Regional inequality in Tunisia:

Is it a geographic misallocation or spatial nature effects?

Jmaii Amal1

Abstract

Using detailed household-expenditure level data from Tunisia, we examines urban-rural inequality in

Tunisia. We use Bayesian regression-based decomposition method to dissect the gap between the two

areas. Results of the present study suggest that the difference between rural poor households and urban

poor households is due essentially to characteristic effects; while for wealthier households both

characteristic and returns to these characteristic effects (for example, efficiency of educational system)

are in charge of this gap. Additionally, the results demonstrate that this is an issue of value, and, more

specifically, an economic development fairness conflict. It is suggested that policy makers should

address a positive discrimination programs in favor of marginalized region.

Keywords: regional inequalities; Bayesian unconditional quantile regression; RIF decomposition;

micro data, Tunisia.

JEL Classification: I31, I24, P25, C14

1 PhD economics sciences, university of Tunis el Manar, Tunis

I. Introduction

“I have cherished the idea of a democratic and free society in which all persons will live

together in harmony and with equal opportunities, ... It is an ideal for which I hope to live

and to see realized ... if needs be, it is an ideal for which I am prepared to die.”

-Nelson Mandela –

Inequality is no longer seen as an inevitable prerequisite for growth, but rather as an obstacle to

both economic growth and poverty reduction (Bourguignon, 2003). A fundamental question in

the field of regional inequality and development is why some regions are rich and others

vulnerable/poor. One explanation is that differences arise from spatial concentrations of

households/individuals with personal characteristics inhibiting growth in their living standards.

Indeed, inequality can efficiently refer to the overall trend of regional development; it does not

completely explain whether certain households are different in their welfare due to geographic

localization, or due to high social/economic disparities within the regions. In such a case,

decompose inequality, which divide aggregate inequality to analyze between-regions

variability, is required for a positive assessment of inequality and for the purposes of effective

social policies. In a quantitative investigation of the impact of changing in economic structure

and urbanization on inequality in Asia, Kanbur and Zhuang (2013) show that economic

structure and urbanization have a positive and significant effect in inequality. Chambers and

Krause (2010) used semiparametric methods to analyze the relationship between income

inequality and economic growth. The authors found that higher income inequality is harmful

for economic growth. Son and Kakwani (2008) found that initial levels of income inequality

and economic development have a positive and significant influence on poverty reduction. In a

study based on a new data set on inequality in the distribution of income, Deininge and Squire

(1996) revealed a systematic link between growth and aggregate inequality variation in parallel

with a significant positive relationship between growth and poverty alleviation.

There has been a wide proliferating literature on inequality decomposition measurement

between regions and its application to micro-data surveys in the developing countries context.

Several studies have been published addressing the source of inequality between rural and urban

households. Nguyen et al. (2007) discuss the welfare inequality between urban and rural areas

from 1993 to 1998 in Vietnam using Machado-Mata decomposition (2005). In this study, they

conclude that inequality differences between the two regions were due to three factors namely

education, ethnicity, and age. Based on a dataset from 65 countries, Young (2013) found that

the gap represents 40% of the average country inequality. In addition, Sicular et al. (2007) used

data from two years respectively 1995 and 2002, to analyze the rural-urban income inequalities

in China. Studying deeply persistent gap between rural and urban sectors can put more emphasis

on the principal factors contributing to this gap and thus improve the decisions required to be

undertaken by policymakers to support regional development.

Poverty and regional inequalities contributed to the rise of the revolution in Tunisia. The

increase in per capita income underestimated persistent regional inequality and poverty,

misrepresenting the situation in the interior where the revolution began. The concentration of

economic activities investment and services in the east regions has increased poverty and

unemployment rate in the west, particularly for youth (Gana, 2012; Boughzala, 2013; Arieff, 2012).

In the spirit of this debate, a legitimate question arises as whether regional inequality in Tunisia

reflects a geographic misallocation problem/a bad government decisions or it’s rather about the

structure and nature of some regions.

To response to this question, this study is organized as follows. First, we began by a Tunisian

background information. Section three presents the data and the model. Section four provides

the results of the proposed methodology. Finally, we conclude and give some policies

recommendations.

