Poverty, Inequality and Oil Exploitation in Chad
Revised final report Submitted to Partnership for Economic Policy (PEP) By
Gadom Djal Gadom Laboratory of Studies and Research in Applied Economics and Management (LAEREAG), University of N’Djamena, Chad (Lead researcher) & Djossou Gbetoton Nadège Adèle Department of Economics and Management, University of Abomey Calavi, Benin
Kane Gilles Quentin Department of Economics and Management, University of Yaoundé II, Cameroon
Mboutchouang Kountchou Armand Department of Economics and Management, University of Yaoundé II, Cameroon
Chad
May 2016
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Acknowledgements
This study was carried out with financial and scientific support from the Partnership for Economic Policy (PEP) with funding from the Department for International Development (DFID) of the United Kingdom (or UK Aid), and the Government of Canada through the International Development Research Center (IDRC). The authors are also grateful to Abdelkrim Araar for technical support and guidance, as well as to Manuel Paradis and Luca Tiberti for for the enriching comments and suggestions.
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Abstract
This paper analyses the impact of the emergence of the petroleum sector on household wellbeing in Chad. A special attention is given to determine the main determinants of the distribution of the petroleum rent, and how this latter can be improved in order to be socially more efficient. To monitor the change in wellbeing over time, we estimate a synthetic index of multidimensional wellbeing, and this using two household surveys, collected in 2003 and in 2011. Our results show that the multidimensional inequality has slightly increase between 2003 and 2011 from 0.591 to 0.609. The Growth Incidence Curve indicates that the poorer and richest classes have earned in wealth but for the large middle class, the wealth have decreased. Conditional NIGIC suggest that the progress in Housing and durable goods is well distributed across the income distribution in Chad. But the education progress failed given that the income distributional pattern of improvements in education lies under 0. This implies that the public resources are not effectively allocated and used efficiently in the education project in Chad. In the issue of oil revenue redistribution policy, the Difference-in-Difference panel results show that people living in departments which received greater oil share compare to national levels are better off than others. Oil revenue helps significantly in improving the MD wellbeing but the poorer departments require more attention on the issue of oil revenue investment.
Keys words: Poverty, Inequality, Oil exploitation, Chad, Redistribution policy.
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Executive summary
Assessing the pro-poorness of economic events, like growth, governmental intervention or economic shocks, has become a general topic of policy discussion in several new development paradigms. This is especially relevant in the Chadian context characterized by a boom in oil exploitation since 2003, actually main source of growth, a high poverty rate and unequal oil revenue repartition among regions.
This study analyzes the effect of oil exploitation on multidimensional wellbeing through oil revenues investment between 2003 and 2011 in Chad. Oil provides a bulk of funding to finance the Chadian development projects (infrastructures, education, health, etc.). It covers 88% of exports on the average since 2004 (PND, 2013). There are different reasons that can justify this form of investigation. First, the petroleum rent may not have an instant monetary impact in the shorter term. Indeed, it is expected that the petroleum rent will be more devoted to improve the non-monetary dimensions of wellbeing, like the provision of education and health services, the improvement in the economic infrastructures, like roads and transportation services etc. To monitor the non-monetary wellbeing over time we start by estimating a synthetized multidimensional index of wellbeing. Based on this, we assess the pro-poorness of growth in non-monetary wellbeing; we evaluate the impact of oil revenues redistribution policy on socioeconomic outcomes.
In this study, we use Data from Chad household consumption and informal sector surveys (ECOSIT 2 and ECOSIT 3 conducted in 2003 and 2011 respectively), the first and second General Population and Housing Census (GPHC) carried out in 1993 and 2009 respectively, as well as information about oil activity and distribution of oil revenue in Chad. ECOSITs provide rich information about housing, education, health and durable good used as wellbeing indicators.
The main findings from this analysis are:
The poorer and richest classes have earned in wealth but for a large middle class, the wealth have decreased. The growth has been pro-poor in Chad between 2003 and 2011 even if the situation of the poor in urban areas requires more attention to improve their access to infrastructure and facilities.
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Conditional NIGIC suggest that the progress in Housing and durable goods is well distributed across the income distribution in Chad. But the education progress failed given that the income distributional pattern of improvements in education lies under 0.
The multidimensional inequality has slightly increased between 2003 and 2011 from 0.591 to 0.609. This depict that redistributive and investment policies implemented in the country since the oil production emergence seems to be inefficient in terms of poverty and inequality reduction
Finally the results show that people living in departments which received greater oil share compare to national levels are better off than others. In addition, far the department is from the capital Ndjamena, lower is the average MD well-being.
Based on these results, policy implications can be drawn
The policies aiming to improve the poor well-being in Chad have to be accompanied by efficient investments in education, environment facilities (lighting type, hygienic garbage vacation, cooking energy, etc.) and to facilitate the household’s access to durable good.
Since the multidimensional inequality has increased in Chad, the redistribution policy targeting Housing, Education and Durable Goods have to be improve in order to promote the development of the middle class because, more the middle class emerges in country better is the reduction of inequality.
In order to better improve the average MD well-being at national level, oil revenue redistribution policy in Chad have to target more the poorer departments. Government has to invest so in the departments which are far from the capital city.
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Table of contents
1 Introduction ...... 7 1.1 Context of the study ...... 7 1.2 Research questions and objectives ...... Erreur ! Signet non défini. 2 Literature review...... Erreur ! Signet non défini. 2.1 Natural resources, growth and redistribution ...... Erreur ! Signet non défini. 2.2 The pro-poorness of the non-monetary wellbeing ...... 11 2.3 Empirical studies using diff-in-diff approach ...... 12 3 Methodology ...... 13 3.1 Data ...... 13 3.2 Empirical strategy ...... 14 3.2.1 Assessing the Multi-Dimensional index of wellbeing ...... 14 3.2.2 Non-income Pro-poorness measure ...... 15 3.2.3 Difference-in-Difference strategy ...... 16 4 Application and results ...... 19 4.1 Multiple Correspondence Analysis and non-monetary dimensions of well-being ...... 19 4.1.1 Selection of the primary indicators to assess the MDW scores ...... 19 4.1.2 Description of selected dimension between 2003 and 2011 ...... 20 4.1.3 Seconde Multiple Correspondence Analysis………………………….…………..21 4.1.4 Multi6dimensional index of inequality..…………………………………………..22
4.2 Pro-poor growth analysis………………………………………………………………….23 4.3 Impact of oil revenue on wellbeing ...... Erreur ! Signet non défini.5 5 Conclusion ...... 26 6 References ...... 27
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1. Introduction
1.1 Context of the study
Since independence in 1960, chronic unstable security situation in Chad has hindered growth and poverty reduction. The country has been experiencing the oil exploitation in 2003. Since, agriculture and primary services which covered 36.7% and 49.6% of GDP before oil producing have not been sufficiently considered (PND, 2013). During the 1990s, the growth rate dropped to 3% per year (INSEED, 2013)1. This low economic performance was mainly due to the recurrent political instability and inadequate levels of investments (Gadom, 2012).
However, oil industry has had a significant stimulatory effect on GDP growth. The oil investments made between 2000 and 2003, and the oil production which started in October 2003 have greatly accelerated economic growth since 2000. The GDP growth rate which was 3% average in the 90s, reached 7% on average from 2001 to 2013 (INSEED, 2013). Oil provides a bulk of funding to the Chadian government. It covers 88% of exports on average since 2004 (PND, 2013). In addition, the commercialization of refined oil from "Djarmaya"2 since 2010, and the derivatives products (gas) have helped to strengthen the financial capacity of the Chadian authorities. Oil activity covers, on average, over 30% of GDP (WDI, 2015) and provides at least 75% of ordinary budget revenues (BEAC, 2013)3.
[Figure 1 here]
Oil revenues are used to finance major investments in Chad. The allocation of oil revenue, under the control of CCSRP4, is moving towards the so-called priority ministries namely infrastructure, education, health, social affairs, agriculture, in order to facilitate the access to public services (sanitary facilities, access to school, roads, drinking water, etc.). In general, public investments are dominated by spending on physical infrastructure that absorbs an average of 57 % of the investment budget since 2004. Social spending (education, health, transfers and subsidies) have increased. According to World Bank (2013), investments in the health sector and social affairs have more than doubled in 6 years, from $ 12.8 million in 2005 to $ 27.4 million in 2011. These
1 National Institute of Statistic, Economic and Demographic Studies. 2 Djarmaya is the site where the oil industry is installed. 3 Bank of Central African Countries. 4 CCSRP : Collège de Contrôle et de Surveillance des Revenus Pétroliers. 7
investments have resulted in the construction of hospitals across the country (modern hospital, maternity services, health centers, etc.). Further, investments in the education sector increased from 10.4 to 22.4 million dollars and result in the construction of several modern schools. These various investments have led to the improvement of child enrollment rate, child mortality, access to drinking water, to electricity, to health centers, to school, etc. (PND, 2013). Oil revenue is the main source of growth in Chad.
It is acknowledged that higher economic growth better benefits the poor when levels of inequality are lower and the gains of development are equitably distributed. The growth led by natural resources could be associated not only with the widening in differences of living standards across regions but also within regions (Buccellato and Mickiewicz, 2009). Inequality is a common concern, strongly perceived by people as well as by governments that redistribute resources through taxation and public expenditure. It is well known that economic growth in African countries is highly based on raw materials and natural resources, especially oil revenues in the case of Chad. However, the dismal performance of most countries is often ascribed to the resource curses because of weak linkages between the exploitation of mineral resources and the rest of the economy, thus the ability of government intervention to provide minimal poverty reduction is mitigated (Thorbecke, 2009, 2013).
Despite the economic performance due to oil, Chad does not record good indicators of development. For example, the country ranks 184th over 187 countries in 2013 according to its Human Development Index (UNDP, 2013). Similarly, poverty indices are bad compared with those of sub-Saharan Africa. The results of the third Consumption and the Informal Sector Survey in Chad (ECOSIT 3) show that 46.7% of Chadians live in extreme income poverty in 2011. The depth of poverty is around 19.7%, one of the highest gaps in Central Africa in particular and in Sub-Saharan Africa in general (WDI, 2014).
