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Keywords:

  • access to clean water;
  • land reform policy;
  • poverty;
  • Southern Africa

Abstract

  1. Top of page
  2. Abstract
  3. 1 Introduction
  4. 2 Background and Literature
  5. 3 Data and Model
  6. 4 Empirical Results
  7. 5 Concluding Remarks
  8. References

This article examines the effect of land reform on poverty in Southern African Development Community countries while controlling for factors that have been shown to be important in explaining poverty in Africa. The percentage of the population without access to clean water is used as a proxy for poverty. Empirical results provide evidence that countries that embarked on land reform experienced an increase in the percentage of the population without access to clean water for the period 1990–2007. More specifically, our results show that the impact of the willing seller–willing buyer land reform approach is higher than the impact of the expropriation land reform approach during the study period.


1 Introduction

  1. Top of page
  2. Abstract
  3. 1 Introduction
  4. 2 Background and Literature
  5. 3 Data and Model
  6. 4 Empirical Results
  7. 5 Concluding Remarks
  8. References

The poverty issue in Africa has attracted the attention of economists, politicians, governmental and non-governmental organisations interested in finding a lasting solution to the problem.3 Several potential poverty alleviating tools and policies have been adopted by a majority of African countries . The most controversial poverty alleviating tool that has been in use within the Southern African Development Community (SADC) region for the past three decades is land reform. Land reforms in SADC are an attempt to reverse pro-colonial era land distribution practices which marginalised the native majority. The pro-colonial land distributions practices are believed to be the main reason behind the poor standards of living of the native majority in the SADC region (May, 1998; Sibanda, 2001; Moyo, 2004; Dlamini, 2007; Tshuma, 2012). One way to measure the success or failure of land reform is to empirically test its impact on poverty. Since lack of access to clean water resources remains a major issue within the SADC region, water deprivation provides an important measure of poverty. Sullivan et al. (2003) argue that since water is a fundamental basis of all life, it would be impossible to rescue anyone out of extreme poverty without an adequate access to water. In the light of the importance of the issue, an interesting and empirical question is how land reform policies enhance or hinder the access to clean water in SADC?

This study contributes to the poverty literature by examining the effect of land reform on water deprivation, a non-monetary measure of poverty.4 Specifically, this article empirically examines the effect of land reform on poverty, where poverty is measured by the percentage of the population without access to clean water. While there are several measures of poverty, an important measure of the poverty situation in Africa which has received minimal attention, is the deprivation of clean water. We make use of data from 14 SADC countries during the period from 1990 to 2007. Land reform is measured by two indicator variables, namely the market-based willing seller–willing buyer (WSWB) approach and the expropriation or confiscation approach. In addition to the land reform specific variables, the study controls for other factors that have been shown to be important in explaining poverty.

The main finding of this article is that land reform failed to alleviate poverty in SADC countries from 1990 to 2007. Specifically, our results show that countries that adopted the WSWB or the expropriation land reform approach experienced higher poverty levels when compared to countries that did not adopt the two land reform approaches. Also, the magnitude of the detrimental effect associated with the market-based approach is higher than that of the expropriation approach, ceteris paribus. In addition to the ineffectiveness of land reform policy, our results also indicate that two other factors, namely a larger percentage of population living with HIV/AIDS, and urban bias as measured by the ratio of urban population to rural population worsen access to clean water in SADC countries. Conversely, we find that a larger ratio of migrant remittance inflows over GDP to improve access to clean water in SADC countries.

The remainder of the article proceeds as follows: 'Background and Literature' provides the background and literature; 'Data and Model' provides the data description, the empirical model and variable description; 'Empirical Results' analyses the empirical results and 'Concluding Remarks' concludes the article.

2 Background and Literature

  1. Top of page
  2. Abstract
  3. 1 Introduction
  4. 2 Background and Literature
  5. 3 Data and Model
  6. 4 Empirical Results
  7. 5 Concluding Remarks
  8. References

2.1 Pro-Colonial Era Land Distribution Practices and the Arguments for Land Reform

The land inequality situation in the SADC region is mainly a result of the pro-colonial era land distribution practices which drove the indigenous African majority away from natural clean water sources, fertile soils and mineral rich areas.5 Such land distribution practices led to issues including but not limited to: congestion; distortions in land allocation; huge land inequality; and a high spread of contagious diseases such as cholera; influenza and tuberculosis in the SADC region communal areas (Binswanger & Deininger, 1993; May, 1998).6 A case in point is Soweto, a South African suburb located in the Gauteng province not very far from Johannesburg. Soweto is characterised by an unusually high population density, lack of clean water sources, inadequate infrastructure and epidemics of infectious diseases such as cholera, influenza and tuberculosis. In 2010, the population density of South Africa was 41.21 people per square kilometre while Soweto's population density was 7666 people for every square kilometre (Index Mundi7; Soweto Vibe8).

