World Bank, 2014: 37). The fact that countries tend to specialize in different stages of manufacturing in the global value chain is called “vertical specialization” by Balassa (1967) and Findlay (1978), “slicing up of the value chain” by Krugman (1995), and “fragmentation” by Jones and Kierzkowski (1990). In this study, it will be preferred to use the vertical specialization concept from here on.
According to the traditional theories, the impact of the increase in exports on employment is considered positive, while that of the increase in imports is considered negative. For example, in the Heckscher-Ohlin-Samuelson model, international trade affects employment in two channels. First, what is called “the scale effect”, is that the increase in international trade integration creates an increase in the output of export sectors and this means an increase in labor demand. Secondly, “the substitution effect” shows that the effect of international trade, which increased in parallel with the increasing import competition, means a decrease in employment in the import sector by substituting domestic production (Van Ha & Tran, 2017: 532).
In the context of the global value chain, trade of intermediate goods between countries has increased as a result of the specialization of each country at certain tasks or specific steps in the manufacturing process. Therefore, exports of countries do not consist entirely of domestic value added; it consists of both domestic and foreign value added. Within the framework of this new phenomenon, for example, it can be said that the global value chains, which may be disrupted as a result of important events affecting the whole world, can disrupt the different ←66 | 67→macroeconomic variables of many countries in this chains. Determining the effect of foreign intermediate input included in exports of countries, that is vertical specialization of exports, on macro variables is important in determining a country’s foreign trade policies in the globalizing world.
Conditions such as the introduction of new businesses in the economies or pulling out of the market of existing businesses, moving various production processes abroad for advantages such as cheap labor power and/or using foreign input during the production stage directly are the situations seen in economies during the reallocation of the production factors required to take advantage of comparative advantage. In this respect, that process has the potential to create both employment and unemployment. Therefore, in addition to the analyzes made with total export and import data, the analyzes made with the data of domestic and foreign value added included in exports have gained importance.
Differentiating from the literature examining the effect of international trade on employment using general variables such as exports, imports and total trade, there is a relatively new developing literature that takes into account the intertwined structure of countries’ production processes and investigates the impact of value added content of trade on employment. The study of Feenstra and Hanson (1996a) investigates how outsourcing affects employment demand in America for the period 1972–1992. They found that outsourcing for the period 1972–1979 and 1979–1990 correlated positively with relative increase in nonproduction workers, and weakly and negatively with relative average annual incomes. Amiti and Wei (2009) investigated the employment effect of service offshoring for the United States over the period of 1992–2000. The study concluded that the service offshoring process has no significant effect on employment with aggregated data in 96 manufacturing industries. However, the service offshoring process caused unemployment when manufacturing industries are aggregated to 450 industries.
The employment generation potential of Turkey’s exports was analyzed with input-output method by Mıhçı, Akkoyunlu-Wigley and Dalgıç (2016). The study, which has calculated domestic value added at sectoral level using the method of Feenstra and Hong (2007), has determined that the employment generation potential of exports has decreased due to the decrease in the domestic value added component of exports for the period of 1995–2008. The firm-level data also has indicated the same results for the period 2003–2012.
Xikang et al. (2012) evaluated the effect of exports on total domestic value added and employment for China in the years 2002 and 2007 with using input-output methodology. The findings have suggested that traditional ←67 | 68→manufacturing exports generated higher total domestic value added and employment than high-technology manufacturing exports. The study has interpreted the higher decline in the total employment generated from 2002 to 2007 as the large gains in labor productivity. Wei (2016) examines the impact of imports and exports by conventional trade and exports by processing trade on the labor market performance of China for the period of 1981–2012 with using vector error correction model. The results have showed that there is a long run relationship between the variables and labor market effects of vertical specialization have become important increasingly. The study has suggested using a vertically specialized trade strategy for Chinese government to encourage employment for more inclusive growth.
Shen and Silva (2018) focused on the direct effect of the Chinese economy to the U.S. labor market. Increasing in exposure to value added exports from China has effected share of manufacturing employment negatively. The study also showed that this rising exposure of value added exports from China increases U.S.’s dependency on the position of Chinese exporting industry in the global value chain. Another study, Shen, Silva and Wang (2018) found wage polarization among workers caused by U.S. occupational exposure to value added imports. Sasahara (2019) study, which conducts global input-output analysis with a focus on the USA, China and Japan, confirmed that the increase in exports means increasing in employment, but emphasizes that the impact of exports varies across destination countries. The study also underlined that exports with higher domestic value added such as textile and service sectors create a greater employment effect.
