Pakistan panel household survey: sample size and attrition.
The socio-economic databases in Pakistan, as in most countries, can be classified into three broad categories, namely registration-based statistics, data produced by different population censuses and household survey-based data. The registration system of births and deaths in Pakistan has historically been inadequate [Afzal and Ahmed (1974)] and the population censuses have not been carried out regularly. The household surveys such as Pakistan Demographic Survey (PDS), Labour Force Survey (LFS) and Household Income Expenditure Survey (HIES) have been periodically conducted since the 1960s. These surveys have filled the data gaps created by the weak registration system and the irregularity in conducting censuses. The data generated by the household surveys have also enabled social scientists to examine a wide range of issues, including natural increase in population, education, employment, poverty, health, nutrition, and housing. All these surveys are, however, cross-sectional in nature so it is not possible to gauge the dynamics of these social and economic processes, for example the transition from school to labour market, movement into or out of poverty, movement of labour from one state of employment to another. A proper understanding of such dynamics requires longitudinal or panel datasets where the same households are visited over time. Since panel surveys are complex and expensive to carry out, they are not as commonly conducted as the cross-sectional surveys anywhere in the world and in Pakistan they are even rarer.
One of the available panel surveys in Pakistan has been conducted by International Food Policy Research Institute (IFPRI) over a period of five years from 1986 to 1991 covering 800 households. The IFPRI sample comprised rural areas of only four districts with no representation from Balochistan and urban areas of the country. In these five years the sampled households were almost visited biannually. Another two-round panel data available in the country is that of the Pakistan Socio-Economic Survey (PSES) carried out by the Pakistan Institute of Development Economics (PIDE) in 1998-99 and 2001 in the rural as well as urban areas of Pakistan. Both the IFPRI and the PSES panels could not be continued after the above-mentioned rounds.
In 2001, the PIDE took a major initiative, with the financial assistance of the World Bank, to revisit the IFPRI panel households after a gap of 10 years. The sample was expanded from four to 16 districts, adding districts from all four provinces. Continuing to be a rural survey, it was named the Pakistan Rural Household Survey (PRHS). The second round of the PRHS was carried out in 2004 while the third round was completed in 2010. The third round marked the addition of the urban sample to the existing survey design of the PRHS, as a result--the Survey was named as the Pakistan Panel Household Survey (PPHS).
Attrition bias can affect the findings of the subsequent rounds of a panel survey, so it is important to examine the extent of sample attrition and determine whether it is random or has affected the representativeness of the panel sample. After conducting three rounds of the PRHSPPHS there is a need to evaluate the panel dataset for attrition bias. The present paper looks into the socio-demographic profile of the sample over the three rounds and evaluates the presence, or otherwise, of an attrition bias. The paper, thus, has three major objectives, which are to:
(a) Describe the sample size of three rounds of the panel survey
(b) Analyse the extent of sample attrition and analyse whether it is random, and
(c) Examine the socio-demographic dynamics of household covered in three rounds.
2. SELECTION OF DISTRICTS AND PRIMARY SAMPLING UNITS (PSUs)
As noted earlier, the IFPRI panel (1986-1991) was limited to the rural areas of four districts, namely Dir in Khyber Pakhtunkhwa (KP), Attock and Faisalabad in Punjab and Badin in Sindh. A rural sample based on these districts cannot be considered representative of the rural areas spread across more than 100 districts of the country. To give more representation to the uncovered areas 12 new districts were added to the PRHS-1 round carried out in 2001. From KP two new districts, Mardan and Lakki Marwat, were added to give representation to the Peshawar-Mardan valley and the Kohat-Dera Ismail Khan belt, respectively. The Hazara belt of KP still needs to be added for an even better representation. Three districts from south Punjab (Bahawalpur, Vehari and Muzaffargarh) and one district from central Punjab (Hafizabad) were also included in the PRHS-I. By this addition, all the three broad regions of Punjab, north, central and south, have their representation in the panel survey (Table 1). The three added districts from Sindh were Mirpurkhas, Nawabshah and Larkana. Balochistan was not part of the IFPRI panel so the PRHS included three districts from Balochistan, namely Loralai, Khuzdar and Gawadar (Table 1).