The foregoing issues prompted SADC countries, with the exception of Mauritius and Seychelles, to pursue land reform to raise the standards of living of the native majority. During the past three decades, land reform has been at the heart of poverty alleviation efforts in the SADC region, where a majority of the countries attained independence from colonial rule between 1965 and 1994. Poverty alleviation is among the well-documented potential benefits of land reform (Besley & Burgess, 2000; Thomas, 2003; Clover, 2005; Juana, 2006; Tshuma, 2012). A few studies highlighted some of the conditions which have to be met if at all land reform is to succeed in improving the lives of the beneficiaries. The granting of land, with well-defined secure land rights, allows the land recipients to use land as collateral to gain access to credit and product markets (Wily, 2000; Carter, 2003; Deininger, 2003; Moyo, 2004; Clover, 2005; Lahiff & Cousins, 2005; Place, 2009; Tshuma, 2012); the provision of farming training and financial support by the government to the land beneficiaries (May, 1998; Carter, 2003; Lahiff & Cousins, 2005); the assignment of land rights to women (Deere & León, 2001; Carter, 2003) are among those conditions.

As documented earlier, the main land reform approaches in the SADC region are WSWB and expropriation. The WSWB approach is favoured by institutions such as the World Bank as it is less disruptive to economic activity. The main argument against this approach is that it is too slow to achieve land reform objectives as evidenced in Namibia, South Africa and Zimbabwe (Cliffe, 2000; Sibanda, 2001; Fortin, 2005; Dlamini, 2007). To ensure a fast paced redistribution of land, most SADC countries have consequently adopted land reform policies that were based on expropriation (with full compensation, partial compensation or without compensation). While expropriation provides a quick way to redistribute land, this approach is not favoured by Europeans and the World Bank. Expropriation of land in Zimbabwe prompted the European Union, Australia, Canada and the United States to sanction the country, contributing to the collapse of the economy between 2006 and 2009 (Richardson, 2005).

2.2 Water Deprivation as a Measure of Poverty

Since this study makes use of water deprivation, a non-monetary measure of poverty, it is important to document the water situation in SADC and the literature that has attempted to use water deprivation as a measure of poverty. A discussion of the clean water situation in SADC is, therefore, in order. The World Bank's Africa Development Indicators data for the period 1990–2007 show that, on average, about 36 per cent of the SADC population had no access to clean water. During the same period, 46 per cent of the SADC rural population had no access to clean water. A closer look at the statistics reveals a different history across SADC countries and within countries over time. For example, in 1990 only 1 per cent of rural population in Mauritius did not have access to clean water compared to 85 per cent in Madagascar during the same period. However, by 2007, the lack of access to clean water improved in some countries. For example, while Mauritius remains the country with the lowest percentage of rural population without access to clean water, estimated at 1 per cent just like in 1990, Madagascar, Mozambique and Democratic Republic of Congo top the list with about 70 per cent of rural population without access to clean water. It is clear that the lack of access to clean water is a grave problem in rural areas.

Despite the significance of the issue, only a few studies have attempted to measure poverty based on water deprivation. Among the leading research on water poverty is the work by Sullivan and her co-authors who came up with an index that measures water poverty. Sullivan et al. (2003) developed a holistic tool to measure water stress at both the household and community level called the water poverty index (WPI). The WPI is intended to provide politicians and decision-makers with an indicator that they can use to make informed decisions relating to water sector interventions for development purposes. The components of the WPI include measures of: access to water; water quantity; quality and variability; domestic and industrial water uses; capacity of water management; and environmental aspects. Along the same line, Sullivan et al. (2006) highlight some applications of the WPI at different spatial scales. Convincingly, Sullivan et al. (2006) not only acknowledge the difficulties posed by disconnections between hydrological and socio-economic factors but also caution against using the index for comparisons across nations.

