the determinants of working poverty: microeconometric

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The Determinants of working poverty:

Microeconometric Application to Palestine Refugees

Prof. M. Bensaïd, Agdal-Rabat University, Morocco Prof. A. Ibourk, Marrakesh University, Morocco

Prof. F. Lapeyre, UCL University, Belgium

First Draft

October 2009

1. Introduction

Based on a survey of almost 10,000 individuals, the present paper aims at analysing

the current living and working conditions of Palestine Refugees (PRs) in Jordan, Lebanon, the Syrian Arab Republic, the West Bank and the Gaza Strip. It represents the first comprehensive attempt to estimate working poverty levels of registered PRs in these five fields of UNRWA’s operations based on a random sampling procedure for individuals from UNRWA’s 4.3 million registered refugees file1. The objective is to provide a better understanding of the socio-economic profile of the PRs in the five fields.

The socio-economic conditions of PRs in the five fields of UNRWA’s operations are deeply rooted in the history of conflict in the Middle East since 1948. The establishment of the State of Israel led to forced migration and the expulsion of Palestinian civilians from their homes. Nearly three quarters of a million Palestinians lost their homes and livelihoods in the aftermath of the 1948 Arab-Israeli War and became refugees in neighbouring countries and various other countries around the globe. After the second Israeli-Arab war in 1967, large segments of land belonging to Palestinians were occupied by Israel and many Palestinians’ were forced to leave their homes leading to a second wave of displaced persons and refugees. These people lost their property, lands, homes and livelihoods. Most of them moved to the West Bank and Gaza Strip, which were not occupied at the time, as well as to Jordan, Lebanon and the Syrian Arab Republic.

Most of the PRs were first in 1948 and later in 1967 placed in refugee camps. Until now a large number of Palestinian refugees live in refugee camps. Overall, more than one third live in refugee camps but their proportion varies considerably from one country/territory to another. There are as many as 56% living in refugee camps in Lebanon and 55% in the Gaza Strip, but comparatively fewer in the Syrian Arab Republic (29%), the

1 UNRWA registered refugees represent approximately three quarters of Palestinian refugees

worldwide.

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West Bank (27%) and Jordan (18%). Thus PRs experience very different economic conditions and events in the five fields of UNRWA’s operations.

It is important for an understanding of our results to stress the specific situation in the Gaza Strip and the West Bank. Since the beginning of the second Intifada, the economy and living conditions in these territories have sharply deteriorated; the situation there is marked by slight improvements alternated by periods of worsening conditions, depending on the intensity of the conflict and Israeli military interventions (PCBS, 2006a). Israel first imposed external and internal closures when the political and humanitarian situation started to deteriorate in late September 2000, and steadily tightened them in April 2002 when Israeli military forces reoccupied West Bank cities and towns and introduced unprecedented measures such as large scale curfew and closures. Israeli security forces’ repeated incursions into the Gaza Strip and the West Bank have severely affected the population through greater restrictions on mobility, destruction of civilian property and economic facilities and higher levels of violence. Israeli restrictions on movement of goods and people both inside the Palestinian territories and between them and Israel, and a sharp increase in military interventions have completely paralysed the Palestinian economy and brought it to the brink of collapse (World Bank, 2003). The private sector has been devastated by the conflict, as the extremely high level of risk and confrontation, physical damage, sustained restrictions on movements and mass material deprivation have resulted in a sharp decline in production, trade, employment and investment. Physical damage has included the bulldozing of agricultural land, the demolition of agricultural and industrial establishments, as well as the destruction of infrastructure.

The paper aims to estimate the working poverty in the various fields covered by the survey, where the working poor are defined as workers whose households are poor. Two approaches of poverty are used here. In the first approach - relative poverty, are poor those households whose income is below 50% of median income. In the second one - subjective poverty, are poor those who consider that their income is insufficient to cover their needs and the needs of their household.

The paper has a number of specific objectives. The first one is to determine and analyze the factors linked (positively or negatively) to working poverty according to the two approaches of poverty selected, i.e. relative and subjective poverty. The second one is to develop a model (simple probit model) that can estimate the risk of working poverty for the different kinds of explicative variables (factors). The third specific objective is to verify, through a bivariate probit model if the results of the two approaches of poverty are contradictory of convergent.

We think that this paper contributes to the following issues: 1. There exists today a large literature on theoretical and methodological issues related

to working poverty. But until recently the concept was applied essentially in developed countries (USA and then EU), and it was rarely exploited to analyse the links between poverty and the labour market in developing countries. This paper shows that this analytical tool is meaningful when applied to non developed countries;

2. The paper tries to apply the concept and measure of working poverty to the specific population of Palestinian refugees in their spatial, economic, political and military context(s) linked to the different fields (countries and territories) of action of the UNRWA. To our knowledge this is the first essay to analyze the working poverty of Palestinian refugees in so large spatial distribution2;

2 By stressing the specificity of the NEP survey, we do not want to disqualify other studies and surveys conducted in the past, especially since the 1990s and in particular by the Norwegian Institute for Applied Research FAFO. However, no other survey has been conducted before, based on a sampling drawn from UNRWA’s own list of registered refugees and administered at the same time throughout the five fields of

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3. The measurement of working poverty is based on microeconomic data and not on macroeconomic estimations, which is rather rare even in developed countries. Microeconomic data are more scientifically reliable and more helpful for action than macroeconomic estimations. The lack of direct measurements of the living conditions of employed population is the main reason behind the use of macro-based models (Economic and Social Commission for Asia and the Pacific, 2007).

4. Given that Palestinian refugees covered by the NEP survey are distributed in five geographical fields, the data collected have two important dimensions: individual and spatial dimensions. This is helpful to estimate the spatial differences and common characteristics;

5. As a consequence of the very definition of working poverty, the data collected combine two fields of research and analysis which are often separated: employment and living conditions. This constitutes an enriching approach but a very constraining one in that it poses difficult methodological and empirical problems concerning the statistical construction of the concept of working poverty;

6. The paper also combines the use of two approaches of poverty to estimate and to analyze the working poverty. Moreover, the results of these two approaches are compared by a specific model. This mean that our ambition is also to discuss methodological issues;

7. Finally, we combine different kinds of analysis: univariate, bivariate and multivariate.

We should remember that the analysis below are based on a survey conducted in September 2005, and give a picture of the situation at that time (Bocco, Brunner, Husseini, Lapeyre, Zureik, 2007). Since then, in the West Bank and Gaza Strip living conditions have worsened considerably as a result of the recent dramatic events; especially after the latest Israeli military intervention in the Gaza Strip.

The rest of this paper is organized as following. Section 2 discusses theoretical and methodological background of the study, that is poverty and working poverty, focusing on the most useful approaches and concepts: relative poverty, subjective poverty, working poor. Section 3 shows the sources of used data and the methodology elaborated to answer our research’s questions. In Section 4, we discuss the main finding of the analysis. Section 5 concludes the paper and discuss the principal implications – at methodological and policy levels – of these findings.

2. Theoretical and Methodological Background

The theme of working poverty poses some theoretical and methodological

challenges due to the fact that it combines two generally separated fields of research. This results in the necessity to combine two conceptual and methodological approaches, the first one focusing on individual factors linked to employment and the second one on the household characteristics linked to its total income and living conditions. 2.1 Working poverty and decent work

In February 2007, the Secretary-General of the United Nations began a two-year

devoted effort in the Commission for Social Development to actions which “promote full

the Agency’s operational activities. An important study worthwhile to mention on the potential of the Agency’s data was produced in the mid-1990s: Endresen and Ovensen (1994).

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employment and decent work for all.”3 Resolutions adopted guided the work of the Inter- Agency and Expert Group (IAE G) in their efforts to expand the MDGs to include a new target for employment and new employment indicators (agreed upon in 2008) (ILO, 2009). The new MDG Target (1B) is – Achieve full and productive employment and decent work for all, including women and young people. This target contains four indicators specifically and directly relating to employment issues:

• Growth rate of labour productivity (GDP per person employed)

• Employment-to-population ratio

• Proportion of employed people living below the poverty line

• Proportion of own-account and contributing family workers in total employment (vulnerable employment rate).

The present paper will only focus on one of the new indicators which is working poverty. By combining these labour market factors with poverty data, working poverty estimates give a clearer picture of the relationship between poverty and employment than that provided by using standard poverty data alone. Working poverty also gives a clear indication of the lack of decent work: If a person’s work does not even provide an income that is high enough to lift the person and the family out of poverty, this job at least does not fulfil the income component of decent work – and very likely other components of decent work are not fulfilled either.

The approach of full employment and decent work is very useful but is insufficient to grasp the complexity of the concept of working poverty. In one side, one can be in unfavorable situations of activity, having precarious employment and low paid job, but do not count as working poor. This is possible if he is a member of a household whose size is low and/or where other members are contributing to its total income so that the household – and then the worker - is not considered as poor. In the other side, one can have a good job with high individual revenues but living in a poor household, so that he is considered as a working poor. This is possible for example when the size of the household is high, especially when the proportion of economically dependent household members is high. This means that the link between work and poverty is difficult to measure and to interpret from the individual perspective (Ponthieux, 2009). If the collective – household – dimension of the phenomenon has been from a long time recognized as a constraint for the analysis of the phenomenon (Danziger & Gottschalk, 1986; Klein & Rones, 1989), it should be now considered as constitutive of the working poverty. Moreover, in the countries where social transfers are organized to face different individual and social risks, the picture becomes more complex since it is possible to escape from working poverty even with non decent work and with unfavourable conditions at the household level thanks to social transfers.

In this paper, working poor are defined as individuals who are working and living in a

poor household whatever the approach of poverty chosen. The principal indicator calculated, i.e. the working poor rate represents the proportion of working poor in total employment. But as is discussed in detail by Ponthieux (2009), if the notion of working poor seems quite intuitive, “going from the notion to a statistical category is not straightforward”. So, we should precise every term in our definition: working and poverty. 2.2. Who is a worker?

This question is less simple than it may seem at first glance. For example, it is

possible to include unemployed persons in the working population, in which case it’s better to speak about active population and about active poor. This is the case in the statistics of

3 See www.un.org/esa/socdev

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working poverty by the American Bureau of Labor Statistics (since the end of the 1980s) and in many studies in France (INSEE). The European Union adopts another criterion by which the persons is considered as worker if he was at work at the time of the survey and if he had been mostly at work during the previous calendar year.

In our case, workers are defined on the basis of statuses of the last three months (before the survey)4 and only those who are actually working are taken in account. 2.3. Approaches to poverty

2.3.1. An income approach

The analyses below focus on quantitative, objective measures of poverty; in other words, on the monetary dimensions of well-being (and, as explained below, a subjective definition of poverty is also used). When estimating poverty using monetary measures, one may choose between using income or consumption as the indicator of well-being. Both conceptual and empirical arguments favour one or the other of these alternatives.