For the rural sample a village or deh is considered as the PSU. Table 1 presents the number of rural PSUs by district. It is noteworthy that there were 43 PSUs (or village/deh) in four districts of the IFPRI panel (Attock, Dir, Badin and Faisalabad). From the 12 new districts, PRHS selected 98 more PSUs (villages/deh) randomly. The total rural PSUs, after all the additions and inclusions, now stand at 141 as can be seen in Table 1. For details regarding each selected PSU, their respective tehsils, districts and provinces see Table A1, A2, A3 and A4 in the Annexure.
It is worth mentioning here that the second round of the panel survey, PRHS-II, was carried out only in the rural areas of Punjab and Sindh. Because of security concerns the other two provinces, K.P and Balochistan, could not be covered in this round.
The urban sample was added in the third round (PPHS) carried out in 2010 in all 16 districts. A selected district was the stratum for the urban sample. All the urban localities in each district were divided into enumeration blocks, consisting of 200 to 250 households in each block. In total, 75 urban enumeration blocks (PSUs) were selected randomly for the third round (PPHS-2010).
The scatter of the selected districts, as can be seen from Figure 1, is a good indicator of the geographical coverage of the districts covered under the PPHS. The sample covers the whole of the country, strengthening its representativeness.
3. HANDLING THE SPLIT HOUSEHOLDS
Before discussing the sample size, it is important to understand how the split households have been dealt with in the panel survey. A split household is defined as a new household where at least one member of an original panel household has moved in and is living permanently. This movement of a member from a panel household to a new household could be due to his/her decision to live separately with his/her family or due to marriage of a female member. If split households are not handled properly, the demographic composition of the sampled households is likely to change over time.
In the rounds two and three of the PRHS-PPHS split households were also interviewed. They, however, were only those households that were residing in the same village as the original panel household. In other words, movement of panel households or their members residing out of the sampled villages were not followed because of the high costs involved in this type of follow-up.
4. SAMPLE SIZE OVER THE DIFFERENT ROUNDS
The size of the sample for each round of the panel survey is shown in Table 2. The total size varies from 2721 households in 2001 to 4142 households in 2010. These variations, as discussed earlier, are for three reasons. First, the PRHS-11 carried out in 2004 was limited to two provinces, Punjab and Sindh, while the other two rounds covered all four provinces. Second, in the PRHS-1I as well as the PPHS-2010, split households were also interviewed (Table 2). Third, urban sample was added in the third round, PPHS, 2010.
As can be seen from Table 2, in the PRHS-I, carried out in 2001, the total sample consisted of 2721 rural households. The sample size decreased to 1614 households in PRHS-II (2004) because of the non-coverage of two provinces. However, 293 split households were interviewed in PRHS-II to raise the total sample size to 1907 households. Table 2 shows that in the PPHS-2010 the total rural households interviewed in four provinces were 2800, out of which 2198 were panel households and the remaining 602 were split households. With the addition of 1342 urban households, the total sample size of the PPHS 2010 accounted for a total of 4142 households (Table 2).
Four features of the three rounds of the panel data are noteworthy, which are as follows:
(i) Urban households, which have been included for the first time in the sample in the third round (PPHS) held in 2010, are not panel households. Essentially, the urban sample can be analysed as a cross-sectional dataset at present and after their coverage in the next round of the survey they can be treated as panel households.
(ii) Split households are not strictly panel households, particularly those where a female has moved due to her marriage. Thus, the matching of split households with the original panel households is not a straightforward exercise. While doing any analysis the split households need to be handled carefully.
(iii) Only the rural sampled households in Punjab and Sindh are covered in all three rounds, so the analysis of the three-wave data is restricted to these two provinces.
(iv) For the analysis of all rural areas covering four provinces, panel data are available for the 2001 and 2010 rounds.
5. SCOPE OF THE PANEL SURVEY
The scope of the panel survey is examined in terms of the types of information (modules) gathered through the structured questionnaires. In all three rounds, two separate questionnaires for male and female respondents were prepared and different modules were included in these questionnaires (Table 3). A two-member team of enumerators, one male and one female, visited each sampled household to gather information. Female enumerators were responsible to fill the household roster and pass it immediately to her male counterpart. Education and employment modules were included in both male and female questionnaires but the relevant information regarding children (under 5 years old), both male and female, was recorded in the female questionnaire. One major objective of the PRHS-PPHS panel survey has been to examine the movement into or out of poverty therefore a detailed consumption expenditure module has been a part of the female questionnaire in all the three rounds. Expenditures on durable items, however, were recorded in the male questionnaire. Health and migration modules were included in PRHS-I and PPHS 2010 rounds. A module on household-run businesses and enterprises was part of the latter two rounds as well.