II. Background: Tunisia five years after the revolution

1. Spatial inequalities: History and evolution

Income distribution is an important indicator for analyzing poverty and economic

development in a country. A better understanding of the pattern and drivers of regional

inequality is critical for enhancing social cohesion and inclusive growth in the region.

Considerable work has been undertaken on regional inequalities related to developing countries.

In a dynamic analysis of the patterns of household welfare in Jordan, Mansour (2012)

highlighted a slight decline in inequality during 2002–10 mostly driven by a regional catching-

up effect. Laithy, Abu-Ismail, and Hamdan (2008) found that despite the moderate national

levels of poverty and inequality in Lebanon, there are striking regional income disparities. In

an analysis of the urban-rural gap in North African countries, Boutayeb and Helmert (2011)

show that, these countries has experienced a considerable development in social, economic and

health indicators. Unfortunately, regions/socio-economic groups of the same country have not

benefited by these improvements equally. Bibi and Nabli (2009) found that MENA region has

relatively a higher level of income/expenditure inequality, compared to other regions. Adams

and Page (2003) revealed that compared to other regions, the MENA has a lower income

inequality and poverty rate due to public sector employment and international

migration/remittances. Regional gap in education has been also of concern in some studies on

Arab countries. In an empirical analysis of inequality of education opportunities in MENA,

Salehi-Isfahani, Belhaj Hassine, and Assaad (2013) show that, inequality in educational

achievements is mainly explained by inequality of opportunities. Likewise, Krafft and Assaad

(2016) highlighted that inequality of opportunity- unequal resources allocations on the basis of

circumstances independent from individuals’ control- can led offends people’s sense of fairness,

causing anger and frustration among marginalized groups.

Tunisia, as a developing country, has implemented since the early years of statehood,

several national programs to reduce poverty and regional disparities. The country has indeed

shown some progress in many areas. The per capita income has raised, public services have

developed, and health indicators have improved and the demographic trends have been

favorable. In fact, the country has opted for an open trade policy since the beginning of the

1990s. This strategy was preceded by the implementation of a Structural Adjustment Plan.

While this policy has led, over the last twenty years, to an increase in growth and income, the

gains from free trade are not equally distributed within the. Economic restructuring,

globalization of capital markets and structural adjustment are synonymous of drawdown of the

permanent workers number, the subcontracting with resort to temporary and seasonal work and

the reduction of costs through deregulation of the labor market.

The deterioration of living conditions among rural population, the increase in the

unemployment rate and the inflation in the consumer prices for basic goods associated to

climate change and water scarcity problems, all threatened the sustainability of growth in these

areas, which has provoked the uprising of the people in the interior regions leading to the 2011

Tunisian revolution. Indeed, after the revolution, Tunisia is boldly emerging from the recession

considered as the longest period of economic downturn ever since the establishment of

statehood. In this paper, we emphasized the failure of agricultural, trade and social policies and

the insufficiency of the measures taken to deal with the vulnerability of the rural population.

More than 800,000 people were unemployed in 2014 and the poverty rate reached 15%

according to data released by the INS. Poverty rate is correlated with many factors such as

unemployment rate, illiteracy, access to health services and especially education (African

Development Bank, 2013). Therefore, poverty in Tunisia is mainly concentrated in rural areas

and in some regions of the country, particularly the Central West, a region that first witnessed

the first sparkles of the revolution. In addition, the lack of an infrastructure in these areas, such

as roads and communication facilities, may limit poor people’s access to information or to the

labor markets.

A strong variation in poverty rates between regions may be the cause of social instability and

population movement. Households with higher level of poverty rate are more concentrated in

the interior regions of the country than in the inland ones. Indeed the measurement of poverty

at the regional level allows a better definition of the priorities for regional development. In fact,

the decrease in the poverty rate compared to the higher consumption disparities with economic

inequalities assert that the GDP growth is biased towards the non-poor.

