Poverty and vulnerability in the Punjab, Pakistan: A multilevel analysis

Accepted Manuscript Title: Poverty and Vulnerability in the Punjab, Pakistan: A Multilevel Analysis Author: Muhammad Masood Azeem Amin W. Mugera Steven Schilizzi PII: DOI: Reference:

S1049-0078(16)30025-2 http://dx.doi.org/doi:10.1016/j.asieco.2016.04.001 ASIECO 1020

To appear in:

ASIECO

Received date: Revised date: Accepted date:

2-3-2015 2-1-2016 29-4-2016

Please cite this article as: Azeem, M. M., Mugera, A. W., and Schilizzi, S.,Poverty and Vulnerability in the Punjab, Pakistan: A Multilevel Analysis, Journal of Asian Economics (2016), http://dx.doi.org/10.1016/j.asieco.2016.04.001 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Poverty and Vulnerability in the Punjab, Pakistan: A Multilevel Analysis Muhammad Masood Azeema,b,*, Amin W. Mugeraa, and Steven Schilizzia,c

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The UWA Institute of Agriculture and School of Agricultural & Resource Economics (SARE), Faculty of Science – The University of Western Australia (UWA), 35 Stirling Hwy, Crawley WA 6009, Australia. Institute of Agricultural and Resource Economics, The University of Agriculture Faisalabad,

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Pakistan. c

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Centre for Environmental Economics & Policy (CEEP), The University of Western

Australia.

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E- mail: [email protected]; [email protected]

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Tel: +61469164499

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Poverty and Vulnerability in the Punjab, Pakistan: A Multilevel Analysis Abstract: This study estimates the prevalence and extent of vulnerability to poverty in the Punjab province of Pakistan. A multilevel model is used to analyse survey data of about 90,000 households distributed across 150 towns/tehsils. Empirical estimates show that the vulnerability rate is higher than the rate of poverty, and poverty-induced vulnerability is higher than risk-induced vulnerability. Moreover, idiosyncratic-vulnerability is higher than covariate-vulnerability. Unlike previous studies that find poverty to be a rural phenomenon, this research shows that poverty and vulnerability are equally high in urban areas. A high level of urban vulnerability adds urgency to anti-poverty interventions given a rapid urbanization in Pakistan.

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JEL classification: I32, O18, R58

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Key words: poverty; vulnerability; multilevel model; idiosyncratic and covariate shocks; Punjab; Pakistan

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1. Introduction

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Antipoverty programmes are mainly based on the traditional measures of poverty such as headcount poverty, poverty gap, and poverty severity (Dercon and Krishnan, 2000; Ligon and Schechter, 2003). Headcount poverty is the proportion of population below poverty line, poverty gap captures the proportionate consumption deficit from poverty line, and povertyseverity is simply the squared proportionate consumption deficit from poverty line (Foster et al. 1984; McCulloch and Calandrino, 2003). Targeting poverty based on these traditional measures is questionable because they provide information only on ex-post poverty once it occurs; these are inadequate to indicate households’ ex-ante vulnerability to poverty. It is quite possible that a household which is not poor at present may face the risk of becoming poor in future. Similarly, a household that is currently poor may become much poorer or less poor in future (Dercon and Krishnan, 2000; Zhang and Wan, 2006; Ozughalu, 2014). For instance, many households in the Punjab province of Pakistan fell into poverty after the devastating 2010-flood although they were economically secure prior to the flooding. As per estimates, 64 percent of the total flood affected population in the Punjab were categorized as ‘extremely vulnerable’ in the sense that they lost everything in the houses without having access to safety-nets (World Bank, 2010). This shows that the observed poverty status of a household at a particular point in time may not be a good guide to determine its likelihood of becoming poor in future.

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The preceding argument suggests the need of incorporating vulnerability as a criterion for allocating antipoverty funds as emphasised by Chaudhuri, et al. (2002) and Skoufias and Quisumbing (2005). Adger (2006) goes beyond this and indicates that vulnerability reduction policies could aim at reducing the headcount vulnerability, vulnerability gap, and vulnerability severity. Headcount vulnerability is the proportion of vulnerable population, vulnerability gap is the average deficit of wellbeing from the threshold level of welfare, and vulnerability severity is the distribution of vulnerability gaps within the vulnerable population. Günther and Harttgen (2009) state that it is also vital to differentiate povertyinduced vulnerability, caused by low expected mean consumption, from the risk-induced vulnerability, caused by variation in consumption even when the expected mean consumption is above the poverty line. Furthermore, the risk-induced vulnerability can be decomposed into vulnerability due to idiosyncratic shocks (household level shocks such as illness of the head of household) and vulnerability due to covariate shocks (community level shocks such as floods). It is important to determine the relative size of each of these dimensions of vulnerability in each country context as they have different policy implications for antipoverty strategies. For example, while households facing risk induced vulnerability would need insurance programmes, households experiencing poverty-induced vulnerability would require interventions to improve their resource endowment such as wealth, education, health (McCulloch and Calandrino, 2003). Similarly, vulnerability due to idiosyncratic shocks would require different intervention strategies from the vulnerability due to covariate shocks partly because insurance mechanisms for both shocks are different1 (Heltberg et. al. 2015; Günther and Harttgen, 2009; McKenzie, 2003).

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This research is motivated by the three most recent observations in the literature on vulnerability. First, although a sufficient work on the theoretical aspects of vulnerability to 1

For example, policy response against covariate risk could be disaster risk reduction or rainfall insurance but the appropriate strategy to deal with idiosyncratic risk may be to increase household access to financial tools such as credit (Heltberg et. al. 2015) 2 Page 3 of 30

poverty exists, yet the empirical studies on implementing these measures are lacking (Klasen and Waibel, 2015; and Povel, 2015). Second, despite of increased frequency of covariate shocks, particularly in developing countries like Pakistan, most of the studies on vulnerability tend to focus only on the impact of idiosyncratic shocks (Kurosaki, 2015). Third, majority of vulnerability studies analyse only rural households (Günther and Harttgen, 2009), consequently urban poverty remains little studied (Christiaensen and Todo, 2014; Haddad et al. 1999). This is especially true in the case of Pakistan where almost all of the limited existing studies on vulnerability to poverty are concentrated on rural households. For example, McCulloch and Baulch (2000) & Baulch and McCulloch (2002) demonstrate that the ‘poverty problem’ in rural Pakistan is mainly of transitory poor rather than chronically poor households; Mansuri and Healy (2002) use data from rural Pakistan and propose a measure of vulnerability that can be used to forecast vulnerability with either panel data or a set of repeated cross-sections. Similarly, Kurosaki (2006a) demonstrate that idiosyncratic risk in rural areas of district Peshawar is much smaller for landed households; Kurosaki (2010) applies a range of vulnerability measures to the dataset from rural areas of district Peshawar and suggests that a whole vector of vulnerability measure should be used to identify vulnerable households. Recently, Kurosaki (2015) finds that access to financial institutions protect rural households from health related idiosyncratic shocks and more landed households are less vulnerable to covariate flood shocks. Although, the contribution of these studies is substantial, yet a rapid urbanization in Pakistan in recent times (Khan et al. 2015) makes it essential to investigate vulnerability of households located in urban areas.