Since 2003, the government has set up National Poverty Reduction Papers (NPRP1 from 2003 to 2006 and NPRP2 from 2008 to 2011). These strategies are set as support to the law 001/PRC/99 in 1999 stating the management and allocation of oil revenues across the country in order to better contribute to poverty reduction. But the goal of reducing poverty by half in 2015 is not realized. The incidence of poverty is reduced from only eight (8) points between 2003 and 2011,
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while the intensity of income inequality captured by the Gini index increased by 6.9%, from 0.394 to 0.421 in the same period (World Bank, 2013).
Unfortunately, the clumsy allocation of oil revenues across the country does not account for the disparities among regions. It is not concerned with the unequal levels of development and poverty reduction needs in more poor regions (Mandoul, Logone Occidental and Tandjilé). The distribution of oil revenues is done unevenly and does respect neither the population density of the regions, nor the level of well-being. Then, it is important to evaluate the distributional changes in the Chadian oil take-off period since 2003, as oil revenues are the main source of economic growth in the country.
1.2 Research questions and objectives
The oil revenues investment should be used to address the development issues at national and sub-national levels. In other words oil revenues should contribute to improve households’ wellbeing which includes monetary and non-monetary dimensions. The monetary dimension of wellbeing does not give more information about the capabilities of household such as access to health and education basic infrastructures, and household environmental facilities (Araar, 2009). Therefore, it’s important to know how disparities related to multidimensional inequality has changed between 2003 and 2011.
The main question raised by this study is: How does oil exploitation affect the wellbeing in Chad? Specifically the research tries to answer the following questions:
i) What is the extent of multidimensional inequality between 2003 and 2013 in Chad? ii) Has growth in Chad between 2003 and 2011 been pro-poor? iii) What is the local effect of the oil revenues redistribution policy on socioeconomic outcomes?
To answer these questions, the study investigates the effect of oil exploitation on wellbeing in Chad. Specifically, the study aims to:
i) Estimate a composite index of multidimensional wellbeing (MDW) between 2003 and 2011 in Chad; ii) Assess the pro-poorness of growth in MDW between 2003 and 2011 in Chad;
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iii) Investigate the impact of oil revenues redistribution policy on socioeconomic outcomes at local level in Chad.
2. Literature review
2.1 Natural resources, growth and redistribution
Recently, researches have linked inequality to natural resources. Hence, the redistribution policy issue is relevant in the context of natural resource exploitation. The starting point when analyzing the relationship between natural resources, growth and redistribution is the “resource curse” theory which implies that the countries largely endowed growth less than resource poor countries (Sachs and Warner, 1995).
Natural resource dependence can impact growth through inequality. While some authors, as Lopez-Feldman et al., (2006), Goderis and Malone (2008), Mehlum et al., (2012), Howie and Atakhanova (2014) show that natural resources can reduce inequality. So, according to Goderis and Malone (2008) the boom in mineral resources reduces income inequality in the short run, but in the long run, these inequalities return to their original level. The similar results are established by Mallaye et al., (2014) for which there is a non-linear relationship (U-shaped) between oil rent and inequality. Howie and Atakhanova (2014) in turn establishes, that the resource boom reduces inequality. Moreover, unlike rural areas, the quality of institutions is the most important factor in reducing inequality in urban areas.
Contrary to the previous studies which advocated the existence of an indirect relationship between dependence on natural resources and both growth and inequality, Gylfason and Zoega (2003) demonstrate theoretically and empirically the existence of direct link. Therefore, dependence on natural resources leads to two effects: a decrease in the growth and increasing inequality.
In general, it appears that the initial conditions, governance and democracy, among others are important for a nation to transform the abundance of natural resources to a blessing, by reducing poverty and inequality. The most famous example was the case of Norway, where natural resources have improved the living standard of population and reduce income inequality. The main factors that led to this success are: the redistribution of public spending on social security
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and social services, the accumulation of human capital through investment in education and health, and the creation of a stabilization fund (Mehlum et al., 2012).
Because of its strategic place and importance of disposable income, oil is the natural resource with the highest probability of occurrence of the resource curse (Ross, 2004; Alexeev and Conrad, 2005; Omgba, 2010). Therefore, the question for oil rich countries is: how is it possible to be so rich, but yet so poor?
2.2 The pro-poorness of the non-monetary wellbeing
The analysis of the pro-poorness of growth in literature focuses extensively on the income dimension of wellbeing and lead to neglect the non-income dimension of wellbeing. However, as noted by Kakwani and Pernia (2000) poverty is multidimensional phenomenon, only considering single dimension in the pro-poorness analysis is “futile”. Therefore, pro-poor growth analysis should be undertaken using multidimensional approach as noted by Berenger and Bresson (2012). For instance, Grosse et al (2008) extended the so use growth incidence curves (GIC) in non-income dimension such as education, vaccinations, stunting, mortality and a composite multidimensional well-being measure. This method helps to analyze improvements in different income groups. The empirical results, when considering the case of Bolivia in the period 1989- 1998 suggest that growth was pro-poor in both income and non-income dimension in the relative and the weak absolute sense. Uppal (2014) analysing the pro-poorness of growth in both unidimensional and multidimensional approach in India suggest that in general poverty had declined, but the growth had not been pro-poor over the period of the study.
In African contest, Lachaud (2007) concluded that the growth was pro-poor in both monetary and non-monetary dimensions over the period 1994-2003 in Burkina Faso. However, contrary to other studies, growth was not pro-poor in absolute sense in both national and rural area. Evidence of anti-poor5 monetary growth was found in absolute and relative sense in the cities. In addition, the reduction in monetary or non-monetary poverty due to growth largely depends on the initial level of development. In the case of Morocco, over the period 1987-2004, El Bouhadi et al., (2010) analysed the pro-poorness of growth in the multidimensional approach by constructing a well-being composite index (WCI). When national economic was pro-poor in the
5 Anti-poor growth is a situation where the negative growth raises poverty, even if there is improves in inequality (Kakwani et al, 2004). 11
relative terms at the national level. The story was totally different in urban and rural area. Indeed, both poorest and richest households were positively affected by economic growth in urban area, contrary to the rural area, where the situation of the poorest and the richest households was more deteriorated. Moreover, contrary to Lachaud (2007), Berenger and Bresson (2012) and El Bouhadi et al., (2010) found not consistent results between the multidimensional approach and the monetary framework of pro-poor growth. Oyekale (2015) analysing the pro-poorness of growth using the multidimensional approach in Nigeria over the period 1999-2008 showed that in general the growth was not pro-poor in many zones and states.
At all, the use of non-income approach in the pro-poorness of growth is not only the simple translation of what is consider as up to date in the poverty analysis, but mainly depends of the policy relevance of the results. Indeed, by focusing on the multidimensional of pro-poor growth, policy makers can be more efficient by targeting the most affected income groups or regions and design focused policies.
2.3 Empirical studies using diff-in-diff approach
The analysis of the impact of the mining/oil production or mining/oil revenues transfers on social and economic behaviour has been raised out by several recent studies. These studies, similar to ours, have used difference-in-differences (DID) approach to estimate the distributed effects of valuable natural resources on the producing districts or producing regions wellbeing or on the recipients of abundant resources.
Postali (2009) evaluate whether royalties distributed under the new law have contributed for the development of benefited municipalities in Brazil. The difference-in-differences estimators show that royalty receivers grew less than municipalities that did not receive such resources. The resource-dependent countries exhibit more anemic economic growth, even after controlling for state-specific effects, socio-demographic differences, initial income, and spatial correlation (James and Aadland, 2011). In term of poverty alleviation, Mabali and Mantobaye (2015) have observed the spillover effects of oil revenues in Chad and found evidence that the nonmonetary poverty, as social indicator, didn’t decrease in the oil producing region compared to others.
But the impact of royalties or rents could be positive in some cases. The nature of the impact depends on the institutions’ quality and the redistribution policy. Postali and Nishijima (2013)
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show that royalties had a positive impact on household’s access to electric wiring, piped water and waste collection, as well as in the decrease of illiteracy rate. In the same vein, Zambrano et al., (2014), assess the systematic differences in district-level welfare outcomes between mining and non-mining districts. They found evidence that the condition of being mining-abundant district have a significant impact on the pace of reduction of poverty rates and inequality levels.
Diff-in-diff models are generally used to perform the socioeconomic impact analysis. Apart the analysis of the curse of natural resources, the diff-in-diff is also used to assess the effects of health insurance. In this issue, Finkelstein (2007) used the Continues diff-in-diff model to estimate the impact of the introduction of Medicare on health in US. He founds that the overall spread of health insurance between 1950 and 1990 may be able to explain about half of the increase in real per capita health spending over this time period.
As limit, Mabali and Mantobaye (2015) criticize the fact that diff-in-diff estimator is based on the assumption that only unobservable characteristics are source of bias. They suggest that other observed variables (economic structure, social capital …) can affect outcome in the oil producing Region. Bertrand et al. (2004) raise out the limit of diff-in-diff by emphasizing on the fact that it tends to overstate the significance of interventions. To fill this gap, Munasib and Rickman (2015) use synthetic control method to appreciate the impact of oil and gas production from shale formations in US countries. But the use of this method needs several observation points over time. Mabali and Mantobaye (2015) tackle the diff-in-diff disadvantage by combining it with propensity score methods (PSM), and sustain that a diff-in-diff, compared to synthetic control method, gives unbiased estimates based on the assumption that the selection bias is constant over time.
3. Methodology
3.1 Data
Data to be used for this research come from several sources. First, we will use the Chad household consumption and informal sector surveys ECOSIT 2 and ECOSIT 3 conducted in 2003 and 2011 respectively by the National Institute of Statistics, Demographic and Economic Studies (INSEED). These are databases representative at national, regional, and departmental levels collecting information related to household characteristics, living conditions, income,
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employment, education and other variables. We recall that ECOSIT2 survey coincided with the beginning of the Chadian oil exploitation and ECOSIT 3 has been carried out 8 eight years later.
A specific harmonization is necessary to better match administrative units and derive the appropriate sample and its size given the CCSRP data about oil revenue redistribution across the country, and the ECOSITs data from which socioeconomic outcomes are derived. ECOSIT 3 presents 20 regions and 73 departments, while they are 12 and 67 according to the CCSRP. In this context, our sample size corresponds to the smaller number of each unit, that is, 12 regions and 67 departments6.
