Israel has a Failed State Index Score Due to the West Bank...

Israel According to the US-based Failed States Index (FSI), Israel ranks number 67 and is in greater danger of becoming a failed state than the likes of Congo, Cuba and Jordan. Despite Israel’s booming economy, high life expectancy and low unemployment, part of the explanation for its low FSI ranking is the West Bank. According to the Fund for Peace’s methodology, Israel/West Bank is considered one entity when determining its Failed States Index score. The issues Israel/ West Bank scored highest on the FIS were; external intervention (8), factionalized elites (8), human rights (8), group grievance (9.3), and refugees (8). Other sources of Israels instability stems from the Israeli–Palestinian conflict, inequality, internal breakdown and†¦show more content†¦In response to attacks Israel began building the anti-terrorism fence to stop West Bank terrorists from killing Israeli citizens. Whatever analysis is put on the history, there can be no disputing that the Palestinian Territories (Gaza and the West Bank) have been occupied by Israel since the Six Day War and that this occupation has had profound implications for the development of the two occupied territories. The opposing sides have been in an almost continuous state of armed conflict. The longer the Israeli-Palestinian conflict continue, the weaker Israel as a modern state will become. Regional Implications from Israel’s Instability The West Bank continues to remain an occupied territory with Israel being the occupying power. Israels policies in the West Bank have displaced a million Palestinians, forcing them into refugee status which in turn has led to a refugee influx in neighboring country, Jordan. Palestinian existence in the West Bank is being increasingly confined to ghettos cut off from farmland, schools, medical care and from other Palestinians. Some would argue that Israels aggressive policies against Palestinians provoke terror attacks on Israeli citizens. The resort to terrorist acts by Palestinians, especially suicide bombings in crowded public places, has caused them to lose much of the international sympathy their cause would otherwise benefit from. Similarly, the cruelties of Israelis to the Palestinians, especially in the ongoing siege of Gaza,Show MoreRelatedRebolusyong Siyentipiko6106 Words   |  25 PagesArabia 5. New Delhi 6. Myanmar 7. Thailand 8. Peru 9. Nigeria 10. Wales 1. United Kingdom UK retail sales flat in November BRCs Helen Dickinson: We will see a flurry of activity as Christmas approaches UK retail sales failed to grow in November, adding to fears that consumers are reining in spending ahead of Christmas. Sales volumes were flat in November compared with October, the Office for National Statistics (ONS) said. 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Health Care Reform Project, Part I - 1775 Words

Health Care Reform Project Part One May 23, 2011 HCS440 Health Care Reform: Managed Care In this country there are numerous concerns about health care economics. Several factors contribute to the increase of health care costs. One area of concern is the impact of managed care on health care finances. Managed care has been around since the early 1970s. The definition of managed care is a set of contractual and management methods implemented to manage the financing and delivery of health care services. Initial implementation of managed care was for health care cost saving (Getzen Moore, 2007, p. 203, para. 1). Though Managed care initially addressed several health care finance issues, there are still problems with the current†¦show more content†¦The United States along with other countries try to find ways to control medical costs. In 2009, the United States changed HMOs into accountable health organizations in which hospitals and doctors form an alliance and take financial and management responsibilities for their patients (Rodwin, 2010). France changed how the y did their billing and started using state managed care. By 2012, Japan should be processing all claims through electronic billing. Instead of fee-for-service, Japan is using per-diem and bundled payments to pay physicians and hospitals. No matter how a country looks at it managed care tools are still around, and they will always be. The government just needs to listen to the voice of its people (Rodwin, 2010). Solutions to Managed Care People are aware that managed care has caused patient free will to be lost in the sprint to cut price. Insurance companies in this sense control patients rather than self-monitoring or by a physician. One wants to make their choices. 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Public Relations and Communication Management

Question: Discuss about the Public Relations and Communication Management. Answer: Introduction: There are certain factors which make a conversation interesting. Mr. David Firkin's presentation must have missed those aspects. The reasons are discussed under: First of all, Mr. Firkin has presumed that all the staffs have enough knowledge about computer and thats why he used some tough technical terms about the computer network in his presentation. The staffs may be familiar with computers, but they may not have enough professional knowledge to understand those technical terms and these leads to a tedious presentation to them. Secondly, Mr. Firkin didnt interact with the audience. Participation of both parties makes a conversation more interesting. Maybe he talked too much and didn't talk to the staffs. This process contributed to the boringness. Thirdly, perhaps he didn't use any visual aids in his presentation. In a presentation, the audience likes to see power point presentations, video clips and colorful pictures. All these components make a presentation more interesting. The staffs listened too much for a long time and didnt get anything to see. It caused a dull presentation. Lastly, maybe he used some topics which seemed unrelated to the staffs and they got bored (Grunig 2013). To get the contract, Mr. Firkin should make another presentation which will include the following factors: Mr. Firkin should know who his audience is. It means he should know about their background and level of knowledge about computers. And based on that, he should prepare the presentation. The presentation should be built on the topic as off-topic presentations lead to boringness. He should focus on the needs of the staffs (adamsinstitute.ku.edu). Mr. Firkin should involve the staffs in the communication. It means he should let the staffs express their views about the topic as this process leads to successful communication. Another important aspect is using the visual aids. Mr. Firkin should use enough power points slides, video clips and colorful pictures to emphasize his talking points in the presentation. This process will make the presentation interesting and acceptable to the staffs (Yen 2015). Mr. Firkins presentation should have the Take-Home Message Persistent. It means he should present the topic in such a way that the audience can remember the key message of the presentation for a long time. He should emphasize the key points properly and there should be enough materials information to support the key points. There should be a flow of logic in the presentation. The presentation should have a proper beginning, middle and end. Mr. Firkin should be an entertainer. He can tell humor and also can use anecdotes to entertain the audience. This process will keep the staffs mentally present in the presentation. But he should not overdo it. Most importantly, Mr. Firkin should be a good listener while giving the presentation. He should listen carefully the issues faced by the staffs. Only then he will be able to deliver the proper solution. And lastly, Mr. Firkin should practice the entire presentation several times before he presets to the audience as practice makes a man perfect (van Ginkel et al. 2016). References: Grunig, J.E., 2013.Excellence in public relations and communication management. Routledge. https://adamsinstitute.ku.edu/. (2016).sites/adamsinstitute.drupal.ku.edu/files/docs/Improving_Oral_Presentations.pdf. [online] Available at: https://adamsinstitute.ku.edu/sites/adamsinstitute.drupal.ku.edu/files/docs/Improving_Oral_Presentations.pdf [Accessed 9 Sep. 2016]. van Ginkel, S., Gulikers, J., Biemans, H. and Mulder, M., 2016. Fostering oral presentation performance: does the quality of feedback differ when provided by the teacher, peers or peers guided by tutor?.Assessment Evaluation in Higher Education, pp.1-14. Yen, D.C., 2015. The use of peer assessment and reflective discussion to improve student participation and oral presentation performance in a technical communication classroom.Journal of Techno-Social,6(2).