On the empirical side, in household surveys – the main source used for this purpose – expenditure estimates are of a higher quality than income estimates: different non-sampling errors usually affect the latter due to lack of reporting and underreporting. As a consequence, many analysts argue that, provided the relevant information obtained from a household survey is detailed enough, consumption is a better indicator of poverty. In this case, expenditure surveys play a major role in the direct estimation of thresholds to produce the poverty line, because they provide information on consumption patterns used to establish the minimum requirements of goods and services. In addition, such surveys provide data for indirectly estimating the non-food components of a household’s expenditures. Conceptual considerations are also advanced to argue in favour of using expenditure estimates, as income is only one factor that enables the consumption of goods (others include access and availability). Moreover, it is a better proxy for a household’s actual standard of living and ability to meet basic needs, as it reduces the impact of temporary fluctuations in current income by taking into account savings, access to credit markets and other coping strategies.

However, expenditure data can be adequately measured only through a comprehensive, disaggregated inquiry into the various goods and services bought and received by household members. In most low-income and medium-low-income countries such surveys are carried out only sporadically because they are expensive and difficult to implement, especially in situations of war or conflict. Consequently, in many countries income data, collected in multipurpose household surveys, appear to be the only alternative basis for assessing poverty. In the analyses below, an income-approach was chosen because, on the one hand the questionnaire did not include a section detailed enough to obtain a comprehensive picture of the consumption pattern of the respondents and, on the other hand, the aim of the project was not to define a precise measure of absolute objective poverty but to provide a profile of different income-based subgroups.

Thus, using income is a second best choice. But one should not be dogmatic about using consumption data for poverty measurement, as the use of income for such a measurement may have its own advantages, as we have noted (Coudouel, Hentschel and Wodon, 2002). Moreover, measuring poverty by income allows a distinction to be made between sources of income and an in-depth analysis of the sources of income by different income groups, as is done below. One can also argue that households’ capacity to purchase

4 To guarantee the comparability between the statistics of poverty and the statistics of labour market, it would be better to base these statistics on a year-period – as is the case in the European Union and United States, but our survey does not permit this option. If one is inactive on the three months period but has worked before this period, he is excluded from the working population.

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goods and services is best reflected by income. For example, a given unit may be spending above the poverty line, but only as a consequence of borrowing or not paying bills (as in the Palestinian territories); this household should thus be considered poor, as it is not clear whether it will be able to maintain this purchasing power.

2.3.2. Equivalence and economies of scale

Having adopted an incomes approach to establish socio-economic profiles, it is important to adjust households’ income according to their size and composition, as a child typically needs less food than an adult. Otherwise, household income can be quite misleading in terms of the well-being of individuals in a given household. Usually, the highest poverty rates are more likely to be found in the largest households with 10 or more members. But we also have to consider economies of scale, as larger households generally can benefit from sharing commodities or from purchasing produce in bulk, which might be cheaper.

There is no consensus on how these weights should be calculated. In our analyses we use the commonly used OECD scale, where a one-adult household is given an adult equivalent of 1, a two-adult household is given an adult equivalent of 1.7, and a three-adult household is given an adult equivalent of 2.4. The weight given to children is 0.5, which presumably reflects their lower needs (e.g. for food or housing space). Thus the household size is not measured here in number of persons but in terms of adult equivalents, as each member of the household counts as some fraction of an adult equivalent.

2.3.3. Relative versus absolute poverty line

Once we have calculated an aggregate income measure at the household level, which takes into account differences in households’ demographic composition, the next step is to define a poverty line which will provide the cut-off points separating the poor from the non-poor. In the economic literature, there are two main ways of setting objective poverty lines: relative and absolute.

An absolute poverty line is anchored in some absolute standard of what households should be able to count on in order to meet their basic needs. For monetary measures, these absolute poverty lines are often based on estimates of the cost a nutritional basket considered minimal for the health of a typical family, to which a provision is added for non-food needs. A popular method in the literature is the so called food-energy-intake (FEI) method, which seeks to establish the value of per capita consumption at which a household can be expected to fulfill its calorific requirement. The poverty line is defined by the level of per capita consumption at which people can be expected to meet this requirement. Such an absolute poverty line guarantees that longitudinal and international comparisons are consistent in the sense that two individuals with the same level of welfare are treated the same way. The conceptual problem that arises when working with absolute poverty lines is the issue of the definition of the basket of basic needs according to the standard of living and social norms of the country.

However, our survey does not attempt to provide the full information needed to estimate the consumption expenditures or income level at which a person’s typical food energy intake is just sufficient to meet a predetermined food-energy requirement. Once again, such a poverty measure should be based on a comprehensive household survey that provides a comprehensive picture of the pattern of household expenditures. Considering the many theoretical and practical problems related to defining a poverty line, we decided to provide a relative poverty line which is set at 50 per cent of the median total household income of the reference population. The relative poverty line is a measure that is widely used in developed countries as it provides a good picture of the proportion of the poorest in

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a society (an objective poverty line being inappropriate in that case, as most people from developed countries have more than $1 purchasing power parity (PPP) per day).

The relative poverty line is attractive because it is simple and transparent. It is also effective in terms of identifying a population subgroup upon which to focus attention. To the extent that the objective is to identify and target today’s poor, a relative poverty line is appropriate.

We then provide a socio-economic profile of the subgroups identified. When defined in this way, it is a truism that “the poor are always with us”, but it is helpful to have a measure such as this in order to target programs that are geared to helping the poorest segment of the population (Duclos, 2002). Such a relative poverty line provides information on inequality among the population and on how large is the poorest segment of the population, but it cannot provide information on the proportion of abject poor.

When undertaking a comparative analysis across fields it is necessary to treat the relative poverty line with some caution. This is because, since it measures mainly inequality of income distribution, rather than basic needs deprivation, it cannot be said that country A is poorer than country B; but what can be said is that there are more relatively poor in country A than in country B. Clearly, such a poverty line will vary with the central tendency of the distribution of living standards, and will not be the same across regions and time. Considering relative poverty at 50 per cent of the median income when national absolute poverty is 14 per cent as in Jordan in 2002 or 40 per cent as in Palestine in 2003, the relative poverty lines adopted here clearly make sense for each country considered in isolation.

But they hardly correspond to a universal notion of a state of extreme poverty and deprivation that is recognizable irrespective of average living standards in each country.

2.3.4. Subjective poverty line

Finally, we also use a subjective measure based on respondents’ perception of food deprivation to measure poverty. Such a measure of poverty is based on the food consumption adequacy question: How would you assess your household’s food consumption in terms of quantity over the past few months? On the basis of the answers to this question, some analytical groups can be derived and analyzed according to their access to basic food consumption.

The results based on perception can be compared with objective measures, defined above, to check their adequacy and explain some discrepancies. Indeed, self-reported measures have important limitations, as subjective measures might reproduce existing discrimination or exclusion patterns if these patterns are perceived as “normal” in the society; in terms of food deprivation this is clearly the case in the Palestinian territories after six years of material deprivation that have lowered expectations, as explained below.

This poverty line provides a subjective poverty indicator, which in some cases can be a good proxy for food security and abject poverty, as mentioned in several theoretical studies (Ravallion and Pradhan, 1998).

Any approach to the measurement of living standards and poverty encounters the problem of the lack of a unique measurement yardstick. According to the World Bank, “any poverty cut-off will reflect some degree of arbitrariness due to the subjectivity of how poverty is defined” (World Bank, 1993). Since this continues to be debated, it is important to keep in mind the difficulties in determining the threshold below which people are considered poor, as the concept of poverty is partly a value judgment by the researcher (Boltvinik, 1994).

From this perspective, the choice of a specific poverty definition and the subsequent definition of a poverty line appear to have major consequences, both for the observed incidence of poverty and for estimating the distribution of the poor by social subgroups (Hagenaars and de Vos, 1988).

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3. Data and Methodology

3.1. Data

On the 7th and 8th of June 2004, the Swiss Government and UNRWA hosted an international conference entitled “Meeting the Humanitarian Needs of Palestinian Refugees in the Near East: Building Partnership in support of UNRWA”. Representatives of 67 countries and 34 international organizations gathered in Geneva to discuss the future of humanitarian assistance to four millions of refugees scattered across the Near East and registered with the UN Agency. To follow-up the conference recommendation, UNRWA entered into a contractual arrangement with the Institut Universitaire d’Études du Développement (IUED) in association with the Institut d’Etudes du Développement of the Université Catholique de Louvain-la-Neuve (UCL) to provide a survey of living conditions and services delivered to the PRs (UNRWA beneficiaries or not) in all five fields; the research project was to be known as the “IUED/UCL Near East Project on the UNRWA Registered Refugees” (NEP). 3.1.1. A Random Sample

The technical specificities of the NEP survey were a key factor in ensuring the relevance and the usefulness of the data obtained. The survey is the first of its kind that is initially based on a random sample drawn directly from UNRWA’s database of PRs and that finally includes the 4.3 million PRs actually living in the 5 fields. This methodological approach is a guarantee against targeting mistakes at the time of the setting up of the refugee sample (selection of non-refugees, for example). Besides, the sample size chosen for this survey (about 2,000 respondents for each field) guarantees findings at the country (or Field) or governorate level. Analysis at smaller geographical levels (in particular: districts, cities, villages or camps) may only be achieved on the condition that they are sufficiently populated so as to be enough represented in the sample. 3.1.2. Reference population

Two reference populations were targeted; they are categorized differently by UNRWA: Bona fide PRs (RRo) and Other Claimants (OC)5. They start at the age of 16 rather than the commonly used age of 18. This threshold was decided with UNRWA as the Agency

5 Bona fide PRs (RRo) : These refugees are UNRWA’s main category of registered persons. UNRWA’s

definition of the Palestine Refugee has evolved over time. Elaborated for operational purposes, namely to determine who is in principle eligible for its various assistance schemes5, its current version states that a bona fide ‘Palestine refugee’ shall mean: “Any person whose normal place of residence was Palestine during the period 1 June 1946 to 15 May 1948 and who lost both home and means of livelihood as a result of the 1948 conflict”. In addition, PRs, and descendants of Palestine refugee males, including legally adopted children, are eligible to register for UNRWA services. The Agency also accepts new applications from persons who wish to be registered as PRs. “Other Claimants” (OC): Additionally to these bona fide refugees, and their descendents, UNRWA has also had to provide since 1950, under the pressure of the host countries’ authorities, assistance to those Palestinians who had lost their means of livelihood but not their homes –or were not able to prove that loss- such as the “Jerusalem Poor” (JP): 1’328 persons in Jordan and 6’744 persons in the West Bank, the “Frontier Villages” (FV) in the West Bank and in Jordan: 26’003 persons in Jordan and 64’640 persons in the West Bank, the “Gaza Poor” (GP): 4’534 persons in the Gaza Strip, the “Compromised Cases” in Lebanon (CC): 1’869 persons in Lebanon, the members of nomadic and semi-nomadic tribes (NT), about 23’500 people recognized as such by UNRWA in the West Bank. Overall, the various categories of “Other Claimants” account for about 130’000 persons. Since 1983, the OC have been entitled to the same services as the “bona-fide refugees” and their names appear in the UNRWA’s registration system.

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has set a high priority on Youth. To prevent possible biases, in each field where the UNRWA-NEP interviews took place, 200 additional respondents were interviewed in the households where another member had been randomly selected; this selection was mainly based on the (young) age of the principal respondent. This procedure allowed us to show that the younger respondents are reliable sources of household information mainly because of the social and cultural context of PRs: almost none of the interviews were carried out privately.