In the context of domestic value added of export, the newly developing literature reveals that exports are not a completely employment-creating process. The aim of this study is to investigate the effect of vertical specialization rate of exports of intermediate goods and services on employment in seven countries consisting of Turkey and the most important trade partners of Turkey which is Germany, England, France, Italy, USA, and Russia. The data of ten sectors for seven countries are used in the study and analysis is carried out for the five-year periods of 2000–2004, 2005–2009 and 2010–2014 with the Two-Stage OLS (2SLS) method.
The organization of this chapter is as follows. After the introduction to basic concepts, theories and literature on the subject of our study, in the second section, beside the data and methodology required for the empirical analysis to be carried out, empirical results will be shared. And finally, in the third section, conclusion was presented.←68 | 69→
2 Data, Methodology and Results
There are three different variables to be used in the analysis. These variables can be listed as follows; vertical specialization rate of exports, total output and number of employees.
The vertical specialization rate of exports of intermediate goods and services (VS, %) to be used in the study was calculated by using data for the period from 2000 to 2014 provided from World Input-Output Database (WIOD)2. These tables, published in 2016, cover 43 countries and the rest of the world and 56 sectors. Sectoral and regional aggregation of input output table has been applied in order to reduce size of table and provide simplicity. High-level SNA/ISIC aggregation criteria of NACE sections3 were used for sectoral aggregation. The group of countries to be included in the analysis except Turkey was determined using foreign trade data by country provided by the Turkish Statistical Institute (TurkStat, 2018). Based on 2014 data, Turkey’s exports by country (X), imports by country (M) and foreign trade volume by country (X + M) were obtained. The first seven countries that have the highest share in foreign trade volume and whose data are published in WIOD were selected. The ranking from the country with the highest share in foreign trade volume of Turkey to the lowest one is as follows; Germany, Russia, China, Italy, USA, UK and France. Thus, aggregated input-output tables consisting of ten sectors and nine countries4 were generated. Then, vertical specialization rates of exports of each sector of the countries were calculated. The second variable to be used in the analysis is the total output (TOT, millions of US$) data for the sectors. This data was obtained from the same aggregated input-output tables.
The number of employees (EMPE, thousands) was obtained from the Socio-Economic Accounts (SEA) table, published in WIOD in 2016, with 56 sectors. This table, which includes 56 sectors, has also been aggregated and reduced to 10 sectors. However, in this table, the number of employees of China is not ←69 | 70→available. After excluding ROW and China, analysis has been conducted with the country group consists of Turkey, Germany, UK, France, Italy, USA and Russia.
Tab. 4.1: Descriptive statistics
After obtaining the annual data of 2000–2014 period for all variables, five-year periods were created. The arithmetic averages of the series were taken according to the five-year periods 2000–2004, 2005–2009 and 2010–20145. The fundamental descriptive statistics for these variables are presented in Tab. 4.1.←70 | 71→
There are two different methods used in the study. The first is a method used in the measurement of the VS variable. The second is the 2SLS method, which will be used to estimate the equation.
The VS variable was calculated using the Hummels, Ishii and Yi (2001) method. According to the method, the main measure of VS can be shown as in Equation (1) (for more details see page 78–81 in Hummels, Ishii & Yi, 2001)
where u is a 1xn vector of one’s, is the n × n imported coefficient matrix, I is the identity matrix, is the nxn domestic coefficient matrix, X is an nx1 vector of exports, is total country exports and n is the number of sectors. Intermediate input values imported from each country (c = 1,2,3,…) and sector i for total output production of sector j can be expressed as . In this case, VS share of each country in total exports is calculated as in Equation (1.2) (Gündoğdu & Saracoğlu, 2016);
The aim of the study is to determine the effect of VS on EMPE and to determine whether the relationship is positive or negative if the effect is statistically significant. For this purpose, based on the study of Shen and Silva (2018)6, while EMPE is taken as dependent variable, VS and TOT are taken as independent variable. The econometric model to be establish within this framework can be expressed as follows;
In this model, is the constant term, and are slope parameters and is the error term.
