1. Introduction

The ultimate purpose of all economic reforms and policies are to increase the human development. Economic growth is considered to be the main contributor for the sustained human development (Ranis and Stewart, 2005) ^[29] and it is the key objective of all economic policies (Asad and Ahmad, 2011) ^[3]. Due to the increased importance and link between poverty and income inequality (Banerjee and Newman, 1994) ^[10] it has become a focal point for the policy maker’s (Cheema and Sial, 2012) ^[12]. The Failure and success of government economic policies depend on the extent of poverty. So it is necessary to categorize the vulnerable population of the country for the public well being.

Along with growth in Pakistan a very little care attended towards poverty, and other socioeconomic factors (Chaudhry et al., 2009) ^[13]. Bourguignon and Chakravarty (2003) ^[11] argued that poverty as an issue arises because everyone desires to identify poverty’s edge for all characteristics like income, consumption on health, food and education. In spite of the fact that every nation desires to reduce both poverty and inequality, there is a lack of consensus on how to plan a holistic approach to tackle this problem (Alesina, 1994) ^[4]. Hence the emphasis on time and era is that to have more concern on this burning issue.

There is often lack of clarity to define the poverty with its occurrence (Shepherd and Prowse, 2008) ^[32]. However, this study focuses on the chronic as well as transient poor households. Households below the poverty line supposed as chronic while transient poverty is connected with the incapability of households to sustain their consumption level when there is any shock in income or individual conditions (Jalan and Ravallion, 1998) ^[22].

Since 1960’s numbers of studies conducted to measure poverty and inequality over time in Pakistan, mostly without seriously taking into account the factors of both. Chaudhry et al. (2009) ^[13] used logit model to estimate poverty by using the income poverty line developed by Malik (1998, 1996) ^{[26, 27]} and inflated it with current prices of the same survey year. They found that land holdings, age of household head and educational level increase the severity of poverty. Siddiqui and Mehmood (2011) ^[34] initiated that year of schooling of households, access to drinking water and energy were verified as a determinant of poverty and inequality within the rural and urban premises. Addison (2007) ^[8] also checked the impact of different exogenous variables like per capita income, female household head, and population aged 65 and above and educational status on poverty.

Inequality with poverty in Pakistan is also a vital subject for policy makers (Haq and Bhatti, 2002) ^[19] because the gap is increased between the provinces over the time (Jamal, 2003) ^[23]. Moreover, inequality in Pakistan is mostly confined to the income and consumption and it is estimated by Naseem (1973) ^[28], Khundkar (1973) ^[24], Mahmood (1984) ^[25], Ahmad (2000) ^[1] and Anwar (2005) ^[6] over the time period by using diverse inequality indices.

Following Ravallion (1998) ^[30] households are the basic unit to calculate poverty. Household consumption can be expressed in the form of expenditure function. Let ¡th household is expressed as Yi, and then an expenditure function may be denoted as:

Where P is a vector of prices of goods and services and Q is a vector of quantity as well. Minimum expenditures e as a function, x is the shifters of poverty while u_y is the utility level of the households. Yi is the available expenditures of the households needed to achieve the utility at a given price level. In order to compute the consumption expenditure of the ¡th household it is assumed that all members of the house have equal need. The Poverty line for the ¡th household is the minimum point of consumption. Chen and Ravallion (2008) ^[15] used purchasing power parity (PPP) prices of 2005 to convert 1.25 USD in order to estimate the population below the poverty line for 15 poorest countries. In terms of USD 2.00 Godoy and Dewbre (2010) ^[16] and World Bank (2008) ^[38] also defined three requirements for the estimation of poverty

•  Total consumption of goods and services by the individuals

•  Exchange rate to convert US dollar into local currency

•  Price of goods in domestic currency

Like them, in this study Consumption of households compared with the official poverty line that was Rs 104.95 per person per day in the year of 2008-09. Poverty is estimated by forming the poverty index Z that determines the poverty status of each household based on the poverty line. Poverty index is categorized as.

If Z₁< 1, then Z = 0, household is supposed to be chronically poor.

If Z₁ ≥1 ≤ 1.2, then Z = 1, household is transient poor.

If Z₁ >1.2, then Z = 2, household is said to be non poor.

Where Z-Score = Y_i / R

Z¡ = poverty index of ¡th household.

Y_i = available expenditures of ¡th household.

R = required expenditures.

Furthermore, this approach has been criticized due to the unavailability of economic rooted theory, not inflating the poverty line in terms of domestic currency prices and as a perceptive measure of Purchasing power parity (PPP) and exchange rate. In one of the earliest work by Ravallion and Chen (2007) ^[31] found that dollar poverty line is considerably simple to compute the poverty in domestic currency. Although the issue of averaging and stability of PPP exchange rate is there.

The basic objective of our study is to determine the contribution of each shifters of poverty and income inequality among different poverty bands at household level in Pakistan. The accomplishment of this study is based on the dollar poverty line developed by the Chen and Ravallion (2008) ^[15]. Using micro data of expenditures, we performed an econometric analysis of poverty shifters and inequality as well.

2. Materials and Methods

The data used for the study is Pakistan Social and Living Standards Measurement Survey (PSLM) 2008-09. The poverty status of household is mostly checked by regression analysis of logit or probit model (Hashmi and Sial, 2005) ^[17]. Therefore in econometric analysis, Multinomial logit model an extended form of the logit model is carried out. In Equation form multinomial logit model with three dependent variables and different explanatory variables is.

Where; Y= dependent variable while

N= a, b, c are the three different categories of poverty.

