MEASURING POVERTY IN -LESTE: USING CENSUS DATA TO DEVELOP A NATIONAL MULTIDIMENSIONAL POVERTY INDEX

Dan Jendrissek*

This paper presents work towards the development of a national Multidimensional Poverty Index for Timor-Leste. The Global Multidimensional Poverty Index for Timor-Leste, based on the Demographic and Health Survey, had been calculated previously, but because of sample size limitations, poverty estimates are only reliable for higher levels of administrative units. This paper’s unique contribution is in demonstrating how this hurdle can be overcome by calculating a census-based Multidimensional Poverty Index that allows for robust estimates at lower subnational levels. By using data from the previous two censuses, the study shows a decline in multidimensional poverty across the country, although persistent deprivations remain in some areas. In addition to a discussion on the global Multidimensional Poverty Index, the paper also includes reviews of previous income/consumption-based poverty studies. These survey-based poverty estimates indicate the existence of an “East-West divide” at the highest subnational level, with more households in the western parts of Timor-Leste experiencing poverty than in the eastern parts. The focus of this study is on lower administrative units of the country which do not replicate those findings. The picture emerging is rather one of hard-to-reach areas away from urban centres being most at risk of being left behind. The study contributes to an ever- growing body of research on poverty measures in the context of countries where reliable data at the subnational level are often scarce. The results and applied methodology presented are, therefore, relevant beyond the immediate country context.

JEL classification: I32

Keywords: Multidimensional Poverty Index, poverty mapping, Timor-Leste, , South-East Asia

* Dan Jendrissek, Department of Health and Social Care, Windsor House, 42 – 50 Victoria Street, London SW1H 0TL, United Kingdom (email: [email protected]). The research presented in this paper was made possible through a fellowship grant from the Overseas Development Institute. The Institute had no role in the analytical work carried out, nor in the decision to submit the article for publication. The author would like to thank colleagues working for the United Nations in Timor- Leste for their vital input, especially Nick McTurk whose thoughts on methodological issues were invaluable. Asia-Pacific Sustainable Development Journal Vol. 28, No. 1, June 2021

I. INTRODUCTION

This paper presents work carried out to develop a national Multidimensional Poverty Index for Timor-Leste based on census data. The Oxford Poverty and Human Development Initiative has published the Multidimensional Poverty Index for Timor-Leste based on the 2016 Demographic and Health Survey (Oxford Poverty and Human Development Initiative, 2019). However, because of the inherent sample size limitations, this analysis is only available at the municipality level, the highest subnational level. As discussed in more detail below, local areas within municipalities can vary widely in terms of people’s income, consumption and overall living standards. Aggregate statistics tend to mask these inequalities. Consequently, to carry out effective spatial targeting of poverty, data at lower administrative levels are needed. The main contribution of this paper is the presentation of this kind of disaggregated data.

After contextualizing the research within the wider country context, the global Multidimensional Poverty Index for Timor-Leste is reviewed, and examples are given of countries in the wider region that have made multidimensional poverty measures an essential part of domestic policy design.

In addition to the global Multidimensional Poverty Index, income and consumption- based approaches to measuring poverty are reviewed, such as the national and international poverty lines of Timor-Leste and the small area estimations of poverty. Strengths and limitations of these approaches are discussed before indicating how a national, census-based Multidimensional Poverty Index can be used for detailed mapping of a Multidimensional Poverty Index at lower administrative levels. The results of the census-based Multidimensional Poverty Index is then compared with the global, survey-based Multidimensional Poverty Index and the monetary poverty estimates.

The present paper also complements the recent Timor-Leste Voluntary National Review (Timor-Leste, 2019) of the country’s progress in realizing the Sustainable Development Goals. While data are available for reporting on the proportion of the population living below the national poverty line (Goal indicator 1.2.1, discussed in more detail below), to date, no indicators exist regarding poverty in all its dimensions according to national definitions (Goal indicator 1.2.2). The findings of this study contribute towards closing this data gap.

The work presented in this study is not to be understood as a finished product. First, methodological questions remain: as this is the first time a census-based Multidimensional Poverty Index has been calculated, the objective was to stay as close as possible to the global Multidimensional Poverty Index methodology, but not all

2 Measuring poverty in Timor-Leste

global indicators could be replicated using census data. Malnourishment information, for example, is not collected in the Timor-Leste census, therefore questions remain on how that poverty indicator should be treated methodologically if the aim is to stay close to the global methodology.

Second, the methodology does not yet hold the status of being “official” statistics and is likely to be adjusted in the future according to the wider country context and government priorities.

Accordingly, references to “national Multidimensional Poverty Index” are the “suggested” or “proposed” national Multidimensional Poverty Index.

Despite these caveats, the work presented constitutes an innovative approach to poverty mapping. It outlines how a national Multidimensional Poverty Index based on census data can produce a more fine-grained picture than one based on survey data, which has not been done in the case of Timor-Leste. In addition, the work shows how a census-based Multidimensional Poverty Index allows for the tracking of poverty levels over time by comparing the 2015 Census with the 2010 Census.

In this respect, the work presented here is relevant beyond Asia and the Pacific and can easily be replicated for other low-income countries where similar census data are available.

II. COUNTRY CONTEXT

To start with, in this section, the research is contextualized in the wider country context. Despite being an oil-exporting country that has made remarkable economic progress over the past decade, Timor-Leste is still one of the poorest countries in the region. Reliable poverty data are, therefore, essential to tackle the many challenges the country is encountering.

After almost twenty-five years of Indonesian occupation, the Timorese people voted to restore independence in 1999. Only hours after the referendum, the Indonesian military and militias started a campaign that led to the destruction of 70 to 90 per cent of the country’s infrastructure, including health facilities, schools, water supply, roads and irrigation systems (CAVR, 2013, p. 300; Albrecht and others, 2018, p. 1). After intervention from the International Force East Timor and a handover to the United Nations Transitional Administration in East-Timor in 2000, the country seemed to be preparing for a transition away from the past conflict. However, tensions resurfaced in 2006 when a violent confrontation between the armed forces and police linked to a complex set of political and institutional conflicts (Brady and Timberman, 2006), triggered another extended outbreak of violence for several months. The violence

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led to the displacement of more than 100,000 people and created insecurity, often dividing communities along regional divisions.

Another United Nations mission was initiated in August 2006, which lasted until 2012. Since then, the country has largely been at peace, but severe challenges remain (for a more detailed overview of the recent history of Timor-Leste, especially during the United Nations peacebuilding missions, see, for example, Menon (2019)).

Timor-Leste ranks 110 out of 119 countries on the Global Hunger Index 2018, scoring 34.2 which was classified as “serious” and bordering on “alarming”. Only Afghanistan, Sudan, Haiti, Yemen, Chad, Zambia, Madagascar, Sierra Leone and the Central African Republic ranked lower (Concern Worldwide and Welthungerhilfe, 2018).