For the five-year periods of 2000–2004, 2005–2009 and 2010–2014, the coefficients were estimated by using the cross-sectional data generated by ←71 | 72→taking the arithmetic averages of the series and the effects were examined separately by periods. As a result of the ten sectors in the seven selected countries, there are seventy observations in the analysis.
However, in the model to be estimated, the fact that there is a two-way relationship between TOT and EMPE points to the problem of endogeneity. In order to test whether the results obtained from the regression can be interpreted, it is assumed that there is an endogeneity problem in TOT. Instrumental variable will be selected for each period to represent the TOT variable consisting of a five-year arithmetic average. An instrumental variable that can explain EMPE but will minimize the impact of EMPE on TOT and represent TOT for the period should be included in the model. For this reason, TOT data of the beginning year of the five-year period will be used as an instrumental variable instead of the five-year average TOT data of that period. Therefore, the relationship between EMPE variable consisting of the average of five years for each period and TOT will be minimized. For example, instead of TOT0004, the TOT series of 2000 (symbolized as TOT2000) will be used as the instrumental variable.
Stata 11.1 and “ivregress 2sls” command was used for regression estimation. The results are estimated as “robust” in order to eliminate the heteroscedasticity problem. The 2SLS analysis consists of two stages and post-estimation procedures.
In the first stage, it is investigated whether the instrumental variable to be used represents the explanatory variable that is assumed to be have an endogeneity problem. In the model prediction of the first stage, the endogenous variable is taken as dependent variable and the instrumental variable is taken as independent variable. In this model, it is checked whether the coefficient of the instrumental variable is statistically significant or not. If the coefficient is statistically significant, the instrumental variable can be used in the second stage. In the second stage, Equation (3) is estimated using the instrumental variable.
After this two-step estimation, Wooldrige (1995), which is one of the post-estimation procedures, is used for endogeneity testing and weak instruments testing. The null hypothesis of the endogeneity test is that the variables are exogenous. In this test, ve F statistics are reported. If the null hypothesis is rejected according to the two statistics, the variable is considered to be endogenous. So the coefficients which is estimated by using the instrumental variables can be interpreted. If the variable is exogenous according to result of statistic but the variable is considered endogenous according to result of F test, ←72 | 73→this variable is considered to be endogenous and estimated coefficients can be interpreted. If null hypotheses are accepted according to the both two statistics, since there is no endogeneity problem in the model, the coefficients obtained with the OLS estimator should be interpreted.
Tab. 4.2: The results of the 2000–2004 period
In the weak instruments test, F statistics are reported and the null hypothesis is that “instruments are weak”. If the F statistic is not significant, the instrument used is considered to be a weak variable. Stock, Wright and Yogo (2002) study states that if the 2SLS estimator and an instrument variable are used, the F statistic should exceed 10.
2.3 Empirical Results
The estimation results of the model for the first five-year period are illustrated in Tab. 4.2. The t statistic of TOT2000, which will be used as an instrumental variable instead of TOT0004 in the second stage, is statistically significant as can be seen from the first stage results. Also, when the results of post-estimation procedures are examined, it is seen that the TOT variable is exogenous according to the test result, and it is endogenous according to the F test. ←73 | 74→Although the test reveals that the variable is exogenous, the result of the F test, which gives the result at the level of regression, is accepted. In addition, according to the weak instrument test, the instrumental variable (TOT2000) was found to be not weak. So the regression results of second stage are robust and the coefficients can be interpreted. According to the regression results obtained from the second stage, the effect of VS0004 on EMPE0004 is found statistically insignificant for the 2000–2004 period.
Tab. 4.3: The results of the 2005–2009 period
Results for the second five-year period are reported in Tab. 4.3. It was observed that the effect of TOT2005, which will be used as instrumental variable in the second stage, on TOT0509 was positive and statistically significant at the level of 1 %. Considering the post-estimation procedure to determine whether the endogenous regressors in the model are in fact exogenous, it seems that the initially accepted assumption of endogeneity is confirmed by both and F test.
