exemplification essay about corruption is the root cause of poverty

Essay on Corruption Is The Root Cause Of Poverty

Students are often asked to write an essay on Corruption Is The Root Cause Of Poverty in their schools and colleges. And if you’re also looking for the same, we have created 100-word, 250-word, and 500-word essays on the topic.

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100 Words Essay on Corruption Is The Root Cause Of Poverty

Introduction.

Corruption is a dishonest act by those in power. This includes bribery, fraud, or embezzlement. Poverty, on the other hand, is a state of being extremely poor. The two are closely linked. This essay explains why corruption is the root cause of poverty.

The Cycle of Corruption and Poverty

Corruption leads to poverty because it affects economic growth. When leaders steal public funds, they reduce the money available for development. This leads to poor infrastructure, low-quality education, and inadequate health services. These conditions result in poverty.

Corruption and Inequality

Corruption also increases inequality. The rich become richer while the poor become poorer. Corrupt leaders often favor their friends and family, leaving the majority poor. This unequal distribution of resources leads to poverty.

Corruption and Poor Governance

Poor governance is another result of corruption. When leaders are corrupt, they ignore the needs of the people. They fail to provide basic services like clean water, healthcare, and education. Without these services, people remain poor.

In conclusion, corruption is a major cause of poverty. It creates a cycle of poverty, increases inequality, and results in poor governance. To fight poverty, we must first fight corruption.

250 Words Essay on Corruption Is The Root Cause Of Poverty

Corruption is a serious problem in many countries. It is an act of dishonesty by people in power to gain personal benefits. This essay explains how corruption is the main reason for poverty.

Corruption and Public Funds

Corruption often leads to misuse of public funds. People in power can take money meant for public services, like schools and hospitals, for their own use. This means less money is available to help people in need, leading to poverty.

Effect on Economy

Corruption also harms the economy. When corrupt officials demand bribes, it discourages businesses. Fewer businesses mean fewer jobs, which can lead to higher poverty rates.

Impact on Society

Corruption creates a society where dishonesty is rewarded and honesty is punished. This can make poverty worse, as honest people may find it harder to get jobs or services.

In conclusion, corruption is a root cause of poverty. It takes money away from public services, hurts the economy, and creates a dishonest society. To fight poverty, we must first fight corruption.

500 Words Essay on Corruption Is The Root Cause Of Poverty

Corruption is a major problem in many parts of the world. It is like a disease that harms society. One of the biggest impacts of corruption is poverty. This essay will explain how corruption is the root cause of poverty.

What is Corruption?

Corruption is when people in power behave dishonestly for their own gain. This can mean taking bribes, stealing money, or not following rules. It is a problem because it means that resources are not shared fairly. This leads to many other problems, one of which is poverty.

Corruption and Poverty

Corruption can cause poverty in many ways. Firstly, when government officials are corrupt, they may take money that is meant for public services. This means that schools, hospitals, and roads do not get the funding they need. This can lead to a lack of education, poor health, and a lack of jobs, all of which can cause poverty.

Secondly, corruption can discourage businesses. If a business owner has to pay bribes to operate, they may decide it is not worth it. This can lead to fewer businesses and fewer jobs, which again can cause poverty.

Thirdly, corruption can lead to inequality. If the rich and powerful can avoid paying taxes by being corrupt, this means that there is less money for public services. This can lead to a bigger gap between the rich and the poor, and more poverty.

Examples of Corruption Leading to Poverty

There are many examples of corruption leading to poverty. In some countries, corrupt leaders have stolen billions of dollars. This money could have been used to improve education, healthcare, and infrastructure. Instead, it ended up in the pockets of a few people, while the majority remained poor.

In other cases, corruption has led to a lack of investment. Businesses are scared off by the high levels of corruption, leading to fewer jobs and more poverty.

In conclusion, corruption is a major cause of poverty. It leads to a lack of public services, discourages businesses, and increases inequality. It is important that we fight against corruption if we want to reduce poverty. This can be done through education, stronger laws, and by holding those in power accountable for their actions.

To sum up, corruption is like a weed that strangles the growth of a healthy society. If we want to see a world without poverty, we must first tackle the problem of corruption.

That’s it! I hope the essay helped you.

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How Poverty Is a Direct Result of Corruption

H ead to any of the most disadvantaged places in America and ask local leaders what is holding their community back, and invariably you will hear a story about the local poor. They don’t want to work, don’t behave like they should, and have become dependent on government welfare programs. This story is centuries old. Indeed, the narrative of the shiftless poor inhabits a perpetual space in the nation’s collective consciousness.

These days, though, the biggest story about welfare cheats isn’t about the poor making off with a few dollars in undeserved aid. Any such fraud is dwarfed by the actions of Nancy New, a nonprofit leader in Mississippi, and John Davis, director of the state’s welfare agency, who, from 2017 to 2020, scammed a government program meant to help impoverished children in Mississippi, the nation’s poorest state, to the tune of nearly $80 million. It’s the largest public corruption scandal in the state’s history.

Rather than alleviating poverty through cash aid, child care, or job training, New and Davis used New’s nonprofit Mississippi Community Education Center to line their own pockets and those of a number of celebrity athletes, among other dubious schemes. Pulitzer Prize-winning reporter Anna Wolfe uncovered the scheme, but the whole thing might not have made national headlines but for the involvement of Super Bowl champion quarterback Brett Favre . Favre was paid $1.1 million by New’s nonprofit for speaking events that, according to Mississippi state auditor Shad White, did not happen. Another $5 million went to build a volleyball stadium at Favre’s alma mater, the University of Southern Mississippi, with the justification that the funds would be used to host events for underserved youth. To date only one such event has occurred.

More From TIME

While the Mississippi story is particularly shocking, our five years of research in America’s most disadvantaged places has shown that government corruption is disturbingly common. We saw firsthand how members of the local elite exploited the community’s meager resources—even aid meant to help the most vulnerable—through corruption of all kinds, a pattern enduring across generations. Through hundreds of interviews with local leaders and ordinary citizens alike in our nation’s most disadvantaged places—clustered in central Appalachia, South Texas, and the historic Cotton Belt—we learned that many people assume that the poor are eager to take advantage of the dole unless proven otherwise, a guilty-until-proven-innocent framework. Yet the same people who are eager to blame the poor will often discount a case like New’s, dismissing her as just one “bad apple.”

Take Crystal City, Texas, which has a poverty rate of close to 30%, according to the U.S. Census Bureau. A major obstacle to bringing new jobs to town that might drive that poverty rate down is that the city is still reeling from 2016, when the mayor, the city manager, and three current or former members of Crystal City’s city council were convicted in a conspiracy and bribery scheme. Yet another council member had already gone down on human trafficking charges, leaving only one member of the city council to run the town. The broader region has seen an economic boom from fracking in recent years, bringing in new hotels and restaurants along with new jobs. Yet Crystal City has missed out on this surge completely. Residents we spoke to complained that local government was largely non-functional when it should have been vying for a piece of the pie.

Read More: America Looks at Poverty All Wrong

In Clay County, Kentucky, which has a poverty rate of nearly 36%, The City of Manchester’s long-serving mayor Daugh White and several of his cronies pleaded guilty to racketeering and conspiracy charges in 2007 for pursuing kickbacks from companies bidding on city contracts. Just before White’s demise, a set of reformers decided to take him on. But in their efforts to unseat White and his coalition, the reformers employed an age-old eastern Kentucky tactic—vote buying. After a federal RICO investigation, they also found themselves in the clink.

Our government must find new ways to get resources to where they are most needed. Since at least the New Deal, there has been an expectation that aid from the government will flow not directly to the needy, but to local governments, who will distribute resources for the betterment of their community. While we met many honest and well-meaning local officials through our research, this approach is a recipe for corruption because all too often the officials responsible for delivering this aid to the poor are self-dealing people. A federally administered expanded Child Tax Credit, such as the one briefly implemented by the Biden administration during the Covid-19 pandemic, is one way to get aid directly to poor families while circumventing the open pockets of local elites. Targeted funding to local governments should be made in full recognition that especially in these places, the investments are at risk of not getting to where they are most needed. Government agencies must build in safeguards to avoid graft.

A broader problem in getting resources to the places of greatest need is that for decades, the government has invested in places through policies by soliciting proposals from the communities themselves. While this may sound like a good thing, rarely is the process driven by experts in regional development. Instead, local elites—with their own self-interest—typically control the undertaking. Expertise, not cronyism, is needed to determine which strategies are most likely to lead to meaningful gains.

In many of America’s most disadvantaged regions, corruption has exerted a chokehold that has kept local communities from thriving. In the words of John Kerry, civic corruption is “an opportunity destroyer because it discourages honest and accountable investment; it makes businesses more expensive to operate; it drives up the cost of public services for local taxpayers”—a toxic alchemy. Deeply disadvantaged communities cannot thrive until more people scrutinize the actions of the local elites who run them.

Correction: This article originally said Nancy New's nonprofit was called Mississippi First. It is called the Mississippi Community Education Center.

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Corruption and Poor Governance- The Major Causes of Poverty in Many Third world Countries

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The Cost of Corruption

Besides being immoral, corruption tends to be an inefficient way to bring together buyers and sellers. Because it is by definition illegal—even when it is common and accepted—corruption naturally tends to be private and secretive. In a private arrangement, there is no guarantee that the good or service being bought carries a market price. That is, another buyer may be willing to pay more, or another seller may be willing to sell for less, but it is impossible to know because the market is not public. Further, resources are expended in the effort to maintain the secrecy of the transactions. Finally, corrupt contracts, because they are illegal, must be enforced in extralegal fashion. Thus a corrupt system is an inherently inefficient system.

In some cases corruption might be reasonably viewed as the most expedient option. For example, in a country with excessive bureaucratic hurdles to starting a business, bribery may be a way to speed the process. This is only a stop gap, of course, since it leaves in place a system that serves the corrupt by providing numerous opportunities to demand bribe money and to tilt the playing field to the advantage of friends, family and political patrons.

Corruption as an Obstacle to Development

The monetary cost of corruption is slower economic growth. Studies have shown that decreasing corruption leads to significant gains in foreign direct investment, which is one of the chief spurs to development. Economist Shan-Jin Wei has argued that corruption is proportionately more damaging to economic growth than is taxation of business. In terms of its discouraging effect on investment, Wei found, “An increase in the corruption level from that of Singapore to that of Mexico is equivalent to raising the tax rate on multinationals by over twenty percentage points.”

Turning international business investment away is only one of several ways that corruption stifles growth. “ Corruption and Poverty ,” a review essay commissioned by USAID, summarizes its findings with a long list of negative effects: “Corruption impedes economic growth by discouraging foreign and domestic investment, taxing and dampening entrepreneurship, lowering the quality of public infrastructure, decreasing tax revenues, diverting public talent into rent-seeking, and distorting the composition of public expenditure.” (Rent seeking is an economics term referring to any effort to profit not by adding value but by manipulating a market’s social or political environment in order to gain an artificial advantage.)

The authors of the review also point out that corruption exacerbates income inequality. They find that this link is due to several negative effects of corruption, including distortion of the “legal and policy frameworks allowing some to benefit more than others”; “unfair distribution of government resources and services”; and “lower income households (and businesses) pay[ing] a higher proportion of their income in bribes” than wealthier households.

The Corruption Perception Index

In light of these observations, it is no surprise that nations with high levels of public sector corruption face major development challenges (while nations with relatively low levels of political corruption tend to thrive economically). This is apparent from even a cursory look at Transparency International ’s Corruption Perception Index (CPI), which measures the reputations of national governments with respect to corruption. In 2017, the five worst-scoring countries on the CPI were Yemen, Afghanistan, Syria, South Sudan, and Somalia. The top five were New Zealand, Denmark, Finland, Norway, and Switzerland.

Corruption as an Immoral Choice

Although political reforms may diminish inducements to corruption, it is important to remember that every instance of corruption is an individual decision. Even where corruption is common and widely accepted, people can choose not to engage in it. And even where it is rare and stigmatized, people may initiate a corrupt transaction.

Corruption is a serious violation of justice, because it benefits some at the expense of others without legitimate reason. It erodes the bonds of trust between people within a community, and between people and the government officials who are supposed to represent their interests. Corruption also tends to favor the well-connected while locking out those who are poor and lack high-level connections.

Fighting Corruption

One strategy for combating corruption is simply to limit the extent of state authority. Expansive regulation (such as business licensing) creates ample opportunity and temptation to rig the process in favor of one or another participant. Where government has narrowly defined powers that keep it out of such decision-making, the rule of law is encouraged and corruption discouraged.

Thus, it’s important that government regulatory law curtail the discretion permitted to individual officials. Where gatekeepers in the bureaucracy have wide leeway to interpret and apply the law, the potential for corruption increases. This strategy is no substitute for virtue, of course, since a measure of interpretive leeway is necessary and inevitable in any system of government.

The costs of fighting corruption through government channels also must be realistically assessed. Government anti-corruption initiatives may be costly and may possess some of the same features that promoted corruption in the first place (e.g., layers of bureaucracy and limited resources that force bureaucrats to choose who to focus on and who to ignore).

Thus, while corruption must never be viewed as an acceptable practice, advocates of the rule of law should be realistic about the prospects for its elimination. The inherent weaknesses in political initiatives to limit political corruption also should serve as a spur for cultures to combat corruption by other means, as through the work of civil institutions (e.g., churches, fraternal organizations) in emphasizing the cultivation of personal virtue. When people become convinced that corruption is harmful to society and damaging to their own moral integrity, and when they possess the courage to act accordingly, then a culture is better positioned to make progress in the fight against corruption in politics and the market.

exemplification essay about corruption is the root cause of poverty

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Does Corruption Cause Poverty?