Here it determines the probability of ith household facing one of the jth outcomes (of being chronically poor, transient poor or non poor).

Where (J) = 0, 1, 2

β = coefficient of explanatory variables

Multinomial logit model estimates the log odd ratio like the logit model.

(Hoffmann, 2004) ^[21]

3. Results and Discussion

Table 3.1 articulated the situation of poverty in Pakistan; as 34.6 percent of households are extremely poor. These are the poor that experience deprivation for a long time period (Hulme and shepherd, 2003) ^[18]. The households that are just transient poor and reside on the edge of the poverty line while a little bit shock could trip them into the well of extreme poverty. Poverty estimated by Chen and Ravallion (2008) ^[15] was about 22.5 percent population of Pakistan below the 1.25$ poverty line in 2005 that percentage was much better as compared to its neighboring countries like India, Nepal and Bangladesh where poverty was 41.6, 54.7 and 50.5 respectively.

Table 3.1. Distribution of the households according to their poverty status in Pakistan (2008-09)

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Table 3.2. Multinomial Logit Analysis. Socio Economic Shifters Affecting Poverty Status of the Households

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The Results of the multinomial logit model in case of negative association of educational level, house ownership, and asset ownership to the probability of being poor is related to the panel data results of Arif et al (2011) ^[9]. Coefficients of male headed households, age factor of households, asset ownership and educational status (above primary level) is significant at 95 percent confidence level in case of transient poor households. While occupational status, asset ownership and male headed households also significantly impact the non poor households. It is revealed that when the household head is a male likelihood of transient poor reduced as compared to the houses where heads is female. Chaudry et al. (2009) ^[13] showed that gender type has significant impact on poverty level of households. While poverty reduced in the averaged aged group of 30 to 40 years. It supports the fact that as more adults concerned with income cohort chances of poverty reduced. Qureshi and Arif (2001) ^[37] also showed that poverty rises gradually within the age level between 49 years of age.

In the developing countries rural households; people used to live in joint family system, only the education of household head is not only matter (Scott, 2000) ^[36]. That is the reason average educational level was taken as a shifter of poverty. The average education level of household’s was also inversely related to the poverty level. However Middle passed households had more chances to be not transient poor. Maximum absorption of poverty is found within the lowest level of education (Qureshi and Arif, 2001) ^[37].

The ownership of both land and livestock reduced the chances by being transient poor. There were significantly less chances to be transient poor when households had only land. The Same results were found by Hashmi and Sial (2005) ^[17]. A household had both land and livestock considered more non poor as compared to the household had only land and livestock. Findings of (Arif et al., 2011) ^[9] also indicated the same picture. The ownership of both land and livestock reduced the chances by being transient poor. Occupational choice is also being negatively related to transiently poor in case when the households were employers. Others occupational groups with self-employed status have also a significant level of incidence poverty. However, such results are demonstrated by Arif and Bilquees (2006) ^[5] who found negative relation in type of working of household head and transient poverty. They defined in reference of panel data that many households may be active in economic term but in spite of that they may not be able to come out of poverty.

Participation of women in decision making like about education, buying food, medicine and purchasing of clothing is also a strong shifter in the sense that a woman can make a better decision than the male counterpart. With the lower status woman had a trend of low power over different holdings, low health services, poor mental health and lesser self respect (Smith, 2005) ^[35]. Table 3.2 revealed in the houses where the woman is the only decision maker chances of transient poverty reduced and condition of non poor households increased. House ownership and dwelling type had no significant impact on the condition of the poverty of the households.

However, the results of this study are according to the common economic theories except the woman’s decision making alone in some matters of life. Assets holding, educational level and occupation reduced the chances of poverty by increasing the average income as well as consumption of the households. (See Chaudhry, 2003, Siddique, 2009) [14, 33]^{[14, 33]}

4. Measurement of Inequality

Poverty is a pervasive issue that accredited to the unequal distribution of income. There are many methods for the estimation of inequality among individuals or households (Atkinson, 1970) ^[7]. One of the most commonly used measure of inequality is Gini coefficient based on the Lorenz curve.

Zero value of the Gini indicates perfect equality while 1 value of Gini coefficient shows perfect inequality. In this study value of Gini cefficient (0.43) is a little bit high as compared to the other studies (Anwar, 2005 & Haq and Zia, 2006) ^{[6, 20]}. It is shown from table 4.1 that the lowest 10 percent holds only 2.63 percent while Top 10 percent of the households had 24.87 percent of the total sample income shares.

Table 4.1. Income Distribution among households according to PSLM (2008-09) in Pakistan

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Graph 1. Lorenz Curve for Income Distribution

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5. Summary and Conclusion

Using PSLM data 2008-09, this study has attempted to find out the shifters of poverty and inequality at the household level in Pakistan. The Household’s categorized as chronic, transiently and non poor on the base of the dollar poverty line. A significant large number of households were supposed as transient poor. About 40 percent of the households due to any external and internal shock may trip below the poverty line.

Multinomial logit model was used to find out the effect of different shifters on the household’s poverty status. Some of the factors were deep and some were not the causal factors of poverty status of the households. Whereas, the analysis highlights the importance of Woman empowerment, age factor, land and livestock ownership with the educational level as a poverty shifter significantly reduced the incidence of transient poverty. Another problem related to poverty is inequality, while 0.43 value of the Gini coefficient showed high income inequality. Top 10 percent of the households had captured a big amount of total sample income. While lowest group of households covered a small amount of the sample income.

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