Despite major improvements in the field of access to clean water, sanitation and hygiene (WASH), more than one third (38 per cent) of all households are still using either an unimproved toilet facility or are practising open defecation. Rural areas are especially at risk of being left behind in this context. A quarter of all rural households in the country are still practising open defecation. While approximately only 5 per cent of households in urban areas have to either defecate in the open or use an unimproved facility, this number balloons to more than 50 per cent in rural areas; every other household, therefore, is very far off from universal access to safely managed sanitation (Timor-Leste, 2019, pp. 80-86).

Considerable work has been directed towards rebuilding the educational capacity of the country since the restoration of independence. With nearly all schools destroyed and almost all international teachers leaving the country at that time, Timor-Leste has had to rebuild the education system from the ground up. Between 2006 and 2011, for example, more than 300 new primary schools alone were built (World Bank, 2018a). Despite these efforts, shortages of classrooms still remain. The Ministry of Education calculates that an additional 1,871 total public classrooms need to be built by 2030 (130 in pre-schools, 1,573 in primary schools and 168 in secondary school) (World Bank, Timor-Leste Ministry of Education and Global Partnership for Education, 2018, p. 5).

Demographic changes are contributing further to the country’s rather difficult position. Overall, fertility rates peaked around 2002 and have declined since then as reproductive patterns have shifted to smaller family sizes, likely supported by broader access to reproductive health services and modern contraceptives. Projections for 2050 range from 3.0 live births per woman in high fertility scenarios to 2.0 live births in low fertility scenarios (General Directorate of Statistics and UNFPA, 2018a).

4 Measuring poverty in Timor-Leste

The downward trend in fertility rates could lead to a demographic dividend, with less dependent elderly people and children relative to the number of workers. For this to occur, employment opportunities need to be created and the workforce must be healthy and reasonably well educated. As some researchers have pointed out, currently, this is not the case for Timor-Leste. Consequently, the country risks not being able to benefit from a demographic dividend because “children are not receiving the necessary investments in their health and education that successful demographic dividend countries have made” (Lee, 2018, p. 6.).

The COVID-19 pandemic is likely to have contributed to this trend, with global supply chains being disrupted and the closure of many reproductive health-care services. Marie Stopes International estimates that globally, the pandemic-related restrictions could lead to three million additional unintended pregnancies (Ford, 2020). Research investigating the disruption in access to reproductive care during the 2013-2016 Ebola outbreak in Sierra Leone shows that pregnancy-related deaths during that time period might have even outnumbered deaths from the virus itself (Sochas, Channon and Nam, 2017).

In summary, Timor-Leste will be facing serious challenges in the coming years. Accordingly, a detailed poverty analysis is vital for evidence-based policy decisions aimed at mitigating these challenges.

III. ADMINISTRATIVE UNITS OF TIMOR-LESTE

In the following sections, reference is made to a number of administrative units that readers might not be familiar with, so the applied terminology is briefly explained below.

At the subnational level, Timor-Leste consists of 13 municipalities (figure 1), ranging from Lautém in the very East of the country to the exclave of in the very West. (including the of Dili, surrounding areas and the island of Atauro) is the municipality with the highest population (approximately 280,000), whereas is the least populous municipality with a population of only 47,000 (General Directorate of Statistics, 2016). Borrowing the Eurostats nomenclature of territorial units for statistics (NUTS) terminology, municipalities can be described as being at NUTS1 level.

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Figure 1. Municipalities of Timor-Leste

Baucau Manatuto Lautém Dili Liquica Liquiçá Manatuto Aieu Ermera Viqueque Bobnaro Manufahi Ainaro Same

Pante Oecusse Cova Lima Suai

Source: General Directorate of Statistics, “Municipalities of Timor-Leste” (Dili, 2018). Note: The boundaries and names shown and the designations used on this map do not imply official endorsement or acceptance by the United Nations.

Municipalities are further subdivided into 65 administrative posts (figure 2), about the same size as the European NUTS2 regions.

Administrative posts are then split into more than 400 sucos (NUTS3 level equivalent, figure 3), which are further subdivided into so-called aldeias.

As explained below, most poverty analysis of Timor-Leste is only available at the highest subnational level, namely the municipality level. Because of vast inequalities within municipalities, however, these data are too broad to support spatial targeting of deprivations. Accordingly, reliable data below the municipality level are required. The research in this paper is intended to fill this gap.

6 Measuring poverty in Timor-Leste

Tutuala Lospalos Lospalos Lospalos Lospalos m é or acceptance by the or Laut Laut é m Iliomar Iliomar Luro Luro Baguia Baguia Laga Laga Uatucarbau Uatucarbau Uato-Lari Uato-Lari Quelicai Quelicai Baucau Baucau Viqueque Viqueque Ossu Ossu Venilale Venilale Baucau Baucau Viqueque Viqueque Vemasse Vemasse Lacluta Lacluta Laleia Laleia Manatuto Manatuto Barique Barique Manatuto Manatuto Soibada Soibada Laclubar Laclubar Fatuberlio Fatuberlio Laclo Laclo Alas Metinaro Metinaro Turiscai Turiscai Remexio Remexio Lequidoe Lequidoe Same Same Same Same Cristo Rei Cristo Rei Maubisse Maubisse Hato-Udo Hato-Udo Atauro Aileu Aileu Laulara Laulara Aileu Vila Aileu Vila Hato-Builico Hato-Builico Ainaro Ainaro Gleno Gleno Vera Cruz Vera Cruz Ainaro Ainaro Railaco Railaco Dom Aleixo Dom Aleixo Letefoho Letefoho Zumalai Zumalai Ermera Ermera Bazartete Bazartete Atsabe Suai Suai Hatulia Hatulia Suai Suai Bobonaro Bobonaro Liquiçá Liquiçá Cailaco Cailaco Lolotoe Lolotoe Liquica Maucatar Maucatar Maubara Maubara Maliana Maliana Fatululic Fatululic Atabae Atabae Maliana Maliana Tilomar Tilomar Fohorem Fohorem Figure 2. Administrative posts of Timor-Leste Balibo Balibo Fatumean Fatumean Pante Makassar Pante Makassar Oesilo Oesilo Passabe Passabe Pante Macassar General Directorate of Statistics, “Administrative posts of Timor-Leste” (Dili, 2018). of Statistics, “Administrative posts Timor-Leste” General Directorate United Nations. The boundaries and names shown and the designations used on this map do not imply official endorsement designations used on this map do not imply official shown and the The boundaries and names

Nitibe Nitibe Source: Note:

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Figure 3. Sucos of Timor-Leste

Baucau Manatuto Lospalos Liquica

Gleno Aileu

Viqueque Maliana Ainaro Same

Pante Makassar

Suai

Source: General Directorate of Statistics, “Sucos of Timor-Leste” (Dili, 2018). Note: The boundaries and names shown and the designations used on this map do not imply official endorsement or acceptance by the United Nations.