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According to United Nations Projections, the number of people living in urban areas of less developed countries will be doubled from 1.9 billion to 3.9 billion during the period 20002030 (UN, 2001). While it can be certain that urban population in Pakistan, the fifth largest population of the world, will keep growing rapidly, we do not know how many of them will be vulnerable to becoming poor. This study therefore examines vulnerability (to poverty) of households located in the eight major cities, urban and rural areas of Pakistan. We apply a multilevel model of vulnerability measurement to the large survey data of about 90,000 households distributed across all 150 towns/tehsils2 of the Punjab province. More specifically, we seek to answer the following research questions: what is the level of poverty, poverty-gap and poverty-severity in the various regions of the province? What is the level of vulnerability, vulnerability-gap, and vulnerability-severity in the various regions of the province? What is the proportion of poverty-induced vulnerability, risk-induced vulnerability and vulnerability due to idiosyncratic and covariate shocks? These questions help us map expost poverty and ex-ante vulnerability to poverty in various regions of the Punjab.

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This study contributes to the vulnerability literature by bridging the existing gaps in the following ways. First, unlike previous studies on Pakistan that analyse vulnerability from the perspective of rural households, this research takes into account households residing in major cities and urban areas as well. Further, to the best of our knowledge, this is the first study that provides vulnerability analysis at tehsil level. The previous research in Pakistan provides vulnerability estimates either at village level in a single district (see, e.g. Kurosaki, 2006a &b) or at a much aggregated provincial level (see, e.g. Jamal, 2009; del Ninno, et al. 2006). The limitation of the village level studies is that their conclusions may not be generalized for the entire province because of the lack of provincially representative data. On the other hand, the issue with the provincial level studies is that they ignore the heterogeneity in terms of 2

Tehsils are administrative units of various districts in the Punjab. The entire province is divided into 9 divisions, 36 districts, and 150 tehsils/towns. 3 Page 4 of 30

socioeconomic development of different micro administrative units (e.g. tehsils). Neglecting the heterogeneity of micro units (tehsil in the case of this study) may lead to misleading results because higher levels of aggregation of data could results in loss of important information (Bashir and Schilizzi, 2013; Hsiao et al. 2004). The present research is therefore significant because it provides vulnerability estimates that can be aggregated at the tehsils level which is missing in the vulnerability research on Pakistan. Finally, unlike previous studies that estimate headcount vulnerability (see, e.g. Chaudhuri, et al. 2002; Günther and Harttgen, 2009; Zhang and Wan, 2009; and Échevin, 2014), this research estimates vulnerability gap and vulnerability severity. This may help policy formulation aimed at reducing the extent of vulnerability.

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The rest of the paper is organized in the following order. Section 2 discusses the methodology and the data. Section 3 presents the empirical results and section 4 concludes.

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There are two quantitative methods for measuring vulnerability: the econometric method and the indicator method. The econometric methods mainly require panel data which is hard to obtain in the case of developing countries. However, it is possible to measure vulnerability using cross sectional data with some stringent assumptions (Chaudhuri et al. 2002). The main assumption is that the unexplained variance in consumption generating process using crosssection data relates to the stochastic innovation. Further, it is assumed that the variance of household consumption can be explained with observable household characteristics (Günther and Harttgen, 2009). The rationality of these assumptions is an empirical matter that can be debated. However, Chaudhuri et al. (2002) provides various cross validation exercises in estimating vulnerability using cross sectional and a short panel data from Indonesia and Philippine. Their results suggest that cross sectional data sets can be used to predict future poverty.

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Deressa, et al. (2008: pp.5) define the indicator method as a “method of quantifying vulnerability which is based on selecting some indicators from the whole set of potential indicators and then systematically combining the selected indicators to indicate the level of vulnerability”. The issue with this method is that it does not relate these indicators to any economic variable of interest such as poverty. Although a single index value is a good measure of the level of vulnerability in a community, yet it does not give any indication on how a shock would impact on household consumption. Moreover, Luers et al. (2003) criticise the indicator method because the choice of variables and their weights is subjective.

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In this study, we use econometric method3 (multilevel model discussed later) because it is the choice method in the poverty and development literature (Deressa, et al. 2008). However, our econometric approach has a slight modification; we group our explanatory variables into three categories of vulnerability commonly used in the indicator approaches: exposure to shock, sensitivity to shock and adaptive capacity.

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Following Chaudhuri et al. (2002), we define vulnerability as the probability of households’ future consumption (Ch,t+1) falling below a pre-defined threshold4 level such as a poverty line

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2. Methodology

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Riely (2000) explains that the choice of methods in estimating vulnerability depends on three factors: i) goal of the research ii) availability of the data iii) background of the researcher. 4 Azam and Imai, (2012: pp.11) describe ‘without use of a benchmark, the term ‘vulnerability’ becomes too imprecise for practical use’

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over a time horizon5. We preferred to use consumption rather than income as a measure of poverty because the former is relatively stable (Dercon and Krishnan, 2000) and indicates household capacity to smooth income shocks through saving and dissaving (McCulloch and Calandrino, 2003).

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To estimate vulnerability to poverty, some studies prefer to use official poverty line set by the government (see, e.g., Kurosaki, 2006a; Günther and Harttgen, 2009) while others rely on the international poverty line of US$1 or US$2 a day (see, e.g., Zhang and Wan, 2009; Kamanou, and Morduch, 2002). Some studies neither rely on the official nor the international poverty lines, for example, Celidoni, (2012) uses a relative poverty line of 60 percent of the median income of households. Likewise, Pritchett et al. (2000) choose the poverty line so that 20 percent of the population is poor. In a recent study on vulnerability to poverty, Échevin, (2014) chooses a counterfactual poverty line so that 40 percent of the population is extremely poor. These studies suggest that the choice of the threshold level of poverty is arbitrary. In this study, we analyse different poverty lines (see appendix, Table A.1) to arrive at more realistic estimates of poverty that are consistent with other independent studies done in Pakistan. In Pakistani context, we find that the consumer price index (CPI) adjusted official poverty line (i.e. Rs. 63 per adult equivalent per day in the year 2011) and the US$2 a day (i.e. Rs. 77 at purchasing power parity in the year 2011) poverty lines gives unrealistic poverty estimates. Therefore, we use a poverty line of Rs. 180 (i.e. US$2 a day at the market exchange6 rate in 2011)7 that gives the headcount poverty rate of 38 percent. This is consistent with the study by Cheema et al. (2008) who found a poverty rate of 38 percent in the Punjab using the MICS-2003-04. Similarly, Jamal (2013) estimates the ex-post poverty rate in Pakistan at 37 percent.

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The vulnerability threshold is defined as 0.29 for the next two years. This means a vulnerable household has a probability of at least 29 percent of becoming poor in the next two years. The choice of the vulnerability threshold is arbitrary (Chaudhuri, et al. 2002) but generally it is 50 percent for the next year and 29 percent for the next two years (Azam, and Imai, 2012). Moreover, the selected vulnerability threshold is consistent with Günther and Harttgen (2009). The choice of a time frame (next 2 years) is based on the fact that we are estimating vulnerability using a single cross sectional data. With a panel data set, the time frame may be extended.

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2.1. The Empirical Model

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To determine the level and sources of vulnerability, we apply a multilevel model8 proposed by Günther and Harttgen (2009). The basic form of the model is:

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(Eq.1)

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Hoddinott and Quisumbing, (2003: pp.9) state that ‘the time horizon and welfare measures are general; one could think of vulnerability pertaining to the likelihood of being poor next year, in ten years’ time, or being poor in old age’ 6 For a detailed discussion refer to Freeman’s (2009) critique on the superiority of Purchasing Power Parity (PPP) exchange rate over the Monetarily Effective Purchasing Power (MEPP) or simply market exchange rate. 7 In the case of Pakistan, Ali et al. (1999) used inflation-adjusted poverty line of above Rs. 300 for the year 1993-1994. 8 Kim et al. (2010) argue that interventions for poverty prevention should take into consideration the multilevel structure of the population rather than focusing merely on one level.