Lastly, results of the first and second General Population and Housing Census (GPHC) conducted 2009 by INSEED will be used to introduce the demographic weight in order to compute the ratio (Oil share/demographic weight) which indicates if the department is disadvantaged regarding the oil revenues distribution7.
3.2 Empirical strategy
3.2.1 Assessing the Multi-Dimensional index of wellbeing
Mainly, we will use the factorial analysis technic to construct a composite index of well-being, and this, starting from a large set of welfare and of access to facilities indicators. .
There are many dimensions of well-being which can be influenced by oil revenue through its investment and transfers. In this study, we focus on four dimensions of well-being, which are the housing, education, health infrastructures and durable goods according to information available in ECOSIT databases8. For these dimensions, we dispose a set of primary indicators (non- monetary indicators), which will be strongly related to the dimension that they represent (see Table 1 below). Considering the fact that all used indicators are categorical, the Multiple Correspondence Analysis (MCA) technique becomes the appropriate method to estimate the
6 In Chad, sub-national administrative units are called regions, departments, districts, and sub-districts in decreasing order of size since the Decree N°419/PR/MAT/02 on 17th October 2002. The lowest local unit to be retained depends on the government policy of oil production distribution across the country as well as data availability. 7 It will be useful to account for the estimated demographic growth rate provided annually by INSEED. This procedure will consist to adjust the values of the demographic control variables from 1993 to 2003. The second and most recent GPHC has been conducted in 2009. 8 These dimensions reflect the sectors where oil revenues are mostly spent according to the National Poverty Reduction Papers (NPRP1 from 2003 to 2006 and NPRP2 from 2008 to 2011). 14
individual scores of well-being. In this vein, the individual i non-monetary wellbeing can be quantified following the formula below:
K J k w I jk i, jk W k 1 jk 1 (1) i K
where K is the number of categorical variables, J k the number of categories for indicator k , I the binary indicator taking 1 if the individual has the category j and w is the i, jk k jk normalized first axis score of the category jk .
3.2.2 Non-income Pro-poorness measure
The measurement techniques of pro-poor growth are largely focusing on income dimensions. Thin attention has been put on multidimensionality of pro-poor growth. However, given that poverty is multidimensional phenomenon, the pro-poorness measure will be also multidimensional (Kakwani and Pernia, 2000). In this end, we follow Grosse et al., (2008) inspired by Klasen (2008), by extending the Growth Incidence Curve analysis to non-monetary dimensions. We measure the pro-poor growth using outcome based welfare indicators as housing infrastructures and environmental facilities, basic education infrastructures and durable goods selected via the Multiple Correspondence Analysis (MCA) principles. Thus, to obtain the non- income growth incidence curve (NIGIC), we rank the individuals by individuals’ scores of well- being computed via the non-income indicators indicated above and generate the population centiles based on this ranking. In addition, the conditional NIGIC approach is apply to assess the income distributional pattern of improvements in non-income dimensions of well-being. To do this, we rank the individuals by income and calculate the growth of non-income achievements for these income percentiles. The conditional NIGIC has advantages to analyze how progress in a particular aspect of human welfare (e.g. education or housing) is distributed across the income distribution.
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3.2.3 Difference-in-Difference strategy
Several studies are devoted to assess the link between household wellbeing and the revenues derived from the mineral exploitation especially mining activities and oil exploitation9. It is worth established that the channels of benefits depend on the type of exploitation. For instance, the channel of benefits from oil exploitation is drastically different from that of gold mining. Indeed, we can find a large number of employers who can activate in gold mining, and this in the inverse of oil exploitation. While the benefit in gold mining is meaning the received wages, that of oil exploitation can be manifested through the expansion of the government public spending on public services, as for instance the education and health services.
A specific objective of this study is to analyze the impact of oil transfers on local socioeconomic outcomes, which is highly based on the content of oil revenues redistribution policy in Chad. Indeed, management and allocation of oil revenues across the country is organized by the law 001/PRC/99 from 1999. This law does not explicitly account for oil activity in different localities. The allocation of direct oil revenues is not based on the geographical localization of oil extraction since the allocative law of the budget is not based on the level of production of the localities. On the contrary, oil revenues coming from different producing localities are entirely collected by the central government which decides the allocation throughout the country through the CCSRP organ10.
On average, all regions and department receive a proportion of oil revenues allocated by the central government. However, it is acknowledged that to better address poverty issues, the redistribution must be done according to development needs in different localities. Then, under the assumption that these needs are highly correlated to the size of the population in each geographic unit (department), one could consider a ratio indicating for each department whether the redistribution policy has been favorable or not to its demographic needs. The ratio is given as follows:
9 See Loayza et al. (2013) and Zambrano et al. (2014) for an extensive review. 10 Consequently, the context of oil revenues redistribution policy in Chad differs from several countries in their strategic management of natural resources. For instance, the CANON law in Peru highlights the mining activity in districts by allocating a part of mining transfers to producing districts (see Loayza et al. 2013, Zambrano 2014). 16
oild rd (2) Dd
Where oild represents the percentage of oil revenues received by the department d , while
11 Dd indicates its demographic weight . A ratio rd 1 shows that the part of oil revenues received by the department is lower than what its population represents compared to the national population. Thus, such a redistribution seems disadvantageous for this department given that the percentage of oil revenues received does not match its demographic needs. Conversely, a ratio rd 1 indicates that the redistribution policy is favorable for the considered department, while for a ratio equal to one, the demographic needs are exactly matched. The table 1 in Appendix presents some departmental evidences.
In order to proceed to our identification strategy, the ratio rd allows us to build two groups of department according to the treatment oil transfers. The first group is represented by departments with ratio is greater or equal to 1. The second group is constituted by departments disadvantaged by the redistribution policy with ratio less than 1. To sum up, in this setting of N departments in
Chad, NN1 departments scoring a ratio rd 1 will be the treatment group. The remaining
NNN01 departments represents the control group.
Following Zambrano et al., (2014) but with different identification strategy, we also assume that for each department dN1, , there are two potential outcomes. First, Yd (0) denotes the outcome that would be realized by department d if it had not received oil transfers that at least match with its demographic needs. On the other hand, Yd (1) denotes the outcome that would be realized by department d after receiving oil transfers which are not disadvantageous considering its demographic needs. Assume that the probability of getting a ratio rd 1 is independent from any observable characteristics of the recipient departments out of their respective demographic
11 The percentage of oil revenues will be computed through data from CCSRP based on the average amount of direct oil revenues redistributed throughout the country between 2008 and 2011. Information before 2008 are not available, while data after 2011 go beyond the scope of this study. However, demographic weights are given by the second General Population and Housing Census conducted by INSEED in 2009. These demographic weights will be easily imputed in year 2011 under the assumption that the population has not highly changed between the two dates. 17
12 weights . Thus, the difference YYdd(1) (0) represents the causal effect at the departmental level13. Then, we will use the difference-in-difference (DID) approach to estimate the average effect of the treatment. As Zambrano et al. (2014), our basic model follows the one discussed by Imbens and Wooldridge (2009).
In terms of estimation strategy, we propose a model mainly based on the expected relationship between the received departmental oil revenue budget and the improvement in access to the different public services, which they mainly contribute in the MD wellbeing measurement. Unfortunately, and related to different factors, the distribution of oil revenue was not unequal across the 62 Chadian departments between 2003 and 2011. As a benchmark reference, we assume that the treated department are those that have received a per capita oil revenue higher than that at national level. The rest of departments are assumed to form the non-treated or counterfactual group. By considering the department as the main unit of analysis, we can construct a panel of two waves (2003, 2011), and this, in order to assess the impact of budget oil revenue on MD wellbeing at the departmental level.
Based on the nature of data –panel- and on the assumed form of treatment (treatment between the two periods: specifically, we are based on the budget of 2009), the difference in difference panel model is the appropriate one to assess the impact of treatment and to deal with the bias selection problem. We recall the two popular specifications of the panel models, to be the fixed effect model and the random effect model.
(3) where is the outcome –average MD score- in department i at time t, is a set of covariates of department i at time t, is a dummy variable that it is equal to 0 in pre-treatment period and 1 in the post treatment period, is a dummy variable that it is equal to 1 for the treated department i in pre and post treatment periods and zero otherwise. Note that the term
12 It is acknowledged that this assumption denoting that the treatment is exogenous seems strong and less realistic given the proposed analytic framework. Therefore, it is expected to go further by considering analyses in the case of endogenous treatment. 13 These two potential outcomes are mutually exclusive; only one of them can be realized. 18
disappears in the FE model, otherwise, we will have perfect collinearity between a given combinations of the dummies and the .
The RE model is based on the assumption that the departmental specific effects ( are uncorrelated with the explanatory variables overtime of the same department (Unrelated effects assumption). In the case of presence of the heteroscedasticity, the auxiliary test is the appropriate one and it can be used to select between the two models (i.e RE and FE). If the Unrelated effects assumption is satisfied, one can adopt the RE model (Mundlak, 1978 Wooldridge, 2010).
4 Application and results
4.1 Multiple Correspondence Analysis and non-monetary dimensions of well-being
As was already reported, the first objective of this paper was to assess the individual's multidimensional wellbeing (MDW). Remember that the main part of oil exploitation benefits can be in a non-monetary form, like the improvement of access to school and to health centers, the improvement of access to the road network, to markets, etc. Since the MDW is based on a large number of dimensions, it may be helpful to synthesize its level in a composite index. In practice, for each dimension of wellbeing, like education, we may have a set of primary indicators. Our strategy was to use the factorial analysis approach and precisely the Multiple Correspondence Analysis (MCA) to quantify the MDW. Intuitively, the factorial analysis seeks to determine the inter-correlation between the different indicators to summarize the information and to quantify the studied aspect, like the MDW in our case.