2. Inequality in the Tunisian post-revolution context

Regional inequality is a driving factor behind Tunisia’s economic/political crisis. So long as

the west Tunisia remains ignored compared to its more-developed east, discontent and unrest

will plague its political and economic recovery. Statistics show that during the past two

decades, poverty rates have declined and the overall economic situation has improved.

However, large parts of the country have been neglected and as a result, regional disparities

has been exacerbated. For example, the gap in poverty rates between the capital and the rest of

the country shows that the regional variation in terms of living standards increased between

2010 and 2015 (figure 1).

Up to now, the adopted economic and social development does not correspond to good regional

governance objectives that Tunisia has to achieve. Hence, the disappointment expressed by of

many Tunisians as they had better expectations after the revolution. Many Tunisians has

expressed feelings of distrust towards public policies that devoted little regard for regional

inequalities.

2010 2015

Fig 1: Poverty rate of Tunisia 2010 vs 2015

III. Methodology

1. Data

This paper uses the consumption expenditure survey (CES)2, this survey is conducted by the INS every

five years and provides socio-demographic and economic characteristics of households and individuals.

Indeed, for 2010, it takes a representative sample of 11281 households with 50371 individuals. The data

includes information concerning the head of the household, composition; area of household’s location

and total household’s consumption expenditures. The choice of explanatory variables is based on the

literature (Nguyen et al. (2007); Sicular et al. (2007); Skoufias and Katayam , 2011) and is validated by

the AIC and BIC criteria1. Therefore, we use household size, the proportion of children under 15 years

2 The ideal is if we can made comparative analysis between household situation of 2010 and 2015. Unfortunately, the last household survey of consumption and expenditure, Tunisia 2015 – micro data – is not yet available for researcher.

old in each household and the gender of the household head and the age. As far as the household

education and employment characteristics, we have included the variable of the schooling of household

head: illiterate (as reference), primary, secondary and higher level. For employment variable, we select

four sectors respectively governmental sector (as reference), private sector, self-employed and

agricultural sector. Since the frequency of transfers from foreign countries, we use a dummy variable

indicating depending on whether a household had received transfers from abroad or not.

2. Choice of the model

The principal question this paper tries to answer is whether spatial factors has any effect on

well-being controlling for observable non-geographic characteristics of the households and

whether access to public and private assets compensates for the effects of an adverse

geography. To address this question, we have used Bayesian regression-based decomposition

method.

a) Bayesian unconditional quantile regression

Introduced by Koenker and Bassett (1978), quantile regression models aim at modeling the

effect of the explanatory variables on the conditional distribution of the outcome variable. They

have been increasingly used in empirical labor market studies, to describe parsimoniously the

entire wage conditional distribution (see e.g. Buchinsky 1994, Chamberlain 1994, Machado and

Mata 2001). While the quantile regression models are useful, they have some restrictive.

b) Recentered Influence Function (RIF)

RIF-regression (Fortin, Lemieux, and Firpo, 2011) is a convenient tool to conduct OB type

decomposition for other method besides the mean such as quantiles regression. Therefore, when

we perform quantile model, RIF-regression will be considered as rescaled linear model.

According to this definition, rescaling factor relies on the estimate of the interest quantile

density:

𝑅𝐼𝐹(𝑦, 𝑄𝑞) = 𝑄𝑞 +𝑞 − 1{𝑦 ≤ 𝑄𝑞}

𝑓𝑦(𝑄𝑞)

The distributional statistic can be written based on the conditional expectations of its recentered

influence function: We, then, can perform an OB decomposition using the RIF as response

variable (dependent variable).

IV. Empirical Results 1. Source of rural-urban inequality

When the models are estimated separately for rural and urban areas, it is possible to see the

differences in the factors affecting consumption expenditures. The rural variable was added (as

a dummy variable) in the pooled model, and its coefficient is statistically significant at a level

of 1% with a negative sign. Most variables of both rural and urban models are significant. The

quantile regression results for both rural and urban areas are given in the table 4 and table 5.

[Place table 1 here]

The finding demonstrates that gender variable is statistically significant in the urban model for

the means regression as well as for the quantile estimates. From table 4, we reveal that men’s

expenditures are consistently lower than those of women, but these differences are much greater

in the upper quantiles. We note that this variable is not significant in the 10th percentile.