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where, represents the log of consumption per adult equivalent per day; denotes the characteristics of households located in various tehsils of the Punjab; denotes the coefficients to be estimated; and represents the error term. The variables in the equation contain two subscript and , that represent the hierarchical nature of the data at a household and tehsil level.

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Equation 1 gives the mean level of consumption of household located in tehsil . In order to determine the variance of consumption, various tehsil characteristics that affect the intercept and slope can be included in equation 1:

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(Eq.3)

The final form of the model can be obtained by substituting Eq. 2 and Eq.3 in Eq.1 as

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(Eq.2)

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(Eq.4)

The first three terms on the right hand side of equation 4 denote the deterministic part, while the last three terms represent the stochastic part of the equation. The stochastic terms and are the unexplained variances across communities and represents the unexplained variation of households’ consumption within communities. We estimate9 equation 4 in five steps as suggested by Hox (2010). Step 1: the intercept only model is estimated by specifying only the hierarchical levels (i.e. households and tehsil levels) of the data. This provides estimate on the inter-class correlation. Step 2: we include household level variables in the model. The deviance of this model from the intercept only model informs on the improvement in the model. Step 3: we include tehsil/towns level variables in the model which explains variation in consumption across communities. Step 4: we include interaction terms in the model and assess whether any explanatory variable has a significant variance component across communities. Step 5: In the final specification of the model, we include only those interaction terms that are statistically significant.

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We allow the variance of consumption to depend on household and community characteristics as:

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(Eq.5)

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(Eq.6) ,

(Eq.7)

Finally, the expected mean and variances (idiosyncratic variance , covariate variance and total variance ) of consumption per adult equivalent can be determined with equations (4) to (7). We report equations (5) to (7) separately as these are estimated independently. In all these equations, the explanatory variable represents the characteristics of households and variable represents various tehsils characteristics. Household characteristics consists of the following variables: wealth score, education of the head of households, proportion of earning members in each households, age of the head of household, household size, gender of the head, occupation of the head, number of children in 9

The estimation is carried out using Maximum Likelihood Method with the help of ‘mixed’ command in Sata-13. 6 Page 7 of 30

household, number of elderly women (age > 55 years) in a household, location of households (rural vs urban) and administrate divisions. Tehsil characteristics include average years of schooling, number of basic health units, percentage of landlords, and the population density in each tehsil. A check on the Variance Inflation Factor (VIF) does not indicate the presence of multicollinearity in the data. More specifically, the VIF values range between 1.04 and 4.05, which are much less than the VIF threshold of 10 (Leahy, 2000).

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We estimate the impact of idiosyncratic and covariate shocks on household vulnerability (to poverty) in three steps:

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Step 1: we regress log of consumption expenditures on a set of household and tehsil characteristics as denoted in equation 4. This step provides the estimated mean consumption along with residuals. It is assumed that the community level error terms10 ( and ) determine the impacts of covariate shocks, while the household level error term ( ) captures the impact of idiosyncratic shocks.

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Step 2: we square the residuals generated in the first step and regress them on a set of household and community characteristics as indicated in equations 5 to 7. This provides the estimated variance of consumption. The dependence of the residuals on households and community characteristics makes it heteroscedastic; therefore it cannot be estimated using ordinary least square (OLS) that assumes homoscedasticity. The expected log of consumption and its variance are estimated using the maximum likelihood method because it gives unbiased and efficient estimates (Hox, 2010).

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Step 3: the expected log of mean consumption and its variance (i.e. idiosyncratic, covariate and total variance) can be determined with the estimated parameters of equations 4 to 7. These estimates can be used to estimate the vulnerability of households to idiosyncratic and covariate shocks as:

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denotes the probability of being poor (range 0 to 1),

(Eq. 8)

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where,

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line and is the variance of consumption. Depending on the type of vulnerability being estimated, this variance could be idiosyncratic, covariate or total variance. The rest of the notations in equation 8 are same as described earlier.

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is the log of the poverty

We can also decompose household vulnerability into poverty and risk induced vulnerability. A household is poverty induced vulnerable if the expected mean consumption ( already lies below the poverty line ( . On the other hand, a household is risk induced vulnerable if the expected mean consumption ( is above poverty line ( but a high variation in

The error term carries a deterministic component , which means that it is possible to track the channels through which covariate shocks impact household consumption. However, we do not analyse this in the present research as the focus of this study is to estimate the impacts of idiosyncratic and covariate shocks on household’s consumption. 10

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lead to estimated vulnerability that is above the vulnerability threshold of

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An important limitation of the method used in this study is the overestimation of vulnerability to poverty if the measurement error is too high. To test for the robustness of vulnerability estimates for alternative specifications of measurement errors, we proceed in three steps. First, we estimate the variance of consumption using equations 5 to 7. Second, we adjust the estimated variance downward by up to 50 percent. More specifically, the assumed measurement errors (i.e. 10%, 20%, 30%, 40% and 50%) in the estimated variance are subtracted separately from the variance estimates generated in the first step. This provides five values of estimated variance net of measurement error for each household in the dataset. Third, we specify each of the five estimated variance net of measurement error separately in equation 8. The estimates from equation 8 provide vulnerability measures after correcting for the measurement errors.

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2.2. Vulnerability Measures: Headcount, gap and severity

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As per Adger (2006), vulnerability measures can be obtained in line with Foster-GreerThorbecke class of poverty measures (Foster et al. 1984). Most of the axioms that hold in FGT measures also hold in the case of vulnerability measures as well because both are related concepts. The general form of the equation to estimate the vulnerability headcount, gap and severity can be construed from Adger (2006) as: /

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consumption 0.29.

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where, represents the vulnerability measure; denotes the total number of households; indicates the number of households that are vulnerable; is the threshold level of vulnerability, while is the actual probability of being vulnerable that is determined by equation 8; and α is the sensitivity parameter. is determined by the following formula: ∗ ∗ 1 1 , where, is the vulnerability threshold, is the vulnerability line and represents future number of years. As discussed earlier, we have defined a time horizons of 2 years and the vulnerability line as 0.50, this gives vulnerability threshold value ( ) of 0.29. If α 0 then ⁄ and equation 9 is reduced to a measure of headcount vulnerability. This measures the proportion of vulnerable households in a population. If, α 1, the equation measures the vulnerability-gap. This denotes the depth of vulnerability measured by the distance from to . Finally if α 2, the equation measures the vulnerability-severity or the skewness of vulnerability among the vulnerable.

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2.3. Data

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We use the Multiple Indicator Cluster Survey (MICS) of 2011 jointly undertaken by the Bureau of Statistics Punjab, United Nations Children’s Fund (UNICEF), and United Nations Development Programme (UNDP). This survey consists of approximately 90,000 households across the Punjab province of Pakistan. The survey provides information on more than 100 socio-economic indicators such as nutrition, child mortality, water and sanitation, education, health, unemployment, housing, remittances, social safety nets and subsidies. This is the fourth round of the survey, with a larger sample size and more indicators as compared to the previous surveys.

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The MICS-2011 survey was collected using a two-stage stratified sampling design. In the first stage various Enumeration Blocks (EBs) were defined in each of the rural and urban

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domains of the province. Within each EB, a complete listing of households was done. In the second stage, a systematic sample of 16 households in the rural areas and 12 households in the urban areas was taken with a random start. The selection of the number of households in each of the 150 tehsils of the Punjab was based on the proportionate population size of each tehsil. The total numbers of households interviewed were 95,238 with a 97 percent response rate. We found consistent information on 89,329 households, which is the actual sample used in this study.