4.1.1 Selection of the primary indicators to assess the MDW scores
We realize a preliminary MCA that takes into account 23 variables from 4 dimensions of wellbeing. This step allowed the choice of the variables that will be used to construct the MDW index. We used a graphical representation (Figure 2) of the variables to better look at their spreads on the first axis and thereby, to appreciate the discriminating power of each variable. We conclude that the following variables have a low discriminating power: Consultation, Raison of dissatisfaction, Sanitary, and Bicycle. Variables which did not deal either with the FAOC property or the discriminating power criterion have been removed. Finally, one dimension
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(Health and basic infrastructures) and a total of five variables have been removed after the first MCA.
[Figure 2 here]
4.1.2 Description of selected dimension between 2003 and 2011 Finally, three dimensions and 18 variables have been retained to run the final MCA (table 2).
[Table 2 here]
Before presenting the results of the MCA, we present an overview of the evolution of the selected MDW dimensions between 2003 and 2011 by living area.
a) Housing infrastructures and environmental facilities
The evolution of housing infrastructure and environmental facilities between 2003 and 2011 appears ambiguous (Table 3). While improvement was observed for some indicators such as nature of the walls, roof and ground, decline was observed for some others as lighting type, garbage vacation and nature of toilet. On the one hand, the proportion of household using cement for walls has slightly increased from about 10% to 13% respectively at national level. The proportion of household that used straw or/and banco for walls decreases from about 88% to 80% respectively. However, the situation is different according to the living area. In rural area, the proportion of household using cement for Walls has slightly decreased from about 8% to 6%. There was increase in the proportion of household using solid root and cement for ground at national level. This dynamics was similar in both urban and rural area. On the other hand, between 2003 and 2011, there was a decline at national level in the proportion of household using modern lighting type (from about 7% to 3%), hygienic garbage vacation (from about 24% to 9%), and hygienic toilet (from about 12% to 7%). Moreover, the magnitude of decline is more pronounced in rural area compare to urban.
[Table 3 here]
b) Education and basic infrastructures
Between 2003 and 2011, the proportion of household head knowing to write at national level has decrease, from about 26% to 19% respectively (Table 4). The dynamics is the same in both rural and urban area, with more magnitude in urban area. The story is different when considering the
20
proportion of household head that experimented problem at school. Indeed, there was not improvement in this indicator at national level, and in both rural and rural area.
[Table 4 here]
c) Durable Goods
The possession of durable goods has slightly increased in households between 2003 and 2011 in Chad (Table 5). For instance, at national level the possession of radio has increased from about 23% to 43%, 0.7% to 0.9% for fridge, 0.6% to 2.8% for fan, and 0.2 to 0.4% for air conditioning. Such improvement was observed in both rural and urban area for radio and fan possession. However, decrease was observed in urban area for the proportion of household that possesses fridge, car and phone.
[Table 5 here]
4.1.3 Second MCA application results
The results show that the explanatory power of the first axis has increased from about 68 to 79 percent. The discrimination power of the first axis is more than 50 percent and can be named multidimensional wellbeing access axis. The First Axis Ordinary Consistency (FOAC) property is checked for all variables (Table 6).
[Table 6 here]
The Table 6 summarizes the first factorial axis derived from the second MCA. It distinguishes social classes from rich to poor. The possession of durable goods such as: air conditioning, fridge, car and fan characterizes the rich class in Chad between 2003 and 2011. Living in urban areas improves wellbeing, even if the individual owns house or not. This can be explained by the fact that in urban areas, the living conditions are more comfortable than in rural areas. In urban areas, houses are built using cement for the grounds and walls. Individuals have access to modern sources of lighting, hygienic toilet and garbage vacation. Furthermore, houses are spacious (more than 5 bedrooms) and individuals use electricity or gas to cook.
Poor are similar in their characteristics. They lack every durable goods: air conditioning, fridge, car, fan, phone and even radio. Thus they can have any access to information and communication. Poor live in rural areas where there is no modern source of lighting and wood is
21
the most important cooking mean. Poor individuals housing are not safe and sound. The following materials are generally used to build houses and lead to very little resistance to natural disasters: straw/banco, thatched roof and clay. Moreover, in poor environment, garbage is thrown away in nature and there is no hygienic toilet. This suggests that poor live in unhealthy environments. Poor households as those: living in rural area, without radio, cooking with wood, without hygienic toilet and garbage vacation, and do not know to write.
4.1.4 Multidimensional index of inequality
The density curves by dimensions below indicate that individuals in urban areas have better scores in education, housing and durable goods than those in rural areas. This suggests that individuals living in rural areas are the poorest taking into account education, housing and durable goods dimensions. The natural explanation of this fact is that the development in infrastructures will firstly concern the dense regions in population.
[Figure 3 here]
Estimation of MDI index are reported in the table 24. As it can be shown, multidimensional inequality has slightly increase between 2003 and 2011 from 0.591 to 0.609. This depict that redistributive and investment policies implemented in the country since the oil production emergence seems to be inefficient in terms of poverty and inequality reduction. Improvement in those policies appear to be necessary. More specifically, when looking at the relative contribution of each dimension to the MDI index, we observe that, the Durable Goods dimension contributes mostly to the multidimensional inequality between 2003 and 2011. The contribution of both housing and educational dimensions to the multidimensional inequality increased respectively from 28.59% to 30.76% and from 31.78% to 33.14%.
However, inequalities between the rich and the poor in Durable Goods have decreased from de 39.62% to 36.10% between 2003 and 2011. This suggest that household welfare in terms of durable goods is effective. Chadian have slight facility to hold the care, fridge, phone, air conditioner, etc. Even if, some improvement are needed, since the durable goods dimension remains the main contributor in multidimensional inequality in Chad.
[Table 9 here]
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In Panel A of Figure 4, we show the evolution in multidimensional MD wellbeing between 2003 and 2011, and this, across the Chadian administrative departments. The highest improvements where specifically registered in Ennedi Ouest, Ennedi Est, Lac Wey, Barh Azoum, N'Djamena and Kabbia. Inversely, the negative or lowest performances where in Haraze Mangueigne, Lac Léré, Tibesti and Dar Tama. Among the explanations of this unequal development one can thinks on the level of benefits from the oil revenue. Indeed, as we can observe in Panel B, the Ennedy departments have received the highest per capita oil revenue, and this, through the allocation of oil revenue across departments. The fruits of the relatively high departmental budget of oil revenue was the remarkable increase in MD wellbeing within this region. Obviously, for the rest of departments, we observe a positive linkage between the departmental oil revenue and the improvement in MD wellbeing. The exception was the department of Tibesti, and where other factors may explain its bad performance.
[Figure 4 here]
4.2 Pro-poor growth analysis
Economic policies that are to improve the households’ welfare have to promote economic growth and to facilitate access to resources and basic public services (health, education, housing, etc.). Economic growth is the important element in the Poverty-Growth-Inequality nexus. The growth is pro-poor if it reduces both inequality and poverty. However, strategies to reduce inequality and poverty or to improve the well-being of households through growth cannot produce the expected effects if they do not target both monetary and non-monetary dimensions of well-being. This section explores the non-monetary aspect of pro-poor growth, analyzing the density curves of the scores and the growth incidence curves of wealth at the national level and according to the area of residence between 2003 and 2011.
[Figure 5 here]
Figure 5.a. shows that the log of scores, computed by the MCA method, follow normal distribution. The density curve has shifted to the left side in 2011 indicating the improvement of overall well-being. Figure 4.b. indicates that the density –population proportion- with low MDW levels have decreased between 2003 and 2011. But an accurate analysis of the evolution of
23
multidimensional wellbeing following the social classes (rich, poor) can be made through the growth incidence curve.
[Figure 6 here]
The growth incidence curve of wealth takes on a U shape (Figure 6). It shows that 20% of the poorest and 20% of the richest benefited from public services and facilities whereas the wealth of the large middle class decreased between 2003 and 2011. The wealth of the first poorest decile group has registered a positive growth. For the middle class, located between the 40th and 70th percentile, the growth was negative. This results suggest that the poorest who are mainly constituted by the household from rural areas have earned from the public services improvement (education, health, etc.) thanks to oil revenues investment in this areas. The richest who live in urban areas benefited more from facilities largely concentrated in towns as N’Djamena for example.
[Figure 7 here]
An analysis of growth incidence curve following the residence areas produces interesting results as indicated in Figure 7. This result shows that poor's access to basic services (education, health and housing) has not improved in urban areas between 2003 and 2011. In other words, the public services, available in urban areas are not easily accessible to urban poor. However, in rural areas, GIC suggests that the poorest have benefited from the wealth generated during this period but the wealth of the rich class has declined. Therefore, the situation of the poor in urban areas requires more attention to improve their access to infrastructure and facilities.
The non-income growth incidence curves by dimension of wellbeing (figure 8) shows the growth in education lies under 0 implying that the growth in this dimension is not pro-poor in the “weak absolute” sense as demonstrated by Grosse et al, (2008) or Klasen, (2008). There is slight improvement in durable goods dimension from 10 to 100 percentiles. The NIGIC in the Housing side takes U shape as the overall non income incidence curve (figure 8 above) means that the housing has a determinant effects in the global wellbeing improvement.
[Figure 8 here]
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The conditional NIGIC (figure 9) shows how progress in a particular aspect of human welfare (education, housing and durable goods) is distributed across the income expenditures distribution. The results suggest that the progress in Housing and durable goods is well distributed across the income distribution, means that the public social spending programs have reached the targeted income-poorest population groups. In return, the education progress failed given that the income distributional pattern of improvements in education lies under 0. This implies that the public resources are not effectively allocated and used efficiently in the education project in Chad.
[Figure 9 here]
4.3 Impact of oil revenue on wellbeing
We proposed a Difference-in-Difference panel model approach to highlight the impact of oil revenue on MDW. Our basic assumption is that the redistribution policy can help to improve individuals’ living standards across localities since investments in social sectors like health, education, water provision, infrastructures are mainly financed by oil revenues in Chad. The obtained results are summarized in table 10 in appendix. Although scales are not equals, both fixed effect (FE) and random effect (RE) models provide similar results about the impact of oil redistribution on multidimensional wellbeing between 2003 and 2011 in Chad. Indeed, the interaction term between time and treatment is positive and significant showing that people living in departments which received greater oil share compare to national levels are better off than others. Therefore, oil revenue helps significantly in improving the MD wellbeing and the poorer departments must receive more attention on this issue.