Besides the instrumental variable used (TOT2005) was found to be not weak. The results of the model regressed with the robust instrumental variable confirmed to be endogenous indicate that the effect of VS0509 on EMPE0509 is negative and statistically significant at the level of 5 %. Likewise, the effect of TOT2005 on EMPE0509 is positive and statistically significant at 1 %.←74 | 75→
Tab. 4.4: The results of the 2010–2014 period
The results of the third five-year period are presented in Tab. 4.4. From the first stage results, it is observed that the effect of TOT2010 on TOT1014 is statistically significant at %1. When the post-estimate procedures are examined, although the TOT2010 instrumental variable used is found to be robust, the endogeneity assumption accepted at the beginning of the 2SLS analysis could not be confirmed and TOT2010 is found to be in fact exogenous. Therefore, the model should be estimated using the OLS estimator without interpreting the regression and coefficient estimates obtained from the second stage of 2SLS.
Although the assumption of internality is rejected as a result of empirical analysis, employment and total output are known to affect each other in theory. For this reason, in the new model to be estimated with OLS, the TOT2010 variable that represents the total output values of the 2010 will be used. Tab. 4.5 shows these OLS results.
According to the results of Breusch and Pagan (1979) heteroscedasticity test, the null hypothesis of homoscedasticity was rejected and heteroscedasticity assumed. In order to obtain more robust standard deviation values, the model was estimated as robust. In the period 2010–2014, the effect of VS1014 on EMPE1014 is negative and statistically significant at the level of 5 %.←75 | 76→
Tab. 4.5: OLS results of the 2010–2014 period
The question of how exports affect employment has been the subject of research for many years. Various studies examining this relationship have concluded that there are positive, negative or no relation between the two variables as a result of their analysis. However, new phenomenon such as the fact that countries, which are part of the global value chain, use foreign intermediate goods in the products they produce to export have changed the structure of this relationship. This fact necessitated the relationship between the two variables to be handled again with a different dimension. For this purpose, the answer to the question of how the vertical specialization rate of exports affects employment was sought in the study. The effect of vertical specialization rate of exports of intermediate goods and services on employment have been investigated for three five-year periods in seven countries consisting of Turkey and Germany, England, France, Italy, USA, Russia by using 2SLS method.
The effect of vertical specialization of exports on the number of employees was found statistically insignificant for the 2000–2004 period. Nevertheless, this effect was found to be negative in the period of 2005–2009 and 2010–2014. This effect of the vertical specialization rate, which is the indicator of the backward participation in the global value chain, has shown that this new fact creates an employment-reducing structure in the countries discussed and also creates a process that shifts the employment potential towards foreign countries.
Amiti, M., & Wei, S. J. (2009). Does service offshoring lead to job losses? Evidence from the United States. In Marshall Reinsford and Mmatthew ←76 | 77→J. Slaughter (Eds.), International Trade in Services and Intangibles in the Era of Globalization. University of Chicago Press. ISBN 0-226-70959-0.
Balassa, B. (1967). Trade Liberalization among Industrial Countries. New York: McGraw Hill.
Breusch, T. S., & Pagan, A. R. (1979). A simple test for heteroscedasticity and random coefficient variation. Econometrica: Journal of the Econometric Society, 47(5) 1287–1294.
Chang, P., Bayhaqi, A., & Yuhua, Z. B. (2012). Concepts and trends in global supply, global value and global production chains. Asia-Pacific Economic Cooperation, APEC#212-SE-01.4.
EUROSTAT. (2008). NACE Rev. 2: Statistical Classification of Economic Activities in the European Community. Luxembourg: Office for Official Publications of the European Communities. ISBN 978-92-79-04741-1.
Feenstra, R. C., & Hanson, G. H. (1996). Globalization, outsourcing and the wage inequality. American Economic Review, 86(2), 240–245.
Feenstra, R. C., & Hong, C. (2007). China’s exports and employment. NBER Working Paper, Working Paper No: 13552.
Findlay, R. (1978). An Austrian model of international trade and interest rate equalization. Journal of Political Economy, 86, 989–1008.
Gündoğdu, C., & Saracoğlu D. Ş. (2016). Participation of Turkey in global value chains: An analysis based on World input output database. ERC Working Papers in Economics, 16/10, Economic Research Center, Middle East Technical University.
Hummels, D., Ishii, J., & Yi, K. M. (2001). The natüre and growth of vertical specialization in world trade. Journal of International Economics, 54, 75–96.
Jones, R. W., & Kierzkowski, H. (1990). The role of services in production and international trade: A theoretical framework. In R. W. Jones & A. Krueger (Eds.), The Political Economy of International Trade. Oxford: Basil Blackwell.