By Walden Bello

Not surprisingly, the international financial institutions have weighed in. The World Bank has made "good governance" a major thrust of its work, asserting that the "World Bank Group focus on governance and anticorruption (GAC) follows from its mandate to reduce poverty - a capable and accountable state creates opportunities for poor people, provides better services, and improves development outcomes."

World Bank and Transparency International data show that the Philippines and China exhibit the same level of corruption, yet China grew by 10.3 percent per year between 1990 and 2000, while the Philippines grew by only 3.3 percent. Moreover, as a recent study by Shaomin Lee and Judy Wu shows, "China is not alone; there are other countries that have relatively high corruption and high growth rates."

Limits of a Hegemonic Narrative

The "corruption-causes-poverty narrative" has become so hegemonic that it has often marginalized policy issues from political discourse. This narrative appeals to the elite and middle class, which dominate the shaping of public opinion. It's also a safe language of political competition among politicians. Political leaders can deploy accusations of corruption against one another for electoral effect without resorting to the destabilizing discourse of class.

While in power, Thaksin brazenly used his office to enlarge his corporate empire. But the rural masses and urban lower classes - the base of the so-called "Red Shirts" - have ignored this corruption and are fighting to restore his coalition to power. They remember the Thaksin period from 2001 to 2006 as a golden time. Thailand recovered from the Asian financial crisis after Thaksin kicked out the International Monetary Fund (IMF), and the Thai leader promoted expansionary policies with a redistributive dimension, such as cheap universal health care, a one-million-baht development fund for each town, and a moratorium on farmers' servicing of their debt. These policies made a difference in their lives.

Thaksin's Red Shirts are probably right in their implicit assessment that pro-people policies are more decisive than corruption when it comes to addressing poverty. Indeed, in Thailand and elsewhere, clean-cut technocrats have probably been responsible for greater poverty than the most corrupt politicians. The corruption-causes-poverty discourse is no doubt popular with elites and international financial institutions because it serves as a smokescreen for the structural causes of poverty, and stagnation and wrong policy choices of the more transparent technocrats.

The Philippine Case

The case of the Philippines since 1986 illustrates the greater explanatory power of the "wrong-policy narrative" than the corruption narrative. According to an ahistorical narrative, massive corruption suffocated the promise of the post-Marcos democratic republic. In contrast, the wrong-policy narrative locates the key causes of Philippine underdevelopment and poverty in historical events and developments.

The complex of policies that pushed the Philippines into the economic quagmire over the last 30 years can be summed up by a formidable term: structural adjustment. Also known as neoliberal restructuring, it involves prioritizing debt repayment, conservative macroeconomic management, huge cutbacks in government spending, trade and financial liberalization, privatization and deregulation, and export-oriented production. Structural adjustment came to the Philippines courtesy of the World Bank, the IMF, and the World Trade Organization (WTO), but local technocrats and economists internalized and disseminated the doctrine.

Corazon Aquino was personally honest - indeed the epitome of non-corruption - and her contribution to the reestablishment of democracy was indispensable. But her acceptance of the IMF's demand to prioritize debt repayment over development brought about a decade of stagnation and continuing poverty. Interest payments as a percentage of total government expenditures went from 7 percent in 1980 to 28 percent in 1994. Capital expenditures, on the other hand, plunged from 26 percent to 16 percent. Since government is the biggest investor in the Philippines - indeed in any economy - the radical stripping away of capital expenditures helps explain the stagnant 1 percent average yearly growth in gross domestic product in the 1980s, and the 2.3 percent rate in the first half of the 1990s.

In contrast, the Philippines' Southeast Asian neighbors ignored the IMF's prescriptions. They limited debt servicing while ramping up government capital expenditures in support of growth. Not surprisingly, they grew by 6 to 10 percent from 1985 to 1995, attracting massive Japanese investment, while the Philippines barely grew and gained the reputation of a depressed market that repelled investors.

When Aquino's successor, Fidel Ramos, came to power in 1992, the main agenda of his technocrats was to bring down all tariffs to 0-5 percent and bring the Philippines into the WTO and the ASEAN Free Trade Area (AFTA), moves intended to make trade liberalization irreversible. A pick-up in the growth rate in the early years of Ramos sparked hope, but the green shoots were short-lived. Another neoliberal policy, financial liberalization, crushed this early promise. The elimination of foreign exchange controls and speculative investment restrictions attracted billions of dollars from 1993-1997. But this also meant that when panic hit Asian foreign investors in summer 1997, the same lack of capital controls facilitated the stampede of billions of dollars from the country in a few short weeks. This capital flight pushed the economy into recession and stagnation in the next few years.

As for agriculture, the liberalization of the country's agricultural trade after the country joined the WTO in 1995 transformed the Philippines from a net food-exporting country into a net food-importing country after the mid-1990s. This year the China ASEAN Trade Agreement (CAFTA), negotiated by the Arroyo administration, goes into effect, and the prospect of cheap Chinese produce flooding the Philippines has made Filipino vegetable farmers fatalistic about their survival.

During the long Arroyo reign, the debt-repayment-oriented macroeconomic management policy that came with structural adjustment stifled the economy. With 20-25 percent of the national budget reserved for debt service payments because of the draconian Automatic Appropriations Law, government finances were in a state of permanent and widening deficit, which the administration tried to solve by contracting more loans. Indeed, the Arroyo administration contracted more loans than the previous three administrations combined.

When the deficit reached gargantuan proportions, the government refused to declare a debt moratorium or at least renegotiate debt repayment terms to make them less punitive. At the same time, the administration did not have the political will to force the rich to take the brunt of bridging the deficit, by increasing taxes on their income and improving revenue collection. Under pressure from the IMF, the government levied this burden on the poor and the middle class by adopting an expanded value added tax (EVAT) of 12 percent on purchases. Commercial establishments passed on this tax to poor and middle-class consumers, forcing them to cut back on consumption. This then boomeranged back on small merchants and entrepreneurs in the form of reduced profits, forcing many out of business.

The straitjacket of conservative macroeconomic management, trade and financial liberalization, as well as a subservient debt policy, kept the economy from expanding significantly. As a result, the percentage of the population living in poverty increased from 30 to 33 percent between 2003 and 2006, according to World Bank figures. By 2006, there were more poor people in the Philippines than at any other time in the country's history.

Policy and Poverty in the Third World

The Philippine story is paradigmatic. Many countries in Latin America, Africa, and Asia saw the same story unfold. Taking advantage of the Third World debt crisis, the IMF and the World Bank imposed structural adjustment in over 70 developing countries in the course of the 1980s. Trade liberalization followed adjustment in the 1990s as the WTO, and later rich countries, dragooned developing countries into free-trade agreements.

Because of this trade liberalization, gains in economic growth and poverty reduction posted by developing countries in the 1960s and 1970s had disappeared by the 1980s and 1990s. In practically all structurally adjusted countries, trade liberalization wiped out huge swathes of industry, and countries enjoying a surplus in agricultural trade became deficit countries. By the beginning of the millennium, the number of people living in extreme poverty had increased globally by 28 million from the decade before. The number of poor increased in Latin America and the Caribbean, Central and Eastern Europe, the Arab states, and sub-Saharan Africa. The reduction in the number of the world's poor mainly occurred in China and countries in East Asia, which spurned structural readjustment policies and trade liberalization multilateral institutions and local neoliberal technocrats imposed other developing economies.

China and the rapidly growing newly industrializing countries of East and Southeast Asia, where most of the global reduction in poverty took place, were marked by high degrees of corruption. The decisive difference between their performance and that of countries subjected to structural adjustment was not corruption but economic policy.

Despite its malign effect on democracy and civil society, corruption is not the main cause of poverty. The "anti poverty, anti-corruption" crusades that so enamor the middle classes and the World Bank will not meet the challenge of poverty. Bad economic policies create and entrench poverty. Unless and until we reverse the policies of structural adjustment, trade liberalization, and conservative macroeconomic management, we will not escape the poverty trap.

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The way out of poverty and corruption is paved with good governance

Sri mulyani indrawati.

But, in some ways, corruption is only a symptom. Anti-corruption must be paired with efforts to enable governments to govern openly and fairly, to provide services and security to their citizens, and create an environment that fosters jobs and economic growth. These are the attributes of good governance and effective institutions, and helping countries achieve them is a major focus of our work in low-income countries around the world. Here are three ways we are going about it.

We focus on institution-building. Prosperity and the quality of a country’s institutions typically go hand in hand. Governments with well-run, accountable institutions are better able to deliver public goods and support an environment that can generate jobs and growth. Public sector performance is particularly important to the world’s poorest people, who rely disproportionately on government services, and improving service delivery is essential to leaving poverty behind. World Bank specialists in nearly 100 countries provide expertise and training to governments to strengthen public administration and public financial management -- systems that are the key to ensuring fiscal resources are spent efficiently, effectively, and accountably. We’ve seen significant results. Between 2011 and 2015, 50 million people in the poorest countries gained access to better water services, 413 million people received essential health services, and 102,000 kilometers of roads were constructed or improved. In Comoros , we’ve helped the government strengthen economic management, so that more information on national finances is available to the public than ever before. In Côte d'Ivoire , our support has helped the government bring in the private sector and prepare energy , transport and port infrastructure projects. Impending reforms of the financial sector promise to promote investment in agriculture , agribusiness and manufacturing. Data is crucial, and one of our priorities is building the statistical capacity of our client countries. Last year, we helped 32 countries (including 11 fragile states) do this; 18 countries now use statistics to design and monitor policies and promote accountability and transparency. For example, Bolivia has completed agricultural and housing censuses and three rounds of household surveys to strengthen the planning and evaluation of public programs and policies.
We help countries mobilize the resources necessary for delivering services. Fifty percent of low-income countries raise less than 15% of their gross domestic product in taxes. By contrast, the OECD average is about 34% . The reason for this discrepancy? The poorest countries grapple with a wide range of problems: businesses — both foreign and domestic — that avoid paying taxes, large numbers of informal businesses that aren’t on the books, weak revenue administrations, poor governance, lack of international tax cooperation and the public’s mistrust. This is not just a question of raising taxes, but of designing tax systems that are fair and accountable and don’t hinder economic growth. We’ve supported tax reforms and improved tax administration for years, both within countries and in international fora such as the G20. And we just launched a Global Tax Team to expand this work. From 2012 to 2014, Mauritania increased the amount it collects in taxes by nearly 50 percent through reforms to improve public resources management. In Pakistan , tax collection in the Sindh province increased by 24 percent in a single year. While development assistance will remain critical in the fight against poverty, it won’t be enough to achieve the ambitious goals we have set. We must help countries mobilize domestic resources – the largest untapped resource for development - to become self-sufficient and to provide quality services to citizens.
We promote transparency and accountability. Openness about the use of public resources builds trust between citizens and their governments. It can make public spending more targeted and effective. This is why we work with governments to make their budgets and the way resources are used more transparent, This also reduces fraud and corruption, and makes citizen voices heard. Tunisia is among 40 countries using our public expenditure database tool to make detailed public spending data more open and accessible. Also in Tunisia, our research quantifying the value of illicit trade and identifying the scope and cost of state capture has helped increase transparency and improved the ability of Tunisians to hold their government accountable. In Moldova , more than 2,200 public servants and other employees received e-government training. People can now access more than 880 government datasets and 131 electronic public services. In Nigeria , the number of public contracts awarded through open competition grew by 85 percent in 2015, up from 20 percent growth in 2009. The three-pronged approach of improving institutions, raising more domestic resources, and engaging citizens is the closest thing to a development silver bullet. Chronic mismanagement and corruption demoralizes citizens and undermines their trust in the state; corruption deepens poverty, leaving the poor vulnerable to exploitation and bribery in return for services such as health care and education; denying citizens participation in their governments stunts their full potential. For all of these reasons, the World Bank considers strong governance and effective institutions essential to putting the poorest countries on the path to self-sufficiency.
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This paper demonstrates that high and rising corruption increases income inequality and poverty by reducing economic growth, the progressivity of the tax system, the level and effectiveness of social spending, and the formation of human capital, and by perpetuating an unequal distribution of asset ownership and unequal access to education. These findings hold for countries with different growth experiences, at different stages of development, and using various indices of corruption. An important implication of these results is that policies that reduce corruption will also lower income inequality and poverty.

I. I ntroduction

Government officials may use their authority for private gain in designing and implementing public policies. This phenomenon—defined broadly as corruption ( Tanzi, 1997a )—may result in enriching these officials as well as private individuals who obtain a larger share of public benefits or bear a lower share of public costs. In this way, corruption distorts the government’s role in resource allocation. It has been argued ( Tanzi, 1995 ) that the benefits from corruption are likely to accrue to the better-connected individuals in society, who belong mostly to high-income groups. Thus, corruption would affect not only broad macroeconomic variables, such as investment and growth, but also income distribution. It has been further contended that corruption increases poverty by creating incentives for higher investment in capital-intensive projects and lower investment in labor-intensive projects ( United Nations Development Programme, 1997 ). Such a bias in investment strategy deprives the poor of income-generating opportunities.

To date, no empirical evidence has been presented to corroborate the relationship between either corruption and income distribution or corruption and poverty. This paper seeks to ascertain if such relationships are supported by cross-country data.

Many studies have investigated the efficiency implications of corruption through its impact on investment, growth, and expenditure allocations. The empirical results show that corruption lowers investment and, consequently, economic growth ( Mauro, 1995 ; Knack and Keefer, 1996 ). There is some discussion in the literature on whether the negative impact on growth operates through reduced private investment or through reduced public investment. The recent paper by Tanzi and Davoodi (1997) provides evidence that corruption actually increases public investment, especially investment in unproductive projects, and squeezes expenditure allocations for operations and maintenance, thereby lowering the productivity of the public capital stock. The paper also shows that corruption tends to reduce government revenue, which limits the ability of the government to provide goods and services critical to its population. In a somewhat similar vein, Mauro (1997) shows that corruption distorts the composition of public expenditure; corrupt governments spend relatively less on education because of the limited scope for collecting bribes under this type of spending. This does not mean, however, that education spending is exempt from corrupt practices; in fact, in many developing countries, government payrolls are inflated by ghost workers—workers who are on the payroll but who do not actually exist, including ghost teachers ( Abed et al, 1998 ).