However, in order to check robustness of our results and choose between FE and RE models, we start by testing for the assumption of homoscedasticity of the error terms. The modified Wald test for groupwise heteroscedasticity rejects this assumption and then suggests the use the auxiliary test to test for the unrelated effects assumption of the RE model. The level of error (P- value) of maintaining the homoscedasticity assumption is about 29%. Then, the auxiliary test rejects the unrelated effects assumption of the RE model. The level of error (P-value) of maintaining the assumption is about 17%which support the use of the FE model.
25
Also, we used some covariates in addition to the treatment with the aim to control for some heterogeneity effects. These variables are especially the population density per km square and its squared value, as well as the distance of the department to Ndjamena and its squared value. Obviously, there are a large number of the other covariates that can explain the MD wellbeing. However, we prefer to avoid the redundancy, since these covariates where used already as basic indicators of MD wellbeing. RE model shows that the far the department is from the capital Ndjamena, the lower is the average MD wellbeing. However the relation between distance to Ndjamena and the levels of MD wellbeing is nonlinear as the squared distance is positive and significant.
[Table 10 here]
5 Conclusion
The objective of this study is to analyze the effect of oil exploitation on household wellbeing between 2003 and 2011, and to investigate the impact of oil redistribution policy on wellbeing at the local level in Chad. The study employs the framework based on the construction of a multidimensional wellbeing scores with the MCA technique. It also uses the Continues Difference-in-Difference model to assess the impact of increase in the governmental budget wellbeing. Data used come from Chad household consumption and informal sector surveys (ECOSIT 2 and ECOSIT 3 conducted in 2003 and 2011 respectively), the first and second General Population and Housing Census (GPHC) carried out in 1993 and 2009 respectively, as well as information about oil activity in Chad. The data on oil revenues are from the Control and Monitoring College of oil revenues (CCSRP).
The MCA analysis shows that the first axis factory explains about 79% of the total inertia. Three dimensions of wellbeing indicators have contributed to this explanation: Housing infrastructures and environmental facilities, Education and basic infrastructures, and Durable goods. The possession of durable goods such as air conditioning, fridge, car and fan characterizes the rich class in Chad between 2003 and 2011. The possession of durable goods has been improved at national level. Such improvement is also confirmed in both rural and urban areas. In return, the proportion of household head knowing to write at national level has decrease, from about 26% to 19%. In terms of inequality dynamic, we observe that the multidimensional inequality has
26
slightly increased between 2003 and 2011 from 0.591 to 0.609, and both housing and educational dimensions contribution to the multidimensional inequality have increased over time, respectively from 28.59% to 30.76% and from 31.78% to 33.14%. The durable goods dimension is the main contributor to the MDI index increase but its contribution decreases from 39.62% to 36.10% between 2003 and 2011.
In addition, the GIC indicate that the poorer and richest classes have earned in wealth but for the large middle class, the wealth has decreased. The growth has been pro-poor in Chad between 2003 and 2011 even if the situation of the poor in urban areas requires more attention to improve their access to infrastructure and facilities. These results suggest that the policies aiming to improve the poor wellbeing have to be accompanied by the efficient investment of oil revenues in education, environment facilities (lighting type, hygienic garbage vacation, cooking energy, etc.) and facilitate the household’s access to durable good. The urban localities require more attention to better help the poor for access to facilities and services. Conditional NIGIC suggest that the progress in Housing and durable goods is well distributed across the income distribution in Chad. But the education progress failed given that the income distributional pattern of improvements in education lies under 0. This implies that the public resources are not effectively allocated and used efficiently in the education project in Chad. In the issue of oil revenue redistribution impact, the Difference-in-Difference panel results show that individuals living in departments which received greater oil share compare to national levels are better off than others. Since the wealth for a large middle class have decreased, the multidimensional inequality has increased and the individuals living in departments which received greater oil share are better off than others, government should set a specific policy to promote the middle class emergence and better orients the oil revenue investment in the poorest departments.
6. References:
Alexeev, and Conrad, M. (2005). The Elusive Curse of Oil. Working paper Series (SAN05-07). Robert Terry Stanford Institute of Public Policy.
Araar, A. (2009). The hybrid multidimensional index of inequality. Working Paper No. 09-45. CIRPEE.
BEAC. (2013). Tableau de bord. Banque des Etats de l'Afrique Centrale. 27
Berenger, V., and Bresson, F. (2012). On the “pro-poorness” of growth in a multidimensional context. Review of Income and Wealth, 58(3), 457-480.
Buccellato, T., and Mickiewicz, T. (2009). Oil and gas: a blessing for few. hydrocarbons and inequality within regions in Russia Forthcoming in “Europe-Asia Studies”.
CCSRP. (2012). Rapports annuels 2004-2012. N'Djamena: Collège de Contrôle et de Surveillance des Revenus Pétroliers.
El Bouhadi, A., Elkhider, A., and El Kchirid, M. (2010). Can economic growth be pro-poor in Moroco? the no-monetary approach. Applied Econometrics and International Development, 10(2), 176-191.
Finkelstein, A. (2007). The aggregate effects of health insurance: evidence from the introduction of medicare. The Quarterly Journal of Economics, 122(1), 1-37.
Gadom, D. G. (2012). Soutenabilité des ressources épuisables: cas du pétrole tchadien: Edition Universitaire Européenne
Goderis, B., and Malone, S. W. (2008). Natural resource booms and inequality: theory and evidence. Working Paper 2008. CSAE 1-41.
Grosse, M., Harttgen, K., and Klasen, S. (2008). Measuring Pro-Poor Growth in non-income dimension. World Development, 36(6), 1021–1047
Gylfason, T., and Zoega, G. (2003). Inequality and economic growth: do natural resources matter? In T. Eicher S. Turnovsky (Eds.), Inequality and growth: theory and policy implications. Cambridge: MIT Press.
Howie, P., and Atakhanova, Z. (2014). Resource boom and inequality: Kazakhstan as a case study. Resources Policy, 39 71–79.
Imbens, G. W., and Wooldridge, J. (2009). Recent developments in the econometrics of program evaluation. Journal of Economic Literature, 47, 5–86.
INSEED. (2013). Profil de pauvreté au Tchad en 2011. National Institute of Statistical Economic and Demographic Studies.
James, A., and Aadland, D. (2011). The curse of natural resources: an empirical investigation of U.S. counties. Resource and Energy Economics, 33, 440–453.
Kakwani, N., and Pernia, E. (2000). What is pro-poor growth? Asian Development Review, 18(1), 1–16.
Lachaud, J.-P. (2007). Measuring pro-poor growth in Burkina Faso: utility or capabilities space? Revue d'économie du développement, 15(5), 5-46.
28
Loayza, N., Teran, A. M., and Rigolini, J. (2013). Poverty, inequality, and the local natural resource curse. IZA Discussion Paper No. 7226.
Lopez-Feldman, A., Mora, J., and Taylor, E. J. (2006). Does natural resource extraction mitigate poverty and inequality? Evidence from rural Mexico and a Lacandona rainforest community. Working Paper 06-005. Giannini Foundation of Agricultural Economics.
Mabali, A., and Mantobaye, M. (2015). Oil and regional development in Chad: impact assessment of Doba oil project on the poverty in Host region. Studies and Documents No. 15. CERDI.
Mallaye, D., Yogo, T. U., and Timba, G. T. (2014). Oil rent and income inequality in developing economies: Are they friends or foes? . Conference Oxford.
Mehlum, H., Moene, K. O., and Torvik, R. (2012). Mineral rents and social development in Norway. In H. Katja (Ed.), Mineral rents and the financing of social policy. Basingstoke: Palgrave Macmillan.
Munasib, A., and Rickman, D. S. (2015). Regional economic impacts of the shale gas and tight oil boom: a synthetic control analysis. Regional Science and Urban Economics, 50, 1–17.
Mundlak, Y. (1978). On the pooling of time series and cross section data. Econometrica, 46, 69- 85.
Oyekale, S. A. (2015). Non-monetary assessment of pro-poor growth in Nigeria: evidence from demographic and health surveys. Journal of Developing Areas, 49(1), 107-132.
PND. (2013). Plan National de Développement. Chadian Ministry of Planing, Economy and International Cooperation.
Postali, F. A. S. (2009). Petroleum royalties and regional development in Brazil: the economic growth of recipient towns. Resources Policy, 34, 205–213.
Postali, F. A. S., and Nishijima, M. (2013). Oil windfalls in Brazil and their long-run social impacts. Resources Policy, 38(1), 94-101.
Ross, M. (2004). What do we Know About Natural Resources and Civil War? Journal of Peace Research, 41(3), 337–356.
Sachs, J. D., and Warner, A. M. (1995). Natural Resource Abundance and Economic Growth. Working Paper 5398. NBER.
Thorbecke, E. (2009). The Anatomy of Growth and Development in Sub-Saharan Africa. Framework paper prepared for the AERC Collaborative Project on Understanding the Links between Growth and Poverty in Africa.
Thorbecke, E. (2013). The interrelationship linking growth, inequality and poverty in Sub- Saharan Africa. Journal of African Economies, 22(AERC Supplement 1), 15-48. 29
Torres, N., Afonso, Ó., and Soares, I. (2013). A survey of literature on the resource curse: critical analysis of the main explanations, empirical tests and resource proxies. Working Paper 2013-02. CEF.UP.
UNDP. (2013). Rapport sur le développement humain 2013. New York: United Development Programme.
Uppal, A. (2014). Multidimensional poverty in India: has the growth been pro-poor on multiple Dimensions? Paper Prepared for the IARIW 33rd General Conference. Rotterdam.
WDI. (2014). World Bank World Development Indicators. Washington DC: World Bank.
WDI. (2015). World Bank World Development Indicators. Washington DC: World Bank.
Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data: MIT Press
World Bank. (2013). Dynamic of poverty and inequality since the emergence of oil production. Poverty Reduction and Economic Management 4 Africa Region.
Zambrano, O., Robles, M., and Laos, D. (2014). Global boom, local impacts: mining revenues and subnational outcomes in Peru 2007-2011. Working Paper No. 509. Inter-American Development Bank.