Each round of the survey has had certain specific areas of focus. Agriculture, for example, was the main focus of the PRHS-I when information even at the plot level was collected from the land operating households. In the other two rounds only a brief agriculture module was included. The main focus of the PRHS-II was mental health, dowry, inheritance and marriage-related transfers. The PPHS-2010 was conducted at a time when inflation was high and the nation had also faced some natural disasters including droughts and floods. In the latest round modules on shocks, food security, subjective wellbeing and overall security were specially included in the questionnaire.
In short, the scope of the three rounds of the panel survey is wide. A variety of social, demographic and economic issues can be explored from these rounds. While some core modules are common to all rounds, there are others that are specific to a certain round. Some of the information is, thus, cross-sectional in nature but can be linked to the household socio-demographic dynamics made available through the core modules.
6. AN ANALYSIS OF THE SAMPLE ATTRITION
As shown earlier, in the PRHS-PPHS data have been collected from the same households over three points of time- 2001, 2004 and 2010. It is common in such surveys that some participants (households) drop out from the original sample for a variety of reasons including geographical movement and refusal to continue being part of the panel. This attrition of the original sample represents a potential threat of bias if the attritors are systematically different from the non-attritors. It can lead to 'attrition bias' because the remaining sample becomes different from the original sample [Miller and Hollist (2007)]. If the participating units, however, are not dropped out systematically, meaning that there are no distinctive characteristics among the attriting units, then there is no attrition bias even though the sample has decreased between waves. It is, therefore, important to examine the attrition bias in our panel survey.
6.1. Theoretical Considerations (1)
Attrition in panel surveys is one type of non-response. At a conceptual level, many of the insights regarding the non-response in cross-sections carry over to panels. According to Fitzgerald, et al. (1998), attrition bias is associated with models of selection bias. Their statistical framework for the analysis of attrition bias, which has been used by several other studies [see for example, Alderman, et al. (20000; Thomas, et al. (2001); Aughinbaugh (2004)], makes a distinction between selection of variables observed in the data and variables that are unobserved. Alderman, et al. (2000) believe that, 'if there is sample attrition, then it has to be seen whether or not there is selection of observables. Selection of observables includes selection based on endogenous observables, which occurs prior to attrition (e.g. in the first round of the survey). Even if there is selection of observables, this does not necessarily bias the estimates of interest. Thus, one needs to test for possible attrition bias in the estimates of interest as well' [Alderman, et al. (2000)].
Assume that the object of interest is a conditional population density f(y|x) where y is scalar dependent variable and x is a scalar independent variable (for illustration, but in practice making x a vector is straightforward):
y = [[beta].sub.0] + [[beta].sub.1] + [epsilon], y observed if A=0 ... (1)
where A is an attrition indicator equal to 1 if an observation is missing its value y because of attrition, and equal to zero if an observation is not missing its value y. Since (1) can be estimated only if A=0 that is, one can only determine g(y|x, (A= 0)), one needs additional information or restrictions to infer f(x) from g(x), which can be derived from the probability of attrition, PR(A=0\y, x, z), where z is an auxiliary variable (or vector) that is assumed to be observable for all units but not included in x. This leads us to the estimation of the following form:
[A.sup.*] = [[delta].sub.0] + [[delta].sub.1] x + [[delta].sub.2] z + V ... (2)
A = I if [A.sup.*][greater than or equal to] 0 ... (3)
If there is selection of observables, the critical variable is z, a variable that affects attrition propensities and is also related to the density of y; conditional on x. In this sense, z is "endogenous to y". Indeed, a lagged value of y can play the role of z if it does not have structural relationship with attrition. Two sufficient conditions for the absence of attrition bias due to attrition of observables are either (1) z does not affect A or (2) z is independent of y conditional on x. Specification test can be carried out of either of these two conditions. One test is simply to determine whether candidates for z (for example, lagged value of y) significantly affect A. Another test is based on Beketti, el al. (1988), and is known as BGLW test. It has been applied by Fitzgerald, et al. (1998) and Alderman, et al. (2000). In the BGLW test, the value of y at the initial wave of the survey (yn) is regressed on x and on A. This test is closely related to the test based on regressing A and x and y., (which is z in this case); in fact, two equations are simply inverses of one another [Fitzgerald, et al. (1998)]. Clearly, if there is no evidence of attrition bias from these specification tests, then one has the desired information on f(y\x).