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The MICS dataset collects detailed socio-demographic information of households (education, age, household size, occupation, earning members), standard of living (asset ownership, fuel, sanitation, type of house), quantities of foods consumed, and monetary expenditures on nonfood items. The data contain wealth score index that ranges from -2.5 to + 2.7. This is calculated through factor analysis of 38 poverty related variables such as households’ possession of electricity, radio, television, and main material used to build floor, walls, and roof of house. About 86 percent of the sample households own a house and 42 percent have a house with a mud floor. About 22 percent of households have houses with mud roofs and 7 percent have houses with mud walls. Table 1 provides a summary of asset ownership of the sample households.

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The MICS data provide information on selected shocks experienced by households, such as Flood, Fever, Tuberculosis and Hepatitis. Moreover, it contains information on beneficiaries of social protection that include social safety nets and social security programmes. Safety nets comprise of cash assistance programmes such as Zakat (religious levy), Benazir Income Support Programme, and Watan-Card (flood relief assistance), while social security programmes are the pension benefits for formal public and private sector employees.

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In the appendix (Table A.2), we provide summary statistics of the selected variables by major cities, rural and urban areas of the Punjab. The average monetary consumption expenditures are relatively higher in major cities than other urban and rural areas. We do not find much difference in the average age of the household head, household size, Tuberculosis and Hepatitis shocks to the head, and proportion of earning members among the residents of major cities, urban and rural areas. However, in terms of mean wealth score (WS), residents of major cities are far better (WS = 1.13) than the urban (WS = 0.57) and rural households (WS = -0.49). The average years of education (head of household) are 4.18 in rural areas, while more than 6 years in the major cities and urban areas. Majority of the casual workers belong to the rural areas. Recipients of social safety nets are comparatively higher in the rural areas and they are also more prone to flood shock.

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[Table 1, about here]

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We also obtained price data for various food items from the Bureau of Statistics, Punjab. This data were collected on a weekly basis from the entire 9 divisions of the Punjab over a 4 months period, from June 2011 to September 2011. We used this data to compute a single average price for various food items. We calculate the total monetary consumption expenditures of a household by adding up expenditures on food and non-food items. The data on tehsil characteristics were taken from the Punjab Development Statistics, 2012.

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3. Empirical Results

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3.1. Regression Results

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The Maximum Likelihood estimates are presented in table 2. Most of the explanatory variables are statistically significant and carry expected signs. For example, all the three 9 Page 10 of 30

variables denoting household adaptive capacity (i.e. wealth score, education of the head and proportion of earning members in a household) are statistically significant and positively associated with expected mean and variance of consumption. Unsurprisingly, this indicates that improvement in consumption comes with increased adaptive capacity, holding all else constant. In terms of sensitivity, being a casual labourer and private employee is statistically and negatively associated with mean consumption. This suggests that lack of a stable source of income will lead to low consumption levels. We find that large household size is associated with low mean consumption, which is expected and consistent with the findings of Gerry and Li (2010). However, increased family size is also associated with increase in the variance of consumption, suggesting that these households tend to be more vulnerable. This is in contrast to the government employees who have high mean consumption but low variance of consumption suggesting that these households are less vulnerable. Moreover, as households with low (high) mean consumption also have high (low) variance of consumption, this justifies the assumption of heteroscedasticity and application of Maximum Likelihood estimation procedure instead of OLS which assumes homoscedasticity. Unexpectedly, female-headed households and dependents (that is, number of children and elderly women) carry a positive sign. However, this might be due to the community support available to such households as indicated by Christiaensen and Boisvert (2000). In terms of exposure, location is positive and statistically significant implying that households in rural areas tend to have higher consumption levels relative to those in urban areas. Similarly, relatively more urban divisions such as Faisalabad and Lahore have negative and statistically significant coefficients, reinforcing the notion that consumption levels are low in urbanized areas. With regard to various community characteristics, increase in average years of schooling tends to reduce consumption. However, increase in the proportion of landlords have a positive impact on consumption. Population density is a binary variable with 1 representing the major cities (total of 8 major cities in the Punjab) and 0 otherwise. A positive and statistically significant coefficient implies that consumption is higher in major cities as compared to rural and other urban areas. The ‘test of fit’ values of and indicates that the model explains 44 % of variation in consumption at the community level and 34 % variation at the household level.

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[Table 2, about here]

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3.2. Level and source of vulnerability

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Table 3 presents the poverty and vulnerability rates across the major cities, urban and rural areas of the Punjab. While there is a 38% population in the Punjab who lives below the US$2 a day poverty line, the proportion of the population expected to fall below that threshold (vulnerability rate) is considerably higher (56%). According to Chaudhuri et al. (2002), the mean vulnerability should be reasonably close to the estimated poverty in order to arrive at the correct vulnerability estimates. In the present study, the mean vulnerability is 37% which is very close to the estimated poverty rate of 38% (table 3). Furthermore, table 3 indicates that the highest proportions of the poor and vulnerable live in rural (58 %) and urban areas (56 %) and not in the 8 major cities of the province (43%). The vulnerability to poverty ratio indicates the distribution of vulnerability in the sample. A higher ratio implies a more dispersed distribution and a lower ratio indicates that vulnerability is concentrated among a few (Chaudhuri et al. 2002). Our results suggest that vulnerability is more dispersed in the rural areas (1.49) compared to urban (1.43) or major cities (1.36). These results are interesting as the previous studies in Pakistan (such as GoP, 2001; Anwar et al. 2004; Jamal, 2009) have emphasized that poverty and vulnerability are mainly a rural phenomenon. On the contrary, we find that poverty and vulnerability are equally high in the urban areas. The 10 Page 11 of 30

literature on poverty suggests that people usually migrate to urban areas in hope of higher chances of finding a job and a higher probability of exiting from poverty (Christiansen and Todo, 2014). Likewise, Nguyen et al. (2015) find that rural households coping with agricultural and economic shocks migrate to urban areas as a livelihood support strategy. However, factors like increased population pressure, slum development, and poor service delivery along with environmental degradation leads to increase in poverty in the urban areas (Ward and Shackleton, 2016). Another possible reason of high urban poverty may be that, in comparison with their rural counterparts, the low-income urban households have to spend a largest share of their budget purchasing food (Moser, 1998). Our finding is consistent with Ruel et al. (1998) and Haddad et al. (1999) who suggest that the locus of poverty is gradually shifting from rural to urban areas in developing countries. The challenge for Pakistan’s authorities is therefore enormous because urban areas of the country have expanded 7 times as compared to a 4 times increase in the overall population during the last sixty years (Ahmad and Farooq, 2010).

427

[Table 3, about here]

428 429 430 431 432 433

In the overall Punjab, 31% households face poverty-induced vulnerability while 24% face risk-induced vulnerability. In other words, poverty-induced vulnerability is 1.28 times higher than risk-induced vulnerability as indicated by the ratio of poverty-induced to risk-induced vulnerability. Moreover, idiosyncratic-vulnerability is higher (55 %) than covariatevulnerability (32%); idiosyncratic vulnerability is 1.7 times higher than risk induced vulnerability.