While the WPI is an important breakthrough in analysing the impacts of water deprivation on well-being, the procurement of primary data at the household and community level is quite costly. In addition, the unavailability of data at the national level in most African countries makes it difficult to build WPI's for cross-country studies. However, some components of the WPI index such as lack of access to water are available from the World Bank poverty indicators. We take advantage of the available components of the WPI measure of water deprivation in this study.

3 Data and Model

  1. Top of page
  2. Abstract
  3. 1 Introduction
  4. 2 Background and Literature
  5. 3 Data and Model
  6. 4 Empirical Results
  7. 5 Concluding Remarks
  8. References

3.1 Data Description

This study makes use of a panel of 14 SADC countries for the years from 1990 to 2007. The 14 countries, in alphabetical order, include Angola, Botswana, the Democratic Republic of Congo (DRC), Lesotho, Madagascar, Malawi, Mauritius, Mozambique, Namibia, South Africa, Swaziland, Tanzania, Zambia and Zimbabwe. These countries which are all part of the SADC region, achieved independence within the last 50 years and are all involved in land reform. We focus on the years from 1990 to 2007, for two main reasons: (i) data on access to clean water, our dependent variable, are available from 1990 onwards and (ii) to avoid incorporating the collapse of the Zimbabwean economy, we limit our study to 2007. This allows us to include Zimbabwe, which has been a major player in land reform in the last three decades while avoiding the period of its economic collapse that may bias our results.

With the exception of land reform variables, the variables included in this study are derived from the World Bank's online World Development Indicators. The land reform variables which include WSWB and expropriation (EXPRO) were compiled by the authors. Specifically, we examine the land reform approach in each country. This process involved detailed examination of several reports, newspaper articles and research articles on land reform in Southern Africa.

Table 1 summarises the descriptive statistics of the variables included in our study. Table 1 shows significant socio-economic variability across countries and within countries over time. Although a closer look at individual countries data reveals that the percentage of rural population without access to clean water has declined overtime, it appears that the majority of the SADC countries are still struggling to provide clean water to their inhabitants. Figure 1 highlights the trends in the struggles facing the populations in SADC countries to access clean water.

Table 1. Descriptive Statistics
VariableMeanSDMinimumMaximum
Remittance share of GDP (%)6.0915.342.49E-478.57
Percentage of population With HIV (%)5.574.850.046716.67
Ag. share of GDP (%)19.5013.491.8359.74
Rural population without access to clean water (as a percentage of total population)45.6224.501.0085.00
Percentage of total population without access to clean water (%)35.6920.951.0071.00
Ratio of rural population over urban population (%)2.551.500.6627.65
Willing seller–willing buyer (WSWB)0.290.4501
Expropriation1.041.0203
image

Figure 1. Percentage of Rural Population without Access to Clean Water, 1990–2007

Download figure to PowerPoint

Some observers, including political leaders, explain this widespread lack of basic life necessities as a result of land inequality, and, therefore, the problem calls for land reform policies. Questions still remain as to the effectiveness of those land policies. Are there other factors that could explain the improvement seen in some SADC countries and the deterioration experienced by other countries within the same economic bloc?

3.1 Model and Variable Description

To provide insight into the effectiveness of land reform policies on the well-being of the inhabitants of SADC countries, we model the relationship between access to clean water and land policy variables after controlling for other relevant factors. Our baseline model is expressed as follows:

  • display math(1)

where i = country, t = year (1990–2007), β are parameters to be estimated while μ is an error term.

The dependent variable, Poverty, refers to the percentage of a country's population without access to clean water in each of the 14 SADC countries for the period spanning 1990–2007. Access to clean water is a good proxy for poverty because it is very relevant to people within the SADC region. As these countries are heavily dependent on agriculture, access to clean water can have a major impact on the production of crops, which are often a major part of the country's exports, as well as a main source of food and incomes. Also, water is the most fundamental element of survival, without it we cannot exist.

