Documento de Trabajo No. 03/09

Marzo 2009

Analysing the poverty impact of the enhanced Heavily Indebted Poor Countries (HIPC) initiative in Bolivia

por Fanny Heylen

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Analysing the poverty impact of the enhanced Heavily Indebted Poor Countries

(HIPC) initiative in Bolivia 1. Introduction The purpose of this article is to analyse the impact of debt relief programs on poverty reduction in Bolivia. We will focus on the poverty reduction impact of the HIPC II initiative. Indeed as we will see further among the multilateral debt relief programs (the original HIPC initiative, the enhanced HIPC initiative or HIPC II and the MDRI) it has conceived the clearest link between debt relief and poverty reduction. As this initiative tries to reduce poverty through a decentralized way we will concentrate our action on the link between poverty reduction and the enhanced HIPC initiative resources at the municipal level. In order to do that, we will study the specific impact of debt relief on the evolution of three municipal poverty indicators (the adult literacy rate, the life expectancy at birth and the average schooling years) over the 2001-2005 periods. Before beginning with this analysis it seems important to introduce the Bolivian indebtedness problem and the different debt relief programs (in particular the HIPC II initiative) as well as their link to the poverty reduction.

Bolivia’s indebtedness problem rose in the seventies with a strong increase in its external credits as Bolivia benefited from petroleum and tin price increases. Therefore the debt stock increased from US$ 524.4 million US$ in 1970 to 2312.3 million in 1980. However Bolivia did not succeed in using efficiently the credits contracted, mostly committed to unproductive projects. In the eighties the favourable economic situation of the seventies was reversed and Bolivia was faced with an economic and social crisis as well as a debt crisis as it was unable to pay its scheduled debt services. In 1987 the external debt stock accounted for 4289.5 million. The debt crisis of the eighties strongly affected Bolivia and the attempts of the international financial community to face the indebtedness problem through the Brady Plan1 (for the private creditors), the debt reprogramming under the Paris’ Club2 framework (for the bilateral creditors) and the Structural Adjustment Program3 (for the multilateral organism) were insufficient to reach a long-lasting solution to the external indebtedness problem. It is in this context, in the middle of the nineties, that the HIPC, HIPC II and MDRI debt relief programs were implemented in order to permanently exit the process of repeated debt rescheduling and reach debt sustainability.

Bolivia reached its decision point4 in September 1997 under the original HIPC Initiative as it fulfilled the three criteria of the initiative concerning its poverty level (a PIB per capita sufficiently low), its external indebtedness (VPN of debt to exports and VPN of

1 The Brady Plan (1989) allowed the commercial banks to receive a part of the due reimbursement as well as a

commercial debt reduction for the country. 2 The Paris’ Club is an informal group of official creditors that seeks solutions to reimbursement difficulties of indebted

countries. Bolivia has negotiated its external bilateral debt from1986 to 2001 in eight different Paris’ Club rounds and has reduced its bilateral debt of $US 1.482 million.

3 The Structural Adjustment Program requires conditions for borrowing money from the IFIs (International Financial Institution), mostly through structural reforms.|

4 The decision point is when the executive board of the IMF and the WB decide that the country is eligible to debt relief under the HIPC initiative.

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debt service to exports ratios respectively superior to 250% and 25%) and its reform programs (the obligation to implement a series of structural reforms). Following sustainability analysis, achieved by the analysts of the IMF and the World Bank, targets of the debt VPN /exports and debt service VPN /exports ratios were fixed respectively for Bolivia at 225% and 20%. At the completion point5 in September 2008 Bolivia got a debt service relief of US$ 448 million VPN, from its multilateral and bilateral creditors6 as it had showed good macroeconomic performance, as it had continued to implement structural reforms and to improve its poverty indicators.

The released resources, as Bolivia saw one part of the amount it had to pay under debt service reduced, did not have any restrictions for how they had to be employed but should initially contribute to reaching debt sustainability levels. It was about a fiscal relief, as the resources coming from this debt relief would head toward the government finances. In order to reach the completion point Bolivia had to improve its poverty indicators. Bolivia at the end of the nineties had followed a range of social reforms (under the second generation reforms program) in areas of education, health and rural areas development achieving this independently to the debt relief programs. Therefore the country had accomplished the targets established in the HIPC Initiative and achieved a performing link between debt relief under the original HIPC initiative and poverty reduction. However a significant number of these targets would have been met anyway through the series of reforms. Indeed under the HIPC Initiative the government had not implemented any specific poverty reduction strategies and no link between debt relief and poverty reduction has been designed. We will see that the HIPC II Initiative tried to address its lack.

In 1999, the G7 meeting took place in Cologne (Germany) and during this meeting, was decided to spread the original HIPC Initiative as the original HIPC initiative was judged insufficient to reach external debt sustainability. The HIPC countries should benefit from a deeper, faster and broader relief under the enhanced HIPC Initiative or HIPC II. As the level of 225% of the ratio debt/ exports was judged insufficient to reach debt sustainability, it has been reduced to 150%, a unique threshold for all the HIPC countries. This program should also reinforce the link between debt relief and poverty reduction with the Poverty Reduction Strategy Paper (PRSP) and lead to a reduction of the reform burden in the budget deficit.

Bolivia qualified for this program following a debt sustainability analysis and reached its decision point in February 2000. At the decision point, the IMF and IDA decided to give the floating completion point to Bolivia in view of its strategy against poverty and its macroeconomic stability. To reach this completion point Bolivia should fulfil three conditions: to go on with its reform program, elaborate the PRSP and guarantee the participation of every creditor. The completion point was reached in June 2001 and $US 854 million in VPN were allocated under this program through reduction and cancellation of debt service and its rescheduling from its multilateral and bilateral creditors. The bilateral creditors participated as well in an extra relief program named “mas allá del HIPC” which provided a 100% stock reduction of the ODA (Official Development Aid) debt.

Unlike what was put forward by the original HIPC initiative a clear link between poverty reduction and debt relief program had to be achieved under the enhanced HIPC 5 The completion point is when the executive board of the IMF and the WB decide that the country has fulfilled the

conditions to receive assistance under the initiative. 6 Among the most important creditors: the Inter-American Development Bank, the World Bank (IDA), the Corporación

Andina Fomento, the IMF, other multilateral creditors and several bilateral creditors (from the Paris’ Club for the majority).

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initiative through the PRSP. The EBRP (“Estratégica Boliviana de Reducción de la Pobreza”, the Bolivian PRSP publicised in March 2001) was conceived with the participation of the government (municipal, departmental and national level), the private sector and the civil society under the National Dialogue Law. This allowed on the one hand making the civil society participate in the PRSP elaboration and on the other hand to identify more efficiently the poverty items and to bring solutions to fight these. This made as well every power level participate conceived as a real democratic process.

The main objective of the EBRP is to reduce poverty and to promote human development, through specific social and economic policies. The EBRP included four interrelated strategic components: expansion of employment and income opportunities (focusing on the promotion of rural development– local road, irrigation systems, agricultural infrastructure, rural electrification, promoting access to land, increasing competitiveness, …- on the development of micro-small enterprises, the microfinance and of technical assistance and improving, as well as maintaining and increasing the road infrastructure); capabilities building of the poor (through improving educational quality and access, improving health services conditions and access and improving habitability conditions-basic sanitation and housing); increase security and protection for the poor (expanding social protection programs, improving comprehensive child care, developing risk prevention and guaranteeing the legal security of assets); promotion of integration and social participation (deepening the Popular Participation and Decentralization initiatives and reducing inequities and barriers based on ethnic discrimination). In addition, actions should be taken to improve greater equity in favour of ethnic groups and the indigenous people as well as to improve the preservation of the environment. A favourable institutional environment had to be established in order to assure the sustainability and the implementation of the EBRP. It had to be done through deepening and improving the system of decentralized public administration, establishing transparent mechanisms for the allocation and administration of resources targeted at poverty reduction, promoting the institutional strengthening and professionalization of the public bodies in charge of the implementation of the EBRP and that also had to recognize explicit social control mechanisms and fight corruption within the public administration at every level.

The allocation system of the EBRP resources is based on a decentralisation process, the resource allocation priorities determined at the municipal level. The resources for the EBRP come from different sources, the HIPC II Initiative resources accounting in 2002 for 52% of the total7. There is a process of social and economic control at each level realised by the civil society and specific institutions. Indicators 8 had been specified over 2015 in order to follow the evolution of poverty reduction and to evaluate the efficiency of the EBRP.