Kahirapan free essay sample

Smoke is a collection of airborne solid and liquid particulates and gases[1] emitted when a material undergoes combustion or pyrolysis, together with the quantity of air that is entrained or otherwise mixed into the mass. It is commonly an unwanted by-product of fires (including stoves, candles, oil lamps, and fireplaces), but may also be used for pest control (fumigation), communication (smoke signals), defensive and offensive capabilities in the military (smoke-screen), cooking (smoked salmon), or smoking (tobacco, cannabis, etc. ). Smoke is used in rituals, when incense, sage, or resin is burned to produce a smell for spiritual purposes. Smoke is sometimes used as a flavoring agent, and preservative for various foodstuffs. Smoke is also a component of internal combustion engine exhaust gas, particularly diesel exhaust. Smoke inhalation is the primary cause of death in victims of indoor fires. The smoke kills by a combination of thermal damage, poisoning andpulmonary irritation caused by carbon monoxide, hydrogen cyanide and other combustion products. We will write a custom essay sample on Kahirapan or any similar topic specifically for you Do Not WasteYour Time HIRE WRITER Only 13.90 / page Smoke particles are an aerosol (or mist) of solid particles and liquid droplets that are close to the ideal range of sizes for Mie scattering of visible light. This effect has been likened to three-dimensional textured privacy glass[citation needed] — a smoke cloud does not obstruct an image, but thoroughly scrambles it. Contents [hide] 1 Chemical composition 1. 1 Visible and invisible particles of combustion 2 Dangers of smoke 2. 1 Smoke corrosion 3 Secondhand smoke inhalation 4 Measurement of smoke 5 Medicinal smoke 6 See also 7 References 8 External links Chemical composition[edit] This section needs additional citations for verification. Please help improve this article by adding citations to reliable sources. Unsourced material may be challenged and removed. (April 2011) The composition of smoke depends on the nature of the burning fuel and the conditions of combustion. Fires with high availability of oxygen burn at a high temperature and with small amount of smoke produced; the particles are mostly composed of ash, or with large temperature differences, of condensed aerosol of water. High temperature also leads to production of nitrogen oxides. Sulfur content yields sulfur dioxide, or in case of incomplete combustion, hydrogen sulfide. Carbon and hydrogen are almost completely oxidized to carbon dioxide and water. Fires burning with lack of oxygen produce a significantly wider palette of compounds, many of them toxic. Partial oxidation of carbon produces carbon monoxide, nitrogen-containing materials can yield hydrogen cyanide, ammonia, and nitrogen oxides. Hydrogen gas can be produced instead of water. Content of halogens such as chlorine (e. g. in polyvinyl chloride or brominated flame retardants) may lead to production of e. g. hydrogen chloride, phosgene,dioxin, and chloromethane, bromomethane and other halocarbons. Hydrogen fluoride can be formed from fluorocarbons, whether fluoropolymers subjected to fire or halocarbon fire suppression agents. [2] Phosphorus and antimony oxides and their reaction products can be formed from some fire retardant additives, increasing smoke toxicity and corrosivity. Pyrolysis of polychlorinated biphenyls (PCB), e. g. from burning older transformer oil, and to lower degree also of other chlorine-containing materials, can produce 2,3,7,8-tetrachlorodibenzodioxin, a potent carcinogen, and other polychlorinated dibenzodioxins. Pyrolysis of fluoropolymers, e. g. teflon, in presence of oxygen yields carbonyl fluoride (which hydrolyzes readily to HF and CO2); other compounds may be formed as well, e. g. carbon tetrafluoride, hexafluoropropylene, and highly toxic perfluoroisobutene (PFIB). [3] Emission of soot from a large dieseltruck, without particle filters. Pyrolysis of burning material, especially incomplete combustion or smoldering without adequate oxygen supply, also results in production of a large amount of hydrocarbons, both aliphatic (methane, ethane, ethylene, acetylene) and aromatic (benzene and its derivates, polycyclic aromatic hydrocarbons; e. g. benzo[a]pyrene, studied as a carcinogen, or retene), terpenes. Heterocyclic compounds may be also present. Heavier hydrocarbons may condense as tar; smoke with significant tar content is yellow to brown. Presence of such smoke, soot, and/or brown oily deposits during a fire indicates a possible hazardous situation, as the atmosphere may be saturated with combustible pyrolysis products with concentration above the upper flammability limit, and sudden inrush of air can cause flashover or backdraft. Presence of sulfur can lead to formation of e. g. hydrogen sulfide, carbonyl sulfide, sulfur dioxide, carbon disulfide, and thiols; especially thiols tend to get adsorbed on surfaces and produce a lingering odor even long after the fire. Partial oxidation of the released hydrocarbons yields in a wide palette of other compounds: aldehydes (e. g. formaldehyde, acrolein, and furfural), ketones, alcohols (often aromatic, e. g. phenol, guaiacol, syringol, catechol, and cresols), carboxylic acids (formic acid, acetic acid, etc. ). The visible particulate matter in such smokes is most commonly composed of carbon (soot). Other particulates may be composed of drops of condensed tar, or solid particles of ash. The presence of metals in the fuel yields particles of metal oxides. Particles of inorganic salts may also be formed, e. g. ammonium sulfate, ammonium nitrate, or sodium chloride. Inorganic salts present on the surface of the soot particles may make themhydrophilic. Many organic compounds, typically the aromatic hydrocarbons, may be also adsorbed on the surface of the solid particles. Metal oxides can be present when metal-containing fuels are burned, e. g. solid rocket fuels containing aluminium. Depleted uranium projectiles after impacting the target ignite, producing particles of uranium oxides. Magnetic particles, spherules of magnetite-like ferrous ferric oxide, are present in coal smoke; their increase in deposits after 1860 marks the beginning of the Industrial Revolution. [4] (Magnetic iron oxide nanoparticles can be also produced in the smoke from meteorites burning in the atmosphere. )[5] Magnetic remanence, recorded in the iron oxide particles, indicates the strength of Earths magnetic field when they were cooled beyond their Curie temperature; this can be used to distinguish magnetic particles of terrestrial and meteoric origin. [6] Fly ash is composed mainly of silicaand calcium oxide. Cenospheres are present in smoke from liquid hydrocarbon fuels. Minute metal particles produced by abrasion can be present in engine smokes. Amorphous silica particles are present in smokes from burning silicones; small proportion of silicon nitride particles can be formed in fires with insufficient oxygen. The silica particles have about 10 nm size, clumped to 70-100 nm aggregates and further agglomerated to chains. [3] Radioactive particles may be present due to traces of uranium, thorium, or other radionuclides in the fuel; hot particles can be present in case of fires during nuclear accidents (e. g. Chernobyl disaster) or nuclear war. Smoke particulates have three modes of particle size distribution: nuclei mode, with geometric mean radius between 2. 5–20 nm, likely forming by condensation of carbon moieties. accumulation mode, ranging between 75–250 nm and formed by coagulation of nuclei mode particles coarse mode, with particles in micrometer range Most of the smoke material is primarily in coarse particles. Those undergo rapid dry precipitation, and the smoke damage in more distant areas outside of the room where the fire occurs is therefore primarily mediated by the smaller particles. [7] Aerosol of particles beyond visible size is an early indicator of materials in a preignition stage of a fire. [3] Burning of hydrogen-rich fuel produces water; this results in smoke containing droplets of water vapor. In absence of other color sources (nitrogen oxides, particulates ), such smoke is white and cloud-like. Smoke emissions may contain characteristic trace elements. Vanadium is present in emissions from oil fired power plants and refineries; oil plants also emit some nickel. Coal combustionproduces emissions containing aluminium, arsenic, chromium, cobalt, copper, iron, merc ury, selenium, and uranium. Traces of vanadium in high-temperature combustion products form droplets of molten vanadates. These attack the passivation layers on metals and cause high temperature corrosion, which is a concern especially for internal combustion engines. Molten sulfate and lead particulates also have such effect. Some components of smoke are characteristic of the combustion source. Guaiacol and its derivatives are products of pyrolysis of lignin and are characteristic of wood smoke; other markers aresyringol and derivates, and other methoxy phenols. Retene, a product of pyrolysis of conifer trees, is an indicator of forest fires. Levoglucosan is a pyrolysis product of cellulose. Hardwood vssoftwood smokes differ in the ratio of guaiacols/syringols. Markers for vehicle exhaust include polycyclic aromatic hydrocarbons, hopanes, steranes, and specific nitroarenes (e. g. 1-nitropyrene). The ratio of hopanes and steranes to elemental carbon can be used to distinguish between emissions of gasoline and diesel engines. [8] Many compounds can be associated with particulates; whether by being adsorbed on their surfaces, or by being dissolved in liquid droplets. Hydrogen chloride is well absorbed in the soot particles. [7] Inert particulate matter can be disturbed and entrained into the smoke. Of particular concern are particles of asbestos. Deposited hot particles of radioactive fallout and bioaccumulated radioisotopes can be reintroduced into the atmosphere by wildfires and forest fires; this is a concern in e. g. the Zone of alienationcontaining contaminants from the Chernobyl disaster. Polymers are a significant source of smoke. Aromatic side groups, e. g. in polystyrene, enhance generation of smoke. Aromatic groups integrated in the polymer backbone produce less smoke, likely due to significant charring. Aliphatic polymers tend to generate the least smoke, and are non-self-extinguishing. However presence of additives can significantly increase smoke formation. Phosphorus-based and halogen-based flame retardants decrease production of smoke. Higher degree of cross-linking between the polymer chains has such effect too. [9] Visible and invisible particles of combustion[edit] Smoke from a wildfire Depending on particle size, smoke can be visible or invisible to the naked eye. This is best illustrated when toasting bread in a toaster. As the bread heats up, the products of combustion increase in size. The particles produced initially are invisible but become visible if the toast is burned or cooled rapidly. Smoke from a typical house fire contains hundreds of different chemicals and fumes. As a result, the damage caused by the smoke can often exceed that caused by the actual heat of the fire. In addition to the physical damage caused by the smoke of a fire – which manifests itself in the form of stains – is the often even harder to eliminate problem of a smoky odor. Just as there are contractors that specialize in rebuilding/repairing homes that have been damaged by fire and smoke, fabric restoration companies specialize in restoring fabrics that have been damaged in a fire. Dangers of smoke[edit] Smoke from oxygen-deprived fires contains a significant concentration of compounds that are flammable. A cloud of smoke, in contact with atmospheric oxygen, therefore has the potential of being ignited – either by another open flame in the area, or by its own temperature. This leads to effects like backdraft and flashover. Smoke inhalation is also a danger of smoke that can cause