IV. PREVIOUS POVERTY ESTIMATIONS

This section includes a review of the most recent estimations of poverty for Timor- Leste and a discussion on the strengths and weaknesses of the studies. Monetary and multidimensional poverty estimates are discussed and compared to the findings of the proposed national Multidimensional Poverty Index.

Global Multidimensional Poverty Index

The Multidimensional Poverty Index, first presented in 2010 by the United Nations Development Programme (UNDP) Human Development Report Office and more recently revised in 2018, has been used to measure multidimensional deprivations in more than 100 countries, covering more than 90 per cent of the population in lower- and middle-income countries (Alkire and Jahan, 2018). The Index is calculated using 10 indicators across the three poverty dimensions: health; education; and living standards.

The “global” Multidimensional Poverty Index published by UNDP and the Oxford Poverty and Human Development Initiative is based on a standardized methodology and incorporates data from globally comparable surveys, such as the Demographic and Health Surveys and the Multiple Indicator Cluster Surveys. In addition, the flexibility of the Alkire-Foster methodology also allows for the calculation of “national”

8 Measuring poverty in Timor-Leste

multidimensional poverty indices that take into account country contexts and priorities. By their very nature, these national indices cannot be compared to each other in cases in which their indicators and data sources differ.

That being said, many countries in the Asia-Pacific region have made the construction of a national Multidimensional Poverty Index an essential part of policy design (Tiwari, 2020, pp. 11–13). Pakistan, for example, used the district-level findings of the 2014/15 Multidimensional Poverty Index to identify less developed areas with multidimensional poverty rates above 50 per cent to allocate more funds to those areas with the objective to further socioeconomic development. The Government is planning to compute the Multidimensional Poverty Index every other year in order to track progress (UNDP, 2019, pp. 31-38).

Viet Nam conducted a poverty census in 2016. Based on the findings of the census, a list of multidimensionally poor and near-poor households was created. Those identified as multidimensionally poor receive certain forms of government support, for example, free health-care services, exemption from school tuition fees or monthly cash transfers for the use of electricity. The Viet Nam Household Living Standards Survey now collects multidimensional poverty data in odd years and consumption and income data in even years (UNDP, 2019, pp. 39-46).

In the wider region, the Multidimensional Poverty Index is part of national development plans in Bhutan, Nepal, Pakistan, the Philippines and Viet Nam, with set targets for the reduction of multidimensional poverty that are complementing monetary poverty measures (UNDP, 2019, pp. 27-30).

The global Multidimensional Poverty Index for Timor-Leste, based on the 2016 Demographic and Health Survey, was calculated (Oxford Poverty and Human Development Initiative, 2019) and is a vital source for comparing poverty in individual municipalities and the wider region. The global Multidimensional Poverty Index, similar to the monetary poverty estimates discussed below, indicates that multidimensional poverty seems to be more prevalent in municipalities in the West of the country, especially in the municipalities of Ermera, Ainaro and the exclave of Oecusse (figure 4). It should be noted that these findings have a political dimension, as the 2006 outbreak of violence occurred at least in part along a regional East-West fault line (Brady and Timberman, 2006).

The 2016 Demographic and Health Survey from which the latest global Multidimensional Poverty Index for Timor-Leste was derived, has a sample of 11,500 households (General Directorate of Statistics, Timor-Leste Ministry of Health and ICF, 2018, p. 2). While this allows for disaggregation at the municipality level, the sample size is not large enough to attain reliable information for lower administrative units. This information deficit can be filled by a “national” Multidimensional Poverty Index based on census data.

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Figure 4. Global Multidimensional Poverty Index value by municipality

AL: Aileu AN: Ainaro BA: Baucau BO: Bobonaro CL: Cova Lima 0.09 DI 0.20 0.18 DI: Dili BA LA ER: Ermera 0.22 LI 0.23 0.20 LA: Lautém AL MT LI: Liquica 0.32 0.20 MF: Manufahi ER VI 0.25 MT: Manatuto 0.5+ BO 0.31 0.21 OE: Oecusse AN MF 0.4-0.5 VI: Viqueque 0.3-0.4 0.2-0.3 0.23 0.32 0.1-0.2 OE CL 0.05-0.1 0-0.05

Source: Oxford Poverty and Human Development Initiative (2019, p. 8). Note: The boundaries and names shown and the designations used on this map do not imply official endorsement or acceptance by the United Nations.

National and international poverty lines

Monetary poverty estimates show a significant decline in poverty rates of Timor- Leste over the past decade. Based on the latest Timor-Leste Survey of Living Standards, the proportion of the population living below the national poverty line declined from 50.4 per cent in 2007 to 41.8 per cent in 2014 (World Bank, 2016).

Table 1. Poverty headcount ratios based on national poverty line, 2007–2014

Per cent of population in poverty (%) (national poverty line) 2007 2014 Timor-Leste 50.4 41.8 Urban 38.3 28.3 Rural 54.7 47.1

Source: World Bank (2016).

Exploring the international extreme poverty line of $1.90/day,1 there is a decline from 37.4 per cent in 2007 to 22 per cent in 2014, meaning that, on average, the rate

1 In September 2020, the World Bank released revised estimates of global poverty from 1981 to 2017 based on 2011 purchasing power parities (PPP). This paper presents the revised estimates that, therefore, do not match previously published figures (such as World Bank (2016)). For further details, see Workd Bank, PovcalNet. Available at http://iresearch.worldbank.org/PovcalNet/home. aspx#.

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of poverty declined by 2.2 percentage points per year, a rate similar to what was shown for neighbouring and the wider East Asia and the Pacific subregions. However, while in both the wider East Asia and the Pacific subregions and Indonesia, extreme poverty fell to far below 10 per cent of the population, approximately one fifth (22 per cent) of the Timorese people were still living in extreme poverty in 2014 (figure 5). As discussed previously (country context), it can be argued that the large-scale destruction of infrastructure by Indonesian military and militias after the independence referendum in 1999 (CAVR, 2013, p. 300; Albrecht and others, 2018, p. 1) and the highly volatile political situation in the years that followed (Brady and Timberman, 2006) delayed economic development, resulting in virtually no reduction of extreme poverty between 2001 and 2007.

Figure 5. Regional comparison of extreme poverty

Share of population living in extreme poverty (below international extreme poverty line of $1.90/day) 45%

40%

35%

30% Timor-Leste 25%

20%

15%

10% Indonesia East Asia and 5% the Pacific 0%

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Source: World Bank, PovcalNet. Available at http://iresearch.worldbank.org/PovcalNet/home.aspx#.