6.2. Extent of Attrition
Table 4 presents the attrition rate for different rounds. Between 2001 and 2010, the attrition rate was around 20 percent while the rate for the 2004 to 2010 period was 25 percent, suggesting some households had dropped in 2004 and re-entered the panel in 2010. For the 2004-10 period, the highest attrition rate is found in Balochistan hinting towards more movement of sampled households than in other provinces.
6.3. Attrition Bias
As stated earlier, the urban sample was included in the panel survey in 2010 for the first time and hence the attrition issue is related to the rural sample. It has also been noted that the PRFTS-II was limited to two large provinces, Punjab and Sindh. All the rural areas were covered in round I (2001) and round III (2010). The attrition bias is examined between the two waves 2001 and 2010. Five models have been estimated where the dependent variable is whether attrition occurred between these two rounds (1= yes; 0 = no), results for which are presented in Table 5. The sample used in these models consists of all 2001 households and all regressors are measured in 2001.
Following Thomas, et al. (2001) and Arif and Bilquees (2006), the first model of attrition includes the only one covariate, In(PCE), where per capita consumption (PCE) is used as a measure of households' economic status. Table 5 presents coefficient estimates from the logit regressions. The first model indicates that there is a statistically significant negative relationship between PCE and the probability of leaving the panel. On average, lower economic status households were more likely to attrite between the two waves, so without weighting, the PPHS-2010 would be lesser representative of lower economic status households than would be a random household survey.
In model 2, two variables, ln(PCE) and ln(househo!d size) have been included. Both PCE and family size (in 2001) are positively and significantly associated with a household staying part of the subsequent round of the panel survey. The third model in Table 5 adds one dummy, that of a household consisting of only one or two members. The association between attrition and PCE and household size still remains negatively significant. On the other hand, small size households (with 1 or 2 members) show a significant association with attrition.
Model 4 included measures related to three characteristics of the head of the household, which are age, sex and literacy. None of these variables turned out to be statistically significant. Two economic variables, ownership of livestock and land, and provincial dummies are added in model 5. Both the economic variables are significantly associated with keeping households part of the panel and maintaining them as non-attritors (see Table 5). Among the provinces, households in Balochistan are more likely to leave the sample than households located in other provinces. It is evident from the multivariate analyses that there is a positive association between leaving the panel and small household size. Improving economic status of the household is statistically significant to keep the household in the sample, so it is mainly the poorer households that are attriting.
As discussed in the beginning of this section, BGLW test, introduced and used initially by Becketti, et al. (1988), is the other method of testing the attrition bias. This test examines whether those who subsequently leave the sample are systematically different from those who stay in terms of their initial behavioural relationships. We estimate the consumption (InPCE) equations as well as poverty equations, dividing the survey participants into two subsets--all 2001 households, and those still in the sample in 2010, labelled as 'Always in' or non-attritors.
Tables 6 and 7 present estimates of OLS regression for consumption equations and logit estimates for poverty equations respectively. A standard set of household and the head of the household characteristics, including age, and literacy of the head of the household, family size, and ownership of dwelling unit and livestock have been entered as independent variables into these equations. All the estimates are significant, as can be seen from Table 6 and Table 7. These estimates indicate a number of associations that are consistent with widely-held perceptions about consumption behaviour and poverty. For example, age and literacy of the head of the households have a positive impact on consumption while they are negatively associated with poverty. A similar pattern of association was also found for family size as it has a positive association with poverty but a negative relation with the per capita consumption expenditure. The ownership of both livestock and land has a positive association with per capita expenditure, but a negative relation with the incidence of poverty.
Our interest here, however, is more in the difference that the attritors might have made to the sample. To ascertain this we apply the t-difference test with the following hypotheses and assumption:
[H.sub.0]: No significant difference between attritor and non-attritor.
[H.sub.1]: Significant difference exists between attritor and non-attritor.