In general, the corruption literature has tended to emphasize the efficiency implications of corruption, while overlooking its distributional consequences. 2 In part, this reflects the belief that the rich or well-connected typically use bribes to be the first in line for a rationed government good or service, and the poor or individuals at the lower end of income distribution obtain the rationed good or service after waiting in line ( Bardhan, 1997 ). In this way, bribes are assumed to clear the market because they reflect individuals’ willingness to pay. These views, similar to the early efficiency-enhancing views of corruption ( Leff, 1964 ; Huntington, 1968 ), ignore that corruption may create permanent distortions from which some groups or individuals can benefit more than others. They also ignore that individuals with high willingness to pay are not necessarily the intended beneficiaries of government programs. Moreover, the distributional consequences of corruption are likely to be more severe the more persistent the corruption, 3 and the more entrenched the vested interests. The impact of corruption on income distribution is also a function of the government’s involvement in allocating and financing scarce goods and services. 4 Finally, empirical work on the distributional consequences of corruption has been hindered by a lack of consistent and reliable cross-country data on income inequality and poverty that only lately has been rectified ( Deininger and Squire, 1996 ; Ravallion and Chen, 1997 ).

This paper is organized as follows. The next section lists arguments on how corruption may affect income inequality and poverty. Section III presents two models of income inequality and poverty. Sections IV and V document the direct and indirect impacts of corruption on income inequality and poverty. Section VI summarizes the results and policy implications of this paper’s findings.

II. C orruption , I ncome I nequality, and P overty

Corruption can affect income inequality and poverty through various channels, including overall growth, biased tax systems, and poor targeting of social programs as well as through its impact on asset ownership, human capital formation, education inequalities, and uncertainty in factor accumulation.

High corruption can lead to high poverty for two reasons. First, evidence suggests that a higher growth rate is associated with a higher rate of poverty reduction ( Ravallion and Chen, 1997 ), and that corruption slows the rate of poverty reduction by reducing growth. Second, income inequality has been shown to be harmful to growth ( Alesina and Rodrik, 1994 ; Persson and Tabellini, 1994 ), 5 and if corruption increases income inequality, it will also reduce growth and thereby limit poverty reduction ( Ravallion, 1997 ). 6

Biased tax systems

Corruption can lead to tax evasion, poor tax administration, and exemptions that disproportionately favor the well-connected and wealthy population groups. This can reduce the tax base and the progressivity of the tax system, possibly leading to increased income inequality.

Poor targeting of social programs

Corruption can affect the targeting of social programs to the truly needy. The use of government-funded programs to extend benefits to relatively wealthy population groups, or the syphoning of funds from poverty-alleviation programs by well-connected individuals, will diminish the impact of social programs on income distribution and poverty.

Asset ownership

High concentration of asset ownership can influence public policy and increase income inequality. In a society where asset ownership is concentrated in a small elite, asset owners can use their wealth to lobby the government for favorable trade policies, including exchange rate, spending programs, and preferential tax treatment of their assets. These policies will result in higher returns to the assets owned by the wealthy and lower returns to the assets owned by the less well-to-do, thereby increasing income inequality. Furthermore, assets can be used as collateral to borrow and invest; therefore, inequality in ownership of assets will limit the ability of the poor to borrow and increase their lifetime income and will perpetuate poverty and income inequality ( Li, Squire, and Zou 1996 ; Birdsall and Londoño, 1997 ).

Human capital formation, education inequalities, and social spending

Corruption can affect income distribution and poverty via its impact on human capital formation and the distribution of human capital. First, corruption weakens tax administration and can lead to tax evasion and improper tax exemptions, as discussed above. Therefore, for a given tax system, the higher the level of corruption, the lower the tax revenue and the lower the resources available for funding public provision of certain services, including education.

Second, corruption increases the operating cost of government, and, therefore, reduces the resources available for other uses, including the financing of social spending that is crucial to the formation of human capital. In fact, higher corruption is found to be associated with lower education and health spending ( Mauro, 1997 ).

Third, wealthy urban elites can lobby the government to bias social expenditure toward higher education and tertiary health, which tend to benefit high-income groups. Corruption can also increase expenditure on tertiary health because bribes can be more easily extracted from the building of hospitals and purchasing of state-of-the-art medical equipment than from expenditure on vaccinations.

Finally, corruption can increase the share of recurrent expenditure devoted to wages as opposed to operations and maintenance ( Tanzi and Davoodi, 1997 ). This lowers the quality of education and health services and affects the ability of the state to improve educational attainment levels.

Uncertainty and factor accumulation

If the “rules of the game” in a corrupt country are unclear and biased toward the well-connected, the poor and the less-well-connected face an added risk premium in their investment decisions. This unequally distributed risk increases expected returns to any investment for the well-connected relative to the less-well-connected. Therefore, low income and poor groups—the less-well-connected—will be discouraged from investing in any resource—human, physical capital, or land—and income inequality and poverty will be perpetuated or accentuated.

III. M odels

A. Corruption and Income Inequality

The empirical model of inequality used in this paper is in the spirit of Atkinson (1997) . It specifies the personal distribution of income in terms of factor endowments, distribution of factors of production, and government spending on social programs. 7 Specifically, the Gini coefficient is assumed to depend on the following variables:

Initial distribution of assets (the initial Gini coefficient for land ownership);

Education inequality (percent of adult population with no schooling expressed as a fraction of percent of adult population with completed secondary and higher education); 8

Education stock or educational attainment (average years of secondary education in population aged 15 and over);

Capital stock-to-GDP ratio;

Natural resource endowment (share of natural resources in total exports);

Corruption (various corruption indices);

Social spending (various spending measures relative to GDP);

Expenditure dummy—equals one when the Gini coefficient is expenditure-based and zero when it is income-based;

Recipient dummy—equals one when the recipient of income or the spending unit is a person and zero when it is a household; and

Net income dummy—equals one when the Gini coefficient is based on net income and zero when it is based on gross income.

Distribution of income-generating assets has an impact on income distribution. Distribution of land is used as a proxy for asset distribution because data on the distribution of other income-generating assets, such as bonds and equity, are available for only a limited number of countries. Inequality in the distribution of land is expected to be positively correlated with income inequality for two reasons. First, the distribution of land has a direct impact on the distribution of income in a given time period, particularly in countries where income from land constitutes a large share of total income. Second, land can be used as collateral for borrowing and investing; therefore, inequitable land distribution limits the ability of the poor to borrow and increase their lifetime income.

Education inequality is expected to be positively correlated with income inequality ( Tinbergen, 1975 ). A more egalitarian distribution of human capital will improve income distribution both by boosting the earning potential of the poor ( Londoño and Szekely, 1997 ) and by limiting the ability of the wealthy to lobby policymakers in their favor. In a similar vein, a higher educational endowment is expected to decrease inequality ( Tinbergen, 1975 ).

A higher capital-output ratio or lower productivity of capital is expected to be associated with higher income inequality. This may happen in developing economies where the majority of economic activity is concentrated in a traditional, low-productivity, unskilled labor sector, but also have islands of high-productivity and high-skilled labor. Similarly, a high natural resource endowment is expected to be associated with higher income inequality because of the high concentration of ownership and rent in this type of wealth as well as the high capital intensity and low complementarity between capital and labor in the natural resource sector. As discussed, corruption is expected to increase income inequality.

Government transfers and spending on social services can constitute a major source of income in poor households. Well-targeted social programs (proxied here by different measures of social spending) are expected to lower income inequality.

Survey-type dummies are included as explanatory variables because differences in measured inequality can be due to differences in the type of survey data used. These are: dummies for type of cash flow (income versus expenditure), choice of recipient unit (household versus personal), and type of income (gross versus net). An income-based measure of inequality is expected to show higher inequality than an expenditure-based measure. This is consistent with aggregate consumption theories in which individuals can smooth their consumption via borrowing and lending while their income fluctuates. Furthermore, measurement errors for income may be higher than for consumption, particularly in developing countries, which tends to inflate measured income inequality. Individual-based Gini coefficients are expected to be higher than household-based ones. This is because poor households tend to be larger than rich ones, and because households are better able to make interpersonal and intertemporal adjustments in expenditure patterns than individuals. The Gini coefficient based on net income should be lower than one based on gross income if tax systems are progressive and redistribute income in favor of the poor.

B. Corruption and Poverty

The model of poverty used in this paper relies on cross-country models that determine overall income growth in the economy. 9 The model expresses the income growth of the bottom 20 percent of the population, a measure of change in poverty, 10 as a function of the following variables:

Aggregate economic growth (real per capita GDP growth rate);

Initial income of the poor (real income of the bottom 20 percent of the population in 1980 measured in purchasing power parity U.S. dollars);

Initial secondary schooling (years of secondary education in population aged 15 and over in 1980);

Education inequality (percent of adult population with no schooling, expressed as a fraction of percent of adult population with completed secondary and higher education);

Initial distribution of assets (the initial Gini coefficient for land);

Social spending (various measures relative to GDP); and

Growth in corruption (various indices).

The rate of change of the income of the bottom 20 percent is chosen as the dependent variable because it is less prone to measurement errors than levels of poverty. 11 Another advantage of this formulation is that it is unaffected by country-specific factors that influence the level of poverty.

It has been argued that resource-rich countries grow less rapidly than resource-scarce countries ( Sachs 1995 , Sachs and Warner, 1997 ). Therefore, natural resource endowment is included in the model to examine if it affects income growth of the poor directly as well as indirectly through aggregate growth.

Initial income of the poor is included to account for diversity in initial conditions among countries. It is also intended to capture the extent to which the poor in one country are catching up with the poor in other countries. If there is a catch-up or convergence effect, the lower the initial income of the poor, the higher their income growth will be. Therefore, the coefficient on the initial income of the poor is expected to be negative.

Initial secondary schooling is included to measure the impact of human capital on the income growth of the poor. A positive coefficient is expected if human capital contributes positively to income growth of the poor. Two measures of distribution of factors of production are included: education inequality and the initial Gini coefficient for land. Each factor-distribution measure is expected to be negatively associated with the income growth of the poor.

Well-targeted social programs are believed to transfer relatively more income to the poor and reduce the incidence of poverty. In reality, it is quite conceivable that much of the benefits of social programs accrue to the middle- and higher-income groups. 12 To assess the impact of social spending on the income growth of the poor, three broad proxies for social spending are tried, all in relation to GDP; these are government spending on (1) social security and welfare, (2) education and health, and (3) the sum of spending items (1) and (2) plus housing and community amenities. Finally, in line with the model of income inequality, various indices of corruption are used to examine whether a higher growth rate of corruption reduces the income growth of the poor.

IV. E mpirical R esults

A. Indices of Corruption: The Stylized Facts

Six corruption indices are used throughout this paper to evaluate the sensitivity of the empirical results ( Appendix II , Table 8 ). All corruption indices are highly correlated, with correlation coefficients ranging from 0.88 to 0.98, and all are statistically significant at the 1 percent level. Because each corruption index refers to a different time period, the high and positive correlation coefficients suggest that a country’s rank in the corruption index is stable over time. However, since the sample of countries differs across the six indices, (ranging from 38 to 87 countries), results may vary depending on which corruption index is used. If the results hold across different corruption indices, it will be an indication of their robustness.

B. Impact of Corruption on the Gini Coefficient

The models of income inequality and poverty are estimated using OLS on cross-country data for 1980-97. (Results from instrumental variable technique, which are similar to the ones from OLS, are also reported.) The income inequality regression is estimated using three specifications. In the first one, the Gini coefficient is regressed on a constant, three survey-type dummies, natural resource abundance, ratio of physical capital stock to GDP, education inequality, initial Gini coefficient for land, and a corruption index. In the second specification, education inequality is replaced with mean years of secondary schooling. The third specification includes both education variables to test for their relative impact on income inequality.

Table 1 reports the results for all three specifications. The explanatory variables account for about 72 percent of cross-country variation in income inequality. In addition, the F-statistic for each regression is statistically significant at the 1 percent level. In all three specifications, the survey-type dummies have the expected signs. Inequality is lower when the Gini coefficient is based on consumption rather than income, higher when the recipient unit is a person rather than a household, and lower when the coefficient is based on after-tax income than before-tax income.

Corruption and Income Inequality: OLS Estimates

(Dependent variable: the Gini coefficient)

The results also suggest that countries with high income inequality tend to have abundant natural resources, low capital productivity, high education inequality, low average secondary schooling, and unequal distribution of land. The estimated coefficients on these five variables are statistically significant at the conventional levels. These findings confirm Atkinson’s (1997) hypothesis that factor endowments and their distribution are important determinants of income distribution.

As regards the impact of corruption on income inequality, it is necessary to first specify the nature of the null and alternative hypotheses. In the absence of prior empirical evidence linking corruption to income inequality, the null hypothesis that corruption has zero correlation with income inequality needs to be tested against the alternative hypothesis of nonzero correlation. The two-tailed test rejects the null hypothesis at the 1 percent significance level. However, rejection of the null hypothesis does not ascertain whether higher corruption is associated with higher income inequality. To prove this, additional tests are needed. The results from these tests show that higher corruption is indeed associated with higher income inequality at the 1 percent level of significance. 13 The magnitude of the effect of corruption on income inequality is considerable. A worsening in the corruption index of a country by one standard deviation (2.52 points on a scale of 0 to 10) is associated with an increase in the Gini coefficient of about 4.4 points ( Table 1 , Column 1). A partial scatter plot based on the regression ( Table 1 , Column 1) is shown in Figure 1 .