The geographical distribution of UNRWA Registered Refugees can be summarized as in the table below:

Table 1. UNRWA registered population

Jordan Lebanon Syria West Bank Gaza Total

Registered refugees (RR) 1'795'326 401'071 426'919 690'988 969'588 4'283'892

RR as % of Total RRs 42 9 10 16 23 100

Existing camps 10 12 10 19 8 59

RR in Camps (RRCs) 284'461 211'593 113'663 182'191 474'079 1'265'987

RRC as % of RRs 16 53 27 26 49 30 Source: UNRWA, Figures as of 30 June 2005, Public Information Office, UNRWA Headquarters (Gaza), 2005.

3.1.3. The main characteristics of UNRWA’s registered population

The main characteristics of UNRWA’s registered population concerning the PRs localization across the five fields are as follow:

- According to Field of registration: In December 2005, 4’349’946 persons were registered with UNRWA (approximately 75% of the total Palestinian refugee population (about 6 million or 20% of the total number of Palestinian refugees in the world). 42% of them were registered in Jordan, 23% in the Gaza Strip, 16% in the West Bank, 12% in Lebanon and 10% in Syria. - According to the Place of residence (official camp vs. non-official camp PRs15): In 2005, 1’278’678 out of 4’349’946 PRs (29%) were living in camps. Lebanon has the highest percentage of camp refugees amongst its own contingent of PRs (53% in 12 official camps), followed by the Gaza Strip (49% in 8 official camps), Syria (27% in 10 official camps), the West Bank (26% in 19 official camps), and Jordan (16% in 10 official camps). However, in actual figures area wide, the Gaza Strip had the highest contingent of camp refugees (479’364), followed by Jordan (286’110), Lebanon (213’349), the West Bank (184’382) and Syria (115’473) (see UNRWA, 2005).

However, it should be bear in mind that UNRWA has neither the present addresses of all their refugees nor detailed information about their geographic distribution in a given moment of time. For this reason, the classical two step selection of the survey sample (random selection of geographical locations and random selection of individuals) was not possible. Furthermore, the very existence of the total list of PR and OC (the sampling frame) in the five fields enabled us to randomly draw the samples directly from the reference populations; such a procedure, according to the scientific literature on sampling, should guarantee a more reliable sample.

This is why, during our preliminary discussions with UNRWA, it was agreed that the samples of these reference populations would be drawn directly from UNRWA’s databases. Following this logic, the IUED/UCL team received in late 2004 a copy of UNRWA’s Family Registration database which included two files, one for the families and the other for the individuals. Time and staff allocated for the address-tracing procedure was considerable. Started in March 2005 and officially completed in June 2005, the address tracing represented a substantial workload and was worth thanks to UNRWA’ staff efforts (teachers, social workers etc.).

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A reasonably representative sample per field would include a minimum of 800 to 1’200 persons in each field (the precision of a sample of 1’000 observations is +/- 3% at a 95% probability), i.e. 4’000 to 6’000 persons overall. However, this confidence interval increases and the precision decreases when we focus on a particular sub group of the reference population (e.g. women). Therefore, a larger sample of at least some 10’000 individuals was used. Such a sample size means a precision of +/-2.2% (at 95% probability) in each field where each of them includes 2’000 refugees6.

3.1.4. Data used

The fact that the concept of working poor is a complex and hybrid one of the working poor – at the intersection of work and poverty spheres – has an important consequence for the collection of data necessary to go from the concept to its statistical construction. Moreover, in the analysis of the determinants of working poverty it is necessary to distinguish between determinants linked to the characteristics of the individual (the working poor itself) like his educational attainment and the quality of his job, and the determinants linked to the characteristics of his household like its size, the number of household’s members contributing to its global income. Finally one can add another factor linked to transfers which can play an important role in the reducing of working poverty

In this paper, the individual factors considered are the age, the sex, the educational Attainment, the type of employer, the sector of work and the number of worked hours. On the other hand, the household characteristics concern particularly those linked to its size and to the number or proportion of members contributing to the household income

3.2. Methodology

This research aims to conduct a comparative analysis of risk factors and groups at risk of poverty in the five fields (regions) of the survey. More specifically, the following questions will be addressed:

- Are the risk factors the same across the five fields? - Do they have the same impact? - Are the risk groups the same across the five fields? To test the probable interdependence between our dependent variable, i.e. the

working poverty, and the different explicative variables, appropriate tests were conducted – Chi-Square and ANOVA – according to the nature of these variables.

To modelize the working poverty, according to relative and subjective poverty, two probit models are estimated: a simple one and a bivariate one.

For the analysis of the determinants of working poverty we have developed a simple probit model. Based on individual data, we estimate a probabilistic model where the

dependent variable iZ is dichotomic (“Working Poor” or “Non Working Poor”). Several explicative variables (Xi) are taken in account: spatial variables, socio-demographic variables, work variables, household variables.

The estimated simple probit model is as following:

[ ] ( ) ( )γσα

iiii XXZP Φ=Φ=== 1Prob ,

where iP is the probability to be a working poor or not that is not conditional to Xi ,

( ).Φ is the distribution function of the error term ε and γ is a vector of parameters to be

estimated. A positive sign (respectively a negative one) of the coefficient calculated shows that

an increase in one explicative variable increases (respectively diminishes) the risk of working poverty. As these coefficients could not directly measure the importance of the changes of

6 See for details on the methodology: Brunner, Al Husseini, Bocco and Calvé (2007).

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the risk associated to every change in the explicative variable, we calculated the marginal effects. The marginal effects show how the probability of current working poverty status change when each explanatory variable change, holding other variables constant.

A bivariate probit model is then developed to modelize the working poverty simultaneously according to the relative and subjective approaches of poverty.

The estimated bivariate probit model is as following:

),(

0* if 0 0* 1

vcorrρRho

εγF XBS*

FFFifFvXαF*

ε==++=

≤==+= f

We observe the variable F to be one if the worker is relatively poor and zero otherwise. ν is the error term which is standard normally distributed. F* is a latent variable.

S is one if the worker is subjectively poor and zero if not poor. ε is the error term

which is standard normally distributed. *S is a latent variable. This methodology is appropriate if the correlation between the error terms (Rho) is

significant: if the correlation coefficient is equal to zero, the two equations could be estimated separately. A positive (respectively negative) sign shows that an increasing of the considered explicative variable does increase (respectively reduces) the risk of working poverty.

4. Main Findings

4.1. Poverty and labour market problems among PRs

4.1.1. Relative poverty in the five fields

A relative poverty line, which represents 50% of the field’s median total household income after transfers, can be set based on measurement of the aggregate income taking into account household size and composition. The relative poverty line enables identification of the poorest in each field in order to analyse their characteristics and needs.

Table 2. Relative poverty line per adult equivalent – 2005 ($ US)

Before Transfers After Transfers

Median 50% of the median

Median 50% of the median

Jordan 57.63 28.81 64.61 32.31

Syria 38.91 19.45 47.08 23.54

Lebanon 62.42 31.21 78.33 39.16

Gaza 31.68 15.84 49.29 24.64

West Bank 59.94 29.97 72.59 36.31

Source: NEP Survey

Measuring relative poverty based on total household income before transfers shows

very high poverty rates in the Gaza Strip and the West Bank: 38% and 30% respectively. Relative poverty rates are lower in the other fields: 25% in Jordan, 23% in the Syrian Arab

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Republic and 26% in Lebanon. However, transfers play a significant role in reducing poverty, especially in the Gaza Strip and the West Bank, where their level is relatively high as a result of humanitarian and socio-economic assistance.

Figure 1. Relative poverty rates before and after transfers, by field

Source: NEP Survey

Regarding cash or in-kind transfers by UNRWA, 68% of the respondents in the Gaza

Strip, 31% in the Syrian Arab Republic, 27% in the West Bank, 22% in Lebanon and 5% in Jordan reported receiving such transfers. However, the amount received varied by field and it should be pointed out that some respondents may have included health and education services provided by UNRWA in their estimates.

Figure 2. Proportion of respondents benefiting from cash and in-kind UNRWA transfers,

by poverty (relative poverty without transfers), by field

Source: NEP Survey

Targeted institutional transfers and transfers from family and friends contribute

significantly to reducing relative poverty. After transfers, relative poverty is still higher in the Gaza Strip (25%) than in the other fields: 18% in the West Bank, 17% in Lebanon, 17% in the Syrian Arab Republic and 16% in Jordan. The results for the Gaza Strip and the West Bank, where UNRWA’s transfers constitute an important component of the income of the poorest (see above analysis of the lowest income quintile), underscore good targeting of the transfer policies that contributes to a significant reduction of relative poverty (Bocco et al., 2006).

2523

26

38

30

16 17 17

25

18

0

5

10

15

20

25

30

35

40

Jordan Syria Lebanon Gaza West Bank

Before Transfers After Transfers

10

4338

81

41

3

28

16

61

22

0

10

20

30

40

50

60

70

80

90

Jordan Syria Lebanon Gaza West Bank

Poor

Non-Poor

13

Next figure shows the importance of UNRWA transfers for the poor. As much as 40% of the total household income of the poor in the Gaza Strip is composed of UNRWA transfers, compared to 13% in the West Bank and 9% in Lebanon.

Figure 3. Share of UNRWA transfers in total household income,

by relative poverty, by field

Source: NEP Survey

Here, we focus on relative poverty, which differs quite significantly between the

Palestinian territories (and to a lesser extent Lebanon) on the one hand and the other host countries on the other. For the West Bank and the Gaza Strip, we consider as relatively poor, households whose income is lower than 50% of the median income, the level of which itself reflects a situation of deep poverty (World Bank, 2004). According to PCBS income data, in 2005 the income of 45.7% of households in the West Bank and 63.1% in the Gaza Strip was below the national poverty line (PCBS, 2006c). However, our results give an idea of the proportion of the poorest – poorest of the poor in the West Bank and Gaza Strip – and indicate the effectiveness of income transfers in reducing the number of the poorest in each field. Moreover, relative poverty gives some information on the level of inequality in each field. The overall level of income in the Gaza Strip is very low, with as much as one fourth of the households having a total income of less than 50% of the median income. 4.1.2 Subjective poverty

The other way of assessing the level of material deprivation of a population is to determine the level of access to basic food needs. Respondents were asked to assess their household’s food consumption over the month prior to the survey as less, equal to, or more than the minimum needed. Two distinct patterns can be discerned from the responses (figure below). On the one hand, in Jordan, the Syrian Arab Republic and Lebanon, more than one third of the respondents stated that their food consumption was less than their minimum needs. On the other hand, in the Gaza Strip and the West Bank the problem of accessing basic food needs seemed, surprisingly, less acute. Indeed, 24% and 28% of the respondents from the Gaza Strip and the West Bank, respectively, declared that their level of food consumption was less than their minimum needs as compared to 39% in Lebanon (where, judging by the responses, food deprivation is the most severe), 36% in the Syrian Arab Republic and 34% in Jordan. The paradoxical situation in the Gaza Strip and the West Bank vis-à-vis the level of absolute poverty prevailing there needs to be explained through further analysis, which is beyond the scope of this paper.