serious injury and death. Many compounds of smoke from fires are highly toxic and/or irritating. The most dangerous is carbon monoxide leading to carbon monoxide poisoning, sometimes with the additive effects ofhydrogen cyanide and phosgene. Smoke inhalation can therefore quickly lead to incapacitation and loss of consciousness. Sulfur oxides, hydrogen chloride and hydrogen fluoride in contact with moisture form sulfuric, hydrochloric and hydrofluoric acid, which are corrosive to both lungs and materials. When asleep the nose does not sense smoke nor does the brain, but the body will wake up if the lungs become enveloped in smoke and the brain will be stimulated and the person will be awoken. This does not work if the person is incapacitated or under the influence of Drugs and/or alcohol Cigarette smoke is a major modifiable risk factor for lung disease, heart disease, and many cancers. Reduced visibility due to wildfire smoke in Sheremetyevo airport (Moscow, Russia) 7 August 2010. Smoke can obscure visibility, impeding occupant exiting from fire areas. In fact, the poor visibility due to the smoke that was in the Worcester Cold Storage Warehouse fire in Worcester, Massachusetts was the exact reason why the trapped rescue firefighters couldnt evacuate the building in time. Because of the striking similarity that each floor shared, the dense smoke caused the firefighters to become disoriented. [10] Smoke corrosion[edit] Smoke contains a wide variety of chemicals, many of them aggressive in nature. Examples are hydrochloric acid and hydrobromic acid, produced fromhalogen-containing plastics and fire retardants, hydrofluoric acid released by pyrolysis of fluorocarbon fire suppression agents, sulfuric acid from burning of sulfur-containing materials, nitric acid from high-temperature fires where nitrous oxide gets formed, phosphoric acid and antimonycompounds from P and Sb based fire retardants, and many others. Such corrosion is not significant for structural materials, but delicate structures, especially microelectronics, are strongly affected. Corrosion of circuit board traces, penetration of aggressive chemicals through the casings of parts, and other effects can cause an immediate or gradual deterioration of parameters or even premature (and often delayed, as the corrosion can progress over long time) failure of equipment subjected to smoke. Many smoke components are also electrically conductive; deposition of a conductive layer on the circuits can cause crosstalks and other deteriorations of the operating parameters or even cause short circuits and total failures. Electrical contacts can be affected by corrosion of surfaces, and by deposition of soot and other conductive particles or nonconductive layers on or across the contacts. Deposited particles may adversely affect the performance of optoelectronics by absorbing or scattering the light beams. Corrosivity of smoke produced by materials is characterized by the corrosion index (CI), defined as material loss rate (angstrom/minute) per amount of material gasified products (grams) per volume of air (m3). It is measured by exposing strips of metal to flow of combustion products in a test tunnel. Polymers containing halogen and hydrogen (polyvinyl chloride, polyolefins with halogenated additives, etc. ) have the highest CI as the corrosive acids are formed directly with water produced by the combustion, polymers containing halogen only (e. g. polytetrafluoroethylene) have lower CI as the formation of acid is limited to reactions with airborne humidity, and halogen-free materials (polyolefins, wood) have the lowest CI. [7] However, some halogen-free materials can also release significant amount of corrosive products. [11] Smoke damage to electronic equipment can be significantly more extensive than the fire itself. Cable fires are of special concern; low smoke zero halogen materials are preferable for cable insulation. When smoke comes into contact with the surface of any substance or structure, the chemicals contained in it are transferred to it. The corrosive properties of the chemicals cause the substance or structure to decompose at a rapid rate. In some instances the chemicals are absorbed into the substance or structure that it comes into contact with, i. e. clothing, unsealed surfaces, potable water piping, wood, etc. , which is why in most cases dealing with a structure fire they are replaced. Secondhand smoke inhalation[edit] Secondhand smoke is the combination of both sidestream and mainstream smoke emissions. These emissions contain more than 50 carcinogenic chemicals. According to the Surgeon Generals latest report on the subject, Short exposures to secondhand smoke can cause blood platelets to become stickier, damage the lining of blood vessels, decrease coronary flow velocity reserves, and reduce heart variability, potentially increasing the risk of a heart attack [12] The American Cancer Society lists heart disease, lung infections, increased asthma attacks, middle ear infections, and low birth weight as ramifications of smokers emission [13] Measurement of smoke[edit] As early as the 15th Century Leonardo da Vinci commented at length on the difficulty of assessing smoke, and distinguished between black smoke (carbonized particles) and white smoke which is not a smoke at all but merely a suspension of harmless water droplets. Smoke from heating appliances is commonly measured in one of the following ways: In-line capture. A smoke sample is simply sucked through a filter which is weighed before and after the test and the mass of smoke found. This is the simplest and probably the most accurate method, but can only be used where the smoke concentration is slight, as the filter can quickly become blocked. Filter/dilution tunnel. A smoke sample is drawn through a tube where it is diluted with air, the resulting smoke/air mixture is then pulled through a filter and weighed. This is the internationally recognized method of measuring smoke from combustion. Electrostatic precipitation. The smoke is passed through an array of metal tubes which contain suspended wires. A (huge) electrical potential is applied across the tubes and wires so that the smoke particles become charged and are attracted to the sides of the tubes. This method can over-read by capturing harmless condensates, or under-read due to the insulating effect of the smoke. However, it is the necessary method for assessing volumes of smoke too great to be forced through a filter, i. e. , from bituminous coal. [14] Ringelmann scale. A measure of smoke color. Invented by Professor Maximilian Ringelmann in Paris in 1888, it is essentially a card with squares of black, white and shades of gray which is held up and the comparative grayness of the smoke judged. Highly dependent on light conditions and the skill of the observer it allocates a grayness number from 0 (white) to 5 (black) which has only a passing relationship to the actual quantity of smoke. Nonetheless, the simplicity of the Ringelmann scale means that it has been adopted as a standard in many countries. Optical scattering. A light beam is passed through the smoke. A light detector is situated at an angle to the light source, typically at 90Â °, so that it receives only light reflected from passing particles. A measurement is made of the light received which will be lower as the concentration of smoke particles becomes higher. Optical obscuration. A light beam is passed through the smoke and a detector opposite measures the light. The more smoke particles are present between the two, the less light will be measured. Combined optical methods. There are various proprietary optical smoke measurement devices such as the nephelometer or the aethalometer which use several different optical methods, including more than one wavelength of light, inside a single instrument and apply an algorithm to give a good estimate of smoke. Inference from carbon monoxide. Smoke is incompletely burned fuel, carbon monoxide is incompletely burned carbon, therefore it has long been assumed that measurement of CO in flue gas (a cheap, simple and very accurate procedure) will provide a good indication of the levels of smoke. Indeed, several jurisdictions use CO measurement as the basis of smoke control. However it is far from clear how accurate the correspondence is. Medicinal smoke[edit] Throughout recorded history, humans have used the smoke of medicinal plants to cure illness. A sculpture from Persepolis shows Darius the Great (522–486 BC), the king of Persia, with twocensers in front of him for burning Peganum harmala and/or sandalwood Santalum album, which was believed to protect the king from evil and disease. More than 300 plant species in 5 continents are used in smoke form for different diseases. As a method of drug administration, smoking is important as it is a simple, inexpensive, but very effective method of extracting particles containing active agents. More importantly, generating smoke reduces the particle size to a microscopic scale thereby increasing the absorption of its active chemical principles. [15] See also[edit] Wikimedia Commons has media related to Smoke. Wikisource has the text of the1911 Encyclop? dia Britannicaarticle Smoke. Air safety Colored smoke Dust Firefighter Fog machine Haze machine Magic smoke Skywriting Smog Smoke bomb Smoke dampers Smoke detector Smoke grenade Smoke hood Smoke screen Smoke signal Smoke test Smoking (cooking technique) Tobacco

2 Theories of Ageing Essays

2 Theories of Ageing Essays 2 Theories of Ageing Essay 2 Theories of Ageing Essay Explain two theories of ageing. Ageing is the accumulation of changes in a person over time. Ageing in humans refers to a multidimensional process of physical, psychological, and social change. Some dimensions of ageing grow and expand over time, while others decline. The ageing process is inevitable and expected to every person as we grow old. There are different theories of ageing and fall into two types that was developed in order to further understand and described how we age.The first type, social and psychological aspect states that aging is natural and programmed into the body, while the second type is the biological aspect of aging theories say that aging is a result of damage which is accumulated over time. Disengagement theory, this is the withdrawal of involvement into social activities. According to Henry and Cumming (1961), this is natural for an older person to experience this mostly because elderly people have restricted opportunities to interact with others due to different reasons like their health.Every person may decline in their physical and mental health as they grow old and loss of social opportunities can be the reason why they disengage into the society, old person can get too tired easily. Retirement is another reason as they lose contact with their colleagues but there are many reasons why people disengage into the society. Everyone expects death, and ones abilities will likely deteriorate over time. As a result, every person will lose ties to others in his or her society.Genetically programmed theory, it says that our genes determine our life span. People often think that if they have a family member that lived over 100 years is more likely that they may inherit that genes. In this theory, human lifespan is due to inherited differences in our DNA. Our lifestyle, diet, activity, stress, aging and numerous other factors can impact our DNA and the way it functions. Aging, therefore, must be inherit in the organism and not simply a result of environmental factors or disease.So aging and death, according to this theory, are not a result of wear and tear or exposure, but are a programmed, natural and necessary part of genetics. In short, we are programmed to age and die. Wear and tear theory is where cells and tissues have vital parts that wear out resulting in aging. Like components of an aging car, parts of the body eventually wear out from repeated use, killing them and then the body.