Our explanatory variables include two land reform specific variables, the variables of interest and five variables designed to control for determinants of poverty. The two land reform specific variables used in the study are WSWB and EXPRO. Specifically, the variable WSWB is a dummy variable which captures the effect on lack of access to clean water of using the WSWB land reform approach. We assign a value of “1” for each year a country used the WSWB land reform approach and “0” otherwise. As stated earlier, WSWB is a market-based land reform programme that allows a land owner to willingly sell his or her land to a willing buyer. The willing seller is usually a land holder of a large farm, which was acquired with or without payment during the colonial era or a land holder who purchased the land post-colonial era. In this case, the willing buyer is the government which buys the land for redistribution. The sign on this variable can be either positive or negative, depending on the effectiveness of the policy tool. The variable EXPRO is designed to capture the impact of the expropriation land reform approach, with full compensation, partial compensation or without compensation, on poverty. We assign a value of “1” for each year a country used the expropriation land reform approach with full compensation, “2” for each year a country expropriated land with partial compensation, “3” for each year a country expropriated land without compensation and “0” for each year a country did not have the expropriation land reform approach in place. Specifically, a value of “1” on the EXPRO variable indicates the use of little or no force, a value of “2” indicates the use of some force and a value of “3” shows the use of significant force in re-distributing the land. Similarly, the sign of the coefficient of this variable is expected to be either positive or negative.

The explanatory variables we use to control for the determinants of poverty in this study include: Urban bias (URBB), remittance (PREM), population infected with HIV/AIDS (PPHIV),9 share of agriculture in gross domestic product (hereafter, AGSHGDP) and time trend (TIME). URBB is a measure of urban bias. It is calculated as the ratio of urban population to rural population. This variable is designed to capture policies that are biased towards the urban population at the expense of rural population. The higher is the ratio, the more pronounced is the bias towards urban population. The variable PREM refers to the ratio of migrant remittance inflows over GDP. Migrants' remittances are defined as the sum of worker's remittances, compensation of employees and migrants' transfers to their families. The variable PPHIV is a measure of the share of adults living with HIV/AIDS out of total population. This variable, a measure of population health, is especially useful because it is a proxy for workers' productivity. The variable AGSHGDP measures the percentage of a country's GDP that results from agriculture. This is an important statistic in Southern African countries as many of them are agriculture dependent. This variable is designed to capture a country's reliance on agriculture, and the sign is expected to be positive. The variable TIME is designed to capture the trend in lack of access to clean water over time. Time could also be capturing economic and political conditions over time within each country. Another possible explanation of the time statistic is technology, as more (or less) exposure to technology could impact access to clean water. The sign on this variable is expected to be either positive or negative as we do not know for sure if things will be better or worse over time.

4 Empirical Results

  1. Top of page
  2. Abstract
  3. 1 Introduction
  4. 2 Background and Literature
  5. 3 Data and Model
  6. 4 Empirical Results
  7. 5 Concluding Remarks
  8. References

We estimated several specifications of Equation (1). We started by estimating Equation (1) with the percentage of rural population without access to clean water as our dependent variable. The results are presented in Table 2. Model 1 results show the coefficient estimates when the land policy variables and time trend are the only regressors included in Equation (1) whereas Model 2 presents the coefficient estimates of all the variables included in Equation (1).

Table 2. Dependent Variable: Percentage of Rural Population Without Access to Clean Water
Independent variableModel 1Model 2
CoefficientsCoefficients
  1. Standard errors in parentheses.*significant at 1 per cent; **significant at 5 per cent; ***significant at 10 per cent.

Constant

0.0106*

(0.229)

0.0467*

(0.336)

Willing seller–willing buyer

0.00467*

(0.218)

0.0377*

(0.253)

Expropriation

0.00155*

(0.104)

0.00459*

(0.0630)

Ag. share of GDP 

0.00534

(0.199)

Per cent of population with HIV 

0.00863*

(0.0651)

Remittance 

−0.000298*

(0.00357)

Urban Bias 

0.0180**

(0.301)

Time

−0.000175*

(0.0143)

−0.00199*

(0.0177)

R-squared0.380.98
F-statistics44.66*212*

Table 2 reveals two important conclusions. First, with the exception of the agricultural share of GDP, all the variables are statistically significant. Second, the estimated coefficients on Willing Seller-Willing Buyer and Expropriation are positively statistically significant, with the coefficient of WSWB larger than that of Expropriation in both specifications. In other words, land reform policy has failed to improve the living standards of the people living in the SADC countries, with the market-based approach having the more pronounced devastating effects. These findings are consistent with the conclusions reached by previous studies, including Anseeuw and Mathebula (2008).