2

8

9

40

13

0,3

2

2

7

3

0 10 20 30 40 50

Jordan

Syria

Lebanon

Gaza

West Bank

Non-Poor Poor

14

Figure 4. Food deprivation, by country/territory

Source: NEP Survey

An analysis of perception of food deprivation among the relatively poor in camps

and outside them does present even more striking results: 33% of the poor from camps in the Gaza Strip reported a problem of acute food deprivation as compared to 60% in Jordan and 68% in Lebanon (figure below). A much larger percentage of respondents reported a feeling of acute food deprivation outside camps than inside in both the Syrian Arab Republic and the Gaza Strip: 67% and 49% respectively in the Syrian Arab Republic as compared to 43% and 33%, respectively, in the Gaza Strip, while in Lebanon more of those living inside camps felt the food shortage more acutely than those living outside them: 68% compared with 44%. In the West Bank and Jordan, there was a less significant difference between residents in camps and outside them.

It must be borne in mind, though, that these results reflect perceptions of respondents regarding food deprivation, and not objective data such as those that could be derived from a food-intake survey.

Nevertheless, it is important to explain why the respondents have such feelings. In the West Bank and Gaza Strip, since the beginning of the second Intifada one of the main coping strategies of Palestinian households was to reduce their spending, including food expenditures, that sharply declined both in volume and quality. As a consequence of the duration of the conflict and of the general level of material deprivation, it could be assumed that the level of expectations with regard to access to basic food needs will be lower than in the other fields where poverty is not so severe. Another explanation is related to the large food aid programs from local and international organizations, which have benefited many Palestine refugees in the West Bank and Gaza Strip, especially the poorest and those living in the camps. As a result of food aid and efficiently targeted policies in both these territories, the poorest Palestine refugees (bottom quintile in income distribution after transfers) might feel less vulnerable to food deprivation than the poorest refugees in the other fields of UNRWA’s operations who do not benefit from food assistance.

The important role of assistance institutions as a principal source of food for the lowest income quintile is reflected in the fact that, for example, as much as 48% of the poorest households in the Gaza Strip reported it as their principal source of food compared to 14% in the West Bank. In the Gaza Strip, 63% of all the respondents and 76% in the lowest income quintile received some food assistance in the three months prior to the survey, compared with respectively, 18% and 32% in the West Bank and 12% and 22% in Lebanon. In Jordan and Syria, food assistance is much more limited. In Syria, 9% of all the respondents and 14% of the lowest income quintile received some food assistance, while in Jordan they were 3% and 7% respectively.

24

28

39

36

34

59

61

54

51

51

17

11

7

13

15

0% 20% 40% 60% 80% 100%

Gaza Strip

West Bank

Lebanon

Syria

Jordan

Less than minimal needs Equal to minimal needs More than minimal needs

15

4.1.3. The labour market situation of the Palestinian refugees

In all five fields of UNRWA’s operations there are very low levels of participation of PRs in the labour market compared to the general population of the host countries and to the rest of the world. Indeed, the labour force participation rate is less than 50% of the working-age population in the Palestinian territories (45% in the West Bank and 49% in the Gaza Strip), and barely 51% in the other fields (Bensaïd and Lapeyre, 2007).

These low levels can be attributed to the combination of demographic, economic and social factors, especially the high levels of unemployment, the young age structure of population, the fact that young people are staying in the education system for longer periods of time, and, particularly, low women’s participation. A significant number of people retreat from the labour market because they think they have no chance of finding a job, particularly in the Palestinian territories because of closures and other Israeli policy measures. Compared with the rates registered for the population of the host countries, the participation rates of PRs are lower both for men and women (see table below). This indicates that the national labour markets are structured in a way that is less favourable for the participation of PRs than for the rest of the population.

Table 3. Labour force participation rates by gender (%) Men’s

participation rate Women’s

participation rate Women’s share in total

labour force

Survey Host country Survey Host country Survey Host country

Lebanon 75 84 24 35 23 30

Syria 75 89 26 39 29 36

Jordan 72 80 25 28 22 24

West Bank 71 70 18 16 20

Gaza 72 64 19 9 19

MENA 79 31 27 * Note: Data for the Palestinian territories are from PCBS (2006b) and for the other fields from the World Bank World Development Indicators 2006 (where he data provided refer to 2004)

An analysis of the employment rate shows, not surprisingly, very low levels in the

Gaza Strip and the West Bank. Only 29% of the working-age population in the Gaza Strip and 34% in the West Bank are employed (figure below) and can satisfy the basic needs of their families. In Jordan, Lebanon and the Syrian Arab Republic, this ratio is 44%, which is 10 percentage points higher than in the West Bank and 15 points higher than in the Gaza Strip.

Figure 5. Employment-to-population ratio by field

Source: NEP Survey

29

34

44

44

44

0 10 20 30 40 50

Gaza Strip

West Bank

Jordan

Syria

Lebanon

16

Thus the figures reveal the acute employment crisis facing Palestine refugees as a

result of the general macroeconomic situation (i.e. low economic growth) in the different fields of UNRWA’s operations as well as other specific institutional and political factors related to the Palestine refugees’ situation in general, and to their difficulty of access to labour markets. Since the working poor rate is a proportion of the working population one should take in account the fact that the employment rate is very low.

Our definition of the working poverty excludes unemployed and long-term unemployed, but one should take this fact in the analysis. Where unemployment and, particularly, long-term unemployment, is very high, the number of working poor would be less than if our reference was active population (as in the USA). The risk of poverty is certainly higher for the households of long-term unemployed persons that others, especially when these unemployed are heads of households.

All the fields of UNRWA’s operations, particularly the West Bank and the Gaza Strip, have high unemployment rates. In Lebanon, Jordan and the Syrian Arab Republic they are 13%, 14% and 15%, respectively, while in the West Bank and Gaza Strip they are 24% and 40% respectively (table below).

Table 4. Unemployment and long-term unemployment

Unemployment rate

Incidence of long-term

unemployment

Proportion of Household heads in long-term

unemployment

Lebanon 13 44 33

Syria 15 23 21

Jordan 14 27 29

West Bank 24 50 46

Gaza 40 51 69 Source: NEP Survey

The incidence of long-term unemployment (i.e. those unemployed for one year or more as a percentage of the total unemployed) (figure above) shows that in all fields, at least one out of five unemployed refugees have been unemployed for more than one year: 23% in the Syrian Arab Republic, 27% in Jordan, 44% in Lebanon, 50% in the West Bank, and 51% in the Gaza Strip. Moreover, a large proportion of the “long-term unemployed” have endured very long-term unemployment (more than two years), especially in the Gaza Strip and the West Bank, though less so in Lebanon.

In the West Bank and Gaza Strip, there is a greater incidence of long-term unemployment, especially among the heads of households. This may well explain the higher levels of poverty of the concerned households in these territories.

4.2. Estimating working poverty among Palestinian refugees

The table below shows working poverty rates for each field. One can see that, according to the relative poverty approach about 30% of the employed persons in Jordan, Syria and Lebanon can be considered as working poor, while the same is lower in the West Bank (24.56%) and Gaza (15.9%).

Table 5. Working poor rate according to relative poverty

Non working poor Working poor

Jordan 71,88 28,12

Syria 69,64 30,36

Lebanon 70,02 29,98

Gaza Strip 84,10 15,90

West Bank 75,44 24,56

17

The same analysis could be done for the subjective approach which shows that

about 30% of the employed persons in Jordan and Syria can be considered as working poor. In Lebanon, this rate is very high since it attains about 36%. For the Occupied Palestinian Territories (OPT) the rate is about 20%.

Table 6. Working poor rate according to subjective poverty

Non working poor Working poor

Jordan 69,50 30,50

Syria 70,79 29,21

Lebanon 64,43 35,57

Gaza Strip 80,85 19,15

West Bank 78,99 21,01

4.3. Profile of the working poor

This section presents and discusses the key factors explaining the working poverty

risk. We present first of all the factors which seem independent of individual or household factors. Then we go to the individual’s factors including those linked to the activity. The last category of factors corresponds to the household’s factors including those linked to the activity (of the other members of the household).

4.3.1. Spatial factors

a) Country/Territory

The highest rates of working poor exist in Syria, Lebanon and Jordan. The Lowest rate is the one prevailing in the Gaza Strip. There is a clear relation of interdependency between the country/territory of residence and the working poverty. This is true according to the two approaches of poverty.

The relatively low working poverty rate in the Gaza Strip must be interpreted very cautiously as the median income is very low. In other words, poor households (below half the median income) in the Gaza Strip are in a situation of abject poverty which is mostly due to exclusion of their members from the labour market (long and long term unemployed, and inactive).

Table 7. Interdependence between working poor rate

and field – Khi2 Test

Relative poverty Subjective Poverty

Non WP WP Non WP WP

Jordan 71,88 28,12 69,50 30,50

Syria 69,64 30,36 70,79 29,21

Lebanon 70,02 29,98 64,43 35,57

Gaza Strip 84,10 15,90 80,85 19,15

West Bank 75,44 24,56 78,99 21,01

Khi-2 Test 48,34 **** 67,88 ***** Significance threshold: **** : 1% *** : 5% * : 10% NS : non significant

18

b) Living in camps/outside camps There is also a clear relation of interdependency between the fact of living in a camp

and the working poverty risk (see next table). According to the relative poverty this is true for all the fields, except for the Gaza Strip because of mass poverty both inside and outside camps. For the subjective poverty approach, this interdependence is significant only in Jordan and Lebanon.

Table 8. Interdependence between working poverty

and living in camps - khi 2 Test

Relative poverty

Subjective Poverty

Jordan 41,03**** 5,61****

Syria 20,63**** 2,51ns

Lebanon 10,94***** 28,80****

Gaza Strip 0,4ns 0,03ns

West Bank 4,95**** 0,07 Significance threshold: **** : 1% *** : 5% * : 10% NS: non significant

As the table below shows, the working poverty rate jumps from 23% outside camps

to 47% inside camps in Jordan, from 26% to 41% in Syria, from 25% to 35% in Lebanon and from 21% to 29% in the West Bank.

Table 9. Working poverty by field and camp/non-camp situation


Situation Non camp Camp Non camp Camp

Jordan Non Working poor 76,98 53,23 71,37 62,37

Working poor 23,02 46,77 28,63 37,63

Syria Non Working poor 74,41 59,21 72,39 67,15

Working poor 25,59 40,79 27,61 32,85

Lebanon Non Working poor 75,11 64,97 73,08 55,88

Working poor 24,89 35,03 26,92 44,12

Gaza Strip Non Working poor 84,30 84,09 80,72 80,91

Working poor 15,70 15,91 19,28 19,09

West Bank Non Working poor 78,71 71,29 78,71 79,54

Working poor 21,29 28,71 21,29 20,46

4.3.2 Individual factors

a) Age

There is no evidence on the interdependency between age and the working poverty. The relationship is significant only in the case of Lebanon (for the approaches of poverty) and in the West Bank (for only the relative poverty approach).