Corruption and Income Inequality

Citation: IMF Working Papers 1998, 076; 10.5089/9781451849844.001.A001

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Table 1 provides another new result. Distribution of education matters more than its mean in affecting income inequality. Specifications (1) and (2) show that education inequality and mean years of schooling matter when entered separately in the regression, but when both are included (that is, Specification 3), mean years of schooling ceases to be significant. This implies that, other things being equal, policies aimed at reducing education inequality through decreasing illiteracy are more important in reducing income inequality than policies aimed at increasing the mean years of schooling without due regard to education inequality.

To put in perspective the magnitude of the impact of corruption on income inequality, it is instructive to compare it with the impact of education on income inequality. A worsening in the corruption index of a country by one standard deviation (2.52 points on a scale of 0 to 10) is associated with the same increase in the Gini coefficient as a reduction in average secondary schooling of 2.3 years. 14

The above regressions used the expanded 1997 corruption index from Lambsdorff (forthcoming). This index has the broadest country coverage of all corruption indices compiled by Goettingen University and Transparency International (1997). To test whether the results are unique to this particular corruption index, the above regressions are estimated using five additional indices of corruption. 15 The results show that higher corruption is still associated with higher income inequality at the conventional statistical levels ( Appendix II , Table 9 ).

A broad measure of social spending, when added to Columns 1, 2, and 3 of Table 1 , is found to have no statistically significant effect on income inequality at the conventional levels. 16 This result is consistent with the observations made by Tanzi (1974) and Alesina (1998) . Even when controlling for the level of social spending, higher corruption continues to be associated with higher income inequality.

Finally, real per capita GDP is added to the previous regression in order to investigate if corruption is merely a proxy for the stage of economic development (Columns 4, 5, and 6 of Table 1 ). The associated coefficients on per capita GDP are significant at the 10 percent level and have a negative sign, indicating that richer countries have, on average, a more equal distribution of income than poorer countries. Higher corruption continues to be associated with higher income inequality, with the coefficient on the corruption index being statistically significant at the 10 percent level. 17 The statistical significance of corruption increases when other indices of corruption are used ( Appendix II , Table 9 ). These findings suggest that corruption is harmful to income inequality even when the impact of real per capita GDP is controlled for. None of these findings change when a broad measure of social spending is also added to the regression. 18 As before, social spending has no effect on income inequality. 19

C. Corruption and Income Inequality: Which Way is the Causality?

The above regression results establish the existence of a statistically significant positive association between corruption and income inequality. However, this association could stem from “reverse” causation, that is, high income inequality could be causing high corruption. Furthermore, the observed association could be due to other factors.

The technique of instrumental variable estimation is used to ascertain whether this is indeed the case. The technique isolates the pure impact of corruption on income inequality by using variables (i.e., instruments) which are correlated with corruption and which have no impact on income inequality and are not influenced by a “third” variable or variables that might be causing both income inequality and corruption. Three such instruments are chosen. They are the proportion of a country’s population that speaks English at home, distance of a country from the equator (referred to as latitude), and an index of ethnolinguistic diversity within each country (referred to as ethnicity). 20 The regression of the corruption index (corruption 5) on a constant and the three instruments produces an adjusted R-squared of 0.54 in which the three variables are individually significant at the 1 percent level with the same signs as found for correlation coefficients.

The results of the instrumental variable estimation of the Gini regression are shown in Table 2 . The estimated coefficients are close to their OLS counterparts in Table 1 ; the estimated coefficients on the corruption index are significant at the conventional significance levels. Table 2 also shows that the chosen instruments are valid at the 1 percent significance level. In sum, these results provide evidence that corruption increases income inequality. The instrumental variable estimates using other corruption indices produce similar results.

Corruption and Income Inequality: Instrumental Variable Estimates Dependent variable: the Gini Coefficient

The results also indicate that a worsening in the corruption index of a country by one standard deviation (2.52 points on a scale of 0 to 10) increases the Gini coefficient by 5.4 points ( Table 2 , Column 1).

D. Corruption and Poverty

The poverty equation is also estimated with the help of OLS, using the same data set for the right hand side variables as for the income inequality equation. A simple regression of the income growth of the poor on aggregate growth (plus a constant) produces a highly significant coefficient with a t-statistic of 2.94 and a R-squared of 0.213. The size of the coefficient on the aggregate growth variable (1.2) indicates that one percentage point increase in aggregate growth is associated with 1.2 percentage points of income growth of the poor. This finding is consistent with the view that, other things being equal, higher growth increases the rate of poverty alleviation.

Table 3 displays the results. All regressions contain the following variables: a constant, natural resource abundance, initial income of the poor, initial secondary schooling, and growth in corruption. 21 The three remaining variables (education inequality, initial Gini coefficient for land, and social spending) are entered one at a time and then all at once to see if the sign and significance of these variables—as well as that of corruption—change. In all these regressions, higher growth in corruption is associated with lower income growth of the poor, with the coefficient being significant in four regressions at the conventional statistical levels. The estimated coefficient on the corruption index is most significant (at the 1 percent level) when the regression includes social spending (Column 4). The latter regression also has a better fit than the regression reported previously when aggregate growth was the only regressor. The results also show that the impact of corruption on poverty is quantitatively important. A one-standard deviation increase in the growth rate of corruption (a deterioration of 0.78 percentage points) is associated with a decline in income growth of the bottom 20 percent of the population of 1.6 percentage points per year ( Table 3 , Column 4). A partial scatter plot based on regression in Column 4 is displayed in Figure 2 .

Corruption and Income Growth of the Poor

Corruption and Poverty: OLS Estimates

(Dependent variable: income growth of the bottom 20 percent)

1/ Multiplied by 10.

2/ Multiplied by 100.

The results also show that countries in which income of the poor has grown faster are those that tend to have fewer natural resources, and have started with lower levels of income and higher average schooling. Income growth of the poor is also higher with lower education inequality, lower initial Gini coefficient for land, and higher social spending (Columns 2, 3, and 4). When the latter three variables are entered simultaneously (Column 5), all variables continue to be statistically significant at the conventional levels, except for education inequality (which changes sign) and the initial Gini coefficient for land (which is no longer significant). 22

E. Corruption and Poverty: Which Way is the Causality?

The above regression results establish the existence of a statistically significant positive association between corruption and poverty. However, high poverty can cause high corruption or the observed association between the two variables can be due to other factors. As in the previous analysis of corruption and income inequality, an instrumental variable estimation technique is used to address these issues. 23

The results are shown in Table 4 . The corruption index has the same sign as the OLS results of Table 3 and is significant at the conventional statistical levels. The estimated coefficients on the corruption index are higher than their OLS estimates. The results also provide evidence that the chosen instruments are valid at the conventional statistical levels. In sum, the evidence shows that corruption increases poverty.

Corruption and Poverty: Instrumental Variable Estimates

1/ Multiplied by 100.

2/ Multiplied by 1000.

The impact of corruption on poverty is quantitatively important. A one-standard deviation increase in the growth rate of corruption (a deterioration of 0.78 percentage points) reduces income growth of the bottom 20 percent of the population by 7.8 percentage points per year ( Table 4 , Column 4).

V. H ow D oes C orruption A ffect I ncome I nequality and P overty?

The regressions in the previous sections have shown that factor endowments, ownership structure of factors of production and corruption, among others, affect both income inequality and poverty. This could be labeled as the direct impact of corruption on income inequality and poverty. However, as argued previously, corruption may also affect poverty and income distribution indirectly through its impact on variables such as factor endowments and factor ownership.

A. Relationship Between Corruption, Factor Endowments, and Factor Ownership

At the outset, each of the five variables representing factor endowments and factor ownership are regressed on a constant and a corruption index. To control for the stage of economic development, real per capita GDP is added to each regression to verify if the simple correlation changes sign or significance. 24

The results are shown in Table 5 . The correlations show that countries with higher corruption tend to have abundant natural resources, higher education inequality, lower mean years of secondary schooling, and more unequal land distribution. Of the five correlations, corruption is statistically significant at the 1 percent level in two regressions (education inequality and secondary schooling); at the 5 percent level in one regression (natural resource abundance); and at the 10 percent level in another (initial Gini coefficient for land). There is no systematic correlation between corruption and the ratio of capital stock to GDP. 25 Once the impact of real per capita GDP is controlled, corruption continues to have the same sign as before in three of the five regressions (education inequality, secondary schooling, and initial Gini coefficient for land); but at a lower level of statistical significance (10 percent) in two regressions (education inequality and initial Gini coefficient for land).

Relationship Between Corruption, Factor Endowments, and Factor Ownership

2/ Multiplied by 100,000.

3/ Multiplied by 1,000.

Although not all the correlations survive after controlling for real per capita GDP, the data are consistent with the view that corruption tends to increase inequality in the structure of factor ownership (that is, education inequality and initial Gini coefficient for land). The results in the previous sections showed that higher education inequality and higher land inequality increase income inequality ( Tables 1 and 2 ) and reduce income growth of the poor ( Tables 3 and 4 ). The results of this section show that corruption not only increases income inequality and poverty directly, it also affects these variables indirectly through higher inequality in education and land distribution.

B. Relationship Between Corruption and Social Spending

The discussion in Section II of this paper underscores the role of social spending in alleviating poverty and reducing income inequality and how corruption can affect these variables through social spending. To determine whether the data support this indirect channel, social spending is regressed on a constant and a corruption index. As in the analysis contained in the previous section, real per capita GDP is added to control for the stage of economic development. 26

The results are shown in Table 6 for three measures of social spending. The correlations show that countries with higher corruption tend to have lower levels of social spending. Of the three simple correlations, two are statistically significant at the 1 percent level (social security and welfare, and total social spending), and one at the 5 percent level (education and health spending). Corruption is statistically significant at the 10 percent level (social security and welfare, education and health spending) and 5 percent level (total social spending) when the impact of real per capita GDP is controlled for.

Relationship Between Corruption and Social Spending

1/ Multiplied by 1,000.

2/ Multiplied by 10,000.

The data are consistent, therefore, with the view that corruption reduces social spending whether or not real per capita GDP is held constant. The results in the previous section showed that higher social spending increases the income growth of the poor ( Tables 3 and 4 ). Together these results show that corruption not only reduces income growth of the poor directly, but also indirectly through lower social spending.

C. Impact of Corruption on Growth

It was argued previously that corruption can perpetuate poverty by reducing growth. To test this hypothesis, the real per capita GDP growth rate is regressed on the same set of variables as the income growth of the bottom 20 percent of population. 27 As noted earlier, higher growth is found to increase the rate of poverty alleviation. The results in Table 7 show that corruption reduces the overall growth rate of the economy. Together, these results indicate that corruption leads to higher poverty by reducing economic growth. 28

Corruption and Growth

(Dependent variable: real per capita GDP growth)

D. Corruption and Progressivity of Taxes

Does corruption increase income inequality by reducing the progressivity of the tax system? To answer this question, the net income dummy is dropped from the Gini regression ( Table 1 , Column 1) and an interaction term between this dummy and the corruption index is added to the same regression. The coefficient on the interaction variable (0.66) has a t-statistic of 2.39, which is statistically significant at 5 percent level. 29 This finding indicates that the impact of corruption on income inequality depends on whether income is measured before or after tax. More important, the positive coefficient on the interaction term shows that the impact of corruption on income inequality is higher when using the after-tax measure of inequality, suggesting that corruption increases income inequality by reducing the progressivity of the tax system.

VI. C onclusions and P olicy I mplications

Corruption interferes with the traditional core functions of government: allocation of resources, stabilization of the economy, and redistribution of income. These functions influence income distribution and poverty in varying degrees, both directly and indirectly.

The budget is the principal vehicle through which any government conducts its core functions. The empirical evidence presented in this paper shows that corruption has significant distributional consequences by affecting both budgetary revenues and expenditures. High and rising corruption increases income inequality and poverty by reducing economic growth, the progressivity of the tax system, the level and effectiveness of social spending, and the formation of human capital. Corruption also increases income inequality and poverty by perpetuating an unequal distribution of asset ownership and unequal access to education. These findings are valid for countries at different stages of economic development, with different growth experiences, and using various indices of corruption. These results hold even when controlling for other factors that affect income inequality and poverty: (1) natural resource endowment; (2) capital productivity; (3) educational attainment; (4) unequal access to education; and (5) distribution of land.

The impact of corruption on income inequality and poverty is considerable. A worsening in the corruption index of a country by one standard deviation (2.52 points on a scale of 0 to 10) increases the Gini coefficient by 5.4 points. A one-standard deviation increase in the growth rate of corruption (a deterioration of 0.78 percentage points) reduces income growth of the poor by 7.8 percentage points per year.

This paper’s findings suggest that the adverse distributional consequences of corruption can be mitigated by: (1) sound management of natural resources; (2) broad-based, labor-intensive growth; (3) efficient spending on education and health; (4) effective targeting of social programs; and (5) a low level of inequality in the access to education.

A central message of this paper is that corruption has significant distributional implications and, given its negative efficiency implications, should be considered harmful to both growth and equity. Therefore, policies that reduce corruption will also reduce income inequality and poverty.

The Gini coefficient and quintile income shares

Data on the Gini coefficient and quintile income shares are taken from Deininger and Squire’s (1996) “high quality” data set. This data set includes observations on the Gini coefficient that fulfill three key requirements for reliability: they must be based on household survey data, the survey coverage must be national, and the surveys must include all income sources.

Natural resource endowment

The proxy for natural resource endowment is the share of natural resource exports in total exports in 1970 ( Sachs and Warner, 1997 ).