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Appendixes
Tables
Table 1: Oil revenues redistribution, poverty and inequality per region Régions Départements Poids Part de rev. Ratio (b/a) démographique Pet (b) (a) BATHA Batha - Ouest 0,0179 0,0026 0,1449 Batha – Est 0,0163 0,0011 0,0662 Fitri 0,0100 0,0007 0,0651 Total Région 0,0442 0,0043 0,0978 BORKOU Borkou 0,0062 0,0012 0,1895 Borkou Yala 0,0023 0,0005 0,2198 Total Région 0,0085 0,0017 0,1977 CHARI BAGUIRMI Baguirmi 0,0190 0,0029 0,1514 Chari 0,0166 0,0017 0,1041 Loug - Chari 0,0168 0,0012 0,0684 Total Région 0,0524 0,0058 0,1098 GUERA Guera 0,0156 0,0037 0,2355 Abtouyour 0,0152 0,0015 0,0970 Barh Signaka 0,0094 0,0007 0,0784 Mangalmé 0,0086 0,0015 0,1712 Total Région 0,0488 0,0074 0,1509 HADJER LAMIS Dagana 0,0171 0,0704 4,1272 Dababa 0,0207 0,0176 0,8507 Haraze Al Biar 0,0136 0,0293 2,1583 Total Région 0,0513 0,1174 2,2855 KANEM Kanem 0,0139 0,0013 0,0965 Nord - Kanem 0,0082 0,0004 0,0541 Wadi - Bissam 0,0081 0,0005 0,0566 Total Région 0,0302 0,0022 0,0742 LAC Mamdi 0,0202 0,0036 0,1776 Wayi 0,0191 0,0016 0,0813 Total Région 0,0393 0,0051 0,1308 LOGONE Lac Wey 0,0300 0,0358 1,1917 OCCIDENTAL Dodjé 0,0096 0,0107 1,1143 Gueni 0,0083 0,0108 1,2981 Ngourkosso 0,0144 0,0143 0,9928 Total Région 0,0624 0,0717 1,1480 LOGONE La Pendé 0,0145 0,0277 1,9085 ORIENTAL Kouh Est 0,0092 0,0117 1,2768 Kouh Ouest 0,0045 0,0046 1,0210 31
La Nya 0,0128 0,0134 1,0511 La Nya Pendé 0,0098 0,0086 0,8832 Monts de Lam 0,0198 0,0140 0,7061 Total Région 0,0706 0,0801 1,1348 MANDOUL Mandoul Oriental 0,0232 0,0455 1,9606 Barh Sara 0,0197 0,0152 0,7701 Mandoul 0,0140 0,0161 1,1491 Occidental Total Région 0,0569 0,0767 1,3489 MAYO KEBBI EST Mayo-Boneye 0,0214 0,0020 0,0952 Kabbia 0,0207 0,0005 0,0244 Mayo-Lemié 0,0074 0,0005 0,0663 Mont Illi 0,0206 0,0033 0,1619 Total Région 0,0702 0,0064 0,0909 MAYO KEBBI Mayo-Dallah 0,0303 0,0013 0,0441 OUEST Lac Léré 0,0208 0,0009 0,0429 Total Région 0,0511 0,0022 0,0436 MOYEN CHARI Barh Koh 0,0278 0,0130 0,4690 Grande Sido 0,0097 0,0049 0,5051 Lac Iro 0,0158 0,0029 0,1862 Total Région 0,0533 0,0209 0,3918 OUADDAI Ouara 0,0298 0,0062 0,2079 Abdi 0,0097 0,0007 0,0691 Assoungha 0,0259 0,0008 0,0311 Total Région 0,0653 0,0077 0,1173 SALAMAT Barh Azoum 0,0165 0,0042 0,2554 Aboudéia 0,0059 0,0036 0,6225 Haraze 0,0050 0,0007 0,1399 Mangueigne Total Région 0,0274 0,0086 0,3128 TANDJILE Tandjilé Est 0,0231 0,0115 0,4993 Tandjilé Ouest 0,0369 0,0173 0,4683 Total Région 0,0600 0,0288 0,4802 WADI FIRA Biltine 0,0153 0,0518 3,3826 Darh Tama 0,0162 0,0017 0,1059 Kobé 0,0145 0,0027 0,1835 Total Région 0,0460 0,0562 1,2199 N'DJAMENA N'Djamena 0,0862 0,4530 5,2559 Total Région 0,0862 0,4530 5,2559 BARH EL GAZAL Barh-El-Gazal Sud 0,0177 0,0023 0,1324 Barh-El-Gazal 0,0056 0,0010 0,1791 Nord Total Région 0,0233 0,0033 0,1436 ENNEDI Ennedi 0,0055 0,0267 4,8705 32
Wadi Hawar 0,0097 0,0008 0,0861 Total Région 0,0152 0,0276 1,8132 SILA Kimiti 0,0277 0,0007 0,0241 Djourouf Al Amar 0,0074 0,0004 0,0604 Total Région 0,0277 0,0011 0,0402 TIBESTI Tibesti Est 0,0013 0,0116 8,9378 Tibesti Ouest 0,0010 0,0003 0,3329 Total Région 0,0023 0,0120 5,1910 Source: From CCSRP (2012), INSEED (2012), and World Bank (2013) from ECOSIT2 and ECOSIT3
Table 2: Final set of indicators considered
Dimension Indicators Modalities Housing infrastructures and Occupation Status by Owner Urbain, Owner Rural, No environmental facilities milieu14 owner Urbain, No owner Rural Milieu Urbain, Rural Number of bedroom One, 2 to 3, 4 to 5, More than 5 Source cooking energy Electricity, Gas, Charcoal, Wood, Other cooking energy Lighting Type Modern, No Modern, Other light type Garbage vacation Hygienic, No Hygienic Nature of walls Cement, Straw/banco, Other type of walls Nature of the roof Solid, Thatched, Other type of roof Nature of the ground Cement, Clay, Other type of ground Nature of the toilet Hygienic, No Hygienic Education and basic Knowing writing Yes, No infrastructures Problem at school Yes, No Possession of phone Own, Do not Own Durable goods Radio Own, Do not Own Possession of Fridge Own, Do not Own Possession of fan Own, Do not Own Possession of air Own, Do not Own conditioning Possession of car Own, Do not Own
14 For the occupational status of housing it was proposed to consider the milieu (urban/rural factor). In urban area, owning the house is an indicator of wealth, but this story is in inverse in the rural area. For this end we cross this categorical variable, Occup_Stat with the variable milieu to generate a new categorical variable with four modalities. 33
Table 3: Housing infrastructures and environmental facilities
2003 2011
Urban Rural National Urban Rural National
Solid 0.19 0.30 0.29 71.49 6.42 18.41 Nature of roof Thatched 99.51 99.60 99.59 27.91 91.61 79.88 Other 0.30 0.10 0.12 0.59 1.97 1.72 Cement 15.82 1.77 3.24 26.92 2.75 7.20 Nature of ground Clay 80.74 91.38 90.27 72.70 95.85 91.58 Other 3.44 6.85 6.49 0.38 1.40 1.22 Cement 23.62 8.86 10.41 43.03 6.60 13.31 Nature of walls Straw/banco 76.24 89.62 88.22 52.73 86.97 80.66 Other 0.14 1.52 1.37 4.24 6.43 6.02 Modern 18.90 6.50 7.80 16.95 0.38 3.43 Lighting type Not modern 80.74 70.08 71.20 82.40 94.93 92.62 Other 0.36 23.42 21.00 0.65 4.69 3.95 Hygienic 66.97 19.83 24.77 29.26 5.19 9.62 Garbage vacation Not hygienic 33.03 80.17 75.23 70.74 94.81 90.38 Nature of toilet Hygienic 38.73 9.47 12.54 26.76 2.93 7.32 Not hygienic 61.27 90.53 87.46 73.24 97.07 92.68
Source : Authors calculation
Table 4: Education and basic infrastructures
2003 2011
Urban Rural National Urban Rural National
Writing Yes 51.44 23.28 26.23 37.14 15.71 19.66 knowledge No 48.46 76.72 73.77 62.86 84.29 80.34
Problem at Yes 66.85 83.41 81.67 67.33 83.95 80.89 school No 33.15 16.59 18.33 32.67 16.05 19.11
Source : Authors calculation
34
Table 5: Durable goods
2003 2011
Urban Rural National Urban Rural National
Own 56.06 19.24 23.10 66.57 38.13 43.37 Radio Don’t own 43.94 80.76 76.90 33.43 61.87 56.63 Own 5.00 0.20 0.70 4.79 0.20 0.90 Fridge Don’t own 95.00 99.80 99.30 95.21 99.98 99.10 Fan Own 5.49 0.04 0.61 14.53 0.25 2.88 Don’t own 94.51 99.96 99.39 85.47 99.75 97.12 Air Own 1.73 0.07 0.24 2.50 0.00 0.46 conditioning Don’t own 98.27 99.93 99.76 97.50 100.00 99.54
Source : Authors calculation Table 6: Summary of the first dimension derived from the second MCA
WEALTH Own air conditioning Own fridge Own car Own fan Own phone Cooking with electricity Cooking with gas Modern lighting source Ground in cement Solid roof Wall in cement Owner of house in urban area No owner of house in urban area Urban Hygienic toilet House with more than 5 bedrooms Cooking with Charcoal Own radio Hygienic garbage vacation POVERTY Owner of house in rural area No owner of house in rural area Rural House with one bedroom House with 2 to 3 bedrooms House with 4 to 5 bedrooms Cooking with wood Other cooking energy No Modern lighting source Other light type Wall in straw/banco Other type of walls Thatched roof 35
Other type of roof Ground in clay Other type of ground No hygienic toilet Knowing or not writing No air conditioning No fridge No car No fan No phone No radio Source : Authors computation