Causes of Corruption Essays

Causes of Corruption Essays Causes of Corruption Essay Causes of Corruption Essay Econ Gov (2008) 9:245–263 DOI 10. 1007/s10101-007-0033-4 O R I G I NA L PA P E R Causes of corruption: a survey of cross-country analyses and extended results Lorenzo Pellegrini  · Reyer Gerlagh Received: 30 September 2005 / Accepted: 20 October 2006 / Published online: 23 February 2007  © Springer-Verlag 2007 Abstract We survey and assess the empirical literature on the sources of corruption Thanks to the improved availability of data, we are able to produce an improved cross-country econometric model to test well-established and more recent hypotheses jointly. We do not ? nd that the common law system, or a past as a British colony predicts corruption. Our results support cultural theories on the causes of corruption, and suggest that a medium-long exposure to uninterrupted democracy is associated with lower corruption levels, while political instability tends to raise corruption. Our results also suggest that the diffusion of newspapers helps to lower corruption levels. Keywords Corruption  · Ethnolinguistic fractionalization  · Democracy  · Political instability JEL classi? ation D72  · H11  · H50  · K42  · O17 L. Pellegrini (B ) Institute of Social Studies (ISS), Kortenaerkade 12, 2518 AX The Hague, The Netherlands e-mail: [emailprotected] nl L. Pellegrini  · R. Gerlagh Institute for Environmental Studies (IVM), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands R. Gerlagh University of Manchester, School of Social Sciences, Economics, Manchester, UK 246 L. Pellegrini, R. Gerlagh 1 Introduction Corruption is a wide spread phenomenon affecting all societies to different degrees, at different times. On the one hand, as corruption scandals have repeatedly shown, bribes are common in all countries notwithstanding differences in income levels and law systems, as they are common in democracies and in dictatorships. Recent scandals over corruption have shown that also supposedly free-from-corruption societies are affected. The ELF scandal demonstrated that corruption was rampant in the management of the French state owned enterprise. 1 The following year, a corruption charge against President Chirac could not be courted because he was shielded by immunity as the head of the state. Also in Germany, the CDU and former Chancellor Helmut Kohl were ? ned for receiving illegal campaign funding. 3 Among Nordic countries (which rank always among the less corrupt in international comparisons), Swedish and Norwegian managers of state owned companies have been found to be involved in bribe-taking. 4 Corruption is not rare even during humanitarian emergencies. According to Transparency Internati onal, an NGO who strives to expose corruption, relief efforts in the aftermath of the South East Asian Tsunami earthquake of 2004 were hampered by corruption. Still, different countries are marked by large differences to the extent of corruption. In some societies, no transaction is ? nalized without corruption having an effect, while in other countries it is considered an exception and rarely tolerated. Figure 1 presents corruption levels worldwide; the ? gure shows that corruption tends to be pervasive especially in developing countries. At the same time, numerous studies have demonstrated the pernicious effects of corrupted practices on- among other things- economic growth, investment, human development and environmental policies. The relevance of corruption for welfare levels requires the understanding of the sources of corruption (and of differences across countries) and the development of policies to address the phenomenon of corruption. Hard evidence of corruption is intrinsically dif? cult to obtain, because of the secrecy surrounding illegal deals, but there are several ways to obtain proxies of the extent of corruption. One such source comes from the pool of international interviews commissioned for the Global Corruption Perception Barometer (Transparency International, 2004). From the barometer, we can see that while around 90% of Brazilians think that petty and grand corruption are ‘very big problems’, around 50% of Finnish interviewees think that petty corruption is ‘no problem at all’ and around 35% think that also grand corruption is no problem. With respect to the personal experiences of individuals with bribery, 1 On the ELF scandal see The Washington Post, Wednesday, February 9, 2000; Page A21. 2 http://archives. cnn. com/2001/WORLD/europe/10/10/chirac. court/. 3 businessweek. com/magazine/content/01_48/b3759151. htm. Corruption Blackens Nordic Region’s Lily-White Image, Agence France Presse, December 4, 2003. 5 transparency. org/pressreleases_archive/2005/faq_tsunami. html#faqti1. Causes of corruption 247 Fig. 1 A higher score (a darker color) indicates higher corruption perception. Our elaboration on data from Kaufmann et al. (2005) we see that more than 50% of Cameroon’s interviewees admit that somebody in their household paid a bribe in the last 12 months, while among Austrian, Canadian, German and Irish interviewees only 1% mentioned exposure to corruption for their households in the last year. Evidence on private ? rms’ expenditures for bribes is available from the World Business Environment Survey 2000. 6 From the executives’ interviews in the survey, we see for example that while more than 90% of Canadian ? rms declare that they did not pay any bribes in the last year, none of the Armenian and Azerbaijani ? rms interviewed declared so. Once we consider the negative effects of corruption on welfare, an obvious research question arises: what is the reason for corruption to be common in some countries while other countries succeed to prevent corruption from hampering their welfare? While theoretical literature on this subject abounds, empirical studies are relatively scarce. Since several indexes of corruption perception have become available over the last few years, it is now possible to test statistically some of the ideas from the theoretical literature. To the knowledge of the authors, the most comprehensive and widely cited econometric analysis of the sources of corruption is a cross-sectional study from Treisman (2000). 7 The study takes into consideration and tests empirically a wide range of theoretical explanations of corruption and ? nds mostly ‘? xed factors’ as the determinants of corruption. That is, the signi? cant explanatory variables are all persistent over time, impossible to change in the short and medium run. The most policy-amenable variable is the ‘exposition to democracy for more than 46 years’. As the sources of corruption are persistent, corruption itself will remain stable over time and can hardly be affected by policies; corrupted countries should 6 See http://info. worldbank. org/governance/wbes/. 7 This relatively recent paper has been already cited in 59 other works according to ISI Web of Science (checked on the 18th September 2005). 248 L. Pellegrini, R. Gerlagh ather learn to live with it. Treisman’s study will be the point of departure of our work. 8 Previous and subsequent empirical studies- mostly cross sectional- have focused more on single issues where the authors have tested a particular theory by inserting a proxy for a speci? c variable into multiple regressions. These studies most often produced results that con? rmed the theories that were tested and, while they are valuable contributions for the identi? cation of likely sources of corruption, they may have over-emphasized the importance of the variables analyzed because of omitted variables biases. Several studies focused on the role of democracy as a determinant of corruption (among the most recent: Sung 2004; Chowdhury 2004 and Bohara et al. 2004), some focused on regulatory burden and economic freedom (Chafuen and Guzman 1999), others have focused on decentralization and federalism (e. g. Fisman and Gatti 2002; Arikan 2004), on natural resources prevalence as a source of income in the economy (Leite and Weidmann 1999), and on legal origins of a country as a determent of corruption (Glaeser and Shleifer 2002). In our paper we will ? rst provide a systematic overview of different theories on the causes of corruption. In this survey, presented in Sect. 2, we distinguish theories that focus on historical roots versus theories that give more attention to contemporary causes. Thereafter, we will assess the different theories and calculate our own estimates of the sources of corruption in a comprehensive econometric model, using indexes of corruption that have become available recently (Kaufmann et al. 2005) and we will cover a large sample of countries. Furthermore, we will make use of some independent variables that are improved proxies for the variables that theory would suggest. The empirical contribution of this paper is threefold. First, due to improved data availability, we can work with a larger data sample that give more power to our statistical tests compared to earlier studies. 9 Second, we have a large set of independent variables available that we can test jointly so that we do not suffer from omitted variable bias, a problem of many previous and partial studies that may have overemphasized the importance of the variables under consideration. This advantage is particularly valuable as it helps us to assess the importance of long-lived versus contemporary causes of corruption, while these sources of corruption have mostly been assessed independently, so far. Thirdly, a crucial advantage over previous authors is the availability of two alternative corruption indexes, which can be used for robustness checks of the ? ndings. Section 3 describes the data that we use for our own econometric analysis, Sect. 4 introduces and discusses the econometric estimates, and Sect. 5 concludes. 8 Lambsdorff 1999 provides a comprehensive survey of earlier empirical results. 9 Speci? cally, when we compare our study to Treisman’s, we ? nd that our sample is larger than 100 countries in most regressions, while Treisman’s sample size is between 44 and 64 countries in the majority of regressions. Causes of corruption 249 2 Theories of corruption’s determinants Theories of corruption’s determinants (and more generally of the quality of government) abound. We will take as a starting point the theories on the sources of corruption that are mentioned in Treisman (2000) and La Porta et al. (1999) as those studies are considered a benchmark in the literature and they provided a powerful battery of empirical tests. To these we will add the most recent ? ndings of empirically backed literature in order to test and build upon their ? ndings. The subsequent econometric estimates will follow a similar approach. 