The HIPC II resources finance for a part the solidarity fund of the municipalities (for 27 million US$ since 2002, 5 million in 2001) financing social sectors as health and education. But the major part is directly allocated to municipalities under the EBRP and the National Dialogue Law 2000. Among them 20% is allocated to the improvement of the education service, 10% to the health sector and 70% to the productive and social infrastructure. Since 2003 they have also financed the SUMI - Seguro Universal Materno Infantil - accounting for 10% of the resources on the special account of the Dialogue 2000 and managed by the Ministry of Health and Sport. These HIPC II resources are allocated to 7 We unfortunately do not dispose of data for the other years of the program implementation. 8 See the annexe for the different indicators

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municipalities according to an equity criteria defined in the National Dialogue Law 2000. This predicts that 30% of the funds is distributed equally to each of the nine department and within each department the allocation to the municipalities is achieved on basis of poverty indicator; the remaining 70% is allocated among all the municipalities according to their poverty level. In order to establish the amount of relief funds for each municipality the population is recalculated according to the following formula:

Recalculated Population = (Population A) x (-1) + (Population B) x (0) + (Population C) x (1) + (Population D) x (2) + (Population E) x (3)

Where the type of population (A, B, C, D, E)9 is determined by the poverty level according to the Unsatisfied Basic Needs (UBN).

70% of the resources from the special Dialogue 2000 account is thus distributed according to the proportion corresponding to the recalculated population in each municipality divided by the total number of all recalculated populations. The allocation within the department (30%) to the municipalities is conceived on the same process. This allowed to focus on the poorest municipalities and to reduce inequalities among them, allocating more resources to the municipalities of the poorest and less populated departments.

Finally the Multilateral Debt Relief Initiative (MDRI) was launched in June 2005 at the G8 summit where it was decided to reduce the external debt to 100% for the heavily indebted poor countries. The MDRI provides an irrevocable cancellation of the debt stock for Bolivia against the IMF (for $US 232.5 million, in nominal terms, in January 2006), the World Bank (for $US 1,511 million, in nominal terms, in July 2006) and the Inter American Development Bank (for $US 1,171 million, in nominal terms, in July 2007). We can notice that the total cancellation under the MDRI was superior to the debt relief under HIPC. It has produced a significant reduction of the public external debt sold in the medium and long term between the end of 2005 and 2007. It leads to a reduction of the interests to pay in the future which increased the total relief to about 3,435 million and an averaged annual relief of 88 million between 2007 and 2045 (BCB 2008). But the MDRI relief incorporated a significant amount of HIPC II relief, which is directed to the municipalities under the PRSP and not to the TGN.

The MDRI objective is twofold: to help the HIPC countries reaching the Millennium Development Goals (MDG’s) and to preserve the financing capacity of the International Financial Institutions. Contrary to the program HIPC II for which a scheme of distribution of the resources to municipalities had been specified in the Law of the National Dialogue, there is neither a precise rule of allocation of the MDRI resources in order to achieve poverty reduction nor a specific control of the resources use.

Table: Debt relief under the HIPC initiative, 1998-2007 (million US$)

1998 1999 2000 2001 2002 2003* 2004 2005 2006 2007 HIPC I HIPC II Mas allá del HIPC MDRI

26,7

84,7

79 0,7

58,8 27,8 9,3

42,6 84,4 29,8

25,7 60,8 16,7

29,4 82,3 54,3

27,5 73,3 47,6

22,8 69,7 50,9 96,6

24,3 63,6 50,5 137,9

Total 26,7 84,7 79,7 95,9 156,8 103,2 166 148,4 240 276,3 * Including the exceptional debt relief from Japan

Source: BCB

9 Population A = population with satisfied basic needs; Population B = population at the poverty threshold; Population

C = moderately poor population; Population D = Indigent population; Population E = marginal population

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The combination of these three programs made it possible to sharply reduce the external indebtedness and to improve the external debt sustainability. The debt stock was reduced from 4523.1 in 1995 to 2257.9 million in June 2008. The level of the debt/exports ratio and the debt service/ exports ratio (linking the debt and its servicing to the availability of foreign exchange earnings) were reduced, accounting respectively for 352.5% and 28.7% in 1996 and 44.5% and 6.6% in 2007. The ratios of debt/GDP and debt service/GDP have also improved indicating the relationship between the debt burden and the capacity of the economy to generate income.

Graph 1: Debt/ exports ratio and Debt service/ exports ratio (%) (1998-2007)

Source: BCB and INE

All of these indicators allow us to conclude that the Bolivian external indebtedness

has improved a lot as a consequence of the different debt relief programs implemented in Bolivia. However we can notice that while the external indebtedness has been reduced the proportion of non-concessional debt in the total has increased accounting for 59% in June 2008. The most important commercial creditor is the CAF, which has increased its importance in the last few years. This could explain the increase of debt service and the negative net transfers of these last few years.

As we have seen these programs intended to contribute to the national poverty reduction strategy. As we have said we will focus on the poverty reduction impact of the HIPC II initiative. Indeed as we have seen the original HIPC initiative did not conceive a clear link between poverty reduction and debt relief resources. It will therefore be difficult to isolate the specific impact of original HIPC resources on poverty. The MDRI was designed to help the country reach the MDG’s but neither has it conceived a clear relation between poverty reduction and MDRI resources and nor has it implemented a control and a specific use of the funds. The enhanced HIPC initiative on the contrary has designed a clear link between debt relief resources and poverty reduction through the PRSP and the earmark the use of funds. However we can notice that the fact to earmark the use of funds does not guarantee the absence of distortion and the effective use. We will restrict our analysis of the program at the 2001-2005 periods as we lacked of data for the municipality’s poverty for recent years.

This paper is organized as follows. We will in the second section try to take a look on the literature over the subject. The third section will introduce our database in order to know its origin, its identity and to have an idea of its value and evolution. We will use this section to introduce the different municipalities incomes in order to see the evolution and the

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importance of the HIPC II resources in the total. We will also examine the different poverty indicators we will use in our analysis. The fourth section outlines the methodology by summarizing the model used and introduces the different specifications we will make. Finally we will interpret the results of the regression, trying to characterize the impact of debt relief programs on poverty through its impact on the selected poverty indicators in the fifth section. Section six concludes the analysis.

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2. Literature survey According to the economic literature on the subject it exists three different channels through which debt relief affects poverty alleviation.

First, debt relief would lead to poverty reduction as it may conduct to economic growth. According to Bhattacharya and Clements (2004) if the external debt can head to higher economic growth helping to finance investment, the diminishing returns to capital predicts that the net benefits of additional investment could decrease as debt intensifies. High levels of debt may hinder growth through the effect of debt overhang (Krugman 1988). The debt overhang occurs when a county’s debt goes beyond its excepted ability to repay and the excepted debt service is likely to be an increase function of the country’s output level. Foreign lenders thus absorb the returns to investment as a consequence the investments made by foreign and domestic creditors –hence the economic growth- are depressed. It also discourages the government incentives to implement structural and fiscal reforms as the creditors will claim for reimbursement if the fiscal position improves. Furthermore the debt overhang creates uncertainty over the government actions and policies to meet its debt services obligations. External debt service could also affect growth by crowding out private investment or shifting the composition of public spending. Other things being equal higher debt services can raise the government interest cost and the budget deficit leading to a reduction in the public saving which may in turn increase the interest rate or crowding out credit available for private investment. Debt service payments can also put pressure on the amount of resources available for infrastructure and human capital formation inducing negative effects on growth. We can see the indirect effect that debt relief has on economic growth by dropping the debt overhang. Several empirical researches have addressed the effect of debt and more specifically debt relief on economic growth.

Pattillo, Poirson and Ricci (2002) used in order to conclude a 3-year average panel data for 93 developing countries over the period 1969-1998. They conclude that debt overhang affects negatively the economic growth up to a threshold of 160-170% of the debt-to-exports ratio and 35-40% of the debt-to-GDP ratio in NPV terms. According to this paper high debt reduces growth by lowering the efficiency of investment rather than its volume. Concerning the debt relief under the HIPC initiative they found that reducing the debt-to-exports ratio from 300% to 150% would increase per capita growth by about 1%. However they moderate these results evocating the disincentives to invest and the bias in the allocation of resources toward economic activities.