Assumption: unequal sample size, unequal variance.
The t-difference test results (see last columns of Table 6 and 7) show that there are no significant differences between the set of coefficients for the sub-sample of those missing in the follow-up versus the sub-sample of those re-interviewed for indicators of either consumption or poverty. These estimates, therefore, suggest that the coefficient estimates of standard background variables are not affected by sample attrition.
The PRHS-PPHS panel is a rich source of information regarding a range of socioeconomic and demographic processes, and a means to understand their dynamics over time. Along with having a few core modules the panel questionnaire is flexible enough to accommodate any particular area of interest in a specific round without affecting the overall efficiency of the survey design. Addition of the urban sample in 2010 to the previously all rural sample has made the panel design even more comprehensive. With three rounds having been carried out so far, in 2001, 2004 and 2010, the panel sample retains its qualities despite all the attritions and the phenomenon of split households.
Table A1 Sample list for Pakistan Panel Household Survey 2010: Punjab Province Code District Code Telisil Code Punjab 1 Faisalabad 1 Faisalabad 1 Jaranawala 2 Gojra 3 Summandri 4 Attack 2 Feth Jang 5 Pindi Ghaip 6 Hafizabad 5 Pindi Bhatian 11 Veliari 6 Mailsi 12 Punjab 1 Muzafar Garh 7 Ali Pur 13 Bahawalpur 8 Ahmed Pur East 14 Province Village Code Punjab Saddon 206RB 1 Sing Pura 2 Jarwanwala Chak 3 Subdarawala 363JB 4 Khalishabad 356JB 5 Summandri 6 Khirala Kalan 7 Thathi Gogra 8 Kareema 9 Hattar 10 Makyal 11 Gulyal 13 Dhock Qazi 14 Khatteshah 53 Nasowal 54 Khidde 55 Bahoman 56 Daulu Kalan 57 Bagh Khona 58 Shah Behlol 59 Purniki 60 Thata Karam Dad 61 Mona 62 Chak No 118-WB 63 Chak No 190 WB 64 Kot Soro 65 Chak No 195 WB 66 Mandan 67 Kot Muzzfar 68 Muradabad 69 Chak No 109 WB 70 Chak N0I66-WB 71 Maqsooda 72 Punjab Mail Manjeeth 73 Makhan Bela 74 Tibbah Barrah 75 Malik Arain 76 Kohar Faqiran 77 NauAbad 78 Kundi 79 Nabi Pur 81 Kotla Afghan 82 Ghunia 83 Chak No 157-N.P. 84 Haji Jhabali 85 Mad Rashid 87 Mukhawara 88 Pipli Rajan 89 Qadir Pur 90 Ladpan Wali 91 Chak Dawancha 92 Table A2 Sample list for Pakistan Panel Household Survey 2010: Sindh Province Code District Code Tehsil Code Village Code Sindh 2 Badin 3 Badin 7 Kerandi 21 Golarchi 8 Kalhorki 22 Shaikhpur 23 Khoro 24 Khirdi 25 Bhameri 26 Walhar 27 Parharki 28 Golarchi 29 Lucky 30 Nurlut 31 Mitho Debo 32 Sorahdi 33 Chakri 34 Fatehpur 35 Mari Wasayo 36 Bajhshan 37 Khirion 39 Kandiari 40 Navvab 9 Daulat Pur 15 Jagpal 93 Shalt Kandhari 94 Khar 95 Sindal Kamal 96 Kaka 97 Bogri 98 Manhro 99 Uttar Sawri 100 Mir Pur 10 KotG. 16 Deh 277 101 Kltas Mohammad Deh 320 102 Deh 346 103 Deh 339A 104 Deh 306 105 Deh 302 106 Deh 285 107 Deh 257 108 Larkana 11 Qantber Ali 17 Chacha 109 Rato Dero 18 Dera 112 Laktia 113 Do-Abo 114 Nather 115 Haslla 116 Sanjar Abro 117 Khan Walt 118 Khuda Bux 120 Naudero 121 Saidu Dero 122 Table A3 Sample list for Pakistan Panel Household Survey 2010: Khyber Pakhtunkhwa Province Code District Code Tehsil Code Village Code KP 3 Dir 4 Blambut 9 Katigram 41 Adenzal Batam 42 Shalt Alam Baba 43 Bakandi 44 Khanpur 45 Kamangara 46 Malakand 47 Khema 48 Khazana 49 