While aggregate statistics, such as national and international poverty lines, are helpful for comparing countries globally, they are less useful for setting domestic policy decisions. Government and development partners require data at lower administrative levels to guide, for example, budget allocation and determine the areas of the country to focus on.

The Timor-Leste 2014 Survey of Living Standards has a total sample size of slightly less than 6,000 households (World Bank, 2016, p. 14), which only allows

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for reliable analysis at the municipality level. In other words, it allows analysts to compare the 13 municipalities of Timor-Leste and to show, for example, that in 2014 the poverty headcount in the exclave of Oecusse (62.5 per cent) was twice as high as in the central municipality of Dili (29.1 per cent). The point is made that poverty levels (expressed as the poverty headcount) are generally “lower in eastern districts and higher in western districts, with the central districts in the middle.” (World Bank, 2016, p. 21).2 However, the associated Gini coefficient for each municipality (ranging from 0.24 to 0.31) indicates the high levels of inequality within municipalities, which raises questions about the usefulness of aggregate statistics for geographically driven poverty interventions. As discussed below, poverty is not evenly distributed within municipalities but is centred in certain “pockets” of the country.

Small area estimation of poverty

As any visitor to Timor-Leste can testify, just within the municipality of Dili the inequality of living standards is striking, with affluent neighbourhoods being within an arms’ reach of sucos comprised of large slum areas (for a definition of slums see UN-Habitat (2015, p. 2)).

Small area estimation of poverty is one approach to capture this heterogeneity and pave the way for geographically targeted interventions. The Demographic and Health Survey is not a World bank product. For a recent World Bank project, researchers have used small area estimations to predict poverty at the suco-level, combining data from the 2014 Timor-Leste Survey of Living Standards, the 2015 Population and Housing Census and the 2016 Demographic and Health Survey (World Bank, 2019b). As a result, it is possible to predict consumption for each one of the households enumerated in the census, which, in turn, allows for poverty predictions at the suco-level. Based on these results, the researchers state that the small area estimations predictions “confirm an already known pattern that poverty headcount rates are much higher in western areas of Timor-Leste than in eastern areas” (World Bank, 2019b, p. 6).

It is worth emphasizing that the poverty rates depicted in figure 6 are predicted based on a statistical model, not on survey data collected from a representative sample of households in each of the respective sucos. The small area estimation approach is filling an important data gap, however, the accuracy of the predictions very much depends on how well the underlying model is able to explain variance in the data.3

2 While the 2014 Timor-Leste Survey of Living Standards seems to support these findings, it should be noted that the exclave of Oecusse, one of the poorest areas of Timor-Leste, is counted here as being part of the western region which may produce a slightly distorted aggregate picture. 3 The authors of the technical report acknowledge that the goodness-of-fit for the underlying beta models is “reasonably low”, but they are confident that the “prediction equations should be reasonably successful” (World Bank, 2019a, p. 8). 12 Measuring poverty in Timor-Leste

Figure 6. Predicted poverty headcount rate

Poverty headcount rate 0.08-0.22 0.23-0.35 0.36-0.45 0.46-0.56 0.57-0.80 Suco Municipality

Source: World Bank (2019b). Note: The boundaries and names shown and the designations used on this map do not imply official endorsement or acceptance by the United Nations.

While the importance of measuring the income and consumption dimension of poverty is undisputed, researchers have argued that in the context of global health and the recent COVID-19 pandemic, it is especially the non-monetary deprivation indicators, such as water, nutrition and cooking fuel, that put people at risk in terms of hygiene, respiratory conditions and weakened immune systems (Alkire and others, 2020). In the case of Timor-Leste, two of these indicators (source of drinking water and cooking fuel) are readily available through census data, which make them potent sources for measuring the non-monetary dimensions of poverty (see Jendrissek (2021) for a copy of the 2010 and 2015 Timor-Leste Population and Housing Census questionnaire). In the following section, the process for calculating a national, census- based Multidimensional Poverty Index is described in more detail.

V. TOWARDS A NATIONAL MULTIDIMENSIONAL POVERTY INDEX: METHODOLOGY

How data from the Timor-Leste Population and Housing Census can be used to construct a national Multidimensional Poverty Index at lower administrative levels is outlined in this section.

While the global Multidimensional Poverty Index discussed above is based on the 2016 Demographic and Health Survey and a sample of slightly less than 12,000

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households, for the suggested national Multidimensional Poverty Index data from the 2015 Timor-Leste Population and Housing Census, containing information from more than 200,000 households, are used. A series of thematic publications based on the latest census have been published, covering such topics as education (General Directorate of Statistics, UNICEF and UNFPA, 2017), fertility (General Directorate of Statistics and UNFPA, 2018a), youth (General Directorate of Statistics and UNFPA, 2018d) and housing characteristics (General Directorate of Statistics and UNFPA, 2018b).

Using the Census as the underlying data source for the construction of the Multidimensional Poverty Index allows for poverty analysis at lower-administrative levels. While, in theory, it would have been possible to produce poverty analyses at the suco level, a decision was made to focus on administrative posts instead. This was done in order to compensate for potential data quality issues at the lowest administrative levels, for example, informal settlements that had not been captured by Census enumerators or the duplication of household identifiers, that, in some cases, made it impossible to differentiate between individual households within the same suco. However, measuring multidimensional poverty at the suco level is still very much on the research agenda. After data from the 2021 Census become available, it should be possible to produce an even more detailed analysis of multidimensional poverty.

The average population size of the 65 administrative posts of Timor-Leste is approximately 18,000, ranging from Fatululic in the municipality of Aileu with slightly more than 2,000 inhabitants to Dom Aleixo in the with 130,000 residents (General Directorate of Statistics, 2016). As discussed below, the Multidimensional Poverty Index methodology is particularly well-suited to account for these differences in the population size of subnational administrative units, as it captures the poverty headcount and the intensity of the poverty.

The Multidimensional Poverty Index methodology is well-documented (see, for example, Alkire and others (2015) and Alkire and Jahan (2018)) and is only repeated in basic terms here. In contrast to absolute poverty measures based on income or consumption, the global Multidimensional Poverty Index differentiates between three dimensions of poverty: health; education; and living standards; these three categories are weighted equally, namely they all count for one third of the total. For each household, a deprivation score is constructed using a number of indicators. If a household is deprived in one third or more of the weighted indicators, it is classified as “multidimensionally poor”, and all household members are assigned the same deprivation score.

Based on this, the multidimensional headcount ratio or poverty incidence (H) can be calculated as

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q H = n (1) where q is the number of persons in multidimensionally poor households divided by the overall population n. The poverty incidence makes it possible to identify what percentage of the population is poor. It does not, however, reveal the intensity of multidimensional poverty (A) which is computed as

q 1 A = q c(k) (2) i=1 where A is the sum of a households’ censored deprivation score c(k) multiplied by the household∑ size over the total number of people in poverty q. In other words, the intensity of poverty expresses the percentage of weighted indicators poor people are deprived in.