Krugman, P. (1995). Growing world trade: Causes and consequences. Brookings Papers on Economic Activity, 26(1), 327–377.
Mıhçı, S., Akkoyunlu-Wigley, A., & Dalgıç, B. (2016). Dynamic analysis of employment and domestic value added generated by foreign demand in Turkey: 1995–2008. METU Studies in Development, 43(1), 289–315.
Sasahara, A. (2019). Explaining the employment effect of exports: Value-added content matters. Journal of the Japanese and International Economies, doi:10.1016/j.jjie.2019.02.004.
Shen, L., & Silva, P. (2018). Value-added exports and U.S. local labor markets: Does China really matter?. European Economic Review, 101, 479–504.←77 | 78→
Shen, L., Silva, P., & Wang, H. (2018). Polarization of American Workers: The Big Squeeze from Occupational Exposure to Value-Added Imports. Kansas State University.
Stock, J. H., Wright, J. H., & Yogo, M. (2002). A survey of weak instruments and weak identification in generalized method of moments. Journal of Business and Economic Statistics, 20, 518–529.
The World Bank (2014). Trading up to high income (Turkey country economic memorandum), Poverty Reduction and Economic Management Unit: The World Bank.
Timmer, M. P., Dietzenbacher, E., Los, B., Stehrer, R., & Vries, G. J. (2015). An illustrated user guide to the world input-output database: The Case of the global automotive production. Review of International Economics, 23, 575–605.
TurkStat (2018). Export and import data by country, Foreign Trade Statistics. https://data.tuik.gov.tr/Kategori/GetKategori?p=Foreign-Trade-104 (available at 12.02.2018).
Van Ha, H., & Tran, T. (2017). International trade and employment: A quantile regression approach. Journal of Economic Integration, 32(3), 531–557.
Wei, W. (2016). Chapter 4: Vertical specialization and increasing productive employment: Comparing impacts of conventional trade and processing trade patterns on labor market in China. In Achieving Inclusive Growth in China Through Vertical Specialization (pp. 71–138). Chandos Publishing. doi:10.1016/B978-0-08-100627-6.00004-7.
Wooldridge, J. M. (1995). Score diagnostics for linear models estimated by two stage least squares. In Advances in Econometrics and Quantitative Economics: Essays in Honor of Professor C. R. Rao, ed. G. S. Maddala, P. C. B. Phillips, and T. N. Srinivasan (pp. 66–87). Oxford: Blackwell.
Xikang, C., Cheng, L. K., Fung, K. C., Lau, L. J., Sung, Y.-W., Zhu, K., Yang, C., Pei, J., & Duan, Y. (2012). Domestic value added and employment generated by Chinese esports: A quantitative estimation. China Economic Review, 23, 850–864.
Yılmaz, B. (2019). The Relationship of the Vertical Specialization and Employment. Doctoral Thesis, Aydın Adnan Menderes University Institute of Social Sciences, Aydın/Turkey.
1 This article is extracted from my PhD thesis entitled “The Relationship of the Vertical Specialization and Employment” (Yılmaz, 2019).
2 For detailed information, please see Timmer, Dietzenbacher, Los, Stehrer & Vries (2015).
3 These 10 sectors consist of high-level SNA/ISIC aggregation of NACE sections into 10 categories. For detailed information and names of sectors please see EUROSTAT (2008:43–44).
4 Turkey, Germany, Russia, China, Italy, USA, UK, France and the rest of the world (ROW).
5 The series for the period 2000–2004 are symbolized as VS0004, TOT0004 and EMPE0004; the series for the period 2005–2009 are symbolized as VS0509, TOT0509 and EMPE0509; the series for the period 2010–2014 are symbolized as VS1014, TOT1014 and EMPE1014.
6 In the study of Shen and Silva (2018: 495), while manufacturing industry employment was taken as dependent variable, net value-added export per employee was taken as independent variable and 2SLS method was used.
- ISBN (PDF)
- ISBN (ePUB)
- ISBN (MOBI)
- ISBN (Book)
- Publication date
- 2021 (March)
- Berlin, Bern, Bruxelles, New York, Oxford, Warszawa, Wien, 2021. 342 pp., 19 fig. b/w, 53 tables.