2. 1 Historical roots of corruption Among the theories that relate the history of countries to their present day levels of corruption, legal theories suggest that the kind of law codes that are in place in a country affect the quality of government, including the level of control of corruption. Historically motivated theories trace the effort of property owners to limit the discretionary power of the Crown as the origin of common law legal codes (Glaeser and Shleifer 2002). Furthermore, they suggest that the actions of the independent judiciary system in countries that adopted the British law code will be conductive to lower levels of corruption (for a discussion in depth, see La Porta et al. 1999). Another theory, also based on the historical roots of corruption, is one that sees former British colonies as having a better civil service code due to the in? uence of the British bureaucracy. In this system, the British civil servant is focused on procedural aspects of the law, which enhances the capability of subordinates and judges to challenge hierarchies in order to enforce the law (Treisman 2000). However, as a result of the method with which colonization was imposed upon countries, this positive effect of British colonization can be questioned. Quoting Macaulay (1843/2001)’ the business of a servant of the [East India] Company was simply to wring out of the natives a hundred or two hundred thousand pounds as speedily as possible, that he might return home before his constitution had suffered from the heat, to marry a peer’s daughter, to buy rotten boroughs in Cornwall, and to give balls in St. James’s Square’. 0 If British colonizers were extracting resources, in addition to establishing legal procedures, the effect of colonization on present corruption deserves to be tested. Another hypothesis, which has been put under scrutiny in previous literature, is that Protestant religion, being relatively less hierarchical when compared to other churches and religions (such as the Orthodox and Catholic churches and Muslims), is less prone to tolerance towa rds power abuses and corruption. Furthermore, the Protestant church has traditionally been apart from the state and played a role of opposition to the abuses of the government (Treisman 2000). 0 Macaulay was in India working for the Supreme Council of India and later became a member of the British Parliament. 250 L. Pellegrini, R. Gerlagh Thus this theory suggests that Protestant countries will be less affected by corruption. Also pointing at long-living causes of corruption are those theories that suggest that more ethnically fractionalized countries tend to be more corrupted (Mauro 1995). One root of the link between ethnolinguistic fractionalization and corruption can be existence of alternative af? liations and obedience with respect to the state. Thus, in ethnically divided societies civil servants and politicians would exploit their positions to favor members of their own ethnic group. Another possible explanation for the effect that ethnolinguistic fractionalization would have on corruption is due to the fact that divided societies tend to under-provide public goods and this, in turn, would augment the dependency on special bounds to obtain essential services from the state. 2. 2 Contemporary causes of corruption Another set of theories relates the level of corruption to institutions, economic structures, and the level of development. These theories provide more of an entrance for anti-corruption policy compared to the theories based on historic variables, as there are some policy-amenable factors among the determinants of corruption. To start with, income levels may affect corruption in several ways. Richer countries can be expected to afford better institutions. Furthermore, many variables correlated with income, such as schooling levels, urbanization and access to mass media, are associated with higher development levels and they decrease the tolerance of the polity towards corruption. It may need some development to recognize corruption as a violation of the border between the public and the private sphere. Thus we expect a real income variable to be negatively correlated with corruption, where causality may run in both directions. The rent-seeking literature emphasizes the link between corruption and possibilities for economic agents to gain access to sources of higher-than-average rents, when state intervention prevents free entry (see Rose-Ackerman 1999). In this perspective, the ? ght against corruption is helped with a reduction of non-generic state regulation. Thus, corruption would be associated to the size of government activities (Chafuen and Guzman 1999; Acemoglu and Verdier 2000). On a similar train of thought, openness to trade and increasing supply of foreign products on the domestic market enhances domestic competition, thereby reducing rents and corruption. Conversely, trade-barriers increase the opportunities for earning extra rents by gaining access to trade allowances, stimulating corruption. Natural resources are a common source of high rents, available to those that have obtained the rights for their exploration and extraction. These rents promote activities aiming at in? uencing policy makers who have power on the distribution of exploitation rights, drawing away resources from other productive activities (La Porta et al. 1999). Thus natural resources abundance would be associated to higher corruption, though we cannot take this effect for granted Causes of corruption 251 since revenues from natural resources could also be used in order to produce public goods, decreasing the need to revert to bribing in order to access them. Another strand of the literature considers the relation between social institutions and corruption, starting with democracy. A negative correlation between democracy and corruption is tautological when based on a substantial de? nition of democracy, since corruption favors the interests of the individual, or a minority, as opposed to the interests of the majority. Once we consider democracy from a procedural perspective (free elections and electoral competitions) the association is less straightforward. Most indexes of democracy are based on the procedural aspects of democracy, and revious empirical studies have found contrasting results. Many papers that focus on democracy and employ few control variables ? nd contemporary democracy to decrease corruption levels (e. g. Hill 2003; Chowdhury 2004; Bohara et al. 2004), while more comprehensive studies do not ? nd such a correlation (Treisman 2000). The experience of some Latin American countries suggests that the transition to democrac y did not produce much of a dividend in terms of a reduction in corruption. A telling example is Mexico. Up to 2000, the country was ruled for seven decades by the PRI, known for its cronies and patronage system. Then, for the ? rst time, a president was elected that was not from the PRI. This was considered a turning point for Mexican democracy, but the following years were marked by corruption scandals that affected virtually all political parties, including the ruling party (the PAN, which has gained power on its platform promoting change and a ? ght against corruption),11 the former ruling party PRI, the other opposition party (the PRD)12 and even the marginal ecologist party. 3 Also in Europe, cases of corruption in long-established democracies abound; the above-mentioned high-level scandals in France are one example. As another example, Italy scores very high on all indexes of corruption, despite having been a democracy with high levels of electoral competition for six decades. Looking at democracy, not from a procedural but from a dynamics point of view, leads us to political sta bility as another variable that may affect corruption levels. In politically stable administrations, bureaucrats face less chances of dismissal and have more opportunity for long-run advancement in their careers, which provides an incentive to build an open and honest reputation for a career development. On the other hand, a permanent position in power may help maintain ‘patronage and corruption’ reputations and relations. There are therefore two alternative and contradictory hypotheses on whether corruption is discouraged or fostered by political stability (Treisman 2000). Apart from the duration of a job in the administration, the wages may also affect the vulnerability to corruption. Higher wages imply higher costs when 11 President Fox has been accused of using illegal funds to ? nance his campaign, see http://news. bbc. co. uk/hi/spanish/latin_america/newsid_2802000/2802161. stm. 12 On the tape-scandals, involving the most important aides of Lopez Obrador, see http://news. bbc. co. uk/hi/spanish/latin_america/newsid_3531000/3531475. stm. 13 On the scandal involving the young leader of the Partido Verde Ecologista, see smas. com/noticierostelevisa/mexico/345598. html. 252 L. Pellegrini, R. Gerlagh a position in the civil service is lost, and a cost-bene? t analysis suggests that higher wages thereby provide an incentive to restrain from corruption (Becker 1968; Treisman 2000). Finally, we look at a rather different institutional variable, recently studied in both theoretical and empirical work: the extent of newspaper circulation, where the press i s supposed to act as a check on those that should represent the public interest (Brunetti and Weder 2003). The hypothesis claims that corruption scandals being freely enquired and exposed by the mass medias, acts as a deterrent for bureaucrats to engage in corruption activities. 3 Data sources on corruption In this section we describe our dataset and the underpinning concept of corruption. The other variables will be described below as they are introduced in the econometric analysis. The variables’ sources are listed in the Appendix together with their descriptive statistics. The assessment of corruption levels across countries is a formidable empirical challenge as corruption is dif? ult to de? ne uniformly, being culturally determined, and even more dif? cult to measure, because illegality implies secrecy. We will use data that complies with the general de? nition of â€Å"abuse of power for personal gains†14 from the World Bank15 (Kaufmann et al. 2005) and Transparency International. 16 The two datasets are similar in the sense that they gather existing measures on the pe rception of corruption, and produce an aggregate index. In this paper, we will use the data from the World Bank in the main analysis and the index from Transparency International as a robustness check. The individual indexes aggregated in order to create the proxies of corruption (or, more precisely of corruption’s perception), range from Gallup’s opinion surveys- where a sample of the polity is asked how common corruption is and at what scale it operates- to surveys of company executives that estimate the share of their companies’ revenues that are spent in bribes. 