The Multidimensional Poverty Index is then expressed as the product of the incidence of poverty (H) and the intensity of poverty (A).

MPI = H * A (3)

This approach is particularly useful for investigating the multidimensional poverty of subgroups, such as urban versus rural poverty or, as discussed here, the multidimensional poverty in different administrative posts.

For the global Multidimensional Poverty Index methodology, a standard set of indicators that allow for comparison between countries is used; as this was the first time a census-based, national Multidimensional Poverty Index was calculated for Timor-Leste, the aim was to adhere to the global methodology as much as possible to allow for comparison and validation.

All but one of the indicators used for the global Multidimensional Poverty Index can be derived from the Timor-Leste Population and Housing Censuses for 2010 and 2015, however, nutrition4 is one of the global indicators of the health dimension that could not be measured using census data.

For the proposed national Multidimensional Poverty Index, the nutrition variable was replaced with an indicator measuring access to health care, and a household was counted as deprived if it was more than 30 km away from the nearest referral hospital5 (figure 7).

4 A household is counted as deprived in this dimension if an adult under the age of 70 or any child is undernourished. Undernourished is defined as an adult with a body mass index below 18.5 and a child whose age-adjusted body weight is more than two standard deviations below the median of the survey population. 5 Distances were calculated in QGIS and are “as the crow flies”, that is, they do not take into account existing road infrastructure and its quality, or the actual time it would take to reach the hospital.

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Even though health services in local communities have been expanded over recent years, the quality of care in the Timorese health-care system remains limited below the highest tier, and referral hospitals are effectively the only institutions in the country offering comprehensive emergency obstetric care. Accordingly, it can be argued that the access to health care indicator is an acceptable substitute in the absence of nutrition data, as the Census does not contain any anthropometric information. The decision to use a 30 km distance was made after consulting local experts with specialist knowledge of the existing roads network. The assumption is that given access to a vehicle, a referral hospital can be reached approximately within an hour from within the 30 km radius. While car ownership in Timor-Leste is low (5 per cent in the 2016 Demographic and Health Survey), 84 per cent of families own a mobile phone (General Directorate of Statistics, Timor-Leste Ministry of Health and ICF, 2018, p. 18), and typically someone in the local community has access to a car, at the very least the xefe suco, the elected administrator of a suco. That said, the 30 km radius is an estimate based on the average time it would take to reach a referral hospital in the majority of cases. This does not, admittedly, hold true for all regions. For example, while the southern tip of Atauro, the island north of the capital Dili, falls within a 30 km radius of a referral hospital, transport off the island by boat would take significantly longer. The same is true for certain remote regions of the exclave of Oecusse.

Figure 7. 30 km radius around referral hospitals of Timor-Leste

Baucau Manatuto Lospalos Liquica GlenoAileu

Ainaro Viqueque Maliana Same Referral hospital Pante Makassar Major town Municipality Suai Administrative post 30km radius

Source: Author’s calculation; General Directorate of Statistics, “Hospitals of Timor-Leste” (Dili, 2018). Note: The boundaries and names shown and the designations used on this map do not imply official endorsement or acceptance by the United Nations.

16 Measuring poverty in Timor-Leste

Apart from access to health care, the other indicators used for the calculation of a national Multidimensional Poverty Index are in line with the global Multidimensional Poverty Index methodology (table 2). Calculations were undertaken in R; to aid reproducibility, the annotated R script as well as the 2010 and 2015 Census questionnaires have been made public and can be accessed through the Mendeley data repository (Jendrissek, 2021).

Table 2. Indicators used for proposed national Multidimensional Poverty Index for Timor-Leste

Dimension Indicator Deprived if… Weight of Poverty Health Access to health care Administrative post is not within a 30 1/6 km radius of a referral hospital. Child mortality Any child has died in the family. 1/6

Education Years of schooling No household member aged 10 years 1/6 or older has completed six years of schooling. School attendance Any school-aged child is not attending 1/6 school up to the age at which the child would complete class 8.

Living Cooking fuel The household cooks with dung, 1/18 standards wood, charcoal or coal. Sanitation The household’s sanitation facility is 1/18 not improved, or it is improved but shared with other households. Drinking water The household does not have access 1/18 to improved drinking water. Electricity The household has no electricity. 1/18 Housing At least one of the three housing 1/18 materials for roof, walls and floor are inadequate: the floor is of natural materials and/or the roof and/or walls are of natural or rudimentary materials. Assets The household does not own more 1/18 than one of these assets: radio, TV, telephone, computer, tractor, bicycle, motorbike or scooter or refrigerator, and does not own a car or truck.

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VI. TOWARDS A NATIONAL MULTIDIMENSIONAL POVERTY INDEX: FINDINGS AND POLICY IMPLICATIONS

The process involved in exploring the global Multidimensional Poverty Index for Timor-Leste shows that at the national level, extraordinary progress has been achieved: the country reduced the incidence of multidimensional poverty from 69.6 per cent in 2009/106 to 46.9 per cent in 2016, the most rapid absolute reduction in the East Asia and the Pacific subregions, with one of the most rapid declines globally in the number of people with no access to drinking water and electricity (UNDP and Oxford Poverty and Human Development Initiative, 2020, p. 12). Timor-Leste, together with Mauritania, Sierra Leone, Liberia and Rwanda, has also achieved the most rapid reduction in multidimensional child poverty in absolute terms (UNDP and Oxford Poverty and Human Development Initiative, 2020, p. 9).

A similar reduction in multidimensional poverty can be observed at the subnational level by applying the methodology outlined above, however, the picture emerging differs substantially from the national level.

Over the five-year period between the 2010 Census and 2015 Census, multidimensional poverty has declined in all administrative posts of Timor-Leste (see annex), but stark differences between different areas of the country remain. Figure 8 shows the picture emerging from the 2015 Census. Administrative posts with overall higher multidimensional poverty are mapped in darker shades of blue, whereas areas with lower Multidimensional Poverty Indices are highlighted in light blue or white. The Multidimensional Poverty Index score of 1 indicates that all households in an administrative post are deprived in all poverty dimensions, whereas a score of zero indicates that no household is deprived in any dimension.

In the map, the five administrative posts with the highest rate of multidimensional poverty are highlighted: Iliomar in the municipality of Lautem; Viqueque and Lacluta in the municipality of Viqueque; and Passabe and Nitibe in the exclave municipality of Oecusse. In total, 60,000 people were living in these administrative posts in 2015, some 5 per cent of the total population of 1.2 million.