Physical capital endowment

The physical capital endowment is the average ratio of the stock of physical capital to GDP, both measured in constant 1987 prices in local currency, between 1980 and 1990 ( Nehru and Dhareshwar, 1993 ).

Human capital endowment

The proxy for human capital endowment is the average years of secondary education in the population aged 15 and over between 1980 and 1995 ( Barro and Lee, 1996 ).

Land distribution

The proxy for the distribution of land is the Gini coefficient for land (circa 1980). It is based on the land rental market and was used by Deininger and Squire (1996) . 30

Education inequality

Education inequality is proxied by the 1980-95 average ratio of the percent of population, aged 15 and over, with no schooling expressed as a fraction of percent of population, aged 15 and over, with completed secondary and higher education ( Barro and Lee, 1996 ).

Six indices of corruption are used. The first (Corruption 1) is from the International Country Risk Guide (ICRG) and the Business International ( BI ) (as used by Tanzi and Davoodi, 1997 ), averaged between 1980 and 1995. The ICRG index reflects the assessment of foreign investors on the degree of corruption in an economy. Investors are asked whether high government officials are likely to demand special payments and whether illegal payments are generally expected throughout lower levels of government as bribes connected with import and export licenses, exchange controls, tax assessment, police protection, or loans. The ICRG index has been rescaled and spliced with the BI index so that the combined index ranges from 0 (most corrupt) to 10 (least corrupt).

Proxies two through six are the Transparency International corruption perception indices for 1995 (Corruption 2), 1996 (Corruption 3), 1997 (Corruption 4), an expanded 1997 index (Corruption 5), and a historical corruption index averaged over the 1988-92 period (Corruption 6). The expanded 1997 corruption index was constructed by Johann Lambsdorff (forthcoming) by applying the same technique as Transparency International, but includes countries for which a minimum of two survey sources were available. The rationale for their exclusion from the Transparency International index (Corruption 4) was the requirement of a minimum of four survey sources on every country to enhance the reliability of the data. By enlarging the number of observations available (from 52 to 101), however, the expanded 1997 corruption perception index compensates for the increased margin of error incurred by using data based on fewer surveys.

Real per capita GDP

The data on nominal purchasing power parity per capita GDP denominated in U.S. dollars have been converted to real data using the U.S. GDP deflator ( International Monetary Fund, World Economic Outlook, 1997 ).

Social spending

Three measures of social spending are used; these are government spending on: (1) social security and welfare, (2) education and health, and (3) the sum of spending items (1) and (2) plus housing and community amenities. These data have been expressed as fractions of GDP, both in local currency, and are from the same source ( International Monetary Fund, Government Finance Statistics, 1997 ).

English language

The proxy for English language is fraction of a country’s population that speaks English at home (Hall and Jones, forthcoming).

Latitude is a country’s distance from the equator (Hall and Jones, forthcoming). This variable is measured as the absolute value of latitude in degrees divided by 90 to place it on a 0-to-1 scale.

The proxy for ethnicity is an index of ethnolinguistic fractionalization for 1960 ( Taylor and Hudson, 1972 ). It measures the probability that two randomly selected persons from a given country will not belong to the same ethnolinguistic group.

APPENDIX II

Corruption Indices

Impact of Various Corruption Indices on the Gini Coefficient Estimated Coefficient and

Corruption, Poverty and Growth (Dependent variable: income growth of the bottom 20 percent)

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The authors would like to thank Vito Tanzi, Željko Bogetić, Benedict Clements, Calvin McDonald, and Edgardo Ruggiero for their comments, and Tarja Papavassiliou for her computational assistance.

Exceptions include Tanzi (1995) and Rose-Ackerman (1997a) . For an exhaustive review of the corruption literature, see Rose-Ackerman (1997b) and Tanzi (forthcoming).

See Bardhan (1997) for a discussion of the persistence of corruption and the empirical section of this paper for supporting evidence.

See Tanzi (forthcoming) for a discussion of the political economy of corruption and the reform of the state.

Growth is harmed because high income inequality creates pressures either for populist programs, which reduce the overall productivity of public resources, or for postponing much needed adjustment to support the growth process (for example, Alesina and Drazen, 1991 ; Laban and Struzenegger, 1994 ; and Alesina et al, 1996 ).

It is possible for income inequality to be high enough that it results in rising poverty, despite high growth ( Ravallion, 1997 ).

The models of Bourguignon and Morrisson (1990) and Londoño and Szekely (1997) are also based on the same underlying principle.

Adult population is defined as population aged 15 years and over.

See Sala-I-Martin (1997) and Sachs and Warner (1997) .

This measure has been previously used by Deininger and Squire (1996) and Birdsall and Londoño (1997) . Income growth of the bottom 20 percent of the population is defined as the average yearly growth rate in real per capita GDP of the bottom quintile of the population, measured in purchasing power parity-adjusted US dollars.

Use of international poverty lines, such as the proportion of the population living on less than US$1 a day, will solve some but not all of the measurement problems.

For evidence on benefit incidence of social spending, see Tanzi (1974) and Alesina (1998) .

There are two separate one-tail tests. In the first, the null hypothesis is that the coefficient on the corruption index is greater than or equal to zero (that is, higher corruption is associated with lower income inequality or corruption has zero correlation with income inequality). In the second, the null hypothesis is that the coefficient on the corruption index is less than or equal to zero (that is, higher corruption is associated with higher income inequality or corruption has zero correlation with income inequality). In both tests, the alternative hypothesis is the complement of the null hypothesis.

This estimate is based on Table 1 , Column 2: (1.72 x 2.52) ÷ -1.85 = -2.3. The estimate is even higher if point estimates from Column 6 of Table 1 are used.

Four indices are compiled by Goettingen University and Transparency International (1997) and one by Tanzi and Davoodi (1997) . See Appendix I for more details.

Similar results are obtained with the two narrower measures of social spending; these are government spending on (1) education and health, and (2) social security and welfare. Regression results that include social spending are not reported. These are available from authors upon request.

The coefficient on the corruption variable is halved relative to regressions which exclude real per capita GDP, the reason being that countries with low levels of per capita GDP have, on average, higher levels of corruption. The simple correlation coefficient between real per capita GDP and the corruption index is negative with a t-statistic of -12.

To determine if these findings hold in the presence of a Kuznets curve, a quadratic term in real per capita GDP is added to regressions in Columns 4, 5, and 6, which already include level of real per capita GDP. No evidence of the existence of a Kuznets curve is found; higher corruption continues to be associated with higher income inequality. The latter findings also holds when growth in real per capita GDP is added to regressions in Columns 1, 2, and 3.

Similar results are obtained when using narrower measures of social spending.

Each instrument changes slowly over time; the second instrument, latitude, not at all. Each is predetermined with respect to future evolution of income inequality. For example, the ethnicity variable refers to 1960 whereas the Gini coefficient and the relative income share data are for the post-1980 period. The English language variable refers to 1988, but it would not be expected to vary substantially in the post-1988 period. The simple correlation coefficient between the corruption index (corruption 5) and each instrument is high and statistically significant. The correlation coefficient is estimated at -0.47 for the English language variable, -0.67 for latitude, and 0.43 for ethnicity. These estimates suggest that countries with low corruption tend to have a high proportion of their population who speak English at home, are farther away from the equator and are fairly ethnically homogenous. In the instrumental variable estimation, all three instruments are used jointly.

Most of the variables included in the regression affect aggregate growth. Hence, aggregate growth is excluded in Table 3 . Including it increases collinearity among the variables, which makes it difficult to distinguish the effect of each independent variable on the dependent variable. Nevertheless, these results are shown in Appendix II , Table 10 . The overall results, particularly with respect to the impact of corruption, remain the same as in Table 3 .

When these two variables are dropped, the adjusted R-squared increases as does the significance of the remaining variables. This regression is given in Column 4.

The technique uses the same instruments as the income inequality regression.

These regressions should not be viewed as structural models of factor endowments and factor ownership. Such an analysis goes beyond the scope of this paper.

Lack of a correlation here does not mean there is no correlation between corruption and the investment-to-GDP ratio. See Mauro ( 1995 and 1997 ).

Mauro (1997) provides similar evidence.

Using a different set of regressors, Mauro (1995) provided the first evidence on the negative impact of corruption on growth.

The results are similar when the same corruption index as in Table 3 is used or when a Mauro-type ( Mauro, 1995 ) specification for the growth regression is tried. Under the latter specification, determinants of growth are initial income, a measure of schooling, population growth rate, investment-to-GDP ratio, and corruption.

All the other variables continue to have the same sign and significance as in Table 1 , Column 1. The interaction term is also significant and positive in other formulations of Table 1 . A high value of the index indicates a high level of corruption.

Klaus Deininger kindly provided the data.

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Front Matter
Does Corruption Affect Income Inequality and Poverty?
I. Introduction
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Does corruption affect income inequality and poverty?

Author(s) : Gupta, Sanjeev; Davoodi, Hamid; Alonso-Terme, Rosa

Organization : International Monetary Fund

Imprint : Washington D. C., IMF, 1998

Collation : 41 p.

Series : IMF Working Papers, WP/98/76

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Studies of the consequences of corruption have mainly focused on economic efficiency. This paper illustrates that corruption can also have distributional consequences. Corruption increases income inequality and poverty through lower economic growth; biased tax systems favouring the rich and well-connected; poor targeting of social programs; use of wealth by the well-to-do to lobby government for favourable policies that perpetuate inequality in asset ownership; lower social spending; unequal access to education; and a higher risk in investment decisions of the poor. Cross-country regression analysis for 1980-97 shows that high and rising corruption increases income inequality and poverty through the above channels. A one-standard-deviation increase in the growth rate of corruption (a deterioration of 0.78 percentage point) reduces income growth of the poor by 7.8 percentage points a year. These findings suggest that adverse distributional consequences of corruption can be mitigated by sound management of natural resources; broad-based, labour-intensive growth; efficient spending on education and health; effective targeting of social programs; and increased access to education.

Access to education, Anti-corruption strategies, Legal framework, Corruption, Economic and social development, Educational management, Central administration, Health, Poverty, Social inequality
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v.9(1); 2017

Social impacts of corruption upon community resilience and poverty

James lewis.

1 Datum International, South Gloucestershire, United Kingdom

Corruption at all levels of all societies is a behavioural consequence of power and greed. With no rulebook, corruption is covert, opportunistic, repetitive and powerful, reliant upon dominance, fear and unspoken codes: a significant component of the ‘quiet violence’. Descriptions of financial corruption in China, Italy and Africa lead into a discussion of ‘grand’, ‘political’ and ‘petty’ corruption. Social consequences are given emphasis but elude analysis; those in Bangladesh and the Philippines are considered against prerequisites for resilience. People most dependent upon self-reliance are most prone to its erosion by exploitation, ubiquitous impediments to prerequisites of resilience – latent abilities to ‘accommodate and recover’ and to ‘change in order to survive’. Rarely spoken of to those it does not dominate, for long-term effectiveness, sustainability and reliability, eradication of corrupt practices should be prerequisite to initiatives for climate change, poverty reduction, disaster risk reduction and resilience.

Résumé

Corruption , existing at all levels of all societies in varying degrees, is a behavioural consequence of power and greed in contexts of inadequate governance. With no published rulebook or formula with which to comply, corruption is covert, repetitively opportunistic and powerfully reliant upon dominance and fear within unwritten and unspoken codes. It is therefore an understatement that, consequently, corrupt practices do not readily lend themselves to scientific analysis. Instead, investigation of its consequences amongst the poor has to be necessarily ad hoc and gathered from relatively few published sources which have become available over time. For the purposes of this assessment of its social impacts upon resilience and poverty, extracts have been gathered of its variety of methods and pervasive consequences; as with corruption itself, its procedures are evasive and do not readily lend themselves to formal research.

Literature on the social impacts of corruption is limited, a definitive analysis of corruption and its social consequences being not, as yet, a practicable undertaking. This short contribution reflects some preliminary investigation of the social impacts of corrupt practices upon the poorer sectors of societies, where and when accessible literature has ensued.

Corrupt practices amongst high level political, commercial and industrial dealings, rightly receiving media attention, may become the commencement of long-term trickle-down consequences for the poor which, at society’s lower levels, are unlikely to attract either scientific or media notice. Whilst the scale of corruption on China, Italy and Africa here receive mention, the impacts of corruption upon the poor of these and other societies in Africa, Bangladesh and the Philippines, for example, reveal social consequences which are here examined and considered against required prerequisites for resilience. Those societies and communities most reliant upon their own resilience to crises of any kind are also the most prone to its erosion by consequences of opportunistic control and exploitation.

An introductory section outlines descriptions of how corruption and its effects are contrary to basic needs for resilience, focussing on erosion of personal capacities and abilities; its significance to poverty and development within less-developed countries being indicated. Detailed analysis of social prerequisites for resilience is described with reference to internationally adopted definitions as a basis for discussion of their interpretation and comparison, both historic and recent. Some worldwide corrupt practices and attitudes to them are described in contexts of resilience theory, its reality and its consequences. Discussion of economic and social consequences of corruption is based upon Transparency International definitions and their shortcomings. Conclusions highlight a relationship between corruption, poverty and their impacts of natural hazards and causes of disasters. Depletion of national incomes by corruption relates to causes of poverty and the need for removal of corrupt practices at all social levels. Improved quality of life may then permit emergence of required prerequisites for resilience.

Introduction

Investigated and published more often as a financial issue (e.g. Drury et al. 2006 ; Klein 2007 ; Transparency International 2016a , 2016b ; Zucman 2015 ), corruption in its various guises imposes wide-ranging social consequences, especially when established long-term to the extent of having become ‘normal’ and when its networks, influences and consequences reach community and domestic contexts.