17 On the one hand, the high correlation of the aggregated indexes, originated from different sources, gives some con? dence on the fact that they are correct proxies for corruption. On the other hand, it should be noted that the de? ition of corruption can be interpreted differently in different cultural contexts and that there can be ‘emotionally driven’ answers to survey: thus, while a corruption scandal is escalating, the interviewees will possibly overrate the level of corruption, just as when the economy is booming interviewees can have a more positive attitude also about the gove rnment and the civil servants. While some of these 14 For an overview of complexities and the evolution in social sciences of the de? nition of corruption see Williams 1999. 15 Available at worldbank. rg/wbi/governance/data. html. 16 Available at icgg. org/. 17 For a full description of all the sources and of the aggregation methodology used by the World Bank, see Kaufmann et al. 2005; for sources and methodology of the Transparency International index, see Lambsdorff 2004. Causes of corruption 253 concerns are taken care of by the way the polls are realized and aggregated, a margin of uncertainty is inevitable (see Kaufmann et al. 2005). Furthermore, as it has been shown extensively in the empirical literature, even though these indexes may de? e the perception of corruption over its essence, it appears that perceptions have an impact on the economy and that the indexes have a high explanatory power when used as independent variables in econometric analyses (for a review see Jain 2001). Lastly, from the descriptive statistics of Table 2, note that the corruption index has a mean of 0 and a standard deviation of around 1. 18 Thus our results can be easily interpreted in standardized form. 4 Empirical analysis In this section we provide cross-country estimates of the sources of corruption, highlighting where and why our data and results differ from previous studies. Following Treisman’s (2000) methods, we will ? rstly test the hypotheses- already summarized in the previous section- based on the more stable variables and subsequently include variables that can change over time and that can more easily be in? uenced by public policies. Our preferred estimation technique is weighted least squares regressions, where the weights of the estimates are the reciprocal of the margins of errors of the corruption index for each individual observation (as estimated by Kaufmann et al. 005). Thus, the estimates of corruption perception that are more uniform across sources are considered more reliable and get a higher weight in the regressions. 19 The dataset on corruption perception is very extended and includes a number of very small countries with typical features (e. g. having an attractive investment climate) that can easily lead to selection bias in our results. 20 For this reason, we omit from the empirical analysis countries that have less than 1 ,000,000 inhabitants. We begin including as independent variables in our regressions a dummy variable for the common law system, a dummy for British colonies including the UK itself,21 a measure of the percentage of protestants in the population, a measure of ethnolinguistic fractionalization, and a resource abundance 18 The standard deviation is exactly equal to 1 in the complete sample, but because of missing data it changes slightly in each sample. 19 Our results hold also for ordinary least squares estimations, but as expected weighted least squares produce more ‘precise’ estimates (i. e. lightly higher t statistics). 20 One example of the special features that very small countries have is the more limited extent of ethnoguistic fractionalization and the fact that they tend to be more open to trade (e. g. Knack and Azfar 2003). Since small countries are included in corruption surveys only when they are more interesting for investors, the over-representation of small countries with good in vestment climate and low corruption levels could easily introduce in the sample a spurious correlation between corruption, openness (negative) and ethnolinguistic fractionalization (positive). 1 Being a former-British colony should affect the degree of corruption because of the lasting effect British occupation has on the organization of the civil service. The UK has that same civil service organization and this is the reason for including the UK together with its former colonies in the dummy. 254 L. Pellegrini, R. Gerlagh measure based on the percentage of fuels, metals, and minerals in exports. These variables are predetermined by the history of the country or by natural characteristics and can be considered ? xed over long time scales. The dependent variable is the corruption index from the World Bank for the year 2004. The results are reported in Table 1. In regression (1), we ? nd results that throw some doubt on many conventional ? ndings. Speci? cally, while as in most previous analysis we do ? nd a strong correlation with some of the country ? xed factors, we ? nd that neither the existence of a common law system or having been a British colony are aspects associated with a country’s corruption levels. This result challenges the hypothesis that having adopted the British law system or having a past as a British colony reduces corruption at present day (e. . Treisman 2000; Glaeser and Shleifer 2002). When we compare Treisman’s work, where he found a British colonial past to be an important determinant of present levels of perceived corruption, with the model and variables used in this study, we ? nd that our dataset is largely expanded (there are up to 107 countries in our regressions, almost doub le the number of countries in the largest dataset from Treisman) and that the index of corruption we are using has a lower standard error associated with it. 22 We ascribe our ? ding to the fact that our dataset includes countries that were not included in previous studies, are former British colonies, and are also perceived to be very corrupt. 23 As a British colonial past tends to overlap with the Common law system,24 collinearity is a risk in considering these highly correlated variables, and we also include them separately in the regressions. 25 We still ? nd the two variables to be individually non-signi? cant. In our analysis, one factor that signi? cantly affects the level of corruption in all regressions with suf? iently large sample size is the percentage of Protestants in the population, which is negatively associated with the level of perceived corruption. This ? nding relates to the theories of culture and the work of those (Weber and Landes 1999 among others) that sugge st that religion is a fundamental character shaping culture. The in? uence of the presence of Protestants over corruption is con? rmed over the different speci? cations and is always statistically very signi? cant. It must be noted that, from regression (2) to (9), the introduction of other independent variables more than halves the coef? ient of Protestant religion prevalence. The conversion of 20% of the population from a non-protestant religion to the protestant religion (a one standard deviation 22 For a thorough comparison of the corruption perception index from Transparency International and the one from the World Bank, see Kaufmann et al. 2005. 23 Our dataset includes former British colonies such as Myanmar and Sudan, which rank among the countries where corruption is perceived to be the highest in the world. Data on these countries has only recently become available. 4 The list of countries, in our dataset, that experienced British control, but did not adopt the British lega l system are: Egypt, Iraq, Jordan, Kuwait, Myanmar, Mauritius, and Oman. Countries, included in the dataset, that adopted the British legal system without being colonies are: United Arab Emirates, Liberia, Namibia, Saudi Arabia, Somalia, and Thailand. 25 We also computed the variance in? ator factors, for both variables, which were well below the conventional level of 10. Causes of corruption Table 1 Regressions results (3) (4) (5) (6) (7) (8) (9) (1) (2) ?0. 85 (0. 30) 0. 3(0. 23) ? 0. 45(0. 51) ? 1. 07 (0. 13) ? 0. 08(0. 27) 0. 03(0. 29) ? 0. 37 ? ?(0. 17) ? 0. 35 ? ?(0. 17) ? 0. 12 (0. 05) ? 0. 71 (0. 08) ? 0. 64 (0. 08) 0. 41 ? (0. 23) 0. 40 ? (0. 21) 0. 40 ? (0. 22) ? 0. 55 (0. 09) ?1. 10 (0. 25) ? 0. 11(0. 21) ?0. 91 (0. 24) ? 0. 13(0. 21) ?0. 73 (0. 25) ? 0. 18(0. 20) ?0. 60 ? (0. 31) ? 0. 16(0. 21) 0. 44 ? (0. 22) ? 0. 53 (0. 09) ?0. 82 ? ?(0. 37) 0. 08(0. 22) 0. 52 ? (0. 29) ? 0. 67 (0. 11) ?0. 39 ? ?(0. 18) ? 0. 15 ? ?(0. 07) 0. 03(0. 08) 1. 29 (0. 43) ?0. 34 ? (0. 19) ? 0 . 08(0. 10) Common law ? 0. 13(0. 2) British colony ? 0. 15(0. 28) Protestants ? 2. 06 (0. 35) ? 2. 21 (0. 34) ? 1. 10 (0. 25) Ethnolinguistic 1. 53 (0. 30) 1. 36 (0. 22) ? 0. 13(0. 21) fractionalization Fuels and minerals 0. 82 (0. 26) 0. 85 (0. 23) 0. 41 ? ?(0. 21) Imports ? 0. 60(0. 47) Income ? 0. 72 (0. 06) Decentralization Contemporary democracy 30 Years of uninterrupted democracy Newspaper circulation Government intervention Political instability Government wage 0. 41 0. 44 0. 77 R2 Number of cases 106 107 106 0. 89 42 0. 78 104 0. 79 106 0. 80 104 0. 81 103 1. 66 (0. 45) ? 0. 04(0. 03) 0. 85 72 WLS estimation with the Corruption Perception as dependent variable and the reciprocal of errors’ estimates as weights. The constants are included in the regressions, but the coef? cients are omitted in the table. Robust standard errors, are in parenthesis under the coef? cients. The variance in? ation factor is lower than 10 for every variable (indicating that collinerity is not a major problem) *, **, ***Correspond to a 10, 5, 1% of signi? cance, respectively 255 256 L. Pellegrini, R. Gerlagh change) would be associated with a reduction in the corruption index by 0. 2 points on an approximate 0–4 scale, that is one ? th of a standard deviation. Associating the prevalence of fuels and minerals with corruption is not a new concept. In an in? uential paper, Leite and Weidmann (1999) found that corruption levels are statistically associated with natural resources and provide a rent-seeking explanation. When compared to their analysis, our substantially expanded dataset st ill ? nds the same association, though the relation is not as strong as in their results. Thus, in our model, a change of 25% in the value of natural resources on exports (i. e. a one standard deviation change) would be associated with a change of 0. 