Although, for the rural model this variable is not significant in the mean and quantile regression,

except in the 25thand 50thpercentiles.

[Place table 2 here]

Results also show that the variable “size of the household” is found to be significant. Ceteris

paribus, with an unit change in the household size, the level of expenditure of household’s

declines by approximately 23.2% for urban areas against 18.4% for rural areas. The breakdown

by age of household heads leads to interesting results in term of social policies. In general,

young people are more exposed to poverty compared to other categories, in both means and

quantiles regression. When looking for the class-age of 31-45 we observe similar results

between rural and urban areas. In most quantiles, this category is not significant. This result

confirms many facts related to the economic/social environment in Tunisia. The increase in the

unemployment rate, in rural area, mainly due to climate change (drought) associated with an

absence of labor market, forced people to internal migration to urban areas. In parallel, we

witness the urbanization of poverty and unemployment caused by the increasing number of

graduate and the inability of the labor market to absorb them. In addition, results show that

categories of age 76 and above have no significant effects or have negative significant effects

of welfare in rural area compared to younger category and compared to urban subgroup. In fact,

for Tunisian case, the rural population is not homogeneous. There is a two-pronged migratory

movement: the departure of skilled young people and the arrival of elderly close to retirement

(Jelili and Mzali, 1998). This phenomenon of older age groups having considerable within-

cohort inequality is an important acknowledged fact and can be clearly observed in the Tunisian

data. These results confirm the urgency of the issue of rural old-age vulnerability in Tunisia.

[Place table 3 here]

Results also show that the agricultural sector has a positively significant coefficient in rural area

at all quantiles excluding the 25thand the 90th. For both urban and rural areas, welfare is

determined by governmental and private sectors, since they are statistically significant with a

positive sign. In the urban model, the effect of these variables is higher in the upper quantile. In

the rural regression, they are much higher at the bottom of the distribution. As indicated in

Tables 5 and 6, returns to education are statistically significant across all quantiles. The findings

show that, for entire distribution, consumption expenditures for people with higher education

levels are higher than illiterate people and those who have just acquired primary education and

secondary education level. In addition, results show that west region is more vulnerable and

have lower welfare than East region. In fact, employments are paying off in the East relatively

to the West and probably for the same reasons: the East gets the lion's share of market’s activity

in Tunisia (manufacturing and services).

2. Counterfactual decomposition results

the overall poverty gap between poor rural and poor urban households may be explained by

the constant term (table 4) of the coefficient effect (14.8%). This means that, even with urban

characteristics, poor rural households will still suffer from a poverty gap of around 9.36%

compared to urban households. This is due to the geography effect, which assigns persistent

spatial differences in living standards to difference in geographic characteristics such as

roads/transport, water, etc.

[Place table 4 here]

Results reveal that differences in the type of employment and education explain respectively

4.58% and 18.32% of the poverty gap between rural and urban areas for the poorest

households (Table 4). Similarly, if the conditions of the labor market in rural areas were

similar to those in the urban areas, the poverty gap could be reduced by 3 points.

Results of the decomposition method revealed that both covariate and returns effects are larger

within higher quantiles, resulting in a wider urban–rural gap. This means that, the principal

reasons of the difference between rural wealthiest households and urban wealthiest households

were twofold: the first one, due to characteristic effect such as education, employment and

household size; the second reason for this difference, is due to the return effect of these

characteristics. Apart from this result, we observe a dominance of characteristics effects at

lower quantile, which attest that differences in characteristics between urban poor households

and rural poor households are more important than differences in returns to those characteristics

(figure 2). This may reflects that the poor earns a wage slightly above the minimum level,

therefore urban-rural change in market yields does not matter at the tail of the distribution.