Table 10. Working poverty by field and age


Non WP WP Non WP WP

Jordan 16 - 17 years old 60,00 40,00 66,67 33,33

18 - 24 years old 66,67 33,33 72,04 27,96

25 - 34 years old 74,65 25,35 71,48 28,52

19

35 - 44 years old 69,78 30,22 67,91 32,09

45 - 64 years old 74,04 25,96 67,31 32,69

65 years and older 76,92 23,08 76,92 23,08

Syria 16 - 17 years old 56,25 43,75 43,75 56,25

18 - 24 years old 64,52 35,48 75,00 25,00

25 - 34 years old 71,53 28,47 69,40 30,60

35 - 44 years old 70,16 29,84 72,98 27,02

45 - 64 years old 68,93 31,07 70,06 29,94

65 years and older 80,77 19,23 65,38 34,62

Lebanon 16 - 17 years old 75,00 25,00 50,00 50,00

18 - 24 years old 67,10 32,90 65,16 34,84

25 - 34 years old 81,55 18,45 72,96 27,04

35 - 44 years old 69,23 30,77 64,62 35,38

45 - 64 years old 60,19 39,81 58,29 41,71

65 years and older 68,42 31,58 31,58 68,42

Gaza Strip 16 - 17 years old 66,67 33,33 66,67 33,33

18 - 24 years old 77,78 22,22 80,56 19,44

25 - 34 years old 86,57 13,43 80,60 19,40

35 - 44 years old 83,33 16,67 86,21 13,79

45 - 64 years old 85,83 14,17 75,83 24,17

65 years and older 88,89 11,11 66,67 33,33

West Bank 16 - 17 years old 66,67 33,33 83,33 16,67

18 - 24 years old 70,00 30,00 81,25 18,75

25 - 34 years old 86,60 13,40 78,95 21,05

35 - 44 years old 68,47 31,53 76,13 23,87

45 - 64 years old 72,03 27,97 80,42 19,58

65 years and older 86,67 13,33 100,00

Table 11. Interdependence between working poverty and age - khi 2 Test

Jordan 4,61 Ns 1,99 Ns

Syria 4,97 Ns 7,97 Ns

Lebanon 25,38 **** 21,31 *****

Gaza Strip 5,61 Ns 7,53 Ns

West Bank 23,30 **** 5,60 Ns Significance threshold: **** : 1% *** : 5% * : 10% NS : non significant

b) Gender

As of the age variable, the gender does not seem to be linked with the working poverty. The only exceptions are Syria and the West Bank according to the relative poverty approach. Moreover, these two cases do not present similar patterns: in Syria, the working poor rate of women is higher than for men while the converse situation prevails in the West Bank where the women’s rate is twofold than men’s rate.

For the subjective approach, only in Jordan the relation is significant: in this country, men Palestinian refugees are more in working poverty than women.

Table 12. Working poverty rate by field and sex


Non WP WP Non WP WP

Jordan Male 70,97 29,03 67,88 32,12

Female 76,81 23,19 78,26 21,74

20

Syria Male 67,23 32,77 69,92 30,08

Female 79,88 20,12 74,39 25,61

Lebanon Male 70,49 29,51 64,48 35,52

Female 68,79 31,21 63,58 36,42

Gaza Strip Male 83,68 16,32 80,08 19,92

Female 87,93 12,07 87,93 12,07

West Bank Male 73,67 26,33 79,00 21,00

Female 86,32 13,68 78,95 21,05


and sex - khi 2 Test

Jordan 1,97 Ns 5,92 *****

Syria 10,07 ***** 1,29 Ns

Lebanon 0,19 Ns 0,05 Ns

Gaza Strip 0,71 Ns 2,08 Ns

West Bank 7,05 ***** 0,50 Ns Significance threshold: **** : 1% *** : 5% * : 10% NS : non significant

c) The status of head of household

The relation between the working poverty and the status of head of household is significant only in Jordan and Syria, for both relative poverty and subjective poverty. In Syria, the two approaches converge, indicating that heads of households have more risk to be working poor than those who are not household heads. This is confirmed in Jordan only in the case of subjective approach, while the relative poverty approach gives a converse result.

Table 14. Working poor rate by the state of being head of household


Non Head of HH Head of HH Non Head of HH Head of HH

Jordan Non WP 67,48 74,50 73,56 67,09

WP 32,52 25,50 26,44 32,91

Syria Non WP 72,79 66,88 75,25 66,88

WP 27,21 33,12 24,75 33,12

Lebanon Non WP 69,98 70,06 64,41 64,45

WP 30,02 29,94 35,59 35,55

Gaza Strip Non WP 81,17 85,15 83,12 80,05

WP 18,83 14,85 16,88 19,95

West Bank Non WP 77,68 74,27 80,26 78,33

WP 22,32 25,73 19,74 21,67


and the state of being head of household - khi 2 Test


Jordan 5,04**** 4,07****

Syria 3,59*** 7,35****

Lebanon 0,02 ns 0,03 ns

Gaza Strip 1,35ns 0,69ns

West Bank 0,96ns 0,34ns Significance threshold: **** : 1% *** : 5% * : 10% NS : non significant

21

d) Educational Attainment

There is a clear relation of interdependency between the working poverty risk and educational attainments of workers. According to the relative poverty this is true for all the fields, except for the Gaza Strip because of mass poverty both inside and outside camps. For the subjective poverty approach, this interdependence is significant only in Jordan and Lebanon.

As the table below shows, for all the fields, the working poverty rate drops from 44.2% for the “no school” to 8.4% for those with higher education. Quite the similar figures exist when using the subjective poverty approach, with working poverty dropping from 49.4% for the non school level to 14.4% for the higher education level.

Table 16. Working poor rate by educational attainment

Relative poverty

7

Subjective Poverty

No school 44,2 49,4

Dropped from elementary school 40,5 44,5

Elementary (or drop. from preparatory school.) 38,7 38,7

Preparatory school (or drop. from voc/high) 31,2 26,1

Vocational training or school 27,0 24,0

High school (or drop. from higher education) 17,2 23,1

Higher education 8,4 14,4

The important role of education is confirmed by the positive impact of language

skills on the reduction of the risk of working poverty. So, the non working poor practice in average 2.48 languages, while the working poor do practice only 1.87 languages.

Table 17. Working poor rate by language skills


Mean Non WP WP Test F Sig Non WP WP Test F Sig

Language skills 2,48 1,87 162,39 **** 2,48 1,91 141,98 **** Significance threshold: **** : 1%

The tables below give detailed results by field according to the two approaches of

poverty. They show that the interdependence between education and working poverty is confirmed for all the fields.

Table 18. Working poor rate by educational attainment and by field - Relative poverty

Jordan Syria Lebanon Gaza Strip West Bank

Non WP WP

Non WP WP

Non WP WP

Non WP WP

Non WP WP

No school 57,14 42,86 52,17 47,83 51,35 48,65 50,00 50,00

Dropped from elementary school 55,56 44,44 45,31 54,69 62,50 37,50 81,25 18,75 63,16 36,84

Elementary (or drop. from preparatory school.) 55,17 44,83 58,39 41,61 61,20 38,80 74,39 25,61 67,42 32,58

Preparatory school (or drop. from voc/high) 61,24 38,76 68,00 32,00 73,01 26,99 76,92 23,08 68,42 31,58

Vocational training or school 67,21 32,79 71,76 28,24 77,91 22,09 75,47 24,53 70,83 29,17

7 0001,0)( 2

0 =χp , which confirms the interdependence between working poverty and education.

22

High school (or drop. from higher education) 83,49 16,51 81,13 18,87 85,87 14,13 85,60 14,40 77,08 22,92

Higher education 90,04 9,96 91,15 8,85 88,89 11,11 95,11 4,89 92,41 7,59

Table 19. Working poor rate by educational attainment and by field - Subjective poverty


Non WP WP

Non WP WP

Non WP WP

Non WP WP

Non WP WP

No school 57,14 42,86 26,09 73,91 51,35 48,65

Dropped from elementary school 52,78 47,22 51,56 48,44 46,67 53,33 78,13 21,88 76,32 23,68

Elementary (or drop. from prep. school.) 56,90 43,10 62,04 37,96 55,85 44,15 71,95 28,05 71,21 28,79

Preparatory school (or drop. from voc/high) 66,29 33,71 78,40 21,60 68,71 31,29 79,81 20,19 79,47 20,53

Vocational training or school 68,85 31,15 74,12 25,88 79,07 20,93 81,13 18,87 77,08 22,92

High school (or drop. from higher educ.) 74,31 25,69 76,42 23,58 79,35 20,65 78,40 21,60 76,04 23,96

Higher education 82,21 17,79 86,46 13,54 88,89 11,11 86,96 13,04 87,34 12,66


and the state of being head of household - khi 2 Test Relative poverty Subjective Poverty

Jordan 98,00**** 46,51****

Syria 87,07**** 72,47****

Lebanon 48,35**** 68,43****

Gaza Strip 33,26**** 10,27ns

West Bank 39,75**** 13,87****

Significance threshold: **** : 1% *** : 5% * : 10% NS : non significant

Clearly, higher levels of education are a good mean to escape from working poverty. When we examine the structure of the labour force by educational attainment, we find that in all the fields the levels of education are high (except in Lebanon).

But, at the same time, this education does not prevent necessarily from unemployment. So, for example, in the Gaza Strip where higher education represents 29% of the labour force, unemployment rate attains 35%. A second example is given by Jordan where workers with higher education are the most vulnerable to unemployment (the highest unemployment rate). Also, in the case of Lebanon where higher education represents only 10% of the labour force, unemployment rate for this category is the highest one (according to educational attainment) in this country.

Table 21. Unemployment rate by educational attainment No

School Elementary

School Preparatory

School Voc.

Training High

School Higher

education Total

Lebanon 6 12 14 18 11 21 13

Syria 5 15 13 11 29 12 15

Jordan 10 10 12 9 15 18 14

West Bank 31 25 24 16 29 21 24

Gaza 51 41 40 43 42 35 40 Source: NEP Survey

Different reasons can help to explain why this is so, depending on the specificities of each country or territory. In the Palestinian territories, those with the lowest education (no school or dropped out of elementary school) experience the highest levels of unemployment. This is due to the collapse of the Palestinian private sector, the small trade

23

sector – which engaged the most unskilled labour before the second Intifada – and limited access to the Israeli labour market that, formerly, absorbed low-skilled labour. But even those with higher education (and the other educational levels) face severe unemployment, and these rates are higher than ever, because of the collapse of the Palestinian economy and the destruction of its labour market.

In Lebanon, the most vulnerable are those with higher education (followed by vocational training), due to the barring of Palestine refugees without citizenship from professional occupations. On the other hand, in the Syrian Arab Republic the Palestine refugees with the highest rate of unemployment are those educated to high school level.

This also shows an inadequate linking of the education system, particularly higher education (and high school degrees), with the labour market requirements. As is well known, most of the countries in the Middle East and North Africa region registered dramatic unemployment rates of educated young people, and particularly those with post-secondary education, a similar pattern to that observed in Jordan and Lebanon. On the other hand, this category of active people has the lowest unemployment rate in the Gaza Strip and among the lowest in the West Bank and the Syrian Arab Republic. In the Palestinian territories and the Syrian Arab Republic, this could be explained by the fact that a high proportion of this category of the refugee population is employed by the public sector, UNRWA and international non-governmental organizations (NGOs).

e) Sector of work

The sector of work is significantly linked to working poverty and this is true for both relative and subjective approaches to poverty. So, working poverty is more present for those working in the primary sector, in the industry, in construction, and in some service activities (transport, cleaning, marketing). Public sectors seem to escape from high levels of working poverty, as is the case of education and health sectors. In the private sector, those who work in offices or restaurants are less vulnerable to working poverty than other private activities.