Contrary to income or consumption-based poverty analyses (such as the national or international poverty line or the small area estimation approach discussed above), for multidimensional poverty at the administrative post level, there is no clear East- West divide. Instead, the image emerging here appears to be one of rural areas in hard-to-access, remote regions away from urban centres being the ones most at risk of being left behind.

6 The underlying data source is the 2009/10 Demographic and Health Survey which ran from August 2009 to January 2010.

18 Measuring poverty in Timor-Leste Lospalos Municipality Administrative post Major town Referral hospital 0.015-0.101 0.101-0.188 0.188-0.274 0.274-0.361 0.361-0.447 MPI (equal intervals) 0.43 Iliomar Baucau Baucau Viqueque 0.39 Viqueque 0.45 Laduta Laduta Manatuto Manatuto Same Same Ailleu Ailleu Ainaro Ainaro Gleno Gleno Liquica Liquica Suai Maliana Maliana Figure 8. Multidimensional poverty at the administrative post level Pante Makassar 0.37 0.37 Author’s calculation based on 2015 Timor-Leste Population and Housing Census. calculation based on 2015 Timor-Leste Author’s The boundaries and names shown and the designations used on this map do not imply official endorsement or acceptance by the United Nations. The boundaries and names shown the designations used on this map do not imply official Passabe Passabe

0.36 Nitibe Source: Note: 0 25 50 75 100 km

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The Multidimensional Poverty Index also allows for an analysis of the underlying drivers of poverty, so that for each administrative post, the indicators with the highest contribution to overall poverty can be highlighted.

At country level, the total census-based Multidimensional Poverty Index declined from 0.23 in 2010 to 0.16 in 2015 (table 3).

Table 3. Indicators’ contribution to overall multidimensional poverty

Census 2010 Census 2015 Difference Demographic and Health Survey 2016a Health Hospital 4.51% 5.10% 1% Child mortality 3.10% 3.07% 0% Education Years of schooling 18.14% 23.75% 6% School attendance 17.43% 16.01% -1% Living Cooking fuel 11.18% 11.72% 1% standards Sanitation 10.16% 8.59% -2% Drinking water 5.93% 5.11% -1% Electricity 9.86% 6.76% -3% Housing 10.91% 11.11% 0% Assets 8.78% 8.79% 0% Total 0.231 0.162 0.21 Multidimensional Poverty Index Headcount (H) 0.484 0.361 0.458 Intensity (A) 0.477 0.448 0.457

Note: a Oxford Poverty and Human Development Initiative (2019).

The poverty headcount (H) indicates the incidence of poverty, namely the proportion of the population that is deprived in one third or more of the weighted indicators; in Timor-Leste, approximately one third (36 per cent) of households were multidimensionally poor in 2015.

The intensity of poverty (A) is the average proportion of dimensions in which poor people are deprived, meaning that multidimensionally poor households were deprived in approximately 45 per cent of the weighted indicators.

The Multidimensional Poverty Index, with scores ranging from 0 to 1, is calculated as a product of the two, thereby taking into account how many people are multidimensionally poor and their degree of deprivation.

20 Measuring poverty in Timor-Leste

The contribution of education to multidimensional poverty at the country level stands out from the above table. The number of children of school age not attending school combined with the number of households in which not a single household member has had at least six years of schooling contributes 40 per cent to the overall multidimensional poverty.7

Exploring the contribution of indicators at the country level can, of course, only give a rough indication of progress made at the subnational level, as aggregate country-level estimates tend to conceal information. For example, while access to improved sanitation and drinking water has increased at the country level, mainly because of improvements in urban areas, large parts of the country, mainly rural areas, still have to practice open defecation and rely on surface water as their main source of drinking water (General Directorate of Statistics and UNFPA, 2018b).

At the subnational level, the methodology suggested here allows for a more fine- grained analysis of multidimensional poverty and, in turn, more targeted interventions. For example, producing a Multidimensional Poverty Index at the administrative post level highlights those areas with the greatest need for support from government and development partners (table 4).

Table 4. Multidimensional Poverty Index at the administrative post level

Rank Municipality Administrative Population Multidimensional post Poverty Index 1 Viqueque Lacluta 6 779 0.45 2 Lautém Iliomar 7 449 0.43 3 Viqueque Viqueque 25 691 0.39 4 Oecusse Passabe 7 879 0.36 5 Oecusse Nitibe 12 113 0.36 ...... 61 Manatuto Manatuto 14 358 0.07 62 Dili Cristo Rei 62 157 0.04 63 Dili Vera Cruz 36 124 0.02 64 Dili Dom Aleixo 129 094 0.02 65 Dili Nain Feto 32 649 0.02

Source: Author’s calculations based on 2015 Timor-Leste Population and Housing Census.

7 It is acknowledged here that there are methodological shortcomings in measuring learning outcomes as years of schooling; future research might explore using learning achievements instead.

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Table 4 contains a list of the administrative posts with the highest and lowest levels of multidimensional poverty. It shows that the Multidimensional Poverty Index levels are lowest in the municipality of Dili, with four out of its six administrative posts having the lowest levels of multidimensional poverty in the country. In contrast, two out of four administrative posts in Oecusse and two out of five administrative posts in Viqueque are among those areas of the country with the highest Multidimensional Poverty Index levels in Timor-Leste.

It is, therefore, worth noting that the Multidimensional Poverty Index methodology allows for the production of an overview of the drivers of multidimensional poverty for each of the 65 administrative posts. Table 5 presents the drivers of multidimensional poverty for Lacluta, the administrative post with the highest Multidimensional Poverty Index score in Timor-Leste. While overall, the Multidimensional Poverty Index for Lacluta decreased by 12 percentage points between 2010 and 2015, with progress being made, particularly in the areas of access to drinking water and electricity, the area in which Lacluta is still severely underserved is the health dimension. As highlighted in figure 7, apart from Suai in the very south-west of the country, there are no major hospitals along the south coast or in the eastern part of Timor-Leste. As a result, access to health care is the main driver of multidimensional poverty for administrative posts, such as Lacluta. Combined, the health and education dimension account for half of Laclutas total level of multidimensional poverty.

Table 5. Indicators’ contribution to Multidimensional Poverty Index, Lacluta

2010 2015 Difference Hospital 28.3% 31.4% 3% Child mortality 1.1% 0.7% 0%

Years of schooling 11.5% 13.1% 2% School attendance 8.8% 7.3% -1%

Cooking fuel 8.9% 10.3% 1% Sanitation 8.6% 7.3% -1% Drinking water 7.7% 5.1% -3% Electricity 7.4% 5.7% -2% Housing 9.2% 9.8% 1% Assets 8.4% 9.2% 1%

Multidimensional Poverty Index 0.57 0.45 -12% Headcount (H) 0.96 0.84 -12% Intensity (A) 0.59 0.53 -6%

22 Measuring poverty in Timor-Leste

Following the methodology suggested here allows not only for highlighting multidimensionally poor areas at lower geographical levels, it also helps in identifying the drivers of multidimensional poverty in each administrative post, thereby forming a potentially vital evidence base for policy decisions and aiding prioritization of interventions by development partners.