Corruption is a cause of low development (Zucman 2015 :34–55) and exacerbates poverty where poverty prevails; corruption, therefore, needs to be included amongst causes of the consequences of poverty, such as debt, incapacity, mental despair and despondency (Ray 1986 ). Within influences as powerful as poverty, corrupt practices, in many forms and over long periods of time, may affect all and every exchange or transaction at every level of society, imposing additional insidious and negative influences upon the emergence of resilience. With little or no hard evidence for outsiders and rarely spoken of to those it does not dominate, in its numerous forms, the invisible, outwardly imperceptible practices of corruption are a cause of debilitating, pervasive and penetrating impacts upon day to day behaviours, ways of life and of well-being (Chabal & Daloz 1999 ; Hartmann & Boyce 1990 ; Hoogvelt 1976 ; Lewis 2008b , 2011b , 2011c ; Ray 1986 ).

Whatever resource and effort may be introduced for its purpose, resilience may be impeded, or may not materialise, where indigenous systems of control prevail and where social capacities are consequently inadequate.

Prevailing incapacities may have been caused by a variety of circumstances, such as: long-term political repression (Lewis 2013a ), ill-considered occupation or re-occupation of hazardous and damaged locations (Lewis 2013b ), direct experiences of catastrophe, deaths, injury, shock or other consequences, or long-term poverty of a degree to so seriously deplete initiative and well-being as to induce physical and mental inertia (Symons 1839 ). Poverty is commonly assumed to be because of a country being poor whilst, in reality, poverty exists in most societies (Lewis & Lewis 2014 ). Any or all of these consequences may have been, or may yet be, experienced over long periods of time, separately or simultaneously, repeatedly or continuously.

For the emergence and organisation of resilience in any context, prerequisites of individual capacity and ability (United Nations: International Strategy for Disaster Reduction [UN/ISDR] 2009 ) are identified as being necessary. Without capacity and without individual qualities ‘to reduce negative consequences’ of disasters and for application to ‘long-term strategies for societal change’ (UN/ISDR 2009 ), it is difficult to envisage how community and organisational resilience could gestate, emerge or formulate. In any context and at any level, if individuals are not resilient then how would community resilience come to prevail?

Many less developed countries are internally perceived as most corrupt (Transparency International 2016b ) and some of the most corrupt are amongst those most vulnerable to natural hazards; Bangladesh, Nepal and the Philippines, for example. For the 20-year period 1996–2015, almost half of all deaths because of all natural hazards occurred in low-income countries (Centre for Research on the Epidemiology of Disasters [CRED]/United Nations International Strategy for Disaster Reduction [UNISDR] 2016a ). In contexts such as these, it is pertinent to ask: how much is a country’s apparent poverty because of corruption in governance and commercial mismanagement, and how many basic components of resilience, such as well-being, capacity and ability could have, indeed should have, been induced and supported in the name of indigenous normal good governance and social development?

Social prerequisites for resilience

Resilience has been defined as:

The ability of a system, community or society exposed to hazards to resist, absorb, accommodate and recover from the effects of a hazard in a timely and efficient manner, including through the preservation and restoration of its essential basic structures and functions. (UN/ISDR 2009 :n.p.)

A definition which may be read either as an assumption that ability exists or as a caution that it may not (Lewis 2013a ).

Resilience theory originated in ‘late 20th century American cities’ (Davoudi et al. 2017 ), in which ‘radical self-sufficiency’, autonomy and ‘self-dependence’ are facts of life for all but the poorest (Lewis 2013a ). What is not known is what kind of ‘community or society’, or what personal, local and national resources, were assumed as the basis of its definition.

Nonetheless, requirements for resilience have come to assume a universal capability of people to absorb stress and to transform and adapt to managing risks. In short, to deal with crises and disasters, people’s capacity being dependent upon demographic, social, cultural, economic and political factors which may vary. Resilient societies are expected to be able to overcome the impact upon them of natural hazards ‘either through maintaining their pre-disaster social fabric, or through accepting marginal or larger change in order to survive’ (UN/ISDR 2009 :n.p.). Required is the capacity to adapt ability in the creation of capability for recovery (Wisner 2016 ). Thus, the concept of resilience is linked to the concept of change (Manyena 2006 ) which may be technological, economic, behavioural, social, cultural (Gaillard 2007 ) or political (Lewis 2013a ), but in conditions of pervasive poverty, there may not be the ability to ‘accommodate and recover’, or for ‘maintenance of social fabric’; least of all the ability, capacity and capability to ‘change in order to survive’ and ‘in a timely and efficient manner’ (UN/ISDR 2009 :n.p.).

The UN/ISDR definition goes further in recognising that resilience ‘is determined by the degree to which the community has the necessary resources and is capable of organising itself both prior to and during times of need’ (UN/ISDR 2009 :n.p.). Consequently, ‘resilience’, once a characteristic of individuals, has come to be widely applied to preventive motivations as well as to post-disaster contexts, and to being relevant to drought, flood, climate, infrastructure, industrial complexes, businesses, cities, communities and administrations and governments and their politically stated objectives (e.g. Resilience-Scan 2016 ). Poverty and resilience cannot be assumed to go together (Boubacar et al. 2017 ); moreover, in realities of the aftermath of any catastrophe, whether or not in conditions of prevailing poverty, is it not more than likely that ‘ability’ may be severely depleted or may not exist at all (Lewis 2013a )? Resilience theory is said to risk becoming ‘another carrier of neoliberal ideologies, politics and practices with negative implications for social justice and democracy’ (Davoudi et al. 2017 :n.p.).

External initiatives applied as preliminaries towards achievement of community resilience over time, for example, by the improvement of living conditions, healthcare and education as described in detail from Bangladesh (Ahmed et al. 2016 ), may assist contexts of socially comprehensive resilience in the short-term. Focus, however, on localised and current conditions may obscure suspected but hidden causes of those conditions and the consequent need for their cessation and prevention; they may be direct or indirect consequences of questionable influences or of corrupt governance nationally and locally (Lewis & Kelman 2012 ).

Notwithstanding inculcation prior to crises to achieve the social capacity resilience requires, capacity may be annihilated or severely depleted in ensuing catastrophe and its aftermath. Despondency, not resilience, may become the reality, expressing not ability but inert disability. Resilience may theoretically pre-exist as a basic human quality but cannot be assumed to prevail regardless of realities of physical, mental and psychological incapacities, especially in contexts of poverty.

Present in any society at any time (Lewis & Lewis 2014 ), an early analysis of poverty in Scotland, France, Belgium, Austria and Switzerland (Symons 1839 :147–148) realised that poverty has ‘… the same effect on the mind that drunkenness has upon the body’ and that poverty was:

… a main instrument in the debasement of mankind … It is not only the parent of ignorance, but it is the greater barrier to enlightenment. When a man’s whole faculties are strained to the utmost from sunrise to sunset to procure a miserable subsistence, he has neither the leisure, aptitude nor desire for information … (pp. 147–148)

It could be assumed from this description that the sufferer would not have had capacity for resilience.

Fifty-three years later, Friedrich Engels ( [1892] 2009 ) wrote of England:

Everything that the proletarian can do to improve his position is but a drop in the ocean compared with the floods of varying chances to which he is exposed, over which he has not the slightest control. He is the passive subject of all possible combinations of circumstances … (p. 144)

It may be impractical to assume resilience where, for example, many populations are striven by conflict and warfare, millions of people are on the move as refugees and migrants, where millions more are in abject poverty and more directly where people are immediate and longer-term victims of catastrophe. Peace and stability may have been achieved in the aftermath of similar experiences but populations may have been left in fear of recurrence, a fear not conducive to the emergence of ability (Lewis 2013a ; Lewis, Kelman & Lewis 2011e ) and a condition which may last for many years.

Whilst communities may be, or may become resilient, they may continue to be vulnerable and at high risk (Sudmeier-Rieux 2014 ), continuingly prevalent causes of their vulnerability (Lewis & Kelman 2010 ) having been bypassed and disregarded by priorities for achieving resilience. Whereas destruction and damage are described in terms of physical impacts, these may transfer as mental, emotional, social and economic impacts upon individuals and communities. For some time, primary resources of resilience, such as capabilities of creativity, energy and leadership, may therefore be scarce commodities. Resilience anywhere will be dependent upon conditions that prevailed before disaster as well as those created by it and upon programmes for development responsive to potential contingencies of environmental hazards and disasters (Lewis 2013b ). Prescribed characteristics of resilience rarely refer to preceding contexts (e.g. Twigg 2007 ), some least positive contexts being described by Lewis and Kelman ( 2012 ).

Resilience may not, therefore, emerge ‘on demand’, commensurately comparable with the origins of catastrophe from whatever source. This would require a different kind of resilience, not on-the-spot reactions to chaos but one that recognises resilience as a long-term process more compatibly aware of political, social and economic causative processes of inequality, vulnerability and poverty (e.g. Lewis 2013a ), of which the social, as well as economic, consequences of corruption and its associated practices are a significant cause.

But stable, equable, fair and considerate communities and their regional and national administrations are a rarity; poverty, expressed according to a country’s median income, exists virtually in all countries as, in its varying degrees and practices, does corruption (Transparency International 2016b ). Where politicians appear to be in power to facilitate their own incomes and lax administrative systems facilitate them to do so, corruption becomes a cause of poverty, a major impediment to equality and the ‘worm-in-the-bud’ of resilience.

Some economic and social consequences of corruption

Corruption, as ‘the abuse of entrusted power for private gain’, has been classified as ‘grand’, ‘political’ and ‘petty’, depending on the amounts of money lost and the sector of governance in which it occurs (Transparency International 2016a ). International scales of corruption, reviewed annually, are based upon internal perceptions of corruption as it is indigenously observed and experienced, a methodology by which it is not possible to compare one perception with another or to know how they were arrived at. This means that whilst corrupt behaviours of politicians or large corporations are reported by the media, they may or may not influence those perceptions upon which international comparisons are based. The international definitions and comparisons by Transparency International are nevertheless a principal comparative scale of corruption and its definitions.

Most corrupt practices operate on, or create, a hierarchical scale of trading, a system that ensures that costs to top-level payers of bribes may be expected to be reimbursed by the receipt of bribes from others, those lower on the scale being recipients of backhanders to them for favours given. Payments would be expected and reimbursed similarly downwards to scales of petty corruption. That socially lowest payers have no-one upon which to claim is how millions of people find themselves in endless poverty – beholden and indebted victims for further exploitation by those richer and more powerful, at whatever level, than themselves. The poor become poorer to the advantage of the rich and poverty and inequality are perpetuated. Realities of corrupt practices upon those already in poverty cannot simply be classed as ‘petty’.

Of Africa, Chabal and Daloz ( 1999 ) argue in support of corruption being ‘the norm … constituting a substantial resource’ (Chabal & Daloz:xxi), taking the view that there has always existed a wide range of activities, inclusive of corruption, which, although illicit from a strictly constitutional or legal point of view, have been regarded as legitimate by the bulk of population (Chabal & Daloz:79). They emphasise however that corruption affects all social strata ‘from billionaires to the lowliest functionary’. Consequently, dichotomy between ‘high’ and ‘low’ or ‘small-’ and ‘large-’ scale corruption is not a determinant factor; neither are differences between financial malpractice, illegal commissions, small graft, open abuse of power, and petty pilfering. Nor do these authors believe some forms of corruption are more reprehensible than others, all forms of corruption being part of an interconnected whole (Chabal & Daloz:98).

Others (e.g. Hoogvelt 1976 ) see corruption as ‘the only means of integrating marginal groups into a disjointed social system’ (Hoogvelt 1976 :132) but where that is the case, corruption should not be allowed to be a licence for social injustice by forcefully keeping in power undeserving elites (Hoogvelt 1976 :137).

Grand corruption in governments’ higher echelons (Transparency International 2016a ), necessarily filters down, with its consequences, throughout all functions of all societies. Politicians and commercial operators, privately and corruptly, are known to have siphoned collectively enormous amounts of money, much of it from development funding, often from their own disaster-prone countries and very often into private bank accounts in the countries that were the origin of the aid (Ndikumana & Boyce 2011 ).

Known as ‘illicit financial flows’ and merged with corruption because of their secrecy, tax evasion and avoidance, and with sources possibly related to more strictly defined corruption, dishonest transactions on a huge scale (Zucman 2015 :34–55) have emerged as evidence of why some countries have remained ‘less-developed’. Money, illicitly taken from external funding intended for development purposes, is a likely cause of reduced domestic investment in basic needs of housing, sanitation, health and education, an explanation of why poverty has prevailed as the principal cause of vulnerability (Lewis 2015 ) and its associated disaster losses and social incapacities, and why such issues have not been matters of development priority by some national governments and indigenous organisations.

A report from the Philippines (Rey 2016 ) concludes that, from 1960 to 2011, approximately $410.5 billion left that country in ‘illicit financial flows’; a figure stated as being 154 times the national budget for health, 52 times that for social protection, 39 times that for education and 25 times that for infrastructure for the same period.

The overall cost to developing countries between 2000 and 2008, of corruption and trade mispricing (trade as a vehicle of monetary transfer), was approximately $6.5 trillion (Kar & Curcio 2011 ), a subsequent United Nations Development Programme (UNDP) report indicating that $197bn, a significant share, had accrued from those countries categorised as least developed (UNDP 2011 ). A more recent report describes illicit financial flows from eight countries, including Bangladesh and Nepal, as a symptom of poor governance and dysfunctional regulation, and having the following consequences:

undermining of domestic resource mobilisation by eroding the tax base
causing greater dependency on official development assistance
reducing domestic investment and slowing poverty reduction efforts and worsening of inequality (UNDP 2014 ).

Illicit financial flows from developing countries worldwide in 2013 totalled $1.1 trillion, a figure greater than the combined total of foreign direct investment and net official development assistance received by those economies in that year. As examples, illicit financial flows between 2004 and 2013 from Bangladesh totalled $5588 million, from Nepal $567m, and from the Philippines $9025m (Kar & Spanjers 2015 ).