Still according to Bhattacharya and Clements (2004) high levels of debt can discourage economic growth in low-income countries, only after reaching a precise turning point estimated at 20-25% of the NPV debt-to-GDP ratio and 100-105% of the NPV debt-to-exports ratio. Their results suggest that the debt service has no effect on real per capita growth. The authors explain this result by the fact that the debt service effect on growth is mainly achieved through its crowding out effect on public investment (following their estimation 1% of GDP increase in debt service reduce public investment by 0.2% of GDP). Seen the weak importance of this latest effect, the authors conclude that debt relief cannot affect strongly public investment but rather have an effect on public consumption or private consumption or investment.

In another more recent study Bhattacharya, Clements and Quoc Nguye (2005) conclude on the positive impact of debt relief on growth using data over the period 1970-

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1999 for 55 developing countries. According to this paper debt relief conducts to an increase of 0.8 to 1.1% in per capita growth rates as well as indirect effect on public investment, a reduction of debt service by 6% would raise the public investment by 0.75 to 1% increasing slightly the growth rate by 0.2%. They argue that if a substantial share of debt relief is devoted to public investment the impact on growth and public investment would be significant. Therefore they recommend to the government to allocate the debt relief resources to public investment, which would lead to poverty reduction unless the increase in public investment results in an increase in the fiscal deficit.

Hussain and Gunter (2005) use a simple macroeconomic model to demonstrate the negative effect of debt service on real GDP growth for all the African countries (HIPC or not) even if the size of the impact varies among countries. The model predicts that the debt services reduces growth by 4.4% per annum and increases poverty by 5.4% per annum on average over all the African countries. The author evaluates the impact of 100% debt relief (debt services of 0) for the HIPCs countries that have reached the enhanced completion point10 predicting an increase in the real GDP growth of 5.6% per year. Considering the impact of HIPC debt relief on growth and poverty the authors conclude that it always has a positive net impact on economic growth as well as on poverty reduction even if the magnitude depends on the country. The authors have also examined the impact of the Term of Trade (ToT) on poverty showing that poverty has increased due to change in the ToT. This provokes an erosion of the benefits of HIPC debt relief. This paper underlines the importance of the 100% debt relief for African countries but explains as well the importance to focus the aid and the policies on a long-term development strategy including necessary structural transformations.

Sachs (2002) used a Solow framework to examine the effect of debt relief on growth. He showed that debt relief or foreign assistance will shift upward the growth curve.

I. Cax (2005) puts into question the Sachs’ model and its assumption that debt relief leads to saving increase arguing that the poor countries often experience a high corruption level this altering the link between debt relief and productive investments. She established that debt relief crowds out existing aid flows in the long run and creates an undesired effect on the composition of existing aid. If this trend persists and the donor transfers are reduced at the point that they hamper the income to lead to positive savings, countries could stay stuck in poverty trap. She adds also that debt relief is not sufficient to enhance growth in low-income countries; other factors have to be examined as social and political characters to qualify debt relief.

A study by Hepp (2005) distinguishes the HIPC countries from the non-HIPC countries to examine the possibility for debt relief to stimulate growth. Using a sample of 122 developing countries (HIPC and non-HIPC) he concludes that if debt relief could potentially increase growth rate and alleviate poverty in developing countries, on average it has no effect. Evaluating this effect in different subsets of developing countries, he found that the growth rate of non-HIPC countries that have not received significant amount of debt relief has benefited significantly from debt relief whereas the HIPC countries growth rate has been unaffected.

Fikru and Getachew (2008) used a comparative and econometric approach to study the relation between debt relief and development in 14 HIPC African countries. They find

10 Condition to reach the 100% debt relief under the MDRI.

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that debt relief is not significant to determine growth on the contrary of foreign direct investment. However this paper finds evidence that the debt burden has a negative effect on GDP growth. According to them there is little evidence that debt relief has increased growth as countries with the highest share of debt relief have experienced reduction in their per capita growth and countries with the lowest share have showed some improvements.

Easterly (1999) investigates for 41 HIPC that debt relief over the past 2 decades has resulted in asset decumulation and new borrowing. According to him debt relief could lead to a perverse incentive effects, the countries borrowing expecting debt forgiveness and postponing policy reforms, this phenomenon is known as moral hazard.

As we can see there is no consensus on the question if debt relief leads to economic growth. In this point we have assumed that economic growth leads to poverty reduction. However this causality is not that clear, Danielson (2001) argues in the Tanzania’s case that economic growth is an unequal process concentrated in some areas where the poor not benefited. He underlines as well the importance of the economic growth distribution among the population in order to reduce poverty. The economic growth does not seem to be a sufficient condition to reduce poverty.

The second channel through which debt relief is linked to poverty reduction is the increase in pro-poor public expenditures. As we have seen debt relief releases resources through a reduction of debt (service) burden increasing resources for public spending. As one of the objective of the HIPC II initiative is reducing poverty this is achieved through the PRSP redaction identifying pro poor expenditures and specify their evolution (Hepp 2005b). Gomanee et al (2003) studied the relation between aid and welfare of the poor. He identifies two impacts, one direct if aid is focused on the poor and one indirect via the aid-financed public spending on social services. He uses two welfare indicators, the human development index and the infant mortality. Studying for 39 countries over the period 1980 to 1998 he found that pro poor expenditures lead to increase in welfare and concluded by this way about the positive impact of aid on welfare. In another study Hepp (2005b) investigated the debt relief impact on per capita health expenditures in a sample of developing countries. He finds that debt relief has little or no effect on health expenditures in HIPC countries (although they are the main beneficiaries) but the level of health expenditures is significantly superior in these countries than in other developing countries. However non-HIPC countries increase their per capita health expenditures more than proportionally if they receive debt relief. The author tries to explain the highest effectiveness of debt relief in non-HIPC countries by the possibly remaining debt overhang in the HIPC countries but as well by the different aid priorities of these countries and by the aid fungibility implying that aid resources directed to health sector may replace government resources. According to Fikru and Getachew (2008) there is as well little evidence that debt relief has improved social service expenditures. This is explained by the authors by various factors as the fungibility of the funds but as well irresponsible governments and lack of institutions. Sahlin (2005) studying the debt relief impact on poverty in Mozambique concluded that the expenditures on priority sectors for poverty reduction have not increased much over the years. In spite of the fact that debt relief has to release resources and to devote them to poverty reduction through increase in pro poor public expenditures, several studies call the effective link between debt relief and increase in public expenditures into question.

Finally the third identified channel would be the change in policies, debt relief under the HIPC initiative is coupled with a series of reforms and conditions for the access that

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could have an impact on poverty reduction. Sahlin (2005) underlines for the Mozambique the importance of the PRSP to stress the necessity of poverty reduction and to construct policies to reach this objective.

The literature we have examined seems to be controversial about the real impact of debt relief on poverty reduction. We can notice that most of these articles apply to the HIPC African countries. It seems to exist only a few articles on the subject addressing the Bolivian case. An article of L. Andersen and O. Nina (2000) studied the effect of debt relief on the municipality poverty reduction. However the authors faced a lack of data, as the municipality poverty indicators were not available in 2000 and have studied the original HIPC impact. A case study over the impact of the PRSP on the health sector has been realised in 2003 by Lanza and Ayllon. Concerning the effect of the transfer from the Dialogue National Account to the municipalities in the health sector, it has been observed that the municipalities had a problem at the start of the implementation to spend the resources showing that the difficulties in the health sector come from a lack of initiatives as well as a lack of resources. The resources spent have principally been concentrated in the infrastructure and the equipment without allocating many resources to the promotion and the prevention work. The authors have however noticed the increase in the health access. This study is interesting in order to see the impact of the resources from the Dialogue National Account to the health sector. Nevertheless it is based on an empirical analysis and has not brought evidence on this impact. This article will try to address this lack.