Table 7: Results of the first MCA | principal cumul Dimension | inertia percent percent ------+------dim 1 | .0254788 68.53 68.53 dim 2 | .0023445 6.31 74.83 dim 3 | .0012041 3.24 78.07 dim 4 | .0007319 1.97 80.04 dim 5 | .0004846 1.30 81.34 dim 6 | .0001839 0.49 81.84 dim 7 | .0000985 0.27 82.10 dim 8 | .0000694 0.19 82.29 dim 9 | .0000317 0.09 82.38 dim 10 | .0000243 0.07 82.44 dim 11 | .000014 0.04 82.48 dim 12 | 2.98e-06 0.01 82.49 dim 13 | 1.50e-06 0.00 82.49 dim 14 | 5.91e-07 0.00 82.49 dim 15 | 4.69e-11 0.00 82.49 ------+------Total | .0371799 100.00 ------overall ------dimension 1 ------dimension 2 --- Categories| mass qualt %inert | coord sqcor contr | coord sqcor contr ------+------+------+------OcupStat_mil | | | Owner Urbain | 4 816 61 | 4467 816 73 | 148 0 0 Owner Rural | 34 781 17 | -766 781 20 | 30 0 0 No owner Urb | 3 681 45 | 3915 681 44 | -12 0 0 No owner Rur | 3 394 2 | -551 372 1 | -445 22 1 ------+------+------+------milieu | | | Urbain | 7 779 102 | 4222 779 116 | 77 0 0 Rural | 37 779 18 | -746 779 21 | -14 0 0 ------+------+------+------Type_Hous | | | Isolated Hou | 27 905 11 | -730 902 14 | 129 3 0 Agglomeratio | 5 792 12 | 1664 785 14 | -493 6 1 Private Hous | 11 924 9 | 1033 924 12 | 54 0 0 Other type o | 0 213 3 | -937 89 0 | -3635 123 6 ------+------+------+------Bedroom | | | One room | 12 350 6 | -483 306 3 | -603 44 4 2 a 3 rooms | 19 599 1 | -185 524 1 | 232 76 1 4 a 5 rooms | 7 413 3 | 495 399 2 | 300 14 1 More than 5 | 5 674 8 | 1266 673 7 | 174 1 0 ------+------+------+------Cook_Ene | | | Electricity | 0 477 1 | 3074 432 1 | -3270 45 1 Gaz | 1 833 13 | 4629 826 16 | -1376 7 1 36
Charcoal | 6 811 14 | 1635 809 16 | -238 2 0 Wood | 35 842 4 | -385 830 5 | 153 12 1 Other cookin | 2 101 2 | -54 2 0 | -1437 99 4 ------+------+------+------LighT | | | Modern light | 2 841 38 | 4442 829 45 | -1763 12 7 No Modern li | 36 381 2 | -122 227 1 | 330 153 4 Other light | 5 674 8 | -1188 576 7 | -1619 98 13 ------+------+------+------Garbage_Vac | | | Hygenic vaca | 7 910 20 | 1939 896 26 | -795 14 4 No Hygenic v | 35 872 4 | -393 846 5 | 226 26 2 Other Garbag | 2 155 1 | 57 3 0 | -1358 152 3 ------+------+------+------Sanitary | | | Hygenic Bath | 18 760 6 | 602 696 6 | 605 65 7 No Hygenic B | 26 760 4 | -418 696 4 | -419 65 5 ------+------+------+------Wall | | | Cement | 5 888 49 | 3484 885 64 | 600 2 2 Straw/banco | 36 824 7 | -471 816 8 | -161 9 1 Other type o | 2 100 8 | -638 64 1 | 1558 35 4 ------+------+------+------Roof | | | Solid Roof | 5 845 66 | 4193 839 81 | 1147 6 6 thatched roo | 38 825 8 | -494 818 9 | -147 7 1 Other type o | 0 44 7 | -963 41 0 | 762 2 0 ------+------+------+------Ground | | | Cement | 2 921 53 | 5442 921 71 | 77 0 0 clay | 40 861 3 | -294 853 3 | 93 8 0 Other type o | 2 326 5 | -921 190 1 | -2566 136 10 ------+------+------+------Toilet | | | Hygenic toil | 4 950 32 | 3249 945 44 | -825 6 3 No Hygenic t | 39 950 3 | -343 945 5 | 87 6 0 ------+------+------+------Write | | | Know writing | 10 617 19 | 1318 616 17 | -199 1 0 Dont know wr | 34 617 5 | -382 616 5 | 58 1 0 ------+------+------+------Problem_Scho | | | Yes | 35 549 4 | -292 549 3 | 39 1 0 No | 8 549 16 | 1264 549 13 | -167 1 0 ------+------+------+------Consultation | | | Authorized p | 6 497 39 | 189 3 0 | 7398 494 307 No Authorize | 1 509 7 | -643 38 0 | 7455 471 51 Missing valu | 37 497 8 | -13 1 0 | -1309 497 63 ------+------+------+------Raison_disat | | | No problem | 4 501 26 | 206 4 0 | 7457 497 207 Probleme | 3 490 20 | -108 1 0 | 7401 489 152 Missing valu | 37 497 8 | -13 1 0 | -1304 497 63 ------+------+------+------Phone | | | Own phone | 2 783 10 | 2575 744 11 | -1943 39 6 Dont own pho | 42 783 0 | -98 744 0 | 74 39 0 ------+------+------+------Radio | | | Own radio | 15 851 15 | 1093 809 18 | 820 42 10 Dont own rad | 28 851 8 | -580 809 10 | -435 42 5 ------+------+------+------Fridge | | | Own fridge | 0 765 38 | 10881 760 42 | -2856 5 3 Dont own fri | 43 765 0 | -90 760 0 | 23 5 0 ------+------+------+------Fan | | | Own fan | 1 855 57 | 9280 855 72 | -912 1 1 37
Dont own fan | 43 855 1 | -181 855 1 | 18 1 0 ------+------+------+------Air_Con | | | Own air cond | 0 749 21 | 11841 740 22 | -4440 10 3 Dont own air | 43 749 0 | -44 740 0 | 16 10 0 ------+------+------+------Car | | | Own car | 0 789 28 | 8433 782 32 | -2631 7 3 Dont own car | 43 789 0 | -87 782 0 | 27 7 0 ------+------+------+------Bicycle | | | Own bicyle | 9 397 10 | 602 235 3 | 1643 162 25 Dont own bic | 34 397 3 | -159 235 1 | -434 162 6 ------
Table 8: Results of the final MCA
| principal cumul Dimension | inertia percent percent ------+------dim 1 | .03783 79.28 79.28 dim 2 | .00179 3.75 83.03 dim 3 | .0007585 1.59 84.62 dim 4 | .0005403 1.13 85.75 dim 5 | .0002213 0.46 86.21 dim 6 | .0000678 0.14 86.35 dim 7 | .0000548 0.11 86.47 dim 8 | .0000198 0.04 86.51 dim 9 | 5.99e-06 0.01 86.52 dim 10 | 3.24e-06 0.01 86.53 ------+------Total | .0477193 100.00
------overall ------dimension 1 ------dimension 2 --- Categories| mass qualt %inert | coord sqcor contr | coord sqcor contr ------+------+------+------OcupStat_mil | | | Owner Urbain | 5 819 75 | 4073 814 77 | 1585 6 12 Owner Rural | 43 802 21 | -697 782 21 | -510 20 11 No owner Urb | 4 738 54 | 3547 682 47 | 4694 56 81 No owner Rur | 4 417 2 | -500 385 1 | -656 31 2 ------+------+------+------milieu | | | Urbain | 8 798 126 | 3840 776 123 | 2963 22 73 Rural | 47 798 22 | -679 776 22 | -524 22 13 ------+------+------+------Bedroom | | | One room | 16 343 4 | -335 342 2 | 88 1 0 2 a 3 rooms | 24 774 1 | -187 766 1 | 88 8 0 4 a 5 rooms | 9 490 2 | 376 490 1 | 17 0 0 More than 5 | 6 778 7 | 1069 766 7 | -623 12 2 ------+------+------+------Cook_Ene | | | Cook Electri | 0 681 2 | 2922 458 1 | -9381 223 9 Cook Gaz | 1 918 16 | 4335 851 18 | -5564 66 29 Cook Charcoa | 8 866 16 | 1469 810 17 | 1791 57 25 Cook Wood | 44 846 5 | -348 844 5 | -70 2 0 Other cookin | 2 123 3 | -87 5 0 | -1892 118 8 ------+------+------+------LighT | | | Modern light | 3 918 47 | 4117 850 50 | -5379 69 85 No Modern li | 46 706 2 | -123 326 1 | 608 380 17 Other light | 6 791 8 | -1031 674 7 | -1974 117 24 ------+------+------+------Garbage_Vac | | | Hygenic vaca | 9 923 24 | 1752 923 27 | -107 0 0 No Hygenic v | 47 923 5 | -336 923 5 | 21 0 0 ------+------+------+------Wall | | | Wall Cement | 7 896 59 | 3134 891 66 | 1079 5 8 Wall Straw/b | 47 831 8 | -425 824 8 | -178 7 1 38
Other type o | 2 58 9 | -538 56 1 | 466 2 0 ------+------+------+------Roof | | | Solid Roof | 6 863 81 | 3801 841 85 | 2880 23 49 Thatched roo | 49 843 10 | -450 819 10 | -354 24 6 Other type o | 1 27 8 | -697 26 0 | 580 1 0 ------+------+------+------Ground | | | Ground Cemen | 3 929 65 | 4990 929 76 | -301 0 0 Ground Clay | 51 913 3 | -275 907 4 | 103 6 1 Other type o | 2 516 2 | -698 350 1 | -2212 166 9 ------+------+------+------Toilet | | | Hygenic toil | 5 970 37 | 2931 968 46 | -720 3 3 No Hygenic t | 50 970 4 | -309 968 5 | 76 3 0 ------+------+------+------Write | | | Writing Yes | 12 614 23 | 1172 600 17 | 843 15 9 Writing No | 43 614 7 | -340 600 5 | -244 15 3 ------+------+------+------Problem_Scho | | | Problem Scho | 45 550 5 | -263 536 3 | -196 14 2 Problem Scho | 10 550 20 | 1137 536 13 | 849 14 8 ------+------+------+------Phone | | | Own phone | 2 866 11 | 2324 768 11 | -3814 98 29 Dont own pho | 54 866 0 | -88 768 0 | 144 98 1 ------+------+------+------Radio | | | Own radio | 19 937 16 | 948 881 17 | 1098 56 23 Dont own rad | 36 937 8 | -503 881 9 | -583 56 12 ------+------+------+------Fridge | | | Own fridge | 0 890 48 | 10180 779 47 |-17616 110 141 Dont own fri | 55 890 0 | -84 779 0 | 145 110 1 ------+------+------+------Fan | | | Own fan | 1 911 72 | 8667 874 80 | -8152 37 70 Dont own fan | 54 911 1 | -169 874 2 | 159 37 1 ------+------+------+------Air_Con | | | Own air cond | 0 921 26 | 11151 764 25 |-23240 157 110 Dont own air | 55 921 0 | -41 764 0 | 86 157 0 ------+------+------+------Car | | | Own car | 1 928 35 | 7922 804 36 |-14291 124 116 Dont own car | 55 928 0 | -82 804 0 | 147 124 1 ------