434

[Table 4, about here]

435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453

The high poverty induced vulnerability implies that a relatively larger population in the Punjab faces structural constraints, such as lack of resource endowments like education, health and physical assets, which makes it harder for the poor to escape poverty. This also implies that even if risk factors are mitigated, there will still be a high proportion of the population that is expected to remain below the poverty line. Our results are consistent with Ligon and Schechter (2003) who suggests that poverty induced vulnerability is higher than risk induced vulnerability in the case of Bulgaria. The authors find that poverty accounts for over half of the measured vulnerability. However, in contrast to Dercon and Krishnan (2000), Ligon and Schechter (2003) and Christiaensen and Subbarao (2004), we find that vulnerability to idiosyncratic shock is more pronounced than vulnerability to covariate shocks. This finding is consistent with finding of Heltberg et. al. (2015), Azam and Imai (2012), Günther and Harttgen (2009), Kazianga and Udry (2006), and Gertler and Gruber (2002). More recently, Gloede et al. (2015) demonstrate that idiosyncratic shocks are more prevalent in Vietnam but covariate shocks dominate in Thailand. According to Günther and Harttgen (2009), relatively higher vulnerability to covariate shocks in most of the previous studies is because of their focus on rural11 households and analysis of few selected shocks. In the context of Pakistan, our finding can be justified by the high prevalence of idiosyncratic shocks. For example, analysing a range of idiosyncratic and covariate shocks, Heltberg and Lund (2009) find that 75 percent of shocks affecting poor households in Pakistan are

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Rural households are usually more prone to covariate shocks (Heltberg et. al. 2015) 11 Page 12 of 30

idiosyncratic. Likewise, Haq (2012) finds that 54% households in Pakistan suffer from idiosyncratic shocks. Although this study does not directly address the question of coping or insurance mechanisms against shocks, yet a high level of idiosyncratic vulnerability point to the lack of effective risk sharing across households. The results of this study have policy implication because idiosyncratic shocks are difficult to target compared to covariate shocks which are geographically clustered (Günther and Harttgen, 2009).

460 461 462 463 464 465 466

We provide statistical significance of our results by reporting standard errors, the lower and the upper bound values of the 95% confidence level (see appendix: Table A.3). For example, the point estimate of vulnerability to covariate shock is 0.32 meaning that 32% households in the Punjab face vulnerability to covariate shocks. We can reject any claim that that the covariate vulnerability is higher than 0.33 and lower than 0.32 with 95% probability. However, with the same probability, we cannot reject claim that the covariate vulnerability lies anywhere between 0.32 and 0.33.

467

[Table 5, about here]

468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483

The geographical distribution of level and sources of vulnerability suggest that the Sahiwal division has a relatively poorer (47%) and more vulnerable (69%) population than the other divisions of the Punjab (Table 5). The Sargodha division has the least poor (28%) and vulnerable (43%) population. Risk-induced vulnerability is highest in the Bahawalpur division (28%). Idiosyncratic-vulnerability is higher in Sahiwal, Lahore, and Faisalabad. Similarly, covariate-vulnerability is relatively higher for Sahiwal (41%), Lahore, Faisalabad and Bahawalpur (39% each). These results reveal that poverty and vulnerability are generally higher in the regions that are primarily dependent on agriculture (e.g. Sahiwal) but less in the regions which are relatively industrialized (e.g. Sargodha). However, this is not true in the case of sources of vulnerability as both idiosyncratic and covariate vulnerabilities are higher in highly industrialized divisions of Lahore and Faisalabad. This may be because of the high population density in these regions. It is interesting to note that the coefficient of population density in table 1 is positive suggesting a relatively higher consumption level with the higher population density. However, regression results provides no indication of the level of vulnerability (Günther and Harttgen, 2009) as it is possible to have a high average consumption but with high variation in consumption.

484 485 486 487 488 489 490 491 492 493 494

The distribution of vulnerability across the entire 150 towns/tehsils of the Punjab is depicted in Fig 1. Panel A of Fig 1 shows that the vulnerability rate is higher than the poverty rate in all of the tehsils; most of the tehsils are above 45 degree line in the scatter plot. This means that there is a greater proportion of the population that is expected to fall below the poverty line in each tehsil than population that is already below the poverty line. Panel B indicates that poverty-induced vulnerability is generally higher for most of the tehsils. The points which are above the 45-degree line suggest that risk-induced vulnerability is higher in these tehsils. Panel C indicates that idiosyncratic-vulnerability is higher than the covariatevulnerability in all of the tehsils. For all the plots, it is worth noting the spread of the tehsils around the 45 degree lines which reflects a considerable re-ranking of the tehsils on these measures.

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495 496

[Figure 1, about here]

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3.3. Extent of vulnerability and poverty in the Punjab

499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514

To better understand the need of measuring the extent of vulnerability, assume that a casual wage-worker is vulnerable with 55% chances of falling below poverty line. Suppose this person faces an idiosyncratic shock (e.g. fever) which increases his/her probability of becoming poor from 55% to 70%. In both scenarios, this person will be counted as vulnerable as he/she has more than an even chance of becoming poor; however, the extent of vulnerability is much higher in the latter scenario. This is explained by Adger (2006. p.278) in the following example: “Take an example of livelihoods of farmers and beach-front property owners in a coastal area all of whom are vulnerable from the risk of coastal flooding. Say the farmers acted to reduce (but not eliminate) their vulnerability through hard coastal defences that changed coastal processes and displace the risk of flooding down the coast such that the owners of beachfront coastal properties were now more vulnerable than previously. A vulnerability measure should be sensitive to this changed distribution of risk (property owners more vulnerable, farmers less vulnerable) even if the total vulnerable population remains the same”. These examples suggest that vulnerability measure should take into account not only the status of vulnerability but also the extent or the distance of vulnerability from the threshold level of vulnerability.

515

[Table 6, about here]

516 517 518 519 520 521 522 523 524 525

Table 6 indicates that poverty headcount, gap and severity are higher and equal in the urban and rural areas as compared to the major cities. The idiosyncratic headcount is highest in rural areas (57%), while vulnerability-severity is highest in urban areas (64%). These measures are lowest in the major cities. The covariate-vulnerability-headcount is highest and equal in rural and urban areas (33%) as compared to the major cities. However, covariate gap and severity are slightly higher in the urban areas. These results re-affirm that in the Punjab poverty is not merely a rural phenomenon as there are almost equal proportions of poor in the urban areas. Moreover, while both rural and urban populations are equally prone to falling below the poverty line, the vulnerability-gap is higher in the urban population. This implies that the impacts of idiosyncratic and covariate shocks are higher in the urban areas.

526

[Table 7, about here]

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527 528 529 530 531 532 533 534 535

Table 7 provides the ranking of the districts in ascending order based on poverty and vulnerability measures. District Mianwali ranks lowest on all these measures except for poverty-severity. District Kasur is ranked highest on all these measures. We do not see much re-ranking of those districts that are either at the bottom or the top. However, there is a considerable re-ranking of the districts which are placed in the middle, for example, Hafizabad, Rahim Yar Khan, and Sargodha districts. This implies that it is important to take into consideration the ranking based on vulnerability headcount when the ex-post poverty rate (headcount) is neither high nor low. This is because of the possibility of having a district with a medium level of headcount-poverty but a relatively high level of headcount-vulnerability.

536

3.4. Robustness of vulnerability estimates

537 538 539 540 541 542

The estimation of vulnerability to poverty using a single cross-sectional data can be criticised because of the strong assumptions that have to be made. The central assumption is that the variance in consumption across households can be used to generate variance of consumption across time. It is assumed that the error term in consumption generating process corresponds mainly to stochastic innovation (i.e. risk). However, the error term stems from three components: i) measurement error; ii) unobserved but deterministic heterogeneity across 13 Page 14 of 30

households; and iii) stochastic innovation. It can be argued that if component (i) and (ii) are large, the empirical results of the vulnerability estimates are overestimated.

545 546 547 548 549 550 551 552

Following Pritchett et al. (2000), we check the robustness of our main results after correcting for the share of measurement error in the estimated variance of household consumption. We re-estimate vulnerability to poverty (Eq. 8) assuming up to 50% measurement error in the variance of household consumption. The results reported in appendix (Table A.4) suggest that vulnerability estimates are robust to alternative specification of measurement errors. For example, if the error term is assumed to be 50%, vulnerability rate (41%) is still higher than the rate of poverty (38%) and idiosyncratic vulnerability (40%) is higher than the covariate vulnerability (30%).