Table 22. Working poor rate by sector of work


Non WP WP Non WP WP

Farming 55,6 44,4 63,7 36,3

Fishing 33,3 66,7 73,3 26,7

Mining/Quarrying 61,1 38,9 55,6 44,4

Craftsmanship 70,2 29,8 69,3 30,7

Industry 63,8 36,2 67,4 32,6

Construction 61,7 38,3 64,6 35,4

Transport 65,7 34,3 65,7 34,3

Commerce 72,4 27,6 71,4 28,6

Restaurants 83,7 16,3 77,9 22,1

Health sector 85,6 14,4 77,3 22,7

Education 90,8 9,2 86,7 13,3

Marketing 64,3 35,7 72,9 27,1

Technical 86,0 14,0 78,9 21,1

Office 90,8 9,2 85,5 14,5

Security 80,8 19,2 76,2 23,8

Cleaning 64, 4 35,6 61,0 39,0

Public sector: other 81, 5 18,5 74,4 25,6

Total 73,4 26,9 72 28

24


and the sector of work - khi 2 Test

Relative poverty Subjective poverty

All fields 209,04**** 89,6****

Significance threshold: **** : 1%

The distribution of working poverty by sector of work and fields confirms the

precedent analysis, since the interdependence between working poverty and sector of work is very significant in each country/territory. But it shows also specific situations in each field. For example, the working poor rate in Lebanon, the West Bank and Gaza is lower in the health sector than in education sector, but it’s higher for Jordan and Syria. Another example is given by the security sector. This sector exhibits one of the lowest working poor rates in the West Bank and Gaza but very high rates in Jordan, Syria and Lebanon. This could be explained by the extreme importance of security activities in the Occupied Palestinian Territories which is regulated by international assistance (and control).

Table 24. Working poor rate by sector of work and by field - Relative poverty


Non WP WP Non WP WP Non WP WP Non WP WP Non WP WP

Farming 55,00 45,00 53,13 46,88 48,72 51,28 70,59 29,41 59,26 40,74

Fishing 100,00 33,33 66,67 20,00 80,00

Mining/Quarrying 42,86 57,14 33,33 66,67 100,00 100,00 100,00

Craftsmanship 70,00 30,00 65,96 34,04 71,21 28,79 72,15 27,85 74,68 25,32

Industry 61,63 38,37 58,04 41,96 61,61 38,39 81,25 18,75 75,00 25,00

Construction 57,81 42,19 56,82 43,18 61,36 38,64 69,23 30,77 64,71 35,29

Transport 61,18 38,82 67,24 32,76 62,75 37,25 64,29 35,71 76,09 23,91

Commerce 75,51 24,49 75,51 24,49 66,88 33,13 88,00 12,00 62,00 38,00

Restaurants 85,00 15,00 88,24 11,76 80,00 20,00 71,43 28,57 85,19 14,81

Health sector 75,00 25,00 78,05 21,95 87,50 12,50 100,00 93,33 6,67

Education 89,74 10,26 92,11 7,89 83,12 16,88 98,31 1,69 92,98 7,02

Marketing 61,29 38,71 68,75 31,25 100,00 72,73 27,27 54,55 45,45

Technical 88,46 11,54 100,00 78,05 21,95 89,47 10,53 75,00 25,00

Office 91,89 8,11 88,89 11,11 90,74 9,26 91,67 8,33 89,47 10,53

Security 37,50 62,50 50,00 50,00 42,86 57,14 93,67 6,33 91,18 8,82

Cleaning 57,14 42,86 40,00 60,00 64,00 36,00 100,00 70,00 30,00

Public sector: other 76,25 23,75 73,98 26,02 88,89 11,11 92,86 7,14 92,86 7,14

Table 25. Working poor rate by sector of work and by field - Subjective poverty


Non WP WP Non WP WP Non WP WP Non WP WP Non WP WP

Farming 70,00 30,00 68,75 31,25 41,03 58,97 82,35 17,65 74,07 25,93

Fishing 100,00 55,56 44,44 100,00

Mining/Quarrying 42,86 57,14 66,67 33,33 100,00 100,00 66,67 33,33

Craftsmanship 68,46 31,54 72,34 27,66 59,09 40,91 77,22 74,68 25,32

Industry 68,60 31,40 66,96 33,04 54,46 45,54 84,38 22,78 85,42 14,58

Construction 54,69 45,31 61,36 38,64 61,36 38,64 78,85 15,63 67,65 32,35

Transport 55,29 44,71 58,62 41,38 66,67 33,33 71,43 21,15 89,13 10,87

Commerce 74,83 25,17 70,41 29,59 61,88 38,13 74,67 28,57 80,00 20,00

Restaurants 65,00 35,00 82,35 17,65 80,00 20,00 71,43 25,33 85,19 14,81

Health sector 82,14 17,86 68,29 31,71 87,50 12,50 88,00 28,57 73,33 26,67

Education 84,62 15,38 86,84 13,16 87,01 12,99 91,53 12,00 84,21 15,79

25

Marketing 70,97 29,03 87,50 12,50 100,00 54,55 8,47 81,82 18,18

Technical 84,62 15,38 90,00 10,00 70,73 29,27 73,68 45,45 87,50 12,50

Office 78,38 21,62 88,89 11,11 85,19 14,81 91,67 26,32 94,74 5,26

Security 62,50 37,50 43,75 56,25 50,00 50,00 86,08 8,33 82,35 17,65

Cleaning 57,14 42,86 40,00 60,00 52,00 48,00 100,00 13,92 75,00 25,00

Public sector: other 70,00 30,00 73,17 26,83 66,67 33,33 80,00 20,00 78,57 21,43


and the state of being head of household - khi 2 Test Relative poverty Subjective Poverty

Jordan 58,31**** 37,32****

Syria 61,88**** 39,46****

Lebanon 54,59**** 55,09****

Gaza Strip 70,74**** 19,96****

West Bank 56,84**** 19,60****


The comparison with the distribution of employment by sector of work is an

important way for the explanation of the combined effect of spatial and sectoral distribution of working poverty. This shows for example that even if working in the activities of the primary sector (farming, fishing, mining/quarrying) is linked to high working poor rates, a little proportion of Palestinian refugees work in them. Another illustrative example is given by the “security” activities. Here, we find high levels of working poverty associated with very low proportion of workers in Jordan, Lebanon and Syria, and low levels of working poverty associated with important proportions of workers in the West Bank and Gaza.

Table 27. Distribution of employed persons by sector of work


Farming 2 4 4 3 5

Fishing 0 0 1 1 0

Mining/Quarrying 1 0 0 0 1

Craftsmanship 15 16 15 14 12

Industry 10 13 13 6 8

Construction 7 10 5 9 16

Transport 10 7 6 5 7

Commerce 17 11 18 13 15

Restaurants 2 2 2 1 4

Health sector 3 5 1 4 4

Education 9 9 9 10 9

Marketing 4 2 0 2 2

Technical 3 2 5 3 1

Office 4 1 6 2 3

Security 1 2 2 14 4

Cleaning 1 1 3 0 3

Public sector: other 9 14 1 12 6

Other 2 1 10 1 1

f) Employment status

The working poverty depends clearly on the type of employer. Following the relative poverty approach, in all the fields those who are paid by UNRWA and by the public sector are less in poverty than others. At the same time, those who are paid by the private sector,

26

those who are self-employed - including as family member – or unpaid family workers are more frequently poor.

Concerning the subjective poverty approach, the dependence is not confirmed for Jordan and the West Bank.

One should note that w age employment does not prevent from the working poverty. Moreover, self-employed are in better situation. Different reasons can explain this fact. First of all, private sectors where Palestinian refugees work are frequently situated in the bad segment of the labour market (informal sector, low pay, flexible hours of work...). Secondly, self-employed could work more hours if they want and if there is an effective demand corresponding to their production.

Table 28. Working poor rate by employment status and by field

Relative poverty Subjective poverty Non WP WP Non WP WP

Jordan Paid by public sector 80,32 19,68 72,34 27,66

Paid by private sector 69,18 30,82 67,41 32,59

Paid by UNRWA 90,48 9,52 80,95 19,05

Paid by local NGO 87,50 12,50 75,00 25,00

Self emp. family member 73,33 26,67 76,67 23,33

Self employed 72,83 27,17 71,74 28,26

Unpaid family worker 60,00 40,00 50,00 50,00

Other 53,85 46,15 76,92 23,08

Several employers 71,43 28,57 85,71 14,29

Syria Paid by public sector 78,90 21,10 75,53 24,47


Paid by UNRWA 100,00 79,31 20,69

Paid by local NGO 60,00 40,00 80,00 20,00


Self employed 75,42 24,58 78,81 21,19

Unpaid family worker 100,00 100,00

Other 60,00 40,00 80,00 20,00


Lebanon Paid by public sector 72,41 27,59 62,07 37,93


Paid by UNRWA 89,58 10,42 83,33 16,67

Paid by local NGO 80,65 19,35 67,74 32,26


Self employed 69,57 30,43 67,39 32,61


Other 47,62 52,38 38,10 61,90


Gaza Strip Paid by public sector 93,65 6,35 86,24 13,76


Paid by UNRWA 98,11 1,89 88,68 11,32

Paid by local NGO 76,92 23,08 61,54 38,46


Self employed 82,22 17,78 77,78 22,22

Unpaid family worker 100,00 50,00 50,00

Other 80,00 20,00 60,00 40,00

Several employers 100,00 80,00 20,00

West Bank Paid by public sector 92,75 7,25 83,33 16,67


27

Paid by UNRWA 81,63 18,37 75,51 24,49

Paid by local NGO 82,35 17,65 76,47 23,53


Self employed 81,25 18,75 79,17 20,83


Other 90,00 10,00 80,00 20,00



and the employment status - khi 2 Test

Relative Poverty Subjective Poverty

Jordan 15,69 **** 0,537 Ns

Syria 40,94 **** 0,060 ***

Lebanon 17,78 **** 0,026 ****

Gaza Strip 43,05 **** 0,073 **

West Bank 44,21 **** 0,836 Ns Significance threshold: **** : 1% *** : 5% * : 10% NS : non significant

g) Hours of work

Atypical number of hours worked are all forms of inadequacy of employment: the first case concerns insufficient volume of employment (in terms of time worked); the second one is related to excessive hours of work. There is a clear relation of interdependency between the working poverty risk and the number of hours worked. According to the relative poverty measure this is true for all the fields.

Working poverty affects both those in underemployment and those who work excessive or very excessive hours. In the first case, underemployment is generally associated with low total revenue per month (per week), not enough to go out of poverty. There is a close links between underemployment (and unemployment) and the other dimensions of economic and social life (income, health, access to basic needs and living conditions), which are now well documented, suggesting that people in such situation are at greater risk of becoming poor (Bhalla and Lapeyre, 2005).

The second case, which concerns excessive hours of work, is more striking. How could it be linked to working poverty? Actually, when people work excessive or very excessive hours, this could be generally – particularly in developing countries - linked to jobs with low remuneration per hour. So, to compensate low earnings, concerned persons try to work more hours or even to seek additional job. Another reason for excessive time of work is when the size of the household is high and the number of people contributing to its income is low (high dependency ratio). In this case, even high remuneration per hour could be insufficient to cover the needs of the household (see the analysis of this factor below). Here, the distinction between individual and household factors does not work.