Political corruption is the manipulation of policies, institutions and rules of procedure in the allocation of resources and financing by decision makers, who abuse their position to sustain their power, status and wealth (Transparency International 2016a ).

An investigation in Bangladesh of self-reported compliance with corporate governance, examined enforcement documents of the Securities and Exchange Commission against actual corporate governance compliance from 2007 to 2011 (Nurunnabi, Hossain & Al-Mosa 2016 ). The authors observe that corruption and lack of enforcement in Bangladesh induced falsification of formal financial reporting under both democratic and military governments (2007–2008). The extent of falsification of information is stated as a cause of alarm for both local and international policy-makers and local and international investors. One thousand one hundred and ninety-four Bangladesh Securities and Exchange Commission’s enforcement documents were evaluated and 20 semi-structured interviews were conducted.

In 2007, the government of China had more than 1200 laws, rules and directives against corruption, but implementation was ineffective. With only a 3% likelihood of a corrupt official being sent to jail, corruption was a low-risk high-return activity. Even low-level officials had the opportunity to amass an illicit fortune of tens of millions of yuan. The secretary to the Chinese Communist Party in Janwei county of Sechuan province acquired 34 million yuan (£3 467 952/$5 096 000) and the colleague of another Chinese Communist Party (CCP) secretary, his city’s anti-corruption chief, collected bribes worth more than 30 million yuan (£346 794 000/$4 497 000; Lewis 2008b ).

Corruption in China is concentrated in those sectors with extensive state involvement, such as infrastructure projects and government procurement, the consequent increased costs of which, during a 10-year period, were estimated as 10% of spending (ending in 2005). Such a depletion of funds contributed to environmental degradation, social instability and inadequate health care, housing and education:

To estimate roughly the direct costs of corruption, we can suppose that ten per cent of government spending, contracts, and transactions is used as kickbacks and bribes or is simply stolen. (Lewis 2008b :n.p.; Pei 2007 :n.p.)

In relative terms, developing countries are the most affected by volumes of wealth held abroad, calculated for 2014 as 30% for those of Africa (Zucman 2015 :53). But between 1970 and 2008, an examination of capital sent from 33 African countries concluded that over that 38-year period, ‘capital flight’ amounted to $735bn, a sum roughly equal to 80% of the combined GDP of those countries during that time. The period of this study indicated that the sum involved was ‘not a transitory product of unusual circumstances but rather an outcome of persistent underlying causes’ (Ndikumana & Boyce 2011 :46). An earlier study by the same authors concluded that this sum was ‘from assets belonging to a narrow, relatively wealthy stratum of populations while, in consequence, public external debts are born by the people through their governments’ (Ndikumana & Boyce 2008 quoted in Shaxson 2011 :158). Similar procedures making use of offshore tax-havens have operated on behalf of the rich and at the cost of the poor within many countries (Shaxson 2011 ). Overall, by its hierarchy of bribery and graft, corruption for the benefit of the few means continued and exacerbated poverty for the many and simultaneous breakdown or malfunction of hospitals, clinics and health care (Ndikumana & Boyce 2011 :74–83, cited in Lewis 2015 ). Corrupt practices are widespread within entire commercial sectors of some countries, and are known to have been causes of serious inadequacies such as building failure (Ambraseys & Bilham 2011 ; Lewis 2005 , 2008a , 2008b ).

Political elites of some developing countries are known to accumulate capital because of the fragility of their position and constant threat to their political survival (Hoogvelt 1976 :137); partly for that reason, large sums are transferred to safer European accounts or to the many global ‘tax-havens’ (Shaxson 2011 ).

In a large scale public works contract in Italy, endemic collusion between levels of administration, elected officials, bureaucrats and private contractors made it obvious that for such abuse of public office for personal gain to persist countrywide, elected officials are necessarily and regularly involved. Extensive and persistent corruption in any sector, could not be regarded as a phenomenon isolated from its broader political context; a political environment of corruption involves a non-benevolent principal rather than being a benign bureaucratic or institutional slippage from a benevolent one (Golden & Picci 2005 ).

Petty corruption refers to everyday abuse of entrusted power by low- and mid-level public officials in their interactions with ordinary citizens, when seeking to access basic goods or services in hospitals, schools, police departments and other agencies (Transparency International 2016a ). These corrupt practices are rarely spoken of and expectations of bribes are rarely applicable to anyone not known to the locality. Without long-term presence and discrete research (e.g. Hartmann & Boyce 1990 ; Ray 1986 ), assured evidence of ‘petty’ corruption remains obscure.

In 2013, a Philippines national survey (Office of the Ombudsman 2014 ) indicated fewer families to have given bribes or ‘grease money’ in 2013 than in 2010. The survey found that more people in ‘the lower income stratum’ were more likely to pay bribes or ‘grease money’ despite ‘their lower financial capacity’, assumed by the report as to ensure government social services essential to them were made available.

Of West Africa, Hoogvelt ( 1976 ), believes corruption affects everyone:

patients offering bribes to nurses in hospital to persuade them to pass on a bed-pan; traffic offenders bribing police officers to waive the fine; tax collectors adding their personal increment to inland revenue extractions; councillors awarding contracts to firms in which they (or their kin) have a financial stake; educational officers giving government scholarships to their cousins; and political candidates buying the votes of entire electoral districts. (pp. 128–129)

Hoogvelt adds that corruption at the law enforcement level, involving lower echelons of civil and public services, is where contact between administrations and the public are most frequent and where, therefore, the greatest volume of corruption occurs – though the amount of damage done and money involved may well be greater at higher levels (Hoogvelt 1976 :130).

Corruption retains society’s levels in place, corrupt behaviours at lower social levels being a microcosm of those at upper levels. Where larger landowners control most land of their district, consequences at lower levels impact upon minor landholders, share croppers and labourers who own little or no land (Hartmann & Boyce 1990 :7); a system that ensures those at each social level will remain at that level, the rich as well as the poor, and the poor will remain beholden to, and controlled by, the rich.

An exception from Bangladesh illustrates the generality: Mahmud was a poor student living in Johir Ali’s house and tutoring his children in exchange for a room and board. After Mahmud graduated from secondary school, Johir Ali is said to have paid a 500 taka bribe to secure him a job as a tahsildar , a government land-tax officer who records the amount of land which tenants held on lease. Tenants were often in arrears with their rent payments and at risk of dispossession, providing Mahmud opportunities to help himself by helping others with their payments. Mahmud charged a fee for his services, but since it was less than the going price of land, most tenants were happy to comply. During his years as a tax officer, Mahmud accumulated considerable capital, which he invested in land and later left his government job to take up the management of his sizeable holdings (Hartmann & Boyce 1990 :55).

Every service demands a kickback or backhander additional to any legal payment that may be required. Larger landholders in line for development aid, such as for the drilling of wells, will buy up numbers of offers to which they may be eligible to sell on to lesser landholders either as wells or as water from wells in their ownership. Larger land owners control lesser landholders and smaller crop growers. From development aid, the poor get temporary employment (e.g. from the use of water), the rich reaping repeated capital gains from the installation of a well (Hartmann & Boyce 1990 :257, 262, 272, 274).

As a consequence of ‘tremendous power’ wielded in Bangladesh by the rural rich, ubiquitous corruption pervades every sector at every level and is stated as being a principal hindrance to the achievement of self-reliance by the rural poor. Wealthy landowners, physicians, shopkeepers, chairmen or members of the union parishad (local government), have long-lasting connections and alliances with government in the capital, officials of all ranks, lawyers, judges and powerful politicians. Sustained by bribes, gifts, marriage and birth, these alliances, enable the rural rich to safeguard their narrow self-interest, ‘committing crimes if necessary and getting away’ (Ray 1986 :24–25).

A study in flash flood prone north-eastern Bangladesh (Choudhury & Haque 2016 ) identifies social power structures, imposed by local political and commercial elites, as serving to diminish local adaptive capacities and consequently as an impediment upon resilience. Petty corruption, in the form of bribery referred to in the study, emerges as an understated but consistent component of impositions upon those in poverty; expressed as the eponymous quotation: ‘We are more scared of power elites than the floods’.

Reports of occasional local optimism (e.g. Hossain 2016 ) need to be set against realities of corruption at all relevant levels and scales (Transparency International 2016b ) and of its social consequences.

Contexts of poverty may be created by corrupt practices at higher levels of government and commercial management (Transparency International 2016a ; UNDP 2014 ), exacerbated and perpetuated by social systems imposed upon people and their communities for purposes of domination and exploitation to facilitate ‘petty’ corrupt practices (Choudhury & Haque 2016 ; Hartmann & Boyce 1990 ; Ray 1986 ).

Contexts of poverty are known to be amongst the most vulnerable and the most disaster-prone (Lewis & Kelman 2012 ). Of countries lowest on the internally perceived international corruption scale (Transparency International 2016b ), several are amongst the poorest developing countries (Ambraseys & Bilham 2011 ). Of the 168 countries on the scale, Myanmar is 147th, Bangladesh is 139th, Nepal is 130th and the Philippines is 95th; of low-income and lower middle-income countries (CRED/UNISDR 2016b ), Myanmar is 147th and Pakistan is 117th. Whilst consistent correlation between corruption and disaster impacts is unlikely, disaster mortality is highest in Haiti, at 158th amongst the lowest on the corruption scale and highest for disaster mortality.

In these and numerous other countries, poverty persists for large numbers of people caused to be at risk by pernicious political, commercial and social realities which result from discrimination and displacement, impoverishment by others’ self-seeking expenditure, denial of access to resources, and corrupt siphoning of public money that may be otherwise spent to the public good (Lewis & Kelman 2012 ); sub-cultures working to favour the few but in opposition to the interests of the many (Lewis 2015 ).

As a perpetrator and perpetuator of poverty and inequality (Alexander 2016 ; Lewis 2011a ; Lewis & Kelman 2012 ), by its various guises and their consequences, corruption is a ubiquitous impediment of abilities to ‘accommodate and recover’, and ‘change in order to survive’, the basic functions of resilience (UN/ISDR 2009 ).

Further, where aspects of national income are diverted to private accounts and payments of bribes are set against declared company profits, the basis upon which national tax incomes are formed is reduced. Income which could have been spent for the benefit of society at large is depleted on such a scale that housing, education, sanitation, nutrition and healthcare (Ndikumana & Boyce 2011 ), for example, are threatened or rendered inadequate (Lewis 2011a ). Corrupt behaviours leading to depletion of national and local incomes are an explanation for why works for basic community development are perceived as necessary for preliminary projects to precede projected inputs for sustainability and resilience (e.g. Ahmed et al. 2016 ).

Until corrupt practices are traced and stopped, it may not be realistic to expect villagers in long-term poverty to turn to new activities merely by advising them to do so: ‘After all, decades of abject poverty has instilled in them a deep fear that trying anything new may be disastrous’ (Ray 1986 :4). Traditionally ingrained corrupt practices may seem inseparable from social norms, the introduction of new practices being seemingly ‘next to impossible’, however essential they may be for longer-term social development to succeed.

Only ‘rugged common sense’ enables the poor to survive decades of exploitation by a ruling urban elite. Famished villagers cannot work towards change to the system by which they are oppressed unless they have achieved a minimum of nutrition and physical strength, ill health being inextricably linked to illiteracy, malnutrition, superstition, unemployment and agricultural backwardness (Ray 1986 :vii–viii, 3–4) – a close comparison with statements made by Symons ( 1839 ) with reference to Edinburgh and European capitals.

Corruption is not only a financial issue; corruption creates social systems compliant to its practices and influences entire societies and the social relationships they contain. In these circumstances and where systemic corruption persists, attempts to induce and to inculcate resilience to hazards and crises, if successful in any short-term, may be unlikely to succeed in any longer-term.

The start of any programme for rural resilience has to be the depletion of those ‘traditionally ingrained corrupt practices’. If famished villagers who have not achieved a minimum of nutrition and physical strength, cannot work towards change to the system by which they are oppressed, then externally applied programmes for purposes of creating resilience are unlikely to succeed in any longer-term. Corruption and its consequences will make any kind of social development programme unsustainable and community resilience is unlikely until individual resilience amongst individuals is itself sustainable (Lewis 2015 ).

Repeatedly, necessary injections of programmes and projects for sustainability and resilience might suggest their temporary presence to be due not to unavailable financial resources but to indigenous illicit misappropriations of financial capital. Corruption denies and impedes personal and community empowerment for change, the basic requirement for disaster risk reduction (Von Meding & Forino 2016 ). How much less vulnerable, and how much more resilient would populations be, without social impediments and financial draining at all levels imposed by corruption in any and all its guises?

Development programmes of wider inclusivity are emerging from responses to climate change and its consequences (Ahmed et al. 2016 ; Kelman et al. 2016 ; Lewis 1999 ). Adjustments for this wider inclusivity could be made to go further and to incorporate measures for annihilation and prevention of corrupt practices which, with poverty reduction, disaster risk reduction and resilience, would be an inclusivity serving to ensure improved long-term developmental effectiveness, sustainability and reliability.

Social consequences of corruption have been examined and considered and found to be negatively influenced against the required prerequisites for resilience. A question that remains is not ‘can resilience exist in contexts of corruption?’ but rather, ‘would the inducement of resilience be less necessary in non-corrupt contexts?’

Acknowledgements

Competing interests.

The author declares that he has no financial or personal relationships that may have inappropriately influenced him in writing this article.