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3. Data In order to analyze the relation between debt relief programs and poverty reduction, we will use the per capita disbursement accumulation to each municipality under the HIPC II initiative over the period 2001-2005 as well as a range of municipality poverty indicators. We dispose of data for 314 of the current 327 municipalities, as 13 of them did not exist in 2001. The HIPC II resources we deal with come from the Unidad de Programación Fiscal a department directly linked to the Bolivian Finances Ministry. We dispose of the resources HIPC transferred to every municipality from August 2001 (date of the beginning of the transfers) to June 2008. The three municipality’s poverty indicators we will use are the literacy rate, the average schooling years and the life expectancy at birth for 2001 and 2005 and we will concentrate ourselves on their evolution over the period. We received this data from the UNDP (United Nations Development Programme) but their source is the INE (Instituto Nacional de Estadísticas). a) The HIPC II resources The municipalities have different sources of financing; they receive transfers from the state and have their own resources mainly through taxation. All these resources allow the municipality to invest and help in its functioning.

Among the transfers received by the municipalities from the TGN (Tesoro General de la Nación) there is the “Coparticipación Tributaria” which has been implemented in 1994 through the Law n°1551 under the second generation reform program. This law indicated that 20% of the national revenue of the TGN (mainly taxation revenue) would be transferred to the municipalities. Under the amount of Coparticipación Tributaria received by the municipalities 75% should be devoted to productive and social investment and a maximum of 25% devoted to current expenditures. These resources are allocated according to the municipality population and should allow redistributing resources previously concentrated in the capital cities. Others instruments have been created in order to help the local development and promote the Bolivia’s development through the municipalities: the Social Investment Fund, the Rural development fund, the National Fund of Regional Development and the resources of the debt relief program under which the state transfers to municipalities the resources it would not have to pay under the debt relief program. Another important transfer, the direct tax on the hydrocarbon (IDH), has been implemented in 2005 with the hydrocarbon law and is currently an important amount of revenue for the municipalities. This income has to be devoted to social sector as the education, the health and the infrastructure… We can notice that many specialists interviewed evocate a hypothetical substitution of the resources HIPC by the resources IDH in the municipality and national poverty reduction strategy. If we consider the Coparticipación Tributaria, the IDH and the debt relief resources, we can notice the growing importance of the IDH since 2005. The Coparticipación Tributaria remains the most important transfer to the municipalities while the HIPC II resources stay marginal.

If we consider the evolution of the per capita disbursement over the period August 2001- December 2007 we can notice that its average (over all the municipalities) and its median were the most important in 2002 and then have showed a decreasing trend. The total disbursement had decreased of 39% between 2002 and 2003. The median of the per capita disbursement follows the same trend of the average but keeps a lower level. This shows that

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the resources released to each municipality differs hardly. Indeed the half of the municipalities receives less than the average. This is explained by the difference of poverty level and population among municipalities determining the resources allocation with the concept of recalculated population.

Graph 2: Main transfers from the TGN to municipalities in average, 1998-2007 (Bs)

Source: Ministerio de Hacienda

Graph 3: Average and median disbursement per capita under the HIPC II initiative, 2001-2007 (Bs)

Source: Unidad de Programación Fiscal

It is interesting to study as well the expenditures from these HIPC II resources per

capita (on average), we can notice that the expenditures per capita are lower than the disbursements in 2001 and 2002 but in 2003 are higher. The following years the amount of disbursement and expenditures are almost similar.

Over the period the accumulation of disbursements and expenditures accounted respectively for 2,431 and 2,180 million of Boliviano. The proportion of the disbursement spent (disbursement/expenditures) has increased from 26% in 2001 to reach its maximum of 136% in 2003 and has decreased then staying around 100%. This means that since 2004 the resources released are spent, in 2003 the municipalities had spent the resources accumulated the two anterior years. We can thus notice that for the first years of implementation of the program an analysis of the impact of the resources HIPC II on the poverty would have been biased as only a weak proportion of the resources has been spent. However this trend has been reversed and the next years the resources allocated were spent. We can thus analyse the impact of the HIPC II resource on the poverty as they are allocated annually within the

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municipalities. We can however notice that the fact to spend resources does not imply an efficient use of these but we do not dispose of efficient indicators to evaluate this.

Graph 4: Annual HIPC II disbursement and expenditures per capita (2001-2007)

Source: Unidad de Programación Fiscal

We can also notice that the proportion in the municipality’s total social expenditures

of the expenditures from the HIPC II resources is marginal. To conclude on this point we use the data from the Ministerio de Hacienda, we can see that the proportion of the municipalities expenditures from the HIPC II resources in their total social expenditures reaches its maximum in 2003 with 19% and remains between 10% and 20% from 2002 to 2004 showing lower percentage in 2001, 2005, 2006 and 2007. Even if the data we use to conclude are not totally reliable, it is still a good approximation and it shows the weak importance of the HIPC II expenditures in the social expenditures of the municipalities. This predicts a weak poverty impact of the HIPC II resources.

The data we will use in our regression is the accumulation of the per capita HIPC II disbursements over the period 2001-2005. The average is 414 Bs; this means that on average over all the municipalities during the period 2001-2005 the disbursement per capita was 414 Bs. The median is 367.1 Bs, this means that in a half of the municipalities the disbursement per capita is inferior and in the other is superior to 367.1 Bs. The fact that the median is lower than the average shows the inequality in the resources distribution. The variance is 82212.72, which is close to 200 times higher than the average. This shows the high inequality in the HIPC II resources allocation among the municipalities, this is explained by the allocation process we have introduced in the first section. The minimum value of the per capita disbursement is 59.03 Bs for the municipality of Colcapirha in the department of Cochabamba and the maximum value is 2040.92 for Ingavi in the department of Pando, which is one of the poorest departments with the lowest population. If we consider the 10% of the municipalities the poorest, according to the UBN (Unsatisfied Basic Needs) of 2001 on average the disbursement per capita is 633.5 Bs, the 10% the richest receive 199.8 Bs, this shows well that the program focus on the poorest municipalities. b) Poverty indicator In order to characterize the municipality’s poverty we have chosen three indicators: the adult literacy rate, the average schooling years and the life expectancy at birth. The purpose is to conceive a profile as complete as possible of the municipality’s poverty evolution between

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2001 and 2005. Each of these indicators is part of the human development index achieved by the UNDP. We would like to include in our analysis data on the infrastructure, as 70% of the HIPC II resources have to be spent in this area. Unfortunately we do not dispose of recent data.

The adult literacy rate is the percentage of persons of 15 years or more who can read and write, it varies between 0 and 100%. The average adult illiteracy rate of 2001 is 79,11% this means that in 2001 on average over all the municipalities 79,11% of the population could read and write. The median in 2001 is 82.33%, which is superior to the average. This shows that the half of the municipalities with the lowest adult literacy rate shows a level inferior to the average. The variable of interest is the difference between the adult literacy rate of 2001 and 2005. Its average is 4,87% and its median is 4.22%. This means that on average the literacy rate has increased of 4.87% between 2001 and 2005 and that a half of the municipalities have augmented their literacy rate of more than 4.22%, the difference between median and average has decreased over the period. We can notice that the variance has also decreased between 2001 and 2005, showing a decrease in the inequality among municipalities. The variance of the difference in the adult literacy rate is 9.81, which is twice higher than this average and shows the dispersion of the values around the average. The minimum value of the difference in adult literacy rate over 2001 and 2005 is 0.23% for Ixiamas in the department of La Paz, the maximum value is 23.09% for Bella Flor in the department of Pando.

Graph 5: Average, median and variance of adult literacy rate, 2001 and 2005 (%)

Source: INE, UNDP

The average schooling years, which is the average number of schooling years for the

population of 19 years or more, varies between 0 and 15 years11. The average over all the municipalities in 2001 was 5.151 years and the median was 5.230 years. The average increase in the average schooling years is 0,551 years (or approximatively 6 months and a half). The median of the difference over the period is 0.518 (or 6 months and 10 days). The variance is quite low with 0.1062, which is less than one fifth of the average. We can however notice that the variance of the average schooling years has increased between 2001 and 2005, accounting for 56% of the average in 2001 and 61% in 2005. This shows an improvement in the average and the median but this improvement is unequally distributed as the variance has increased over the period (but stays however at a quite low level). The

11 This is the reference values determined by the UNDP for Bolivia.

  15

minimum value of the difference in the average schooling years is -0.14 (a decrease of more than one month and a half) for Tacacoma in the department of La Paz and the maximum value for Yunguyo in the department of Oruro with 2.94 (nearly an increase of three years).