Table 9: Calculation of the MDI Dimensions Relative Contributions to the MDI (%) MDI index Housing Education Durable Goods 2003 0.596226 28.59 31.78 39.62 2011 0.615578 30.76 33.14 36.10 Population 0.609208 30.11 32.38 37.51 Source : Authors calculation
39
Table 10: DIF in DIF model - Impact of intense oil revenue on MD wellbeing Random effect Fixed effect Variables model (RE) model (FE) Basic Difference in Difference dummy variables: - 0.092 - 0.037 - Time (0.088) (0.093) - 0.099 - Treatment (0.141) 0.290* 0.349* - Time*Treatment (0.175) (0.177)
Departmental covariates: - 0.002** - Distance to N'djamena (0.001) 0.000*** - Squared distance to N'djamena (0.000) 0.001 - 0.001 - Density of population (0.001) (0.002) - 0.000 0.000 - Squared density of population (0.000) (0.000) Observations 124 124 R2 0.048 0.075 Notes: Discrete change of dummy variable from 0 to 1. Standard deviations in brackets. *p < 0.10, ** p < 0.05, *** p < 0.01
40
Figures Figure 1: Oil production share in GDP
Source: Authors based on World Development Indicators
Figure 2: Projection of the variables on the two first factorial axes after the first MCA
MCA dimension projection plot
15 15 15 15 15
10 10 10 10 10
5 No_owner_UrbOwner_Urbain 5 Urbain 5 5 5 Cook_ElectriCook_Gaz Private_HousAgglomeratio More_than_5_ Cook_Charcoa Score 0 Owner_RuralNo_owner_Rur No_owner_RurNo_owner_UrbOwner_UrbainOwner_RuralScore 0 Rural RuralUrbain Score 0 Other_type_oIsolated_Hou AgglomeratioIsolated_HouPrivate_HousScore 0 One_room2_a_3_rooms4_a_5_rooms One_room2_a_3_rooms4_a_5_roomsMore_than_5_Score 0 Cook_WoodOther_cookin Cook_GazOther_cookinCook_CharcoaCook_Wood Other_type_o Cook_Electri
-5 -5 -5 -5 -5 1 2 1 2 1 2 1 2 1 2 Dimensions Dimensions Dimensions Dimensions Dimensions OcupStat_milieu milieu Type_Hous Bedroom Cook_Ene
15 15 15 15 15
10 10 10 10 10
5 Modern_light 5 5 5 Wall_Cement 5 Solid_Roof Hygenic_vaca Other_type_o Solid_Roof Score 0 No_Modern_liScore 0 No_Hygenic_vScore 0 Hygenic_Bath Hygenic_BathScore 0 Wall_CementScore 0 Other_type_o Other_light_No_Modern_li Modern_lightOther_light_ No_Hygenic_v Hygenic_vaca No_Hygenic_B No_Hygenic_B Other_type_oWall_Straw/b Wall_Straw/b Other_type_oThatched_roo Thatched_roo
-5 -5 -5 -5 -5 1 2 1 2 1 2 1 2 1 2 Dimensions Dimensions Dimensions Dimensions Dimensions LighT Garbage_Vac Sanitary Wall Roof
15 15 15 15 15
10 10 10 10 10 Consultation
5 Ground_Cemen 5 5 5 5 Hygenic_toil Writing_Yes Problem_Scho
0 0 0 0 0 Score Ground_Clay Ground_CemenGround_ClayScore No_Hygenic_t No_Hygenic_tScore Writing_No Writing_YesWriting_NoScore Problem_Scho Problem_SchoScore Consultation Other_type_o Other_type_o Hygenic_toil Consultation
-5 -5 -5 -5 -5 1 2 1 2 1 2 1 2 1 2 Dimensions Dimensions Dimensions Dimensions Dimensions
Ground Toilet Write Problem_School Consultation
15 15 15 15 15 Own_fridge 10 Disatisfatio 10 10 10 10 Own_fan
5 5 5 5 5 Own_phone Own_radio 0 0 0 Own_radio 0 0 Score Disatisfatio Score Dont_own_pho Dont_own_phoScore Dont_own_rad Dont_own_radScore Dont_own_fri Dont_own_friScore Dont_own_fan Own_fanDont_own_fan Disatisfatio Own_phone Own_fridge -5 -5 -5 -5 -5 1 2 1 2 1 2 1 2 1 2 Dimensions Dimensions Dimensions Dimensions Dimensions Raison_disatisfation Phone Radio Fridge Fan
15 Own_air_cond 15 15 10 10 Own_car 10
5 5 5 Own_bicyle
0 0 0 Score Dont_own_air Dont_own_airScore Dont_own_car Dont_own_carScore Dont_own_bicOwn_bicyle Dont_own_bic Own_air_cond Own_car
-5 -5 -5 1 2 1 2 1 2 Dimensions Dimensions Dimensions Air_Con Car Bicycle standard normalization
41
Figure 3: Density curves of the contribution of scores by dimensions
Density curves of the contribution of scores by dimensions
Education Housing
.6
3
2.5
.4
2
1.5
.2
1
Density(f(y)) Density(f(y))
0
.5 0 .066147 .1322941 .1984411 .2645881 .3307351 0 .8964611 1.792922 2.689383 3.585844 4.482306 y y
Urbain Rural Urbain Rural
Durable goods
.8
.6
.4
.2
Density(f(y))
0 0 .9576736 1.915347 2.873021 3.830695 4.788368 y
Urbain Rural
42
Figure 4: The change in average departmental scores between 2003 and 2011
Panel A Panel B
Evoluation of Multidimensional Well-being The distribution of oil revenue across departments The change in average departmental scores bertween 2003 and 2011 The ratio of oil revenue (Tchad 2009) Change between 1.00 and 1.25 (.8832,6.492281] Change between 0.70 and 1.00 (.469,.8832] Change between 0.50 and 0.75 (.1302465,.469] Change between 0.25 and 0.50 [.0244,.1302465] Change between 0.00 and 0.25 Change between -0.25Tibesti and 0.00 Tibesti Change between -0.50 and -0.25 Change between -2.50 and -0.50
Borkou Ennedi Ouest Borkou Ennedi Ouest
Ennedi Est Ennedi Est
Nokou Kobé Nokou Kobé Biltine Biltine Fitri Barh El Gaze Barh El Gaze Fitri Dar Tama Dar Tama
Kanem Kanem Batha Est Batha Est Mamdi Ouara Mamdi Wayi Assoungha Ouara Wayi Assoungha
Dagana Batha Oues Dagana Batha Oues Haraze Al Bi Djourf Al Ah Dababa Mangalmé Haraze Al Bi Djourf Al Ah Dababa Mangalmé Barh Signaka Bitkine Barh Signaka Sila Bitkine N'Djamena Sila N'Djamena Baguirmi Aboudeïa Baguirmi Aboudeïa
Guéra Barh Azoum Guéra Barh Azoum Mayo-BoneyeLoug Chari Haraze Mangu Mayo-BoneyeLoug Chari Haraze Mangu
Mont Illi Lac Léré Mont Illi Kabbia Tandjilé Est Barh Köh Lac Iro Lac Léré Kabbia Tandjilé Est Barh Köh Lac Iro TandjiléBéré Oue Mayo-Dallah Mandoul Orie TandjiléBéré Oue Ngourkosso Mayo-Dallah Mandoul Orie Lac Wey Ngourkosso Dodjé Mandoul Occi Lanya DodjéLac Wey Pendé Grande Sido LanyaMandoul Occi Barh Sara Pendé Grande Sido Nya Pendé Barh Sara Monts de Lam Nya Pendé Monts de Lam
43
Figure 5: The density of the log of scores
a. The density curves b. The difference between density curves
Density curves
.2 .004
.15
.002
.1
0
Density
.05
Density finalDensity - initial
-.002
0 -6 -3.6 -1.2 1.2 3.6 6 Log of scores 2003 2011 -.004 -6 -3.6 -1.2 1.2 3.6 6 Log of scores
Figure 6: Overall Growth Incidence Curve
Growth incidence curve of wealth index ( Order : s=1 | Dif. = ( Q_2(p) - Q_1(p) ) / Q_2(p) )
1.5
1
.5 GIC(p)
0
-.5 0 .2 .4 .6 .8 1 Percentiles of population
Confidence interval (95 %) Estimated difference
44
Figure 7: The Growth Incidence Curves by area
GIC Urban GIC Rural
.3
1.5
.2
1
.1
.5
0
0 -.1
-.5
-.2 .0001 .20008 .40006 .60004 .80002 1 .0001 .20008 .40006 .60004 .80002 1 Percentiles (p) Percentiles (p) Confidence interval (95 %) Estimated difference Confidence interval (95 %) Estimated difference
Figure 8: Non income growth incidence curves by wellbeing dimension
Non Income Growth Incidence Curves Wellbeing: MD score Education Housing
.8
1
.6
.5
.4
0
.2
-.5
0
-1
-.2 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 Percentiles Percentiles
Durable goods
5
0
-5
-10 0 .2 .4 .6 .8 1 Percentiles
45
Figure 9: Conditional non income growth incidence curves
Non Income Growth Incidence Curves Wellbeing: Delfated per capita expenditures Education Housing
0
.6
-.05 .4
-.1
.2
-.15
0
-.2
-.25
-.2 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 Percentiles Percentiles
Durable goods
.8
.6
.4
.2
0
0 .2 .4 .6 .8 1 Percentiles
46