Table 2 also reveals other statistically significant contributing factors to the struggles of rural populations in SADC countries. Specifically, a larger percentage of population living with HIV/AIDS adversely impacts the living standards in the SADC countries, all else equal. In developing countries, particularly in Africa, access to antiretroviral therapy is limited to very few people because of the high cost of the treatment. The findings of this article support the existing evidence on the economic impact of HIV/AIDS epidemic. For example, Fox et al. (2004) conclude that the morbidity associated with HIV/AIDS among the tea state workers in Kenya leads to a decrease in productivity and an increase in absenteeism and financial struggles. Similarly, consistent with the findings of Bezemer and Headey (2008), we find that urban bias exerts a detrimental effect on poverty. According to Bezemer and Headey (2008) rural populations in population-sparse regions of Sub-Saharan Africa are the most disenfranchised of political groups, physically isolated from the centres of power, and often isolated from each other. Previous efforts (Byerlee et al., 2005; Bezemer & Headey, 2008) in contemporary development policy document the pervasiveness of urban bias and conclude that it is the most significant impediment to poverty reduction in the World's poorest countries.

On the other hand, our results indicate that the variable remittance has a positive and significant poverty-reducing effect on the economies of SADC countries. This result is consistent with the findings of Gupta et al. (2007) who examined the impact of remittances on poverty in seventy-six developing countries, including twenty-four in Sub-Saharan Africa.

While results based on the economic impact of land reform policies on the percentage of rural population without access to clean water are appealing, we extend the spatial coverage of poverty to include the population living in the urban areas. In effect, we estimated Equation (1) using the percentage of total population without access to clean water as a measure of poverty. This is to ensure that our results are robust to alternative measures of poverty. Table 3 summarises the results. Column 1 presents the estimates when the only regressors included are the land policy variables and time trend, and column 2 shows the estimates when all the regressors in Equation (1) are included. Several pronounced tendencies emerge upon comparison of the estimation results in Tables 2 and 3. First, all the estimated coefficients in Models 3 and 4 remain statistically stable. Moreover, the variable agricultural share of GDP has now a statistically significant coefficient with a positive sign, meaning that an increase in agricultural activities leads to more people without access to clean water. This result reinforces the adverse impact of land reform policy on poverty in SADC countries. The second observation that is derived from the comparison of the results summarised in Tables 2 and 3 pertains to the magnitude of the coefficient estimates. In particular, land reform policy appears to have a more pronounced effect on the economies within the SADC region when the measure of poverty is the percentage of total population without access to clean water. This is indicative of the magnifying effects of the policy on the economies of the countries in the SADC region.

Table 3. Dependent Variable: Percentage of Total Population Without Access to Clean Water
Independent variableModel 3Model 4
CoefficientsCoefficients
  1. Standard errors in parentheses.*significant at 1 per cent; **significant at 5 per cent; ***significant at 10 per cent.

Constant

0.0185*

(0.229)

0.0482*

(0.319)

Willing seller–willing buyer

0.00789*

(0.222)

0.0496*

(0.245)

Expropriation

0.00259*

(0.103)

0.00466**

(0.0585)

Ag. share of GDP 

0.0122***

(0.195)

Per cent of population with HIV 

0.0108*

(0.0594)

Remittance 

−0.000375*

(0.00348)

Urban bias 

0.0333**

(0.297)

Time

−0.000331*

(0.0145)

−0.00246*

(0.0166)

R-squared0.310.90
F-Statistics36.34*241*

Undoubtedly, land reform policy seems to have far reaching and yet unexpected effects on the economies of SADC countries. For instance, the positive and statistically significant coefficient on the WSWB variable supports the argument that this land reform approach is too slow. Because of its slow pace, not much land has been distributed through this process which has led to civil unrest on the part of frustrated potential land beneficiaries in countries such as Namibia, South Africa and Zimbabwe during the past two decades. The result on the WSWB variable could be capturing the argument that land holders will easily offer marginal land to the government for redistribution purposes. Such land areas could be associated with lack of clean water, lack of rainfall among other problems which could be part of the reasons why landholders are willing to cash in on the land without delay. Moreover, the positive and statistically significant coefficient on the EXPRO variable could be capturing the destabilising nature of this land reform approach. When land is confiscated from the landowner, it could take a long time for the new settlers to use the land as the previous owners usually take the matter to court. During this time, in most instances, production on the land is halted until a ruling on who has the right to farm the land is made by the court. In addition, confiscation comes with international isolation as is the case with Zimbabwe. Economic sanctions affect the imports of water treatment chemicals and this worsens access to clean water as evidenced by the Zimbabwe situation.