To validate the findings for Timor-Leste, and while keeping in mind slightly diverging indicators, the suggested national Multidimensional Poverty Index is also compared to the global Multidimensional Poverty Index based on the 2016 Demographic and Health Survey (Oxford Poverty and Human Development Initiative, 2019). As indicated in table 5, while the poverty headcount and overall Multidimensional Poverty Index on Census data is slightly lower, potentially due to the lack of malnutrition information, the intensity of poverty is very similar between the two.

To explore further how the global Multidimensional Poverty Index compares to the proposed national Multidimensional Poverty Index at the municipality level, namely the level of the global Multidimensional Poverty Index, the proposed national Multidimensional Poverty Index has to be constructed at the same administrative level. Methodologically, this has proven to be slightly problematic for the health dimension of the national Multidimensional Poverty Index, as each municipality either has a referral hospital within its borders or some part of the municipality is within a 30 km radius of one of the six major medical facilities. Obviously, in reality, this has no meaning for places such as Lautem, the westernmost municipality of East Timor, where only a few settlements along the border to Baucau fall within the 30 km radius of the nearest referral hospital; counting the whole municipality as being within a 30 km radius from a referral hospital is, admittedly, misleading. However, to compare the proposed national Multidimensional Poverty Index to the Demographic and Health-based global index, the indicator was maintained and all municipalities were counted as non-deprived regarding access to health care.

Surprisingly, even including the statistical disturbance pertaining to access to health care, the distribution of the proposed national Multidimensional Poverty Index across municipalities is very similar to the global Multidimensional Poverty Index for Timor-Leste (figure 9). As expected from the issues around measuring the health dimension of the Multidimensional Poverty Index, the suggested national Multidimensional Poverty Index headcount is approximately 5 to 10 per cent lower than the global Multidimensional Poverty Index for each municipality. However, the ordinal structure between municipalities is similar: Dili, being the municipality with the least multidimensionally poor households; Oecusse, the municipality with the most people in multidimensional poverty, and so on. It can, therefore, be said that despite being based on different data sets (Demographic and Health Survey versus Census) and a slightly different set of indicators (access to health care versus nutrition), both

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the global Multidimensional Poverty Index and the proposed national index seem to observe a similar prevalence of multidimensional poverty across municipalities.

Figure 9. Poverty headcount comparison, global and national Multidimensional Poverty Index

70% 60% 50% 40% 30% 20% 10% 0%

Dili Aileau Ainaro Baucau Ermera Lautem Liquiça ManatutoManufahi OecusseViqueque BobonaroCova Lima

MPI (DHS) MPI (Census)

Note: MPI, Multidimensional Poverty Index; DHS, Demographic and Health Survey.

This section includes discussions on the findings of the proposed national Multidimensional Poverty Index, but a number of issues and potential shortcomings remain. These issues are addressed in the following section.

VII. DISCUSSION

To reiterate what was written in the introduction, the work presented in this paper should be understood as a work in progress. Accordingly, this section provides a discussion on some of the methodological problems that have yet to be solved and issues requiring additional data sources.

First, the work presented here is based on the 2015 Timor-Leste Population and Housing Census, which inevitably creates a lag between what can be observed through the Census data and the situation of the country today. Stating the obvious, the analysis is not able to capture the impact of, for example, environmental disasters, such as the devastating flash floods and landslides in March and April 2021 (UNRCO, 2021), nor is it able to gauge the country-specific impact of the Coronavirus pandemic.

24 Measuring poverty in Timor-Leste

That said, the Census-based Multidimensional Poverty Index is by no means meant to depict deprivations in real time, but rather it is intended to track socioeconomic development at lower administrative levels over a longer time period. Once the data from the 2021 Census becomes available, it will be possible to track changes in multidimensional poverty over a decade and the methodology outlined here (as well as the algorithm used to calculate the national Multidimensional Poverty Index, which is published alongside this paper) should help facilitate a quick analytical turnaround in the future.

Second, the analysis would benefit from the incorporation of additional data sources. For example, data on the structure and quality of the road network would be vital in the context of access to health services. An administrative post might be more than 30 km away from a referral hospital, however, in cases where a good- quality all-weather road connects a settlement to such a hospital so that potentially life-saving emergency care can be provided in time, the distance between a settlement and a major hospital could be weighted differently. One option could be to include an interaction term and to calculate the “access to health care” indicator as the product of a household’s physical distance from a referral hospital and a variable capturing road infrastructure connectivity.

At the time of writing, however, available data on roads infrastructure were either incomplete or outdated. For example, major infrastructure developments, such as the controversial Suai Highway in the South-Western part of the country (Rahmani, 2019) could not be incorporated into the model, as the relevant shapefiles were not available.

Third, more work is required to adequately account for intrahousehold dynamics and the situation of women and vulnerable people. The Multidimensional Poverty Index, as presented in this paper, does not capture a gender-disaggregated Multidimensional Poverty Index for administrative posts, a requirement for future research.

Finally, it should again be emphasized that the work presented here does not have the status of “official” statistics. Deprivation indicators and weighting still need to be discussed and agreed on by the Government and stakeholders. However, it is the aim of this paper to demonstrate the potential use of Census data for measuring multidimensional poverty at lower administrative levels. In the case of Timor-Leste, this is the first time such an approach has been carried out. It is the hope of the author that the results may contribute to the broadening evidence base needed to reduce poverty effectively.

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VIII. SUMMARY

This paper has shown the contribution of Census data for the calculation of a Multidimensional Poverty Index for lower administrative units of Timor-Leste, one of the poorest countries in the East Asia and the Pacific subregions.

Over the past decade, poverty in Timor-Leste declined significantly, both in terms of income and consumption and across multiple dimensions. This is most likely the result of an expanding public sector and social transfers (World Bank, 2018b, p. 11). However, to accelerate progress and help inform effective spatial policy interventions, data at lower administrative levels are required. Small area estimation is a helpful tool in this matter, but questions remain about the robustness of the underlying model and if policy decisions should be based solely on the monetary dimension of poverty.

A methodology, such as the Multidimensional Poverty Index, combines multiple dimensions of poverty, but to date, the Demographic and Health Survey-based global Multidimensional Poverty Index for Timor-Leste is only available at the highest subnational administrative unit (municipality level). Inequalities within municipalities, however, make spatial targeting of deprivations difficult.