553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576

The data used in this research contain information on few selected shocks experienced by households. We do not include these shocks in generating vulnerability estimates because focusing on selected shocks may lead to econometric problem of “omitted variable bias as various shocks are often highly correlated…..the impact of selected shocks on households’ consumption is therefore likely to be overestimated” (Günther and Harttgen (2009, p. 1223). Inclusion of shocks in consumption regression can provide unbiased estimates of the other independent variables in the regression function (Ward, 2016); however, the focus of this research is not to estimate the marginal effects per se, but to generate estimates of households’ future expected consumption. Our decision of not including few observable shocks in generating vulnerability estimates is consistent with the previous studies (see, e.g. Günther and Harttgen and Ward, 2016). In particular, Ward (2016. p. 543) states “we will not include the shocks when we generate our ex-ante estimates of conditional expected income”.

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However, as a robust check of vulnerability estimates, we specify shocks and social protection variables in the consumption generating function (equation 4). Social protection represents whether household is benefiting from social-safety-nets and social security programmes. The sensitivity estimates are provided in Appendix (Table A.5). The first specification represents the original model used in this study, that is, without adding observations on shocks and social protection. In the second specification, we include Tuberculosis, Hepatitis, Fever, and Flood shocks experienced by households, while in the third specification, we add shocks and social protection benefits to households. Our results indicate that the idiosyncratic, covariate, and total vulnerability rates are consistent under all of the three specifications.

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4. Conclusion

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The earlier research on Pakistan considered ex-post poverty and ex-ante vulnerability to be a rural phenomenon. The present study contest this conclusion by examining household vulnerability to poverty using a unique survey dataset of about 90,000 households collected from rural and urban areas of the entire 150 towns/tehsils of the Punjab province of Pakistan. The results suggest that the headcount vulnerability is equally high in both urban and rural areas. Furthermore, a higher vulnerability gap in the urban rather than rural areas implies that the effects of idiosyncratic and covariate shocks are more pronounced for urban households. A high level of urban vulnerability adds urgency to anti-poverty interventions given a rapid urbanization in Pakistan. We suggest that the further research on the magnitude and determinants of urban vulnerability is essential for effective anti-vulnerability measures.

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Defining vulnerability as the probability of households’ future consumption falling below poverty line, we find that poverty-induced vulnerability is higher than risk-induced vulnerability and idiosyncratic-vulnerability is higher than covariate-vulnerability in most of the towns/tehsils of the Punjab. The relative importance of poverty-induced vulnerability compared to risk-induced vulnerability suggests that variation in households’ consumption is a lesser problem than the likelihood of remaining below poverty line. This makes the task of vulnerability reduction more daunting because even if risk factors are mitigated, there will still be a high proportion of the Punjab’s population that is expected to remain below poverty line. Since we do not observe households' coping strategies, it is difficult to make some reliable recommendations on the type of assistance to be provided to the most vulnerable people. However, vulnerability that is mainly driven by the structural causes or low expected mean consumption (i.e. poverty induced vulnerability) requires long term investment in human capital such as education and health of poor households. On the other hand, households may be assisted in consumption smoothing by provision of social safety-nets in case short-term variation in consumption (i.e. risk induced vulnerability) is the principle cause of vulnerability to poverty. Finally, the distinction between idiosyncratic and covariate vulnerability is important because both require different intervention strategies; household level assistance for the former and overall disaster risk reduction for the latter. Although this study does not directly investigate the insurance mechanism or coping strategies against shocks, yet our finding of a relatively high idiosyncratic vulnerability compared to covariate vulnerability implies that the risk sharing mechanism across households is not functioning well.

611 612 613 614 615 616 617 618 619 620 621 622

We acknowledge the limitation of the statistical analysis in this study using a single cross sectional data. In particular, we had to assume that cross sectional variation in households consumption mirrors inter-temporal variation. An important caveat of this study is that the error terms in the consumption generating process are interpreted mainly in economic terms rather than as measurement errors. A high level of measurement error would tend to overestimate households’ vulnerability to poverty. Although we have provided a robust check for alternative specifications of measurement errors, yet the availability of a lengthy panel data would be extremely valuable to generate statistically more robust vulnerability estimates. Finally, this study has used monetary consumption expenditure to estimate households’ vulnerability to poverty. However, as poverty is a multidimensional construct, it would be desirable to estimate vulnerability using other indicators of poverty such as malnourishment. This is left as a subject of for further research.

623 624 625 626 627

5. Acknowledgement: The PhD scholarship from University of Western Australia (UWA) in support of Mr. Azeem’s research is gratefully acknowledged. The authors would also like to thank Shamim Rafique (Director General, Bureau of Statistics, Punjab) for providing the data used in this study. Our special thanks to Muhammad Usman (Statistical Officer, Bureau of Statistics Punjab) for his valuable assistance in clarifying queries regarding the data.

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Adger, W. N. (2006). Vulnerability. Global Environmental Change, 16(3); 268-281. Ahmad, M. and U. Farooq (2010). "The state of food security in Pakistan: Future challenges and coping strategies." The Pakistan Development Review, 2010: 903-923. Ali, S. S., Tahir, S., & Arif, G. (1999). Dynamics of growth, poverty, and inequality in Pakistan [with Comments]. The Pakistan Development Review, 38 (4); 837-858. Anwar, T., Qureshi, S. K., Ali, H., & Ahmad, M. (2004). Landlessness and rural poverty in Pakistan [with Comments]. The Pakistan Development Review, 43(4), 855-874.

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[Appendix Table A.1-A.4, about here]

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Households (%) (sd) 82 (-0.39) 2 (-0.14) 61 (-0.49) 52 (-0.5) 86 (-0.35) 36 (-0.48) 33 (-0.47) 10 (-0.3) 0.04 (-0.02) 0.2 (-0.04) 4.23 (-0.2) 3.85 (-0.19)

780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796

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Table 1. Percentage of households possessing assets Assets Households (%) Assets (sd) Electricity 94 Iron (-0.21) Radio 6 Water filter (-0.24) TV 65 Donkey Pump or turbine (-0.48) Telephone 7 Watch (-0.26) Fridge/Freezer 47 Non-mobile phone (-0.5) Gas 33 Bicycle (-0.47) Computer 11 Motorcycle/scooter (-0.31) Air Conditioner 6 Animal drawn-cart (-0.24) Washing Machine/dryer 52 Motor ship (-0.5) Air Cooler/fan 94 Bus/Truck (-0.24) Cooking range/microwave 6 Car/Van (-0.24) Stitching Machine 67 Tractor/Trolley (-0.47) Source: Authors’ calculations using MICS-2011 Notes: Figures in parenthesis are standard deviations