5 Concluding Remarks

  1. Top of page
  2. Abstract
  3. 1 Introduction
  4. 2 Background and Literature
  5. 3 Data and Model
  6. 4 Empirical Results
  7. 5 Concluding Remarks
  8. References

Land reform has become an attractive subject to development theoreticians, politicians and international donors concerned with the poverty issue in developing countries, particularly in Southern African countries. This study uses the market-based and expropriation land reform approaches to examine the effect of land reform on poverty as measured by the percentage of the population without access to clean water in 14 SADC countries during the period of 1990–2007. Our empirical results indicate that the land reform approaches pursued by the governments of the SADC countries failed to properly address the poverty issue in that part of the world.

While land reform has been a success in terms of redistribution in countries such as Zimbabwe, it is likely to take some time before the benefits of such a reform can be realised. Our results could be capturing the short-term shock on these economies which is expected when reforms of this nature are undertaken. In the short-run, poor economies such as those in Africa are likely to find it difficult to provide financial support and other necessary infrastructure to land beneficiaries. It also takes time for these poor countries to secure foreign funding from international donors. In most instances, land reforms are not approved by the first world and may attract economic sanctions instead of funding. This lack of financial support from the government may have worsened the ability for land beneficiaries to gain access to clean water resources. Another possible reason is that, access to clean water may not have been incorporated as one of the important goals of most land reform policies in the SADC region. Our findings suggest that policy-makers should incorporate access to clean water in their land reform plans. In addition, policy-makers should ensure that the quality of water continues to be maintained after land has been distributed. Redistributing land alone without investing in the necessary infrastructure in the new communities may not be enough if the policy-makers' main objective is to positively impact the well-being of the land beneficiaries.

Because this study is limited to lack of access to clean water, there could be other indicators of well-being where land reform has had a positive impact. It will be interesting to expand the analysis to examine the impact of land reform on inequality and economic growth not only in the SADC region but in Africa as a whole. Such analyses are the focus of our future research.

Notes
  1. 3

    Some of the well-documented characteristics of poverty in existing literature are: lack of adequate income or assets to generate income; physical weakness due to lack of nutrition or curable sickness; isolation due to location; or lack of access to goods and services, vulnerability and risk to crisis; risk to disease; lack of access to clean water resources; and powerlessness within existing social, political; or economic structures (Woolard & Leibbrandt, 1999).

  2. 4

    Existing literature classifies poverty measures into monetary and non-monetary measures. There is evidence that in poor countries such as those in the SADC region, non- monetary measures of poverty, such as caloric intake, sanitation, education, or access to clean water sources provide better estimates of poverty when compared to their monetary counterparts (Sahn & Stifel, 2000; Daniels, 2011; Kenny, 2012).

  3. 5

    For instance, in South Africa, suppressive laws such as the Glen Grey Act of 1894, and the Native Lands Act of 1913, gave white farmers almost all of the quality arable farmland in South Africa. In Zimbabwe, the Land Apportionment Act of 1930, which divided land into three zones according to three tribes, namely, white people, Shona people and the Ndebele people resulted in 5 per cent of the white minority controlling about 70 per cent of the fertile land in the country.

  4. 6

    Because most of the communal land is not suitable for farming, poor rural peasant farmers continue to till the land destroying the natural plant cover leaving the soils prone to erosion. In addition, the destruction of plant cover through overgrazing and cutting down trees for firewood have negative impacts on the rainfall pattern leading to low crop yields.

  5. 7
  6. 8
  7. 9

    HIV/AIDS refers to human immunodeficiency virus/acquired immunodeficiency syndrome.

References

  1. Top of page
  2. Abstract
  3. 1 Introduction
  4. 2 Background and Literature
  5. 3 Data and Model
  6. 4 Empirical Results
  7. 5 Concluding Remarks
  8. References
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