Graph 6: Average, median and variance of the average schooling years, 2001 and 2005 (years)

Source: INE, UNDP

The life expectancy at birth, the number of years a recent birth will live if the

mortality standards stay constant, is expected to vary from 25 to 85 years10. The average life expectancy at birth in 2001 was 60.48 and its median was of 61.25. The average difference in life expectancy at birth over the period is 2.06 years (nearly two years and a month) and its median is 2.22 years (2 years and two months and a half). The variance is two times higher than the average accounting for 4.88, which means that the improvement is not equally distributed among the municipalities. We can notice that the variance has sharply increased during the period accounting for 42% of the average in 2001 and 70% in 2005. As for the average schooling years the life expectancy at birth has improved but the improvement has been unequally distributed among the municipalities. The extreme values vary between a diminution of 5.5 years (or 5 years and 6 months) for Todos Santos in the Oruro department and an increase by 7.72 (or 7 years and 8 months and a ten days) in Curahuara as well in the department of Oruro.

Graph 7: Average, median and variance of the life expectancy at birth, 2001 and 2005 (years)

Source: INE, UNDP

  16

Graph 8: Average, median and variance of the difference between 2005 and 2001 of the adult literacy rate (%), the average schooling years (years) and the life expectancy at birth (years)

Source: INE, UNDP

  17

4. Methodology In this section we will describe the methodology and the function of the explanatory variables used in the analysis of the consequences of HIPC II debt relief on the evolution of the three poverty indicators over the period 2001-2005.

In order to de that we use a multiple regression model, which allows us to study the effect of the explanatory variable (the per capita HIPC II resources accumulated over the period 2001-2005) on the dependent variable (the difference between the selected poverty indicators of 2005 and 2001) controlling for many other factors affecting simultaneously the dependent variable. The regression model is specified as follows:

y i = α + β x i + γZ’i + εi (1)

where yi is the difference in the poverty indicator (the adult literacy rate, the average schooling years and the life expectancy at birth) between 2001 and 2005 of i-th municipality, xi is the accumulation of resources HIPC II per capita of i-th municipality, Zi is a vector of control variables of i-th municipality and ε i is an iid error term.

The control variables include the degree of urbanization, the age and gender structure of the population and the proportion of Indian population in the municipality. Indeed we could expect an impact of these variables on the evolution of the poverty indicators. They include as well the per capita Coparticipación Tributaria accumulated from 1994 to 2005 and the per capita IDH of 2005 as these transfers focus on municipality’s poverty reduction. We control as well for the municipality’s department which is very important seen the difference among the department in term of poverty and resources. The latest control variables we take account of are the percentage of the municipality’s population that benefits of water and electricity distribution.

In order to estimate this regression model we use the ordinary Least Square method. This method allows us to estimate the model

by

where the parameters of the estimation are the OLS estimates. These are chosen to make the sum squared residuals as small as

possible , hence the name of this method. Indeed the residual u, represents the difference between the model and its estimation, therefore we have to minimize this difference in order to get an estimation as close as possible to the regression model. We minimize the squared sum of the residuals in order to avoid the cancellation effect possibly achieved by the sum of positive and negative residuals. We can use the estimated coefficient of the explanatory variable to predict the variation in the dependent variable. The coefficient on xi measures thus the change in the poverty indicator difference due to one unit increase in the per capita HIPC II resources, holding all the other independent variables fixed.

In order to see how well the OLS regression line fits the data or how the regression explains the dependent variable, we use the R-squared which is the ratio of the explained variation compared to the total variation, the fraction or percentage of the sample variation in

  18

the dependent variable that is explained by the explanatory variables. It is important to notice that the R-squared never decreases and usually increases with an increase in the number of independent variables added to the regression. The adjusted R-squared is an alternative to the R2 to measure the goodness of fit. This imposes a penalty for adding additional independent variables to a model and therefore is an interesting goodness of fit measure.

We can also construct models with interaction terms, including the fact that the partial effect of the poverty indicator difference with respect to the per capita HIPC resources can depend on the magnitude of yet another explanatory variable. We have these specifications:

y i = α + β x i + σ x i * zi+ γZ’i + εi (2) y i = α + β x i + λ x i * mi+ γZ’i + εi (3) where zi is the dummy of the ethnic composition of the municipality12 and mi is the

dummy for the degree of urbanization13 of i-th municipality. We are interested to know the effect of the per capita HIPC II resources on the

evolution of the poverty indicator given the degree of urbanization or the proportion of Indian in the municipality. Indeed we consider that the impact of the per capita HIPC II resources on the poverty indicator difference could depend on the nature of the municipality in term of Indian population and urbanization. This allows us to conclude on the impact of the resources HIPC II on the poverty difference for a certain type of municipality, as it is possible that the HIPC II initiative has a stronger impact in the rural municipalities or in the municipalities with the highest proportion of Indian.

We have studied static regression model, we can still improve this specification by introducing a dynamic component to the regression. We are interested to study the convergence14 phenomenon among the municipalities, as well as the impact of the HIPC II resources in the convergence process. In order to model this we use the specification of Barro and Sala-i-Martin (1992) famous in the study of quantitative measurement of convergence with their concept of σ- and β-convergence. This approach moves from the neoclassical Solow-Swan exogenous growth theory. They use the following statistical model:

where yit is per capita income of i-th economy at time t, yi0 is the initial level of per

capita income, yit/yi0 is the growth rate of i-th region, a is the intercept, T is the length of the period over which the growth rate is calculated, εi is the error term and β is the speed of convergence.

12 We have created a dummy representing four categories of municipalities according to their percentage of Indian

population (0-49%; 49-93.6%; 93.9%-99%; >99%). In order to determine the ethnic nature of the population we use the definition of the UDAPE based on the spoken language of the population (censo 2001).

13 We have created a dummy representing the urbanization of the municipality. In order to classify the municipalities we consider that a municipality is urban is its degree of urbanization is superior to 45%. We use for that the data from INE (censo 2001) computing the degree of urbanization for each municipality. We have chosen the threshold of 45% in order to get a total population urban of 62% of the total which fits to the reality.

14 The convergence is the process with which the poor country grows faster than the rich one, it is also know as the catching up process.

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The model can be simplified using a little algebra and reparametrizing model as:

where c + b log(yit/yi0) is the systematic component (c=Ta, b= -(1-e-β T) and ω is the new error term.

The β-convergence is satisfied in a cross section of economies (here the municipalities) if we find a negative relation between the growth rate of per capita income and the initial level of income, b<0; otherwise there is divergence, b>0.

We can apply this specification of Barro and Sala-i-Martin in our study of the impact of the HIPC II resources on the evolution of the poverty indicators, using this specification:

y 05 – y 01 = α + β1y 01 +β2 x I + γZ’I + ε I (4) where y05 – y01 represents the difference between the poverty indicator of 2005 and

2001 of i-th municipality, y01 represents the initial level of the poverty indicator in 2001 of i-th municipality, x i is the per capita HIPC II resources of i-th municipality, Zi is a vector of control variables of i-th municipality and ε� is the i.i.d. error term.

The coefficient βrepresents the convergence, if it is positive it means that the fact to increase the initial level of one unit increases the difference between 2005 and 2001 in the poverty indicator, there is divergence. On the contrary if the coefficient is negative it means that the fact to increase the initial level of one unit reduces the difference in the poverty indicator. There is convergence and the inequality among the municipalities decreases as the poorest municipalities see their poverty reducing more over the period.

We can still improve this specification, using an interaction term between the per capita HIPC II resources and the initial level of the poverty indicator of 200115. The specification is:

y 05 – y 01 = = α + β1y 01 + δ x i* y 01 + β x i + γZ’i + ε i (5)

where xi* y 01 represents the interaction term between the per capita HIPC II

resources and the initial level of the poverty indicator of i-th municipality. The coefficient δ has to be interpreted as the impact of the per capita HIPC II

resources on the convergence. This allows us to conclude about the contribution of the HIPC II resources to the change in the poverty indicator given the initial level of the municipality in term of the poverty indicator.

15 In order to do that we have to transform the initial level of the poverty indicator in a dummy, we divide the

municipalities in four equal groups according to their initial level of each of the elected poverty indicators.