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Table 2: Maximum likelihood estimates of expected mean and variance of consumption (1) (4) VARIABLES Mean Total Variance Household level characteristics 1. adaptive capacity Wealth score 0.224*** 0.017*** (0.002) (0.002) Education (head) in years 0.011*** 0.0011*** (0.000) (0.000) Earning members (proportion) 0.190*** 0.099*** (0.008) (0.006) 2. Sensitivity Age (head) 0.001*** 0.000*** (0.000) (0.000) Household size -0.066*** 0.002*** (0.001) (0.000) Gender Head (male=0, female=1) 0.027*** 0.020*** (0.006) (0.004) Occupation of the head of household Govt. employee (no=0, yes=1) 0.036*** -0.025*** (0.006) (0.004) Private employee (no=0, yes=1) -0.054*** -0.030*** (0.005) (0.004) Self-employment (no=0, yes=1) 0.017*** -0.017*** (0.005) (0.003) Employer (no=0, yes=1) 0.083*** 0.004 (0.017) (0.012) Labour (no=0, yes=1) -0.072*** -0.017*** (0.004) (0.003) Agriculture employment (no=0, yes=1) 0.169*** 0.008** (0.005) (0.004) Number of children in household 0.054*** 0.001 (0.001) (0.001) Number of elderly women (>55 years) 0.035*** 0.006*** (0.003) (0.002) 3. Exposure Location (urban=0, rural=1) 0.214*** 0.024*** (0.004) (0.003) D.G.Khan 0.075** -0.014 (0.032) (0.011) Faisalabad -0.080*** -0.021** (0.029) (0.010) Gujranwala 0.026 -0.029*** (0.030) (0.010) Lahore -0.100*** -0.011 (0.031) (0.010) Multan -0.044 -0.044*** (0.029) (0.010) Rawalpindi -0.034 -0.027** (0.034) (0.011)

Ac ce pt e

797

20 Page 21 of 30

-0.016 (0.013) -0.014 (0.010) ref

-0.032*** (0.011) -0.001* (0.001) 0.002** (0.001) 0.032*** (0.009)

-0.009** (0.004) 0.000 (0.000) -0.001*** (0.000) -0.005 (0.007)

Bahawalpur Community Characteristics Average years of schooling Basic Health Units (numbers) Landlords (%) Population density1 Interaction terms (hh*community) Pop density × hhsize Pop density × age

an

Pop density × agri- employ

Ac ce pt e

Wealth score×avg schooling

d

Wealth score × landlords

M

Pop density × number of children Labour × landlords

Education ×avg schooling

Labour × basic health units Hhsize × basic health units Constant

eij (household)

-0.010*** (0.002) -0.001** (0.000) 0.135*** (0.033) 0.017*** (0.004) 0.001*** (0.000) -0.001*** (0.000) 0.015*** (0.002) 0.001** (0.000) -0.001*** (0.000) -0.000*** (0.000) 5.362*** (0.022)

cr

Sargodha

ip t

-0.063* (0.038) 0.113*** (0.030) ref

us

Sahiwal

-0.000 (0.001) 0.000 (0.000) 0.027 (0.023) 0.001 (0.003) 0.000** (0.000) 0.000*** (0.000) 0.001 (0.002) 0.000 (0.000) -0.000** (0.000) -0.000* (0.000) 0.159*** (0.007)

798

0.131 (0.001) 0.34 Number of Households 89,329 u0j (community) No. of towns/tehsils Source: author's calculations from MICS-2011 dataset

0.006 (0.001) 0.44 150

799 800

Notes: * p<.05; ** p<.01; *** p<.001. 1dummy for major cities represents population density. All variables are raw cantered (actual value less mean value of the respective variable). The 21 Page 22 of 30

801 802

full model is developed in 5 steps and only statistically significant interaction terms are included in the model.

Table 3: Level of poverty and vulnerability in the Punjab Area Poverty rate Mean vulnerability Vulnerability rate 0.30 0.37 0.37 0.37

1.36 1.43 1.49 1.48

an

us

Source: author's calculations from MICS-2011 dataset Notes: Poverty and vulnerability rates are given as proportions of population, while mean vulnerability ranges from 0 to 1.

0.25 0.33 0.31 0.31

0.17 0.23 0.26 0.24

Idiosyncratic vul.

Covariate vul.

Idiosyncratic /covariate vul*

1.46 1.42 1.20 1.28

0.42 0.55 0.57 0.55

0.23 0.33 0.33 0.32

1.81 1.66 1.72 1.70

Ac ce pt e

Major cities Urban Rural Punjab

Poverty / riskinduced*

d

Table 4: Sources of Vulnerability Area PovertyRiskinduced induced vul. vul.

816 817

0.43 0.56 0.58 0.56

M

808 809 810 811 812 813 814 815

0.31 0.39 0.39 0.38

Vul. to Pov. ratio

cr

Major cities Urban Rural Punjab

ip t

803 804 805 806 807

Source: author's calculations from MICS-2011 dataset. * indicates ratio. All other values are given as proportions of population.

22 Page 23 of 30

cr us

Gujranwala 0.30 0.29 0.42 1.41 0.22 0.20 1.09 0.41 0.23 1.81

Lahore 0.40 0.42 0.65 1.64 0.38 0.26 1.45 0.65 0.39 1.67

Multan 0.42 0.39 0.60 1.42 0.35 0.25 1.38 0.59 0.36 1.65

Rawalpindi 0.28 0.30 0.44 1.53 0.22 0.21 1.05 0.43 0.22 1.91

Sahiwal 0.47 0.43 0.69 1.45 0.41 0.27 1.53 0.68 0.41 1.67

Sargodha 0.28 0.30 0.43 1.54 0.21 0.21 1.00 0.41 0.24 1.69

Ac

ce

pt

ed

M

an

Table 5: Poverty and vulnerability decomposition by divisions of the Punjab Bahawalpur D.G.Khan Faisalabad Poverty rate 0.41 0.36 0.44 Mean Vulnerability (ranges 0 to 1) 0.41 0.35 0.42 Vulnerability rate 0.64 0.54 0.65 Vulnerability to poverty ratio 1.58 1.48 1.49 Poverty-induced vulnerability 0.37 0.28 0.39 Risk-induced vulnerability 0.28 0.26 0.26 Poverty / risk-induced (ratio) 1.32 1.06 1.49 Idiosyncratic-vulnerability 0.63 0.53 0.65 Covariate-vulnerability 0.39 0.28 0.39 Idiosyncratic/covariate (ratio) 1.61 1.88 1.64 Source: author's calculations from MICS-2011 dataset. Note: values are given as proportion except indicated otherwise.

23 Page 24 of 30

Ac ce pt e

d

M

an

us

cr

ip t

Table 6: Extent of poverty and vulnerability in the Punjab Measures Major cities Urban Rural Punjab Poverty* headcount 0.31 0.39 0.39 0.38 Poverty-gap 0.07 0.09 0.09 0.08 Poverty-severity 0.02 0.03 0.03 0.03 Idiosyncratic-vulnerability headcount 0.42 0.55 0.57 0.55 vulnerability-gap -0.35 -0.49 -0.48 -0.47 severity of vulnerability 0.46 0.64 0.61 0.60 Covariate-vulnerability 0.23 0.33 0.33 0.32 headcount vulnerability-gap -0.46 -0.64 -0.63 -0.62 severity of vulnerability 1.04 1.41 1.40 1.37 Source: author's calculations from MICS-2011 dataset. *To calculate poverty measures (headcount, gap, and severity) refer to Haughton and Khandker (2009).