[Place figure 2 here]

3. Conclusion and recommendations

We start by this study by posing the following question: what about regional inequalities in

Tunisia, is it a spatial misallocation or bad choices? To response to this question we used

Bayesian semi-parametric regression based decomposition method.

objective of this study was to dissect regional inequalities in Tunisia. For this purpose, we used

semi-parametric regression based decomposition methods to assess the source of welfare gap

between urban and rural areas. Results of the study are in agreement with the type of policies

that have been established in Tunisia. For several decades, the government has implemented

reforms that promote education in the rural area especially the west region. Nevertheless, it did

not take into consideration the quality of the education program, as a result educated youth, in

these areas, do not have the capacity to succeed in their national competition and are unable to

compete with other graduates in the private market. Indeed, the lack of a good educational level

may limit the opportunities for these individuals to find a decent job. This may explain the fact

that the gap between the two areas is still deep. The issue of equitable access to education,

especially to the higher level, is probably a real issue that must be addressed as part of an overall

review. In other hand, the breakdown by age of household heads has confirmed the urgency of

the issue of rural old-age vulnerability in Tunisia. Effective policies must take into consideration

the heterogeneous nature of elderly-group, associating both personal characteristics (sex,

education...) and the varying contexts in which they live.

The fight against regional inequalities is not just a matter of equality, but also a question of

economic development fairness. We should know that the development of social groups and

deprived regions inevitably involves a strategy to reduce regional inequality. In addition, the

Tunisian state is not only required to build universities and hospitals (etc.), but also to provide

the needed resources to get the rural areas to the level of the urban ones. We recommend a

positive discrimination programs in favor of marginalized region. For example, the government

can start by sending most competent teachers and doctors to work in these areas, and provide

the necessary resources (good infrastructure, access to quality schools, internet, software ...).

By decomposing the gap between urban and rural area, this study has shown that the problem

is about not only equality but also it is an equity issue. Such equity implies that resources should

be allocated equally between areas with regard to the quality. Although, for a social

phenomenon, such as inequality, a dynamic analysis is required. This type of inequality is often

known as income variation/mobility and poverty dynamics and is important, when viewed from

the social welfare and political perspectives, to understand policy changes, an issue left for

future research.

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TABLE 1 - OLS and Quantile Regression of Total Consumption Expenditure Per Capita

Variables Means

Quantile regression

5th percentile

10th percentile

25th percentile

50th percentile

75th Percentile

90th Percentile

95th percentile

Intercept 15.109* (0.085)

13.56* (0.166)

13.72* (0.197)

13.99* (0.085)

14.31* (0.129)

14.96* (0.148)

15.48* (0.204)

15.79* (0.524)

Gender -0.106* (0.015)

-0.025 (0.030)

-0.033 (0.026)

-0.046** (0.020)

-0.098* (0.014)

-0.164* (0.020)

-0.204* (0.032)

-0.134* (0.094)

household size -0.212* (0.003)

-0.224* (0.0059)

-0.229* (0.006)

-0.221* (0.0036)

-0.219* (0.004)

-0.209* (0.005)

-0.202* (0.007)

-0.176* (0.018)

% children (under 15 old)

-0.674* (0.029)

-0.522* (0.056)

-0.525* (0.059)

-0.609* (0.025)

-0.664* (0.024)

-0.761* (0.039)

-0.809* (0.050)

-0.843* (0.178)

Age 0.0144* (0.0027)

0.016* (0.005)

0.017* (0.006)

0.018* (0.0031)

0.020* (0.004)

0.007** (0.004)

0.001 (0.006)

0.004 (0.016)

Age2*100 -0.009* (0.002)

-0.011* (0.004)

-0.012** (0.005)

-0.01* (0.002)

-0.01* (0.003)

-0.002 (0.003)

0.003 (0.006)

0.005 (0.014)

Tenant of house

Free housing

-0.059* (0.019) -0.131* (0.025)

-0.084** (0.037) -0.133* (0.049)

-0.092* (0.028)

-0.114** (0.056)

-0.075* (0.023) -0.116* (0.027)

-0.084* (0.023) -0.140* (0.029)

-0.046** (0.021) -0.106* (0.024)

-0.013 (0.030) -0.180* (0.039)

0.082 (0.117) 0.007* (0.157)