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5. Empirical results In this section we will interpret the results we obtain in the different regressions we have made. We will for the first specification present all the variables, for the further specifications we will focus on the variables we are interested in. We will interpret the five specifications we have realised for each one of the three poverty indicators we have selected. We have to notice that the explicative power of these regressions remains quite weak (an adjusted R-squared lower than 50% for most of the regression), we have therefore to interpret carefully our results.

We can already predict the poor impact of the HIPC II resources on the difference between 2001 and 2005 of the poverty indicators. Indeed as we have seen the HIPC II expenditures have only a weak importance in the total social expenditures. Furthermore the HIPC II resources could affect the municipality’s resources. It is the “additionally” phenomenon which predicts that the increase in the resources for the municipalities under the HIPC II program could reduce the municipalities’ resources (for example conduct to a reduction of the taxation) but as well the transfers from the state. It does not seem that the transfers from the state have decreased however it is possible that the municipalities have reduced their own revenues (for example taxation revenue in order to increase the popularity of the authorities for the election). We will not test this phenomenon in this article but we have to keep this in mind in our analysis.

a) Adult literacy rate (Annex- Table 2) In regression (1) the specification tests the effect of the HIPC II resources on the evolution of the adult literacy rate controlling for a series of factors. We can see that the per capita HIPC II resources accumulated from 2001 to 2005 have a significant positive effect on the difference between the adult literacy rate of 2005 and 2001, showing that an increase in the per capita HIPC II resources of 1Bs increase the variation in the adult literacy rate between 2001 and 2005 by 0.006%. The genre and age structure of the population as well as the degree of urbanization in the municipality does not seem to have an impact on the dependent variable. On the contrary the Indian percentage has a positive impact, for an increase of 1% in the municipality’s Indian population the change in the adult literacy rate between 2001 and 2005 increases by 0.4018%. This shows that a municipality with a higher Indian population has increased more its adult literacy rate between 2001 and 2005. The per capita IDH does not affect the dependent variable, on the contrary the per capita Coparticipación Tributaria has a negative but small impact on it. This shows that increasing this by 1Bs decreases the difference in the adult literacy rate by 0.0012%. The water distribution within the municipality does not affect the independent variable while the electricity distribution does. This has a negative impact on the evolution of the literacy rate, an increase of 1% in the electricity distribution reducing the change in the adult literacy rate of 0.0266%. Finally the departments affect the dependent variable, we have to interpret the results as the difference between each department and the Chuquisaca department (taken as reference). This shows that for all the departments (but for Tarija for which the difference with Chuquisaca is not significant) the fact to pass from Chuquisaca to another department has a negative impact on the evolution of the adult literacy rate. This makes us conclude that Chuquisaca has increased

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by a larger amount its adult literacy rate between 2001 and 2005 than the other departments (excluding Tarija).

The second regression (2) tests the relation between the adult literacy rate and the resources HIPC but as well the interaction between the percentage of Indian in the municipality and the HIPC II resources. The HIPC II resources have still a positive impact on the evolution in the adult literacy rate. The Indian identity does not seem to have an impact on the dependent variable but on the contrary the HIPC II resources seem to have a differential impact on the different ethnic categories. An increase in the per capita HIPC II resources for the first group by 1Bs increases by 0.0041% the dependent variable, for the second group the impact is not significantly different from the first group, for the third group this affects the dependent variable by 0.0146% (0.0041+0.0105) and by 0.0174% (0.0041+0.0133) for the fourth group. This shows us that the resources HIPC are more effective to increase the adult literacy rate for the municipalities with a higher rate of Indian (for the third and fourth categories).

The third regression (3) examines the differential impact of the HIPC II resources on the municipalities rural and urban. The results suggest that an increase by 1Bs in the HIPC II resources for a municipality rural affects the difference in the adult literacy rate between 2001 and 2005 by 0.0066% while for a municipality urban it affects the dependent variable by 0.0037% (0.0066-0.0029). This makes us conclude that the HIPC II initiative had a stronger impact on the adult literacy rate of the municipalities in the rural area.

We examine the dynamic regression, regression (4), using the convergence concept of Barro-Sala-i-Martin. In this specification the HIPC II resources does not have a significant effect on the change in the adult literacy rate. On the contrary the initial level of adult literacy rate has a significant negative effect. An increase of 1% in the 2001 adult literacy rate decreases the change in the adult literacy rate from 2001 to 2005 of 0.1548%. This shows that there is a convergence effect among the municipalities as an increase in the initial level reduces the adult literacy change over the period.

Finally our last specification, regression (5), studies the impact of the resources HIPC II on the convergence process. Examining the impact of being in one of the four group (division of the municipalities according to their adult literacy rate initial level) on the adult literacy rate variation, we can notice that being in the second and fourth group has a negative impact on the dependent variable in comparison of being in the first one, for the third group the difference is not significant. This shows in another way that there is convergence as the fact to be in the group with the lowest adult literacy rate in 2001 has the greatest impact on the adult literacy rate. Concerning the impact of the per capita HIPC II resources for each categories we can notice that for the first group an increase of 1Bs in the per capita HIPC II resources increases the change in the adult literacy rate of 0.0041%, for the second group the impact of an increase in 1Bs in the HIPC II resources is 0.01% (0.0041+0.0059) on the change in the dependent variable, for the third and fourth group the difference with the first group is not significant and the impact is thus similar (0.0041%). The HIPC II resources do not seem to help the convergence process as they do not affect stronger the first group than the others.

In conclusion the per capita HIPC II resources seem to have a positive impact on the variation in the adult literacy rate. This impact is more important for the municipalities with the highest proportion of Indian and for the municipalities in the rural area. The evolution of adult literacy rate seems to show convergence among the municipalities. The HIPC II

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resources have the highest effect on the municipalities that have showed a adult literacy rate in 2001 from 73.25% to 82.31 and being part of the second categories.

Table 2: Regression results. Dependent Variable: Difference in the adult literacy rate between 2005 and 2001

(1) (2) (3) (4) (5) HIPC II resources per capita 2001-2005 .0060059** .004101** .0065603** .0021407 .0041155** (3.48) (2.22) (3.63) (1.27) ( 2.09) Adult literacy rate 2001 (%) -.154808** (-8.32) Adult literacy rate 2001 2: Btw 73.34 and 82.31% -2.425686** (dummy) (-2.00) 1: Less 73% 3: Btw 82.34 and 88% -1.391396 (-1.51 ) 4: More 88% -2.85233** (-2.00) HIPC*Adult literacy rate 2001 HIPC*D2 .0059169** (dummy) (2.11) HIPC*D3 .0012996 (0.64) HIPC*D4 .0051954 (1.27 ) Proportion men (%) -2,801605 6.742282 -3.99625 6.928521 -7.520735 (-0.24) (0.58) (-0.34) (0.70) (-0.82) Degree of urbanization (%) -.6012 -1.063164 .5306653 -.1147083 (-0.90) (-1.37) (0.83) (-0.15) Urbanization (dummy) Urban .2079686 (0.36) HIPC*Urban -.0028686** (-1.97) Indian population (%) .401767** .0411342** .0245893** 0.0331965** (5.77) (5.91) (3.94) (4.80) Indian population (dummy) Groupe 2: less 93.8% .0468417 Group 1: less 49% (0.05) Groupe 3: less 99% -1.159831 (-0.84) Groupe 4: more 99% -1.084895 (-0.67) HIPC*Indian population HIPC*Ind2 .0033275 (dummy) (1.26) HIPC*Ind3 .0104807** (2.67) HIPC*Ind4 .013289** (3.62) Population less than 5 years (%) -17,46026 -16.879 -17.55806 -9.839373 -16.23811 (-1.53) (-1.59) (-1.54) (-1.24) (-1.36) Population between 5 and 14 years (%) -16,87066 .7410333 -15.84666 -12.93829 -22.11264 (-1.50) (0.07) (-1.39) (-1.24) (-1.86) Population between 15 and 64 years (%) -3,943172 2.559842 -4.443177 6.762722 -7.451814 (-0.46) (0.33) (-0.51) (0.92) (-0.88) IDH per capita 2005 .0132078 .0130877 .0128694 .0133672 .0191694** (1.52) (1.22) (1.46) (1.76) (2.12) Coparticipacion Tributaria per capita 1994-2005 -.0011466** -.0010997 -.0011428 -.000869 -.0016291** (-2.20) (-1.75) (-2.19) (-1.86) (-3.06) Water distribution (%) .0017262 .0049017 .0007942 -.0087256 .000753