24 Page 25 of 30

Ac ce pt e

d

M

an

us

cr

ip t

Table 7: District Ranking on Poverty and Vulnerability Measures Districts P0 P1 P2 IV0 IV1 IV2 CV0 CV1 CV2 Mianwali 1 1 5 1 1 1 1 1 1 Bhakkar 2 2 3 2 2 2 2 2 2 Gujranwala 3 8 1 3 3 3 4 3 3 Sialkot 4 4 8 5 5 6 5 6 7 Khushab 5 7 5 4 4 4 3 4 5 Layyah 6 6 6 7 8 8 7 7 6 Jhelum 7 5 4 6 6 5 6 8 8 Rawalpindi 8 10 14 11 11 11 12 11 11 Narowal 9 3 2 8 7 7 8 5 4 Attock 10 9 13 9 9 9 9 9 10 Chakwal 11 11 12 10 10 10 10 10 9 Lahore 12 19 11 13 17 17 15 17 18 B. Nagar 13 13 22 18 15 15 18 13 13 Hafizabad 14 23 18 15 12 12 13 15 17 M. Bahaudin 15 12 10 12 13 13 14 12 14 DG Khan 16 17 15 14 14 16 11 16 19 RY Khan 17 16 28 21 21 22 20 23 22 Lodhran 18 18 19 19 24 26 22 24 24 M. Garh 19 15 9 17 18 18 19 20 20 Sargodha 20 14 17 23 20 23 28 19 16 Multan 21 20 16 20 25 25 21 26 27 N. Sahib 22 27 30 33 30 29 26 29 30 Faisalabad 23 22 24 25 28 30 25 28 29 Chiniot 24 24 21 26 22 21 24 25 23 Vehari 25 25 26 16 19 19 17 22 26 Pakpattan 26 21 25 22 16 14 16 14 15 Sheikhupura 27 31 33 32 29 28 27 30 33 Rajanpur 28 26 23 27 23 20 20 21 21 Jhang 29 29 31 28 27 24 30 27 25 Gujrat 30 32 20 24 26 27 33 18 12 TT Singh 31 28 27 30 32 32 31 32 31 Okara 32 30 32 35 33 33 34 35 32 Khanewal 33 33 29 29 34 35 32 33 34 Sahiwal 34 34 35 31 31 31 29 34 35 Bahawalpur 35 35 34 34 35 34 35 31 28 Kasur 36 36 36 36 36 36 36 36 36 Notes: ranking is given in ascending order. P0 = headcount poverty; P1= poverty-gap; P2= poverty-severity; IV0 = Idiosyncratic-vulnerability-headcount; IV1= Idiosyncraticvulnerability-gap; IV2= Idiosyncratic-vulnerability-severity; CV0 = Covariate-vulnerabilityheadcount; CV1 = Covariate-vulnerability-gap; CV2= Covariate-vulnerability-severity

25 Page 26 of 30

Panel B

45 degree line

0

.1

.2

.3

.4 .5 .6 poverty rate

.7

.8

.2 .3 .4 .5 .6 .7 Idiosyncratic Vulnerability

.8

.9

45 degree line

0

M .9

d

.1

.6

an

45 degree line

0

.1 .2 .3 .4 .5 Poverty Induced Vulnerability

us

Covariate Vulnerability 0 .1 .2 .3 .4 .5 .6 .7 .8 .9

Panel C

ip t

Aggregate vul. rate

cr

Aggregate pov. rate

Risk Induced Vulnerability 0 .1 .2 .3 .4 .5 .6

vulner ability r ate 0 .1 .2 .3 .4 .5 .6 .7 .8 .9

Panel A

Ac ce pt e

Fig 1: Level and sources of vulnerability to poverty in 150 Tehsils of the Punjab, Pakistan

26 Page 27 of 30

Appendix

cr

ip t

Table A.1: Poverty and vulnerability estimates using various poverty lines Poverty lines (Rs.) Poverty Rate Vulnerability Rate 63 (official-CPI adjusted) 2% 0.7% 77 (US$2 at PPP exchange rate) 6% 2.5% 180 (US$2 at 2011 exchange-rate) 38% 56% Source: Authors’ calculations

Table A.2: Summary statistics by area (selected variables)

an

Wealth score Age (head) in years

M

Education (head) in years Household size

Ac ce pt e

d

Earning members in HH (proportion) Female headed HH Govt. employee

Private employee

Self-employment (no=0, yes=1) Employer

Labour (no=0, yes=1)

Agriculture employment (no=0, yes=1) Number of children in household

Number of elderly women in HH Flood shock (no=0, yes=1) Fever shock (no=0, yes=1) Tuberculosis shock (no=0, yes=1) Hepatitis shock (no=0, yes=1)

Urban (mean/sd.) 5.35 (0.47) 0.57 (0.78) 47.02 (13.22) 6.39 (4.99) 6.42 (2.81) 0.30 (0.17) 0.06 (0.24) 0.11 (0.31) 0.11 (0.32) 0.27 (0.44) 0.01 (0.08) 0.26 (0.44) 0.06 (0.23) 0.89 (1.13) 0.23 (0.43) 0.03 (0.17) 0.03 (0.16) 0.01 (0.08) 0.02

us

Consumption Expenditures (log)

Major City (mean/sd.) 5.44 (0.49) 1.13 (0.67) 47.08 (12.98) 6.97 (5.15) 6.30 (2.81) 0.32 (0.18) 0.07 (0.25) 0.11 (0.31) 0.18 (0.38) 0.26 (0.44) 0.01 (0.09) 0.23 (0.42) 0.01 (0.12) 0.88 (1.14) 0.21 (0.41) 0.00 (0.04) 0.02 (0.14) 0.01 (0.08) 0.02

Rural (mean/sd.) 5.35 (0.44) -0.49 (0.83) 47.22 (14.23) 4.18 (4.50) 6.44 (2.90) 0.31 (0.18) 0.05 (0.22) 0.06 (0.23) 0.07 (0.25) 0.11 (0.31) 0.00 (0.06) 0.30 (0.46) 0.29 (0.46) 0.99 (1.17) 0.27 (0.46) 0.09 (0.28) 0.04 (0.19) 0.01 (0.09) 0.02 27 Page 28 of 30

(0.13) 0.03 (0.18) 0.08 (0.27) 8621

Recipient of safety-nets (no=0, yes=1) Recipient of pension benefits (no=0, yes=1) Observations

(0.15) 0.05 (0.22) 0.08 (0.27) 27559

(0.15) 0.07 (0.26) 0.08 (0.27) 53149

ip t

Source: Authors calculation using MICS-2011

Table A.3: Statistical Inferences

0.00 0.00 0.00 0.00

0.55 0.32 0.31 0.24

0.55 0.33 0.32 0.25

Ac ce pt e

d

M

an

Idiosyncratic Vulnerability 0.55 Covariate Vulnerability 0.32 Poverty Induced Vulnerability 0.31 Risk Induced Vulnerability 0.24 Source: Author's calculation using MICS-2011.

cr

Values (Ratios)

95% Confidence Interval Standard Lower Upper Errors Bound Bound

us

Estimates

28 Page 29 of 30

Table A.4: Robustness of vulnerability Estimates

0.26

0.22

0.19

0.29

0.25

0.22

0.18

0.06

0.05

0.05

0.04

3.79 0.5

3.32 0.47

2.85 0.44

2.37 0.41

0.49

0.46

0.43

0.40

us

ip t

0.3

0.31

0.30

0.30

1.58

1.48

1.43

1.33

an

0.31

M

Total standard deviation 0.37 0.33 (est) Idiosyncratic standard 0.36 0.33 deviation (est) Covariate standard 0.08 0.07 deviation (est) Idiosyncratic/covariate 4.74 4.27 Vulnerable to poverty 0.56 0.53 Vulnerable to 0.55 0.52 idiosyncratic shocks Vulnerable to covariate 0.32 0.32 shocks Idiosyncratic/covariate 1.71 1.62 Source: Authors’ calculations using MICS-2011

50% error

cr

Estimates Net of Measurement Error 10% 20% 30% 40% No Error error error error error

Without shocks and Social Protection

Ac ce pt e

First Specification

d

Table A.5: Sensitivity of estimates for different model specifications Specification Idiosyncratic Covariate Total Vulnerability Vulnerability Vulnerability

Second Specification With shocks Third Specification

With shocks and Social Protection

0.55

0.32

0.56

0.55

0.32

0.56

0.55

0.32

0.56

Source: Authors’ calculations using MICS-2011 Notes: values are given as proportion of vulnerable households.

29 Page 30 of 30