How to cite this article: Lewis, J., 2017, ‘Social impacts of corruption upon community resilience and poverty’, Jàmbá: Journal of Disaster Risk Studies 9(1), a391. https://doi.org/10.4102/jamba.v9i1.391

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Is Corruption the Cause? The Poverty Trap

Philippines

The “corruption-causes-poverty” narrative has become a standard tool in the hegemonic discourse kit for leaders in some developing countries - where in fact, Waldon Bello argues, it is neoliberal economic policies that are really to blame for poverty. Thailand’s “Red Shirts” are not, however, being distracted by the “corruption” line the World Bank and IMF are pushing, choosing instead to keep their eyes on the prize - the real answer to poverty - replacing neoliberalism with pro-people economic policies.

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The issue of corruption resonates in developing countries. In the Philippines, for instance, the slogan of the coalition that is likely to win the 2010 presidential elections is "Without corrupt officials, there are no poor people." Not surprisingly, the international financial institutions have weighed in. The World Bank has made "good governance" a major thrust of its work, asserting that the "World Bank Group focus on governance and anticorruption (GAC) follows from its mandate to reduce poverty — a capable and accountable state creates opportunities for poor people, provides better services, and improves development outcomes."

Because it erodes trust in government, corruption must certainly be condemned and corrupt officials resolutely prosecuted. Corruption also weakens the moral bonds of civil society on which democratic practices and processes rest. But although research suggests it has some bearing on the spread of poverty, corruption is not the principal cause of poverty and economic stagnation, popular opinion notwithstanding. World Bank and Transparency International data show that the Philippines and China exhibit the same level of corruption, yet China grew by 10.3 percent per year between 1990 and 2000, while the Philippines grew by only 3.3 percent.

Moreover, as a recent study by Shaomin Lee and Judy Wu shows, "China is not alone; there are other countries that have relatively high corruption and high growth rates." Limits of a Hegemonic Narrative The "corruption-causes-poverty narrative" has become so hegemonic that it has often marginalized policy issues from political discourse. This narrative appeals to the elite and middle class, which dominate the shaping of public opinion. It's also a safe language of political competition among politicians. Political leaders can deploy accusations of corruption against one another for electoral effect without resorting to the destabilizing discourse of class.

Yet this narrative of corruption has increasingly less appeal for the poorer classes. Despite the corruption that marked his reign, Joseph Estrada is running a respectable third in the presidential contest in the Philippines, with solid support among many urban poor communities. But it is perhaps in Thailand where lower classes have most decisively rejected the corruption discourse, which the elites and Bangkok-based middle class deployed to oust Thaksin Shinawatra from the premiership in 2006. While in power, Thaksin brazenly used his office to enlarge his corporate empire. But the rural masses and urban lower classes — the base of the so-called "Red Shirts" — have ignored this corruption and are fighting to restore his coalition to power. They remember the Thaksin period from 2001 to 2006 as a golden time.

Thailand recovered from the Asian financial crisis after Thaksin kicked out the International Monetary Fund (IMF), and the Thai leader promoted expansionary policies with a redistributive dimension, such as cheap universal health care, a one-million-baht development fund for each town, and a moratorium on farmers' servicing of their debt. These policies made a difference in their lives. Thaksin's Red Shirts are probably right in their implicit assessment that pro-people policies are more decisive than corruption when it comes to addressing poverty. Indeed, in Thailand and elsewhere, clean-cut technocrats have probably been responsible for greater poverty than the most corrupt politicians. The corruption-causes-poverty discourse is no doubt popular with elites and international financial institutions because it serves as a smokescreen for the structural causes of poverty, and stagnation and wrong policy choices of the more transparent technocrats. The Philippine Case The case of the Philippines since 1986 illustrates the greater explanatory power of the "wrong-policy narrative" than the corruption narrative.

According to an ahistorical narrative, massive corruption suffocated the promise of the post-Marcos democratic republic. In contrast, the wrong-policy narrative locates the key causes of Philippine underdevelopment and poverty in historical events and developments. The complex of policies that pushed the Philippines into the economic quagmire over the last 30 years can be summed up by a formidable term: structural adjustment. Also known as neoliberal restructuring, it involves prioritizing debt repayment, conservative macroeconomic management, huge cutbacks in government spending, trade and financial liberalization, privatization and deregulation, and export-oriented production. Structural adjustment came to the Philippines courtesy of the World Bank, the IMF, and the World Trade Organization (WTO), but local technocrats and economists internalized and disseminated the doctrine. Corazon Aquino was personally honest — indeed the epitome of non-corruption — and her contribution to the reestablishment of democracy was indispensable. But her acceptance of the IMF's demand to prioritize debt repayment over development brought about a decade of stagnation and continuing poverty. Interest payments as a percentage of total government expenditures went from 7 percent in 1980 to 28 percent in 1994.

Capital expenditures, on the other hand, plunged from 26 percent to 16 percent. Since government is the biggest investor in the Philippines — indeed in any economy — the radical stripping away of capital expenditures helps explain the stagnant 1 percent average yearly growth in gross domestic product in the 1980s, and the 2.3 percent rate in the first half of the 1990s. In contrast, the Philippines' Southeast Asian neighbors ignored the IMF's prescriptions. They limited debt servicing while ramping up government capital expenditures in support of growth. Not surprisingly, they grew by 6 to 10 percent from 1985 to 1995, attracting massive Japanese investment, while the Philippines barely grew and gained the reputation of a depressed market that repelled investors. When Aquino's successor, Fidel Ramos, came to power in 1992, the main agenda of his technocrats was to bring down all tariffs to 0–5 percent and bring the Philippines into the WTO and the ASEAN Free Trade Area (AFTA), moves intended to make trade liberalization irreversible. A pick-up in the growth rate in the early years of Ramos sparked hope, but the green shoots were short-lived. Another neoliberal policy, financial liberalization, crushed this early promise. The elimination of foreign exchange controls and speculative investment restrictions attracted billions of dollars from 1993-1997. But this also meant that when panic hit Asian foreign investors in summer 1997, the same lack of capital controls facilitated the stampede of billions of dollars from the country in a few short weeks. This capital flight pushed the economy into recession and stagnation in the next few years.

The administration of the next president, Joseph Estrada, did not reverse course, and under the presidency of Gloria Macapagal Arroyo, neoliberal policies continued to reign. Over the next few years, the Philippine government instituted new liberalization measures on the trade front, entering into free-trade agreements with Japan and China despite clear evidence that trade liberalization was destroying the two pillars of the economy: industry and agriculture. Radical unilateral trade liberalization severely destabilized the Philippine manufacturing sector. The number of textile and garments firms, for instance, drastically reduced from 200 in 1970 to 10 in recent years. As one of Arroyo's finance secretaries admitted, "There's an uneven implementation of trade liberalization, which was to our disadvantage." While he speculated that consumers might have benefited from the tariff liberalization, he acknowledged that "it has killed so many local industries." As for agriculture, the liberalization of the country's agricultural trade after the country joined the WTO in 1995 transformed the Philippines from a net food-exporting country into a net food-importing country after the mid-1990s. This year the China ASEAN Trade Agreement (CAFTA), negotiated by the Arroyo administration, goes into effect, and the prospect of cheap Chinese produce flooding the Philippines has made Filipino vegetable farmers fatalistic about their survival. During the long Arroyo reign, the debt-repayment-oriented macroeconomic management policy that came with structural adjustment stifled the economy. With 20-25 percent of the national budget reserved for debt service payments because of the draconian Automatic Appropriations Law, government finances were in a state of permanent and widening deficit, which the administration tried to solve by contracting more loans. Indeed, the Arroyo administration contracted more loans than the previous three administrations combined. When the deficit reached gargantuan proportions, the government refused to declare a debt moratorium or at least renegotiate debt repayment terms to make them less punitive.

At the same time, the administration did not have the political will to force the rich to take the brunt of bridging the deficit, by increasing taxes on their income and improving revenue collection. Under pressure from the IMF, the government levied this burden on the poor and the middle class by adopting an expanded value added tax (EVAT) of 12 percent on purchases. Commercial establishments passed on this tax to poor and middle-class consumers, forcing them to cut back on consumption. This then boomeranged back on small merchants and entrepreneurs in the form of reduced profits, forcing many out of business. The straitjacket of conservative macroeconomic management, trade and financial liberalization, as well as a subservient debt policy, kept the economy from expanding significantly. As a result, the percentage of the population living in poverty increased from 30 to 33 percent between 2003 and 2006, according to World Bank figures. By 2006, there were more poor people in the Philippines than at any other time in the country's history. Policy and Poverty in the Third World The Philippine story is paradigmatic.

Many countries in Latin America, Africa, and Asia saw the same story unfold. Taking advantage of the Third World debt crisis, the IMF and the World Bank imposed structural adjustment in over 70 developing countries in the course of the 1980s. Trade liberalization followed adjustment in the 1990s as the WTO, and later rich countries, dragooned developing countries into free-trade agreements. Because of this trade liberalization, gains in economic growth and poverty reduction posted by developing countries in the 1960s and 1970s had disappeared by the 1980s and 1990s. In practically all structurally adjusted countries, trade liberalization wiped out huge swathes of industry, and countries enjoying a surplus in agricultural trade became deficit countries. By the beginning of the millennium, the number of people living in extreme poverty had increased globally by 28 million from the decade before.

The number of poor increased in Latin America and the Caribbean, Central and Eastern Europe, the Arab states, and sub-Saharan Africa. The reduction in the number of the world's poor mainly occurred in China and countries in East Asia, which spurned structural readjustment policies and trade liberalization multilateral institutions and local neoliberal technocrats imposed other developing economies. China and the rapidly growing newly industrializing countries of East and Southeast Asia, where most of the global reduction in poverty took place, were marked by high degrees of corruption. The decisive difference between their performance and that of countries subjected to structural adjustment was not corruption but economic policy. Despite its malign effect on democracy and civil society, corruption is not the main cause of poverty. The "anti poverty, anti-corruption" crusades that so enamor the middle classes and the World Bank will not meet the challenge of poverty. Bad economic policies create and entrench poverty. Unless and until we reverse the policies of structural adjustment, trade liberalization, and conservative macroeconomic management, we will not escape the poverty trap.

Development
IMF/World Bank

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Essay on Corruption Is The Root Cause Of Poverty
Corruption can cause poverty in many ways. Firstly, when government officials are corrupt, they may take money that is meant for public services. This means that schools, hospitals, and roads do not get the funding they need. This can lead to a lack of education, poor health, and a lack of jobs, all of which can cause poverty.
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Corruption And Poverty Essay. 1080 Words5 Pages. INTRODUCTION Corruption is the main cause of poverty and inequality in Asia. Government officials may use their authority for private gain in designing and implementing public policies (Gupta S, Davoodi H and Terme RA, 1998). This is defined as corruption.
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"Corruption and Poverty," a review essay commissioned by USAID, summarizes its findings with a long list of negative effects: "Corruption impedes economic growth by discouraging foreign and domestic investment, taxing and dampening entrepreneurship, lowering the quality of public infrastructure, decreasing tax revenues, diverting public ...
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This review found that few studies examine or establish a direct relationship between corruption and poverty.4 Corruption, by itself, does not produce poverty. Rather, corruption has direct consequences on economic and governance fact ors, intermediaries that in turn produce poverty. Thus, the relationship examined by researchers is an indirect one
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Despite its malign effect on democracy and civil society, corruption is not the main cause of poverty. The "anti poverty, anti-corruption" crusades that so enamor the middle classes and the World Bank will not meet the challenge of poverty. Bad economic policies create and entrench poverty. Unless and until we reverse the policies of structural ...
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In this five-part series I will discuss what the World Bank Group is doing and what we are planning to do in key areas that are critical for ending poverty by 2030: good governance, gender equality, conflict and fragility, creating jobs, and, finally, preventing and adapting to climate change. Twenty years ago, the World Bank took up the fight ...
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This paper demonstrates that high and rising corruption increases income inequality and poverty by reducing economic growth, the progressivity of the tax system, the level and effectiveness of social spending, and the formation of human capital, and by perpetuating an unequal distribution of asset ownership and unequal access to education. These findings hold for countries with different ...
(PDF) The Impact of Poverty on Corruption
The impact of poverty on corruption is an importa nt relationship when the. negative effects of poverty are examined. In this context, some studies have. theoretically reviewed the literature on ...
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Cross-country regression analysis for 1980-97 shows that high and rising corruption increases income inequality and poverty through the above channels. A one-standard-deviation increase in the growth rate of corruption (a deterioration of 0.78 percentage point) reduces income growth of the poor by 7.8 percentage points a year.
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Corruption both causes and thrives upon weaknesses in key economic, political and social institutions. It is a form of self-serving influence akin to a heavily regressive tax, benefiting the haves ...
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Corruption is a cause of low development (Zucman 2015:34-55) and exacerbates poverty where poverty prevails; corruption, therefore, needs to be included amongst causes of the consequences of poverty, such as debt, incapacity, mental despair and despondency (Ray 1986). Within influences as powerful as poverty, corrupt practices, in many forms ...
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Most importantly, the poverty dimension about the impact of corruption is exacerbated. As there are many causes of poverty, corruption has historically been labeled as one of the major culprits.
Does poverty lead to corruption? Or is it corruption that actually
A causality analysis involving 97 developing countries found that corruption and poverty go hand in hand and run in both directions. Corruption leads to poverty by means of poor economic growth and/or bad governance. An economic model postulates that corruption discourages foreign investments, decreases tax revenue and, in turn, induces poverty.
PDF The Causal Relationship between Corruption and Poverty in ASEAN ...
Studies on the causal relationship between corruption and poverty have been carried out intensively. Basically, there are two competing theories in exploring such a linkage. The first theory argues that corruption affects poverty but not the other way around. There is unidirectional causality from corruption to poverty. Corruption
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One cause of poverty in our country is the corruption. Corruption is an unconscionable advantage profit or gain of injustice through the abuse of authority and power. One of the big factors that contributes to poverty in Philippines is corruption a problem that doesn't seem to end. Because of these corruptions funds are getting short for ...
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