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(0.21) (0.63) (0.10) (-1.17) (0.09) Electricity distribution (%) -.0266013** -.0013287 -.0251999 -.0194048 -.020591 (-2.22) (-0.11) (-2.36) (-1.92) (-1.81) Departments La Paz -3.667855** -3.299838** -3.579718 -1.274967** -3.552415** Chuquisaca (-5.52) (-5.27) (-5.51) (2.30) (-5.76) Cochabamba -1.62226** -1.480142** -1.627945 -.2868519 -1.589574** (-2.18) (-2.21) (-2.17) (-0.41) (-2.19) Oruro -5.226559** -5.246902** -5.088384 -1.700845** -5.835954** (-6.00) (-5.50) (-5.82) (2.23) (-6.89) Potosi -2.74163** -2.672598** -2.66781 -1.318791** -2.919524** (-4.99) (-5.26) (-4.87) (2.54) (-5.32) Tarija -1,232734 -1.735988 -1.13687 -.4775505 -1.707786** (-1.14) (-1.60) (-1.07) (0.58) (-1.57) Santa Cruz -1.631989** -1.789028** -1.466053** .144291 -1.296068** (-2.66) (-3.20) (-2.42) (0.30) (-2.38) Beni -3.090696** -3.265442** -2.773357** -.9528092 -3.408523** (-3.34) (-3.27) (-2.98) (1.15) (-3.75) Pando -10.36509** -8.84168 -10.21813 -5.476338 -13.93597** (-2.02) (-1.46) (-1.96) (1.10) (-2.75) Constant 15.04787** 2.545934 15.28692** 14.71467** 22.78463** (2.3) (0.36) (2.33) (2.41) (3.09) Observations 314 314 314 314 314 R-squared 0,4414 0.5133 0.4512 0.5751 0.4906 Adjusted R-squared 0,4053 0.4728 0.4137 0.5461 0.4728 **Significant et 5%

b) Average schooling years (Annex-Table 3) The first regression (1) shows that only a few variables have a significant impact on the change in the average schooling years over the period. The percentage of the municipality population between 5 and 14 years has a significant negative effect, increasing the population in this category by 1% decrease the change in the average schooling years by 3.239422 years. The per capita IDH seems to have a positive impact on the dependent variable as an increase of 1Bs in the per capita IDH increases the variation of the average schooling years by 0.0032009. The Coparticipación Tributaria per capita has on the contrary a negative impact as an increase of 1Bs decreases the dependent variable by 0.0002 years. The water distribution surprisingly shows a negative impact, an increase of 1% conducing to a reduction of 0.0022682 years in the dependent variable. This could be explain by the fact that the water supply indicates the poverty level of the municipality and could predict that decreasing the poverty level could decrease the evolution in the average schooling years. Finally the department variable shows that only the fact to be in Tarija and Pando has a negative effect in comparison of being in Chuquisaca. This means that for these two departments the variation has been lower than in the Chuquisaca department.

In the second specification (2) the per capita HIPC II resources have still no effect on the dependent variable. The coefficient of the Indian dummy implies that being in the fourth categories increases the variation in the dependent variable of 0.7627 in comparison of being in the first one. This means that the municipalities with a higher Indian proportion have increased more their average schooling years. As the per capita resources has no effect on the dependent variable for the first categories the differential with the other categories is not interpretable.

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We cannot conclude about the specific impact of the HIPC II resources on the average schooling years for the municipalities urban and rural using the third regression (3) as neither the effect of the HIPC II resources nor the degree of urbanization is significant.

The dynamic specification (4) shows a divergence process among the municipalities as an increase of 1 year in the initial average schooling year increases the variation of the average schooling years between 2001 and 2005. The per capita HIPC II resources turns to be significantly positive in this specification, showing that an increase of 1Bs in the per capita HIPC II resources increase the change in the average schooling years of 0.0004 years.

Finally the last regression (5) shows that being a municipality with the highest initial level (fourth group) increases the average schooling years by 0.2302 years in comparison to having the lowest initial level (first group). This demonstrates in another way the divergence process as being in the group with the higher initial level increase the improvement in the average schooling years. Considering the impact of the per capita HIPC II resources in each of the categories we cannot conclude as the impact is not significant for the first group.

In conclusion the per capita HIPC II resources do not seem to have a significant impact on the variation of the average schooling years over the period 2001-2005. The only positive impact we have found is present in the regression examining the convergence process. We can therefore underline that the variation in the average schooling years seems to be unequal among the municipalities, showing divergence among them in its evolution.

Table 4: Regression results, Dependent variable: Difference between the average schooling years of 2005 and

2001 (1) (2) (3) (4) (5)

HIPC II resources per capita 2001-2005 .0001192 .000251 .0001356 .0004169** -.0000552 (0.62) (1.45) (0.69) (2.04) (-0.23) Average schooling years 2001 (%) .0935872** (5.46) Average schooling years 2001 D2 -.1041772 (dummy) (-1.08) D3 -.0436673 (-0.32) D4 .2301876** (2.08) HIPC*Average schooling HIPC*D2 .0003896** years 2001 (dummy) (1.97) HIPC*D3 .0003009 (1.10) HIPC*D4 -.0001974 (-0.95) Proportion men (%) 2.278105 2.122701 2.255474 1.669947 1.924973 (1.62) (1.34) (1.60) (1.09) (1.28) Degree of urbanization (%) .0915738 .0881544 -.1066719 -.0388119 (1.11) (0.88) (-1.13) (-0.40) Urbanization (dummy) Urban .0982517 (1.54) HIPC*Urban -.0001808 (-1.62) Indian population (%) .0002645 .0002598 .0013931 .0007925 (0.28) (0.27) (1.59 ) (0.87)

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Indian population (dummy) Groupe 2: less 93.8% .0835754 Group 1: less 49% ( 0.81) Groupe 3: less 99% .0024435 (0.01) Groupe 4: more 99% .7627062** (3.71) HIPC*Indian population HIPC*Ind2 .0000876 (dummy) (0.28) HIPC*Ind3 .0001341 (0.19) HIPC*Ind4 -.001699** (-3.77) Population less than 5 years (%) -1.979964 -2.06047 -1.871699 -

2.306037** -2.383322

(-1.53) (-1.55) (-1.49) (-1.90) (-1.90) Population between 5 and 14 years (%) -3.239422** -3.896204** -3.201212 -

2.927509** -2.943193

(-2.15) (-2.51) (-2.12) (-2.11) (-2.07) Population between 15 and 64 years (%) .1451707 -.4061222 -.2258981 -.6544056 -.3148079 (-0.15) (-0.42) (-0.23) (-0.74) (-0.35) IDH per capita 2005 .0032009** .0037689** .0031829 .0026487** .0031093 (3.04) (3.06) (2.96) (2.66) (2.99) Coparticipacion Tributaria per capita 1994-2005

-.0001868** -0002253** -.0001877 -.0001898**

-.0001731

(-2.81) (-2.96 ) (-2.78) (-2.99) (-2.68) Water distribution (%) -.0022682** -.0021514** -.0022363 -.001727 -.0020764 (-2.06) (-2.20) (-2.02) (-1.63) (-1.87) Electricity distribution (%) .0026277 .0025858 .0029043 .0015129 .0025166 (1.55) (1.21) (1.84) (0.92) (1.40) Departments La Paz -.0671278 -.1086475 -.0580282 -.188676** -.1237423

Chuquisaca (-0.91) (-1.47) (-0.78) (-2.54) (-1.67) Cochabamba -.0929089 -.0961257 -.0925818 -

.1443823** -.1195126

(-1.39) (-1.39) (-1.39) ( -2.19) (-1.82) Oruro -.0515068 -.1143343 -.041435 -

.2529833** -.1241566

(-0.49) (-0.89) (-0.40) (-2.41) (-1.23) Potosi -.0699745 -.065077 -.0693893 -

.1540466** -.1027046

(-1.09) (-1.05) (-1.08) (-2.59) (-1.66) Tarija -.2752427** -.2917414** -.264589 -.2203137 -.2740435 (-2.20) (-2.45) (-2.07) (-1.72) (-2.15) Santa Cruz -.0002812 .0056028 -.2137071 -.0519152 -.0471228 (-0.00) (0.07) (0.09) (-0.69) (-0.63) Beni -.226495 -.2303555 -.2137071 -

.3216355** -.2707988

(-1.74) (-1.76) (-1.61) (-2.46) (-2.07) Pando -1.522882** -1.897134 -1.510804 -

1.642632** -1.479102

(-2.26) (-1.76) (-2.21) (-2.53) (-2.25) Constant .8023972 1.186535 .8187919 .8387031 1.052044 (1.03) (1.3) (1.05) (1.23) (1.30)

Observations 314 314 314 314 314 R-squared 0.3253 0.3625 0.3269 0.3924 0.3817 Adjusted R-squared 0.2817 0.3096 0.2809 0.3510 0.3280 **Significant et 5%

  26

c) Life expectancy at birth The first specification (1) shows us that the per capita HIPC resources has a negative effect on the increase in the life expectancy at birth, as an increase of 1Bs decreases the life expectancy at birth variation of 0.0025 years. The water distribution seems to have as well a negative impact, an increase of 1% reducing the dependent variable of 0.01408 years. The only department showing a significant impact in comparison to the impact in Chuquisaca is Cochabamba as passing from Chuquisaca to Cochabamba decreases the variation in the life expectancy at birth.