Primary

Secondary level

Higher level

0.180* (0.014) 0.455* (0.016) 0.876* (0.024)

0.196* (0.027) 0.444* (0.032) 0.846* (0.048)

0.187* (0.019) 0.442* (0.024) 0.868* (0.044)

0.178* (0.018) 0.424* (0.021) 0.866* (0.019)

0.184* (0.018) 0.458* (0.019) 0.868* (0.026)

0.187* (0.023) 0.476* (0.023) 0.890* (0.047)

0.166* (0.025) 0.474* (0.042) 0.884* (0.051)

0.124* (0.086) 0.529* (0.101) 1.039* (0.152)

sector_Gov 0.104* (0.017)

0.145* (0.034)

0.138* (0.028)

0.128* (0.025)

0.103* (0.027)

0.099* (0.022)

0.085** (0.036)

0.107 (0.107)

sector_Priv

0.116* (0.020)

0.162* (0.039)

0.146* (0.023)

0.139* (0.021)

0.111* (0.019)

0.103* (0.025)

0.103** (0.045)

0.112* (0.122)

sector_Agri 0.014 (0.016)

0.050 (0.032)

0.036 (0.023)

0.030 (0.020)

0.005 (0.020)

0.017 (0.021)

-0.029 (0.030)

-0.051 (0.101)

self 0.072* (0.015)

0.076** (0.030)

0.082* (0.025)

0.085* (0.016)

0.065* (0.016)

0.097* (0.024)

0.069** (0.029)

0.126 (0.094)

West -0.274* (0.0108)

-0.305* (0.021)

-0.308* (0.018)

-0.299* (0.014)

-0.276* (0.015)

-0.254* (0.012)

-0.242* (0.023)

-0.109** (0.066)

Rural -0.266* (0.012)

-0.331* (0.049)

-0.293* (0.019)

-0.270* (0.012)

-0.261* (0.018)

-0.231* (0.016)

-0.218* (0.028)

-0.246* (0.073)

Foreign Transfer 0.188* (0.041)

0.134** (0.080)

0.259* (0.082)

0.179* (0.035)

0.160** (0.071)

0.183** (0.088)

0.265* (0.082)

0.237 (0.254)

Number of observation Pseudo R2

11281

0.572

11281

0.358

11281

0.356

11281

0.359

11281

0.359

11281

0.352

11281

0.336

11281

0.288

Chow Test [K, N-2*K]=

104.70a

Quantile regression and OLS estimates of total observation, with standard deviation in parentheses.

Significance levels are respectively 1% (*) and 5% (**)

a: p-value > F(10, 11281) = 0.000

TABLE 2 - Quantile Regression of the Urban Consumption Expenditure Per Capita

Quantile Regression Estimates Of Urban Observation, With Standard Deviation In Parenthesis.

Significance Levels Are Respectively 1% (*) And 5% (**).

Urban

Variables 5th Percentile

10th Percentile

25th Percentile

50th Percentile

75th Percentile

90th Percentile

95th Percentile

Intercept 13.97* (0.201)

14.13* (0.247)

14.34* (0.125)

14.81* (0.131)

15.41* (0.140)

15.69* (0.215)

16.31* (0.819)

Men -0.036 (0.036)

-0.047** (0.021)

-0.060* (0.022)

-0.111* (0.031)

-0.171* (0.022)

-0.149* (0.041)

-0.037 (0.149)

Household Size -0.244* (0.007)

-0.241* (0.007)

-0.235* (0.005)

-0.240* (0.005)

-0.235* (0.005)

-0.228* (0.008)

0.008 (0.018)

% Of Children (Under 15 Years)

-0.425* (0.068)

-0.406* (0.073)

-0.505* (0.063)

-0.557* (0.051)

-0.613* (0.046)

-0.674* (0.073)

-0.081 (0.123)

Age 0.015** (0.006)

0.016** (0.007)

0.019* (0.004)

0.017* (0.0041)

0.009** (0.005)

0.0002 (0.007)

-0.002 (0.012)

Age2*100 -0.008 (0.005

-0.01 (0.006)

-0.01* (0.003)

-0.01* (0.003)