0 points in our corruption perception index. In regression (2) we include, as a proxy for openness, the share of imports in GDP averaged over of a period of 10 years. We ? nd it not to be a signi? cant predictor of the corruption index. This result differs from Treisman who found imports over GDP to be associated with a decrease of the corruption index, but it corresponds with the results from Knack and Azfar (2003) who have disputed the link between trade intensity and corruption. The difference between results can be ascribed to the propensity of smaller datasets to be affected by selection bias. That is, earlier and less comprehensive surveys tended to include small economies only when they were of interest for international investors who are the main source of funding for the surveys. The selection thus favored small countries that were open and not very corrupt. The inclusion of income as an independent variable in Regression (3), causes some major changes in all coef? cients. 26 Most notably, the ethnic division proxy becomes insigni? cant (and remains insigni? cant after the inclusion of other explanatory variables). It appears that ethnic diversity is not correlated per se with corruption, but through lower income levels or through other variables correlated to income, such as schooling. A similar effect occurs for the coef? cients of the Protestants variable and of natural resources in exports, though these coef? cients remain signi? cant. 27 At the same time, we must emphasize that the inclusion of income as a independent variable runs the risk of creating an endogeneity problem: since corruption, and institutions in general, have been found to affect the growth rate of income, and since institutional quality tends to be stable over time, it is ossible that corruption determines income levels rather than the other way around. 28 A possible strategy for tackling the issue of endogenity is the use of 26 The income variable refers to 2001. 27 Indeed, for natural resources there is a large literature on the ‘resource curse’ and the ‘Dutch disease’, which have shown the de trimental effect that natural resources have on economic growth (Stevens 2003). 28 On the direction of causality between institutions and income there is a large and growing empirical literature. Most of the authors seem to agree that causality runs from institutions to income, rather than the other way around (e. . Rodrik et al. 2004; Acemoglu et al. 2001). For an example of an econometric study ? nding the opposite direction in the causality between growth and institutions, see Chong and Calderon 2000. Causes of corruption 257 instrumental variables. We identify latitude as a good instrumental variable29 (it is correlated with income at 60% and we ? nd no ways through which it could directly affect corruption). Latitude has been suggested as a determinant of income by the proponent of theories of geography as a determinant of economic development (e. g. Sachs 2001). Once latitude is inserted among the independent variables, acting as an instrument for income, the coef? cients of the other independent variables tend to increase in magnitude and to become more statistically signi? cant (not shown in this paper). Most notably, the variables describing democracy, newspapers circulation and political instability (see below) increase their coef? cients. But, equally important, we do not ? nd any of the variables that we found not to be signi? cant in the main analysis to become signi? cant when using the instrument. 0,31 While the income endogeneity issue can be taken care of via the use of instrumental variables, we prefer an alternative solution, using a lagged income variable. The reason is that we are not so much interested in the coef? cient of income itself, which can be biased due to endogeneity, but we are more interested in the use of income as a control variable. Our aim is to ? lter out that part of the coef? cients for other independent va riables that may go through income. We thus use income to improve the robustness of our estimates. Summing up, choosing to present results from the WLS regressions we favor type I errors, but the difference with the results with the instrumental variables would not alter substantially our conclusions. One of the main ? ndings of Treisman (2000) that contradicts existing literature (e. g. Fisman and Gatti 2002) is the trend for federalism to increase corruption. We use as a proxy of power decentralization the share of expenses that are delegated to local authorities as compared to the central government. 32 Once we include such a proxy in our regressions, we do not ? d that decentralization has a positive correlation with corruption. On the contrary, we ? nd the proxy for decentralization to have a negative correlation with corruption, though the coef? cient is not statistically signi? cant. We interpret this result as a 29 A good instrumental variable must be highly correlated with the variable to be instrumented and should not have additional explanatory power. 30 The only except ion is contemporary democracy that becomes signi? cant. In the analysis below, we prefer to use and discuss the measure of medium-term persistence of democracy, because it is signi? ant even with the inclusion of income. In any case, the null hypothesis of exogeneity is not rejected by the Hausman test. 31 A note of caution is needed when we analyze the results with the instrumental variable, because theories that link geographical factors to institutions and through them to income levels have been developed (Hall and Jones 1999; Acemoglu et al. 2001). If these theories are correct, latitude could be used as an instrumental variable for corruption as well, and the interpretation of the 2-stage results would become problematic. For our own dataset, we checked whether latitude could be used as an instrument for corruption in a regression on income and found that latitude would not be a valid instrument, because it retained explanatory power when added to corruption in the regression. 32 Unfortunately, our proxy for decentralization is available just for a small sample of countries. Once more statistics on government ? nance, uniform across countries, are available a more reliable empirical test of the link between decentralization and corruption will be possible. 258 L. Pellegrini, R. Gerlagh weak suggestion that federalism does not increase corruption. 3 One reason for the difference between our ? ndings and Treisman (2000) ? nding can lay in the fact that Treisman uses a dummy variable to characterize a state as federalist or centralist based on an incomplete list of federal countries in Elazar 1995. Apart from its incompleteness, another problem of this list is that an of? cially federalist country may in fact be overly centralized, where Mexico is a case in point. 34 The evidence is less than conclusive, as it is based on a sample of only 42 countries, but deserves to be mentioned as a matter that merits further inquiry (see also Arikan 2004). In the remaining regressions, the decentralization proxy is omitted in order to keep a larger sample of countries. The inclusion of a contemporary democracy variable in regression (4) does not produce a signi? cant coef? cient. This result is different when compared to most of the literature that speci? cally tests the democracy hypothesis. We conjecture that the reason for this is that most other models work with less control variables (e. g. Hill 2003; Chowdhury 2004) or with smaller samples (Sandholtz et al. 2000) leading to omitted variable bias. In the table, we introduced a proxy for democracy from the Polity IV dataset (measuring democratic levels of institutions as judged by a panel of experts, averaged over the period 1994–2003). As an alternative (not reported in the Table), we used a proxy from the Polyarchy dataset (measuring participation and competition at the elections through a mathematical interpretation of elections results, also for the period 1994–2003). None of the two democracy’s proxies seem to affect the corruption level, but only when we exclude the income variable. Thus, it is possible that there is an indirect effect of democracy levels through income on corruption (see Barro 1996; and the literature that has sprung from his work). Constructing a dummy variable for stable democracies35 (where there are no coups, or other major interruptions of the nature of the government, and the country has democratic institutions in terms both of elections and of administration of power), we ? nd that a long exposition to democracy has a mitigating effect on corruption. Thus, a ‘30 years of uninterrupted democracy’ variable [as reported in regression (6)] is signi? ant at 5%. The absolute size and the statistical signi? cance of the coef? cient increases if more decades are included in the dummy. A dummy characterizing countries that experienced 50 years of uninterrupted democracy is signi? cant at 1%. Our ? nding thereby presents 33 To be sure, we also included, as a proxy of the size of the country, the natural logarithm of the population (as in Fisman and Gatti 2002), to account for the fact that countries with different size may have different ‘natural’ centralization levels. Conform with previous literature, we did not ? nd the variable to be signi? cant or to affect the coef? cient of the decentralization variable. 34 In the United States of Mexico, central government spending exceeds the States and the local governments spending together by more than three times. While the Mexican constitution is of federal nature, political power is centralised in the country’s capital. ‘For most of the seven decades of rule by the Institutional Revolutionary Party (PRI), Mexico was a highly centralised one-party polity. State governors, and even many mayors, were named by the president and answered to him, even if they were duly elected, by fraud if need be’, see ‘Mexico’s truncated moves towards real federalism’, March 27th 2003, From The Economist print edition. 35 We set the cut off point at the level of seven on a 0–10 scale of democracy in the Polity IV variable. Causes of corruption 259 an intermediate position where it is not contemporary levels of democracy that are signi? cant predictor of corruption (e. g. Chowdhury 2004), but it is also unnecessary to have 45 years of uninterrupted democracy as in Treisman (2000). According to our estimates, more than 10, but less then 45 years of a persistently democratic regime is suf? cient to produce a signi? cant dividend in terms of corruption reduction. An issue related to democracy that attracted some attention recently is the role of press. We include the variable of newspapers circulation in order to catch the possible effect of that the mass medias can play in reducing corruption. Our empirical ? nding [regression (7)] supports the hypothesis that countries where access to press is more wide spread will have less corruption. 