In the second specification (2) the per capita HIPC II resources does not seem to have any impact on the dependent variable. On the contrary the fact to be a municipality with a high proportion of Indian seems to have a positive impact on the variation in the life expectancy at birth. We cannot conclude on the differential impact over the ethnic categories of the HIPC II resources on the change in the life expectancy at birth as the impact on the first categories is insignificant.

The third regression (3) shows that the HIPC II resources have a significant negative impact on the dependent variable. We unfortunately cannot conclude on the specific impact of the resources HIPC II for the municipalities rural and urban as the difference between both is insignificant.

In the fourth specification (4) we can conclude over a divergence process in the evolution of the life expectancy at birth among the municipalities as an increase of 1 year in the initial level increases by 0.4169 years the variation in the life expectancy at birth. The per capita HIPC II resources do not have a significant impact.

Finally the fifth specification (5) shows the per capita HIPC resources have a significant impact, for the first group an increase by 1Bs decreases the change in the life expectancy at birth by -0.0109 year, for the second one by -0.0065 years (-0.0109+0.01025), for the third group by -0.00278 years (-0.0109+0.00812), for the fourth one by -0.00302 years (-0.0109+0.00788). This seems to demonstrate that the HIPC II resources are perverse to increase the life expectancy at birth and affect more negatively the municipalities with the lowest initial level.

In conclusion the per capita resources seems to have a perverse effect on the variation in the life expectancy at birth over the period. The evolution of the life expectancy at birth shows a divergence process among the municipalities. Concerning the impact of the per capita HIPC II resources on the evolution of the life expectancy at birth for the municipalities according to their initial level we can conclude that the effect is the most perverse for the lowest categories (with the level the lowest initial level) this underlines the negative effect of the HIPC II resources in the divergence process.

Table 4: Regression results, Dependent variable: Difference between the life expectancy at birth of 2005 and

2001 (1) (2) (3) (4) (5)

HIPC II resources per capita 2001-2005 .0001192 .000251 .0001356 .0004169** -.0000552 (0.62) (1.45 ) (0.69) (2.04) (-0.23) Average schooling years 2001 (%) .0935872** (5.46) Average schooling years 2001 D2 -.1041772 (dummy) (-1.08)

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D3 -.0436673 (-0.32) D4 .2301876** (2.08) HIPC*Average schooling HIPC*D2 .0003896** years 2001 (dummy) (1.97) HIPC*D3 .0003009 (1.10) HIPC*D4 -.0001974 (-0.95) Proportion men (%) 2.278105 2.122701 2.255474 1.669947 1.924973 (1.62) (1.34) (1.60) (1.09) (1.28) Degree of urbanization (%) .0915738 .0881544 -.1066719 -.0388119 (1.11) (0.88) (-1.13) (-0.40) Urbanization (dummy) Urban .0982517 (1.54) HIPC*Urban -.0001808 (-1.62)

Indian population (%) .0002645 .0002598 .0013931 .0007925 (0.28) (0.27) (1.59 ) (0.87) Indian population (dummy) Groupe 2: less

93.8% .0835754

Group 1: less 49% ( 0.81) Groupe 3: less 99% .0024435 (0.01) Groupe 4: more

99% .7627062**

(3.71) HIPC*Indian population HIPC*Ind2 .0000876 (dummy) (0.28) HIPC*Ind3 .0001341 (0.19) HIPC*Ind4 -

.0016994**

(-3.77) Population less than 5 years (%) -1.979964 -2.06047 -1.871699 -

2.306037** -2.383322

(-1.53) (-1.55) (-1.49) (-1.90) (-1.90) Population between 5 and 14 years (%) -3.239422** -

3.896204** -3.201212 -

2.927509** -2.943193

(-2.15) (-2.51) (-2.12) (-2.11) (-2.07) Population between 15 and 64 years (%) .1451707 -.4061222 -.2258981 -.6544056 -.3148079 (-0.15) (-0.42) (-0.23) (-0.74) (-0.35) IDH per capita 2005 .0032009** .0037689** .0031829 .0026487** .0031093 (3.04) (3.06) (2.96) (2.66) (2.99) Coparticipacion Tributaria per capita 1994-2005

-.0001868**

-.0002253**

-.0001877 -.0001898**

-.0001731

(-2.81) (-2.96 ) (-2.78) (-2.99) (-2.68) Water distribution (%) -.0022682** -

.0021514** -.0022363 -.001727 -.0020764

(-2.06) (-2.20) (-2.02) (-1.63) (-1.87) Electricity distribution (%) .0026277 .0025858 .0029043 .0015129 .0025166 (1.55) (1.21) (1.84) (0.92) (1.40) Departments La Paz -.0671278 -.1086475 -.0580282 -.188676** -.1237423 Chuquisaca (-0.91) (-1.47) (-0.78) (-2.54) (-1.67) Cochabamba -.0929089 -.0961257 -.0925818 -

.1443823** -.1195126

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(-1.39) (-1.39) (-1.39) ( -2.19) (-1.82) Oruro -.0515068 -.1143343 -.041435 -

.2529833** -.1241566

(-0.49) (-0.89) (-0.40) (-2.41) (-1.23) Potosi -.0699745 -.065077 -.0693893 -

.1540466** -.1027046

(-1.09) (-1.05) (-1.08) (-2.59) (-1.66) Tarija -.2752427** -

.2917414** -.264589 -.2203137 -.2740435

(-2.20) (-2.45) (-2.07) (-1.72) (-2.15) Santa Cruz -.0002812 .0056028 -.2137071 -.0519152 -.0471228 (-0.00) (0.07) (0.09) (-0.69) (-0.63) Beni -.226495 -.2303555 -.2137071 -

.3216355** -.2707988

(-1.74) (-1.76) (-1.61) (-2.46) (-2.07) Pando -1.522882** -1.897134 -

1.510804 -1.642632**

-1.479102

(-2.26) (-1.76) (-2.21) (-2.53) (-2.25) Constant .8023972 1.186535 .8187919 .8387031 1.052044 (1.03) (1.3) (1.05) (1.23) (1.30)

Observations 314 314 314 314 314 R-squared 0.3253 0.3625 0.3269 0.3924 0.3817 Adjusted R-squared 0.2817 0.3096 0.2809 0.3510 0.3280 **Significant et 5%

  29

6. Conclusions As we have seen in the economic literature, the impact of debt relief on the poverty reduction is controversial. In this article we have studied the specific impact of the HIPC II resources on the evolution between 2001 and 2005 of the municipality’s adult literacy rate, average schooling years and life expectancy at birth. The results we have obtained have to be interpreted carefully, however they suggest a positive impact of the HIPC II resources on the evolution of the adult literacy rate while they do not seem to affect the average schooling years. Concerning the life expectancy at birth the HIPC II resources seem to have a negative impact on it suggesting a perverse effect of this program. These results do not allow us to conclude on the question of the impact of debt relief on poverty reduction. It seems interesting to continue research in this sense in order to conclude on the real impact of debt relief on poverty reduction using econometric models more sophisticated as well as using better data.

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Annex Table 1: PRSP indicators