Table 30. Working poor rate by hours of work and by field – relative poverty


Underemployed 7,4 18,8 14,9 26,3 22,3

Excessive hours 10,3 15,9 12,6 15,2 23,4

Very excessive hours 22,8 25,2 23,2 25,5 16,6

More than one job 2,4 4,5 4,5 3,9 4,0 Underemployed = less than 35 hours per week Excessive hours aof work = between 47 and 51 hours per week Excessive hours of work = more than 51 hours per week

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and the number of worked hours - khi 2 Test

Khi-2

Underemployed 112,18****

Excessive hours 56,54****

Very excessive hours 20,23****

More than one job 7,05* Significance threshold: **** : 1% *** : 5% * : 10% NS : non significant

We have also calculated and tested the relation between the average hours worked and the working poverty rate. We find that, according to the subjective poverty approach, there is a significant dependency between the two variables. More precisely, working poor work in average 2.5 hours more than the non poor.

Table 32. Working poor rate by average hours worked - ANOVA test

Relative poverty

Sig Subjective poverty

Sig

Non WP 47,35 46,55

WP 46,79 Ns 48,98 **** Significance threshold: **** : 1% *** : 5% * : 10% NS : non significant

Theses results are confirmed by the fact that excessive and very excessive hours of work is a very frequent phenomena for Palestinian refugees, especially in Jordan, Lebanon and Syria (see the table below).

Figure 6. Proportion of Palestinian refugees working

low, excessive or very excessive hours

4.3.4. Household factors

Individual factors are insufficient to explain the state of working poverty. The characteristics of the household must also be taken in account. Two kind of household factors are considered here for because of their extreme importance: the size of the household and the proportion of persons contributing to the household income.

a) Household size

13

24

29

29

41

23

30

43

40

51

23

25

19

22

14

0 10 20 30 40 50 60

West Bank

Gaza Strip

Lebanon

Syria

Jordan

Low hours Excessive hours Very Excessive hours

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There is a very significant relation of interdependency between the size of the household and the working poverty risk. This is true for all the fields of the survey and in each one. Moreover, there is a large overlap between the results of relative poverty approach and the subjective one.

If we limit our comment to the results of the relative poverty approach, we can see that the size of the household jumps from 5.98 for the non poor workers to 8.20 for the poor ones.

Table 33. Working poor rate by household size - ANOVA


Mean Non

Working Poor Working

Poor F-Test Sig Non

Working Poor Working

Poor F-Test Sig

Household Size 5,98 8,20 447,52 **** 6,44 6,91 18,34 ****

Number of adults in the household 3,58 4,56 161,36 **** 3,80 3,94 3,15 ****

Number of children in the household 2,36 3,61 258,91 **** 2,60 2,93 18,12 ****

Number of females in the household 2,82 3,88 259,76 **** 3,04 3,25 9,31 **** Significance threshold: **** : 1% *** : 5% * : 10% NS : non significant

The positive effect of the household size is due particularly to the higher number of

children and women in the poor workers’ households. High number of women and children is a risk factor of working poverty because of their high level of economic dependency8. This is particularly true where women are largely excluded from the labour market as is the case for women refugees in all the five fields. Actually, there are very low women’s employment rates due mainly to their low level of participation in the labour market and to their high unemployment rates (see table below).

Table 34. Labour market indicators by gender and field

Activity rate Employment Rate Unemployment Rate

Men Women Men Women Men Women

Jordan 72 25 69 16 6 39

Syria 75 26 69 17 8 36

Lebanon 75 24 69 18 9 26

Gaza 72 19 48 6 33 70

West Bank 71 18 58 11 19 44 Source: NEP Survey

The details results by field and according to the two approaches of poverty confirm

the precedent analysis.

Table 35. Working poor rate by household size and by field according to Relative Poverty

– ANOVA test


Household Non WP 5,87 5,79 5,11 7,35 6,08

8 Of course, this is not a universal fact. If children are generally economically dependent, this is not necessarily the case for women. It depends on their participation in the labour market and on the degree of their integration in remunerated economic activities. The Middle East region exhibits the lowest rates of labour force participation and employment rates for women in the World.

30

Size WP 9,18 **** 7,91 **** 7,06 **** 10,14 **** 7,94 ****

Number of adults in the household

Non WP 3,56 3,83 3,35 3,78 3,39

WP 5,10 **** 4,54 **** 4,35 **** 4,77 **** 4,03 ****

Number of children in the household

Non WP 2,27 1,92 1,73 3,57 2,63

WP 4,06 **** 3,32 **** 2,69 **** 5,35 **** 3,90 ****

Number of women in the household

Non WP 2,74 2,67 2,35 3,58 2,93

WP 4,25 **** 3,78 **** 3,29 **** 4,87 **** 3,86 **** Significance threshold: **** : 1% *** : 5% * : 10% NS : non significant

Table 36. Working poor rate by household size and by field according to Subjective Poverty

– ANOVA test


Household Size

Non WP 6,47 6,34 5,57 7,64 6,38

WP 7,54 **** 6,66 Ns 5,92 **** 8,44 **** 7,14 ****

Number of adults in the household

Non WP 3,92 4,04 3,61 3,83 3,57

WP 4,15 Ns 4,05 Ns 3,72 Ns 4,39 Ns 3,48 Ns

Number of children in the household

Non WP 2,52 2,25 1,92 3,81 2,76

WP 3,35 **** 2,58 **** 2,18 **** 4,03 **** 3,61 ****

Number of women in the household

Non WP 3,02 2,94 2,58 3,71 3,10

WP 3,51 **** 3,18 * 2,72 **** 4,10 **** 3,38 ****


b) Number and proportion of persons contributing to the household income

The size of household represents a real risk of poverty and of working poverty when the number and proportion of the persons not contributing to the household income is high. This is clearly the case of children, of elder persons and non working adults. Every additional dependent person is a positive factor of working poverty while every additional member contributing to the household income is a negative factor (reducing the risk of working poverty). So, working poor households have more than two dependent persons (2.32) than non working poor households (see table below). Also, if the number of persons contributing to the household income is 1.76 in non working poor households, this figure drops to 1.58 in the working poor ones.

Table 37. Working poor rate by the number of persons contributing

to the household income – ANOVA test

Relative poverty Subjective poverty

Non Working

Poor Working

Poor Sig Non Working

Poor Working

Poor Sig

Number of dependants in the household 4,39 6,71 **** 3,78 4,26 ****

Number of HH members contributing to HH income 1,76 1,58 **** 1,75 1,61 ****

Number of male above 18 contributing to HH income 1,35 1,34 Ns 1,36 1,31 Ns

Number of female above 18 contributing to HH income 0,46 0,38 *** 0,45 0,44 Ns


31


to the household income and by field – Relative Poverty – ANOVA Test


Number of dependants in the household

Non WP 4,34 3,62 3,53 5,96 4,80

WP 7,74 **** 6,21 **** 5,42 **** 8,99 **** 6,67 ****

Number of HH members contributing to HH income

Non WP 1,68 2,35 1,84 1,51 1,30

WP 1,45 **** 1,75 **** 1,84 **** 1,26 **** 1,28 Ns



to the household income and by field –Subjective Poverty – ANOVA Test


Number of dependants in the household

Non WP 4,94 **** 4,21 **** 3,98 **** 6,28 **** 5,10 ****

WP 6,11 4,96 4,30 7,14 5,90

Number of HH members contributing to HH income

Non WP 1,68 **** 2,30 **** 1,86 Ns 1,49 ns 1,31 Ns

WP 1,47 1,85 1,81 1,38 1,24 Significance threshold: **** : 1% *** : 5% * : 10% NS : non significant

4.4. Analysis of the determinants of working poverty

4.4.1. Simple Probit model

Here we present the results of a simple probit model applied to each of the poverty

approaches and where the working poverty rate is the dependent variable. We also calculated the marginal effect for each approach.

Generally the results of the model confirm the results of the precedent bivariate analysis. The following comments focus only on the results of the relative poverty approach. a) Spatial factors

Palestinian refugees living in Jordan, Syria or Lebanon have more risk to be working poor. So, as showed by the marginal effects, living in Jordan augments the risk of working poverty by 12% compared to those living in the West Bank. This risk is estimated at 10% for Syria and 7% for Lebanon.

Also, the probit model shows that Palestinian refugees living in urban areas have less risk (-12%) to be in working poverty.

For the factor of living in camps, contrary to the results of the previous bivariate analysis that shows a clear and very significant dependence between this variable and the working poverty (except in the Gaza Strip), the probit model does not result in significant relation between the two variables.

b) Individual factors

The relation between adult age and working poverty is not significant: there is no significant difference between the youngest (20-25 years) and the others (more than 25 years). On the other hand, the relations between the working poverty rate, in one side, and the variables of sex, marital status and head of household status, in the other side, are not significant. However, we must note here that this result is a consequence of the aggregation of all the fields. Probably, the construction of a probit model for each field will result in more

32

significant relations. For example, as we have seen in the section of bivariate analysis (profile of working poor), the relation between the working poverty and the status of head of household is very significant in Syria and Jordan and not in the other fields. Probably we will find the same result with the disaggregated probit model (by field)9

. If we turn to educational attainment, the model shows that the relation with working poverty is very significant (except for vocational training). So, low levels of education are correlated with more risk of working poverty while high levels of education are associated with positive probabilities to escape from working poverty. For example, the no school level increased the probability of being in working poverty by 16%, compared to those with preparatory school level (the reference). In the other hand, the probability of being working poor is reduced for those workers who had attended high school or higher education, compared to our reference (preparatory school). For workers with higher education attainment, the probability of being poor is reduced by 23%, while this reduction is only 9% for those with high school attainment. Concerning the sector of work, the model shows significant interdependence with working poverty for those paid by public sector, by private sector and those in the situation of self-employment family member, but these relations are not similar in their sense and in their marginal effects. Working in the public sector is a factor which reduces the probability of being poor by 5%, while working in the private sector or as self-employment family member increases this probability by 7% and 9% respectively. The last individual factor regards the number of hours worked. Here, in one hand underemployment (less than 35 hours per week) increases the risk of working poverty by 6% and, in the other hand, very excessive hours of work (more than 51 hours per week) reduces this risk by 4%. c) Household factors

The model shows that the size of household represents a positive factor of working poverty. So the number of adults and the number of children in the household increases the probability of being working poor by 4% and 6% respectively.

But contrary to the results of bivariate analysis the number (and proportion) of persons contributing to the income of the household is not significantly linked to the working poverty.

Finally, it is worth mentioning that the predictive capacity of our probit model is high. Indeed, the percentage of its correct predictions is 78% regarding the relative poverty approach and 72% regarding the subjective one.

Table 40. Simple probit model Relative Poverty Subjective Poverty

Coef

Sig. Sig.

Marginal Effect Coef Sig

Marginal Effect

Constant -0,20 **** 0,06 0,19 **** 0,06

Spatial factors

Countries/Territories Jordan 0,40 **** 0,12 0,36 **** 0,12

Syria 0,35 **** 0,10 0,25 **** 0,08

Lebanon 0,25 **** 0,07 0,37 **** 0,12

Gaza Strip -0,51 **** -0,15 -0,06 Ns -0,02

West Bank Ref. Ref.

Place of residence Urban -0,39 **** -0,12 -0,07 Ns -0,02

Rural Ref. Ref.