-0.002 (0.004)

0.002 (0.006)

0.003 (0.011)

Homeowner (Ref) Tenant Free Housing

-0.101* (0.037) -0.126** (0.058)

-0.098* (0.021) -0.137* (0.039)

-0.092** (0.027) -0.136* (0.033)

-0.094* (0.021) -0.151** (0.061)

-0.053** (0.025) -0.103* (0.038)

0.004 (0.038) -0.05 (0.057)

0.109 (0.089) 0.167 (0.145)

Illiterate (Ref) Primary Level Secondary Level Higher Level

0.233* (0.035) 0.486* (0.038) 0.913* (0.051)

0.205* (0.031) 0.465* (0.032) 0.877* (0.046)

0.230* (0.022) 0.479* (0.025) 0.907* (0.039)

0.209* (0.024) 0.510* (0.022) 0.863* (0.031)

0.218* (0.023) 0.524* (0.028) 0.913* (0.030)

0.17* (0.039) 0.432* (0.04) 0.835* (0.053)

0.003 (0.078) 0.002 (0.086) 0.038 (0.087)

Sector_Gov 0.142* (0.038)

0.115* (0.025)

0.102* (0.025)

0.089* (0.019)

0.072* (0.019)

0.307* (0.04)

-0.088 (0.155)

Sector_Priv 0.184* (0.043)

0.126* (0.035)

0.115* (0.024)

0.088* (0.023)

0.084* (0.029)

0.313* (0.046)

0.017 (0.178)

Sector Agri 0.013 (0.057)

-0.049 (0.049)

-0.056 (0.048)

-0.036 (0.043)

-0.042 (0.038)

-0.028 (0.073)

-0.299 (0.231)

Self 0.070** (0.034)

0.031 (0.025)

0.030 (0.024)

0.0208 (0.023)

0.036** (0.019)

0.011 (0.033)

0.049 (0.138)

Center-West -0.334* (0.025)

-0.299* (0.014)

-0.303* (0.019)

-0.287* (0.014)

-0.265* (0.020)

-0.246* (0.039)

0.022 (0.105)

Foreign Transfer 0.114 (0.092)

0.246** (0.123)

0.183* (0.056)

0.123** (0.071)

0.081 (0.059)

0.061 (0.096)

-0.329* (0.177)

Number Of Observation Pseudo R2

7261 0.315

7261 0.316

7261 0.330

7261 0.343

7261 0.340

7261 0.330

7261 0.254

15

TABLE 3 - Quantile Regression of the Rural Consumption Expenditure Per Capita

Quantile Regression Estimates Of Rural Observation, With Standard Deviation In Parenthesis.

Significance Levels Are Respectively 1% (*) And 5% (**).

Rural

Variables 5th Percentile

10th Percentile

25th Percentile

50th Percentile

75th Percentile

90th Percentile

95th Percentile

Intercept 13.40* (0.331)

13.92* (0.308)

14.24* (0.203)

14.46* (0.190)

15.19* (0.180)

15.52* (0.228)

15.88* (0.462)

Men -0.026 (0.064)

-0.053 (0.055)

-0.037 (0.036)

-0.106** (0.043)

-0.115* (0.033)

-0.187** (0.074)

-0.084 (0.162)

Household Size -0.203* (0.011)

-0.205* (0.013)

-0.197* (0.007)

-0.193* (0.006)

-0.176* (0.006)

-0.175* (0.007)

0.020 (0.027)

% Of Children (Under 15 Years)

-0.684* (0.122)

-0.724* (0.092)

-0.804* (0.053)

-0.824* (0.059)

-0.962* (0.054)

-0.903* (0.074)

0.289 (0.251)

Age 0.024** (0.010)

0.011 (0.010)

0.017** (0.006)

0.015* (0.005)

0.001 (0.005)

0.004 (0.005)

-0.007 (0.027)

Age2*100 -0.022** (0.009)

-0.009 (0.009)

-0.01** (0.005)

-0.01** (0.004)

-0.0624 (0.005)

-0.003 (0.005)

0.003 (0.023)