6 There are ? nally the theories related to government policies. Thus, we have included in regressions (8) and (9) an index of government intervention in the economy, a proxy of political instability, and a measure of wages in the public sector. The proxy for government intervention in the economy is computed using government consumption as a percentage of the economy, government ownership of businesses and industries, the share of government revenues from state-owned enterprises, and government ownership of property and economic output produced by the government. While we do not ? nd support for the hypothesis that government intervention is associated with corruption, we are cautious as to its interpretation, because an increase in government activities in the economy can refer to both restrictions that provide more opportunities for rents’ appropriation, but it can also refer to higher expenditures in education and health, or in public goods in general. These in turn could be channels through which corruption is diminished. The political instability is obtained averaging the number of â€Å"veto players† changes in the political system in each year. The variable we employ is a proxy of political instability that recognizing the diffuse nature of political power in most countries. It computes the total number of veto players in the political structure and then evaluates the number of them that have changed in a year. The results show that political instability seems to be signi? cantly associated with corruption, thus the shortening of the time horizons of politicians would affect corruption levels. While evidence of this relation is tentative and would deserve further testing, we highlight that this result is mostly due to the increase in sample size and the use of a better proxy for political instability as compared to Treisman’s work. 37 Finally, we add government wage as an independent variable. We add this government-related variable last, because of the fact that the variable is available only for a reduced sample (72 countries). The government wage is obtained 36 We also checked whether the interaction term between contemporary democracy and newspapers circulation would be igni? cant, controlling whether a free press together with a democratic regime would have a special effect on corruption levels. In our regressions the interaction term was not signi? cant. 37 Treisman employed a variable that simply stated the number of government leaders changes in each year. 260 L. Pellegrini, R. Gerlagh by dividing the average wage in the public sector by GDP per capita. The government wage variable has the predicted negative sign, but is signi? cant only at 12%. Since the sample is relatively small, it is possible that with more data on government salaries and a better proxy for the opportunity costs faced by civil servants if they loose their jobs it would be possible to get a signi? cant coef? cient. 38 As a robustness check of our ? ndings, we have run ordinary least squares regressions with identical speci? cations, only with the Transparency International’s corruption perception index for 2004 as a dependent variable. 39 We ? nd similar results, but with a slightly smaller dataset and decreased signi? ance of most coef? cients. 40 There are two differences in the estimates. First, the share of fuels and minerals in exports tends to become statistically insigni? cant in most regressions, and second, the import share in GDP is signi? cant when income is excluded as an independent variable. Subsequently, we used the corruption perception index of the World Bank for the years 1996, 1998, 2000, and 2002 instead of 2004 as a dependent variable to check our results, with the independent variables also referring to earlier years than in our main analysis. Again, results are similar with two exceptions. Fuels and minerals in exports tend to be slightly more signi? cant in some regressions, and newspapers circulation tends to be slightly less signi? cant in some (other) regressions. 5 Discussion and conclusions Our exercise contributes to the existing literature as it questions some central ? ndings of previous econometric studies. Most notably, for some historical characteristics of a country that have been said to be a cause of corruption, we do not ? nd support. In our statistical analysis we do not ? d that the common law system or a past as a British colony (negatively) predict corruption. Furthermore, we do not ? nd any association between decentralization and corruption. Moreover, the link between ethnolinguistic fractionalization and corruption is diminished and becomes insigni? cant once income is included in the regressions. We do ? nd systematic evidence that supports cultural theories of the causes of corruption, in that the presence of Protestants in the population is found to be associated with lower corruption. We also ? nd that richer countries are less corrupt. As mentioned above, caution is needed as there could be reverse causality from institutional quality to income, though the result is upheld using an 38 A better proxy would be the ratio of civil servants pay to service or manufacturing salaries (that are not in? uenced by the share of population employed in the agricultural sector). Van Rijckeghem and Weder (2001) use the ratio of government wages to manufacturing wages and ? nd it to be a signi? cant determinant of corruption levels. Their data sample, though, is limited to 31 countries and data limitations do not allow us to follow their data. 9 The estimates if Transparency International’s margins of error have been shown to be biased (Kaufmann et al. 2005), therefore we preferred the ordinary least squares, as a regression technique, rather than the weighted least squares technique. 40 The sample size ranges between 98 and 67 countries. Causes of corruption 261 instrumental variable. Another ? nding shows that a long expos ure (30 years) to uninterrupted democracy is associated with lower corruption, that political instability tends to raise corruption, and that the diffusion of newspapers is associated with lower corruption levels. Finally, we also ? d some evidence of an association of higher wages in the public sector with lower corruption. What distinguishes our study from previous econometric works is, apart for some different variable choices, a larger sample and we suggest that the most notable differences with earlier studies can be traced to the inclusion of new countries in the present study. We are aware of the limitations on the interpretation of econometric results. Econometrics has a bias towards theories that can easily be quanti? ed. Case studies and more theoretical studies can act as a necessary complement of this type of work (e. g. Johnston 2005). For future research, we hope to follow two approaches. First, we want to investigate determinants of corruption that are amenable to policy changes. This search can be enhanced through the use of econometric analyses that identify the sources of corruption at the micro level (e. g. Fisman and Miguel 2006; Miller 2006; Reinikka and Svensson 2006). Second, we consider another relevant challenge to be the collection of suf? cient data for a panel data approach over a number of decades. The combined cross-country- or cross-region- and intertemporal analyses possible with panel data can be a key to ? ding other sources of corruption. Acknowledgments The research has been funded by the Dutch National Science Foundation (NWO) under contract no. 016. 005. 040. Appendix Table 2 Descriptive statistics Variables Corruption Protestants Ethnolinguistic fractionalization Fuels and minerals Imports Income [ln(GDP) per capita] Decentralization Contemporary democracy Newspapers circul ation Government intervention Instability Government wage Obs 106 106 106 106 105 106 42 104 104 106 105 73 Mean 0. 00 0. 13 0. 33 0. 20 0. 39 8. 38 0. 33 0. 72 1. 10 2. 94 0. 14 3. 25 Std. Dev. 1. 07 0. 21 0. 30 0. 6 0. 20 1. 19 0. 28 0. 29 1. 48 0. 75 0. 10 2. 47 Min ? 2. 47 0. 00 0. 00 0. 00 0. 09 6. 20 0. 02 0. 05 0. 00 1. 50 0. 00 0. 60 Max 1. 55 0. 98 0. 89 0. 97 1. 34 10. 39 1. 33 1. 00 7. 60 4. 70 0. 41 11. 80 Corruption is the perceived corruption index 2004, from the World Bank (see Kaufmann et al. 2005); Protestants is the share of Protestants in the population, Ethnolinguistic fractionalization is a average of ? ve different indexes based on linguistic groups (for the last two variables see La Porta et al. 1999); Fuels and minerals equals to the share of fuels and minerals on exports, 62 L. Pellegrini, R. Gerlagh averaged over 1993–2002, Income in the natural logarithm of GDP per capita in 2001, (the last two variables are from the World Development Indicators 200 4); Decentralization is the expenses of state and local government divided by the central government averaged over 1993–2002 (from the ‘Government Finance Statistics 2004’ of the International Monetary Fund); Contemporary Democracy is the average of the institutional democracy score for the years 1994–2003 from the Polity IV dataset (see Jaggers et al. ; Newspapers Circulation is daily newspapers circulation for 10 people; Imports is a measure of the openness of the economy and equals to the share of imports over GDP, averaged over 1993–2002 (the last two variables are from the World Development Indicators 2004); Government Intervention is an index for 2004 of the in? ence of government on the economy based on government consumption as a percentage of the economy, government ownership of businesses and industries, the share of government revenues from state-owned enterprises, and government ownership of property and economic output produced by the g overnment (from The Index of Economic Freedom 2005); Instability is the percentage of veto players that changed every year, averaged for 1991–2000 (see Beck et al. 000); Government Wage is the average government wage as a multiple of GDP per capita (from Schiavo-Campo 1998); British Colony is the dummy variable for countries that have been under British control (from Treisman 2000 augmented with information from Flags of the World Website http://? agspot. net/? ags/gbcolon. html); Common Law is the dummy variable for countries that adopted the common law system in their commerical code (from La Porta et al. 1999). 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