9 This will be done in an extended version of this paper.

33

Situation of residence :

Camp/non Camp UNRWA official camps -0,003 Ns -0,001 -0,001 Ns 0,001

Outside camps Ref. Ref.

Individual variables

Age Adult 0,21 **** 0,06 -0,25 Ns -0,08

Age class Age2: 20-25 years -0,09 Ns -0,03 -0,14 Ns -0,04

Age3: 26-55 years -0,04 Ns -0,01 0,01 Ns 0,002

Sex Man -0,006 Ns -0,001 0,001 Ns 0,002

Marital Status Married 0,02 Ns 0,01 -0,02 Ns -0,01

Household head Head 0,06 Ns 0,02 0,09 Ns 0,03

Education No school 0,54 **** 0,16 0,63 **** 0,21

Dropped from elementary 0,24 **** 0,07 0,45 **** 0,15

Elementary (or drop. from prep.) 0,15 **** 0,04 0,31 **** 0,10

Preparatory school Ref. Ref.

Vocational training -0,10 Ns -0,03 -0,07 ns -0,02

High school (or drop. from higher educ.) -0,32 **** -0,09 -0,05 ns -0,02

Higher education -0,77 **** -0,23 -0,38 **** -0,13

Sector of work Paid by public sector -0,16 *** -0,05 0,02 ns 0,01

Paid by private sector 0,25 **** 0,07 0,05 ns 0,01

Paid by UNRWA Ref. Ref.

Paid by local NGO 0,10 Ns 0,03 0,13 ns 0,04

Self emp. family member 0,30 **** 0,09 -0,01 ns -0,003

Self employed -0,01 Ns -0,002 -0,16 *** -0,05

Unpaid family worker 0,33 Ns 0,10 -0,01 ns -0,004

Several employers 0,19 Ns 0,06 0,14 ns 0,05

Hours worked per week Less than 35 0,20 **** 0,06 0,02 ns 0,01

Between 47 and 51 0,08 Ns 0,02 0,12 **** 0,04

More than 51 -0,15 **** -0,04 0,05 ns 0,02

Pluri-activity More than one job -0,02 Ns -0,01 0,04 Ns 0,01

Household variables

Household size Number of adults in the household) 0,15 **** 0,04 0,03 **** 0,01

Number of children in the household) 0,21 **** 0,06 0,06 **** 0,02

Number of women in the household) -0,01 Ns -0,002 -0,01 Ns -0,004

Members contributing to the household income

Number of children contributing to the household income) -0,001 Ns -0,002 -0,002 ns -0,001

Number of persons contributing to the household income) -0,002 Ns -0,005 -0,005 ns -0,001

Number of observations 3910 3910

Log-likelihood -1809.241 -2167.745

Degrees of freedom 34 34

Significance threshold 0.000 0,000

Percentage of correct predictions 78% 72%

34


4.3.2 Bivariate Probit Model

The last finding of our analysis concerns the results of the bivariate probit model which focuses on a methodological (and theoretical) issue. The problem is to see to what extent the two approaches of poverty converge or not in their results. The principal conclusion of this second model is that the two approaches converge as indicated by the positive and significant Rho coefficient (+0.32).

Table 41. Bivariate Probit Model


Coef Sig Coef Sig

Constant 0,21 **** -0,90 **** Spatial factors

Countries/Territories Jordan 0,40 **** 0,36 ****

Syria 0,33 **** 0,24 ****

Lebanon 0,23 **** 0,37 ****

Gaza Strip -0,49 **** -0,06 Ns

West Bank Ref. Ref.

Urban versus Rural Urban -0,41 **** -0,07 Ns

Situation of residence : Camp/non Camp

Residence in UNRWA official camps -0,001 Ns 0,001 Ns

Individual variables

Age Adult 0,19 Ns -0,24 Ns

Sex Man -0,001 Ns 0,001 Ns

Marital Status Married 0,02 Ns -0,02 Ns

Household head Head 0,06 Ns 0,08 Ns

Education No school 0,54 **** 0,63 ****

Dropped from elementary 0,24 **** 0,45 ****

Elementary (or drop. from prep.) 0,15 **** 0,31 ****

Preparatory school

Vocational training -0,10 Ns -0,07 Ns

High school (or drop. from higher educ.) -0,33 **** -0,06 Ns

Higher education -0,78 **** -0,38 ****

Sector of work Paid by public sector -0,16 *** 0,02 Ns

Paid by private sector 0,25 **** 0,04 Ns

Paid by UNRWA Ref. Ref.

Paid by local NGO 0,11 Ns 0,14 Ns

Self emp. family member 0,29 **** -0,01 Ns

Self employed -0,003 Ns -0,16 ***

Unpaid family worker 0,33 Ns -0,01 Ns

Several employers 0,19 Ns 0,13 Ns

Hours worked per week Less than 35 0,20 **** 0,02 Ns

Between 47 and 51 0,08 Ns 0,12 ***

35

More than 51 -0,15 **** 0,05 Ns

Pluri-activity Mohre than one job -0,03 Ns 0,03 Ns

Household variables

Household size Number of adults in the household 0,15 **** 0,03 ****

Number of children in the household 0,20 **** 0,05 ****

Number of women in the household 0,001 Ns -0,01 Ns

Members contributing to the household income

Number of children contributing to the household income -0,001 Ns -0,001 Ns

Number of persons contributing to the household income -0,001 Ns -0,001 Ns

Rho 0,32****

Significance threshold: **** : 1% *** : 5% * : 10% ns: non significant

5. Conclusions and Policy Implications

This survey emphasizes Palestinian refugees’ working poverty in the five fields.

Based on a survey which is the first of its kind (based on a random sample drawn directly from UNRWA’s PRs database), it provides a new set of data and information on living and working conditions of PRs in Jordan, Lebanon, Syria, Gaza Strip and the West Bank. Synthesis of the main findings

The first aim of the paper was to give a description of the profile of the working poverty amongst Palestinian refugees by the analysis of the different factors linked (positively or negatively) to working poverty according to two approaches of poverty, i.e. relative and subjective poverty. In doing so, we distinguished between individual factors, including the characteristics of work, and collective factors related to the characteristics of the household.

The second objective was then to develop a simple probit model to estimate the risk of working poverty depending on the different kinds of factors (explicative variables). The model shows that the principal factors that increase the risk of working poverty are:

- living in rural areas; - low levels of education - working in private sector or as self-employed family member; - being in underemployment; - the size of household.

The model shows also that the principal factors contributing to the reduction of the risk of working poverty are:

- living in urban areas; - high levels of education; - working in public sector; - very excessive hours of work.

The third objective of the paper was to verify, through a bivariate probit model, if the results of the two approaches of poverty are convergent. The principal conclusion of this

36

second model is that the two approaches converge indicating that the recourse to relative and subjective approaches of poverty, separately or simultaneously, was useful. The complex linkages between the labour market and poverty

The close links between employment status and the other dimensions of economic and social life (income, health, access to basic needs and living conditions), which are now well documented, suggest that people who are excluded from the labour market or are in situations of unemployment and underemployment are at greater risk of becoming poor (see Bhalla and Lapeyre, 2005). The risk of poverty is more or less dependent on the quality of the link to the labour market of the members of the household. Although employment is a key factor for reducing the risk of poverty, a significant proportion of labour force members of the household - including heads of household - currently working do not earn enough to raise their household income above the poverty line.

The Palestinian refugees suffer from both the lack of employment - as indicated by their very low employment rates - and the cumulative difficulties linked to employment of those who has the chance to work - as indicated by high level of working poverty rates. The great difficulties encountered by Palestinian refugees to find jobs, even if bad jobs, explain why working poverty rates are not extremely high. Could the Palestinian refugees have easier access to the labour market, especially in the West Bank and Gaza Strip, the working poverty would be certainly higher than what was observed. This is due to the fact that Palestinian refugees are concentred in bad segments of the labour market, either in Jordan, Syria and Lebanon, or in Israel. Inside the OPT’s, closures and other Israeli actions constitutes big obstacles for a constitution of a national labour market and of a dynamic private sector.

Traditional analysis of the labour market consider that in the developing countries characterized by the absence of formal systems of social protection, poor people are generally obliged to accept any job even if it is precarious and low-paid. In this context, indecent work is a necessary means to support the family, even if the earned income from it does not always permit the household to escape poverty. This explains how high employment rates may be accompanied by high rates of working poverty as is the case in Sub-Saharan African countries. In developed countries where social protection plays an important role in the composition with the different forms of individual and social risks, including those linked to unemployment, low incomes and the size or composition of household.

In the case of Palestinian refugees and due to their specific status and context, this theoretical approach seems incomplete in that it explains only a part of their conditions: poverty is associated with very low employment rates and with high proportion of long-term unemployment. But even if more Palestinian refugees would like to work, it is often difficult for them to find a job, let alone a decent job, particularly in the West Bank and in the Gaza Strip. In this case, the problem is not only a problem of decent work; it is also a problem of absence of employment opportunities. Moreover, in such a context the labour supply is not really and individual issue but a collective one, so that the pertinent unit of analysis is the household (or the family) and not the individual worker.

Policy implications

These finding could have important policy implications, some of them are briefly discussed below:

- The analysis of the profile of working poor from the Palestinian refugees could be used as a means to ameliorate the design of policies adapted to these populations. So, if the fact of being worker is not sufficient to prevent from poverty, it should not be considered as a discriminatory criterion for public policies or international assistance (UNRWA and other

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agencies). These policies should focus on the principal factors increasing or reducing the risk of working poverty, depending on the specificities of each field.

- The most effective way to reduce poverty is to enable Palestinian Refugees to have a better access to income-generating activities and to expand decent and full-employment opportunities. A household’s standard of living is very sensitive to any fall in income generated by work activities; it immediately translates into a reduction in the level of consumption and well-being, particularly in a situation where savings are considerably diminished or exhausted. Unemployment, precarious jobs and underemployment, as well as exclusion from the labour market are the main factors underlying material deprivation. From this perspective, lifting the closures in the West Bank and the Gaza Strip should be top priority as they are paralysing the Palestinian economy and excluding Palestinians from access to income-generating activities. Meanwhile targeted micro credit, training programmes and entrepreneurship programmes need to be promoted in all the fields of UNRWA’s operations to enable Palestine refugee households to escape poverty.

- The complementary way to reduce poverty and working poverty is to develop specific and adapted systems of formal social protection that can help to reduce the family burden on individual workers. The family solidarity is no longer sufficient to reduce poverty and other formal systems are necessary. Certainly, international agencies like UNRWA play this role but they could not represent a sustainable solution and other national and local institutions must be developed and supported.

- The problem of adequate data: where microeconomic data exist and are open to researchers, they could be very helpful to produce analysis that can be useful for decision makers (governments, international organizations (like UNRWA in our cases), NGOs...). This implies that it is necessary to encourage governments for the production of such microeconomic data and to let them accessible to researchers.

- Another policy implication concerns the results of the bivariate probit model. For example, if the subjective approach proves to be really convergent with objective approaches, it could help to realize rapid and non expensive surveys, necessary to elaborate public policies. Methodological issues could have important consequences on policy issues, but need finer analysis and the generalization of this kind of analysis to other approaches of poverty as the absolute poverty and the multidimensional one.

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