Homeowner (Ref) Tenant Free Housing

0.058

(0.212) -0.058 (0.112)

-0.082 (0.137) -0.054 (0.059)

-0.018 (0.166) -0.070 (0.044)

0.065

(0.125) -0.119* (0.040)

0.057

(0.072) -0.121** (0.064)

-0.144 (0.109) -0.234* (0.054)

-0.392 (0.297) 0.095

(0.226)

Illiterate (Ref) Primary Level Secondary Level Higher Level

0.122** (0.055) 0.334* (0.077) 0.350** (0.203)

0.144* (0.040) 0.331* (0.061) 0.749* (0.127)

0.162* (0.028) 0.315* (0.048) 0.794* (0.099)

0.166* (0.021) 0.362* (0.042) 0.805* (0.070)

0.132* (0.030) 0.355* (0.048) 0.725* (0.074)

0.088** (0.034) 0.389* (0.062) 0.762* (0.113)

0.144

(0.109) 0.332** (0.167) 0.293

(0.484)

Sector_Gov 0.270* (0.090)

0.296* (0.057)

0.205* (0.044)

0.149* (0.039)

0.122** (0.048)

0.098 (0.080)

-0.384 (0.277)

Sector_Priv 0.174** (0.102)

0.147** (0.081)

0.179** (0.069)

0.182* (0.050)

0.094** (0.059)

0.092** (0.042)

0.662** (0.175)

Sector Agri 0.097** (0.052)

0.131* (0.032)

0.084* (0.022)

0.035 (0.024)

0.037** (0.017)

-0.027 (0.022)

-0.261* (0.121)

Self 0.203* (0.077)

0.250* (0.061)

0.252* (0.040)

0.220* (0.040)

0.192* (0.031)

0.166* (0.035)

0.034 (0.160)

West -0.280* (0.044)

-0.270* (0.021)

-0.275* (0.021)

-0.239* (0.022)

-0.233* (0.027)

-0.192* (0.028)

-0.014 (0.096)

Foreign Transfer 0.061 (0.193)

0.089 (0.202)

0.153** (0.072)

0.221* (0.082)

0.572* (0.203)

0.568* (0.139)

-0.228 (0.243)

Number Of Observation Pseudo R2

4020 0.270

4020 0.279

4020 0.294

4020 0.288

4020 0.276

4020 0.265

4020 0.242

16

TABLE 4 - Urban Rural gap (RIF-regression)

Author’s computing from 2010 National Survey on Households’ Budget, Consumption and Standard of Living

Standard errors are in parentheses and (*** significant at 1%, ** significant at 5%,*significant at 10%)

10th percentile 50th percentile 90th percentile

Estimated std Estimated std Estimated std

Reference group: Urban Coef

Estimated log expenditure gap:

E[RIFq (ln(Expu))]-

E[RIFq(ln(Expr))]

-0.633***

(0.023)

-0.564***

(0.018)

-0.614***

(0.025)

Composition effects attributable to

Age, gender, household size,

foreign transfer and logement

-0.069*** (0.021) -0.071*** (0.015) -0.075*** (0.021)

West region -0.070*** (0.011) -0.077*** (0.007) -0.056*** (0.011)

Education -0.116*** (0.020) -0.117*** (0.013) -0.156*** (0.020)

Employment -0.029** (0.013) -0.034** (0.009) -0.035** (0.014)

Agregated characteristics

effects

-0.298*** (0.030) -0.315*** (0.021) -0.337*** (0.025)

Regional structure effects attributable to

Age, gender, household size,

foreign transfer andlogement

0.494 (0.319) -0.079 (0.224) -0.036 (0.353)

West region 0.031** (0.013) 0.003 (0.009) 0.017 (0.014)

Education -0.116*** (0.020) -0.084** (0.033) -0.148*** (0.051)

Employment 0.075** (0.031) 0.041* (0.021) 0.040 (0.033)

Constant -0.094*** (0.330) -0.073 (0.232) -0.117 (0.366)

Agregated coefficient effect -0.335*** (0.036) -0.249*** (0.024) -0.277*** (0.036)