A census-based Multidimensional Poverty Index, as outlined above, is well-suited to close that data gap because it can produce statistically reliable poverty data at lower administrative levels. Comparing results across the past two Censuses, the proposed national Multidimensional Poverty Index is in line with monetary poverty measures in that it observes a significant poverty reduction across all 65 administrative posts (see annex). Furthermore, when scaled up to the municipality level and compared to the Demographic and Health Survey-based global Multidimensional Poverty Index, the two indices’ poverty headcounts display a high degree of similarity, despite diverging methodologies and underlying data sets.

While consumption-based poverty studies at the municipality level indicate an East-West divide, with fewer poor households in eastern municipalities, the proposed national Multidimensional Poverty Index at the administrative post level does not appear to mirror these findings. Instead, it seems to be the less-well connected areas away from urban centres (and away from the Dili municipality in particular) where citizens are most at risk of being multidimensionally poor.

Investigating the different dimensions of poverty has highlighted the contribution of the education dimension to overall multidimensional poverty. The number of households in which no person over the age of 10 has completed six years of schooling, together with the number of households in which at least one child does not attend school up to the age at which the child would complete class 8 accounts for 40 per cent of the overall multidimensional poverty.

26 Measuring poverty in Timor-Leste

The methodology applied for the calculation of a national Multidimensional Poverty Index at the administrative post level aims, for now, to stay close to the established global methodology; all but one deprivation indicator, nutrition, can be replicated using the Census datasets. The nutrition indicator was replaced with an indicator measuring access to health care, which counted a household as deprived if it was located more than 30 km away from one of the country’s six referral hospitals. It was argued that as referral hospitals are the only institutions offering comprehensive emergency obstetric care, the replacement of the nutrition indicator (which could not be calculated using the Census data) with a location-based indicator measuring emergency health-care accessibility was justifiable.

Potential shortcomings of the proposed methodology were discussed. It was stated that future research might benefit from the incorporation of infrastructure connectivity information, as well as from attempts to incorporate a gender dimension into the research design.

In closing, it is the hope of the author that the research presented here will further the development of an “official” national Multidimensional Poverty Index for Timor-Leste and that the data will aid the policy decisions of the Government and development partners alike. Future research will benefit from the methodology and findings outlined here, especially after the 2021 Timor-Leste Population and Housing Census data become available. At that point, it will be possible to track multidimensional poverty at lower administrative levels over a decade and to evaluate the impact accordingly, a vital step towards eliminating poverty in all its forms.

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ANNEX

Table A.1. National Multidimensional Poverty Index of administrative posts, 2010-2015

ADM_ID Municipality Administrative Multidimensional Difference post Poverty Index 2010 2015 101 Aileu Aileu Vila 0.193 0.121 -0.073 102 Aileu Laulara 0.224 0.130 -0.094 103 Aileu Lequidoe 0.238 0.119 -0.120 104 Aileu Remexio 0.332 0.206 -0.126 201 Ainaro Ainaro 0.165 0.108 -0.057 202 Ainaro Hato-Udo 0.271 0.186 -0.085 203 Ainaro Hato-Builico 0.306 0.291 -0.015 204 Ainaro Maubisse 0.365 0.275 -0.090 301 Baucau Baguia 0.321 0.219 -0.102 302 Baucau Baucau 0.146 0.103 -0.042 303 Baucau Laga 0.363 0.234 -0.129 304 Baucau Quelicai 0.343 0.289 -0.055 305 Baucau Vemasse 0.266 0.138 -0.128 306 Baucau Venilale 0.291 0.177 -0.114 401 Bobonaro Atabae 0.289 0.186 -0.103 402 Bobonaro Balibo 0.328 0.255 -0.073 403 Bobonaro Bobonaro 0.311 0.197 -0.114 404 Bobonaro Cailaco 0.370 0.245 -0.125 405 Bobonaro Lolotoe 0.178 0.128 -0.049 406 Bobonaro Maliana 0.131 0.096 -0.035 501 Cova Lima Fatululic 0.195 0.184 -0.011 502 Cova Lima Fatumean 0.233 0.146 -0.088 503 Cova Lima Fohorem 0.276 0.211 -0.065 504 Cova Lima Maucatar 0.221 0.134 -0.086 505 Cova Lima Suai 0.162 0.088 -0.075 506 Cova Lima Tilomar 0.173 0.107 -0.066 507 Cova Lima Zumalai 0.291 0.222 -0.069 601 Dili Atauro 0.162 0.128 -0.034 602 Dili Cristo Rei 0.065 0.041 -0.024 603 Dili Dom Aleixo 0.027 0.015 -0.012 604 Dili Metinaro 0.166 0.130 -0.036

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Table A.1. (continued) ADM_ID Municipality Administrative Multidimensional Difference post Poverty Index 2010 2015 605 Dili Nain Feto 0.035 0.015 -0.020 606 Dili Vera Cruz 0.030 0.020 -0.010 701 Ermera Atsabe 0.384 0.289 -0.095 702 Ermera Ermera 0.251 0.162 -0.089 703 Ermera Hatulia 0.410 0.287 -0.123 704 Ermera Letefoho 0.345 0.256 -0.089 705 Ermera Railaco 0.199 0.148 -0.051 801 Lautèm Iliomar 0.532 0.428 -0.104 802 Lautèm Lautèm 0.224 0.169 -0.056 803 Lautèm Lospalos 0.385 0.263 -0.122 804 Lautèm Luro 0.401 0.262 -0.139 805 Lautèm Tutuala 0.449 0.321 -0.127 901 Liquiça Bazartete 0.205 0.142 -0.063 902 Liquiça Liquiça 0.228 0.184 -0.044 903 Liquiça Maubara 0.323 0.229 -0.095 1001 Manatuto Barique 0.441 0.295 -0.147 1002 Manatuto Laclo 0.296 0.205 -0.091 1003 Manatuto Laclubar 0.352 0.240 -0.112 1004 Manatuto Laleia 0.401 0.266 -0.135 1005 Manatuto Manatuto 0.131 0.071 -0.059 1006 Manatuto Soibada 0.164 0.102 -0.062 1101 Manufahi Alas 0.247 0.149 -0.098 1102 Manufahi Fatuberlio 0.205 0.129 -0.076 1103 Manufahi Same 0.207 0.138 -0.068 1104 Manufahi Turiscai 0.292 0.183 -0.109 1201 Oecusse Nitibe 0.406 0.363 -0.043 1202 Oecusse Oesilo 0.365 0.295 -0.070 1203 Oecusse Pante Macassar 0.273 0.199 -0.074 1204 Oecusse Passabe 0.453 0.365 -0.088 1301 Viqueque Lacluta 0.570 0.447 -0.123 1302 Viqueque Ossu 0.268 0.208 -0.060 1303 Viqueque Uato-Lari 0.248 0.174 -0.073 1304 Viqueque Uatucarbau 0.273 0.223 -0.050 1305 Viqueque Viqueque 0.468 0.393 -0.075

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