Shehzadi 50 Munjal 51 Mardan 12 Taklit Bhai 19 Khan Killi 125 Dagal 126 Jangirabad 127 Saidabad 129 Mian Killi 130 Fethabad 131 Seri Behial 133 L. Marwat 13 L. Marwat 20 Nar Akbar 135 Nar Langar 136 Alwal Khel 138 Gorka 141 Ghazi Khel 142 Table A4 Sample list for Pakistan Panel Household Survey 2010: Balochistan Province Code District Code Tehsil Code Village Code Balochistan 4 Loralai 14 Loralai 21 Sanghri 145 Urd Shahboza 146 Sor Ghand 147 Nigang 148 Marah Khurd 149 Mekhtar 150 Tor 151 Khuzdar 15 Khuzdar 22 Bajori Kalan 153 Ghorawah 154 Bhat 155 Kliat Kapper 156 Sabzal Khan 157 Khorri 159 Par Pakdari 160 Gawadar 16 Gawadar 23 Ankra 161 Chibab Rekhani 162 Dhorgati 163 Grandani 164 Nigar Sharif 165 Shinkani Dar 167 Sur Bandar 168
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(1) This sub-section depends heavily on Arif and Biquees (2006) who have examined the attrition bias between two rounds of the Pakistan Socio-Economic Survey (PSES) carried out in 1998-99 and 2001 by the Pakistan Institute of Development Economics.
Durr-e-Nayab <email@example.com> is Chief of Research at the Pakistan Institute of Development Economics, Islamabad. G. M. Arif <gmarifW;pide.org.pk> is Joint Director at the Pakistan Institute of Development Economics, Islamabad.
Authors' Note: The authors are thankful to Shujaat Farooq for his help in the analysis regarding attrition of the sample. Thanks are due to Syed Majid Ali and Saman Nazir as well for their help in the tabulation for this paper. Usual disclaimer applies.
Table 1 Primary Sampling Units (PSUs) by Province and District Number of PSUs Province Districts Rural Urban (c) Punjab Faisalabad (a) 6 16 Attock (a) 7 4 Hafizabad1 (b) 10 4 Vehari1 (b) 10 4 Muzaffargarh (b) 9 4 Bahawalpur (b) 9 7 Sindh Badin (a) 19 3 Nawab Shah (b) 8 4 Mirpur Khas (b) 8 4 Larkana (b) 11 7 KP Dir (a) 11 2 Mardan (b) 7 6 Lakki Marwat (b) 5 2 Balochistan Loralai (b) 7 2 Khuzdar (b) 7 3 Gwadar (b) 7 3 Total 141 75 Note: PR.HS-I (2001) and PPHS (2010) covered all districts. PRHS-II (2004) was limited to 10 districts of Punjab and Sindh, (a). Districts included in the IFPRI panel. (b). New districts added since 2001. (c). Included only in PPHS-2010. Table 2 Households Covered during the Three Waves of the Panel Survey PRHS-II 2004 Panel Split PRHS-I House- House- 2001 holds holds Total Pakistan 2721 1614 293 1907 Punjab 1071 933 146 1079 Sindh 808 681 147 828 KP 447 -- -- -- Balochistan 395 -- -- -- PPHS-2010 Total Panel Split Rural Urban House- House- house- House- Total holds holds holds holds Sample Pakistan 2198 602 2800 1342 4142 Punjab 893 328 1221 657 1878 Sindh 663 189 852 359 1211 KP 377 58 435 166 601 Balochistan 265 27 292 160 452 Source: PRHS 2001, 2004 and PPHS 2010 micro-datasets. Table 3 Scope of the Panel Survey: Modules included in Household Questionnaires PRHS-(2001) PRHS-II (2004) Modules Male Female Male Female Household Roster [check] [check] [check] [check] Education [check] [check] [check] [check] Agriculture [check] x [check] x Non-Farm Enterprises [check] x x x Employment [check] [check] [check] [check] Migration [check] x [check] x Consumption [check] [check] [check] [check] Credit [check] x [check] x Livestock Ownership x [check] x [check] Housing x [check] x x Health x [check] x [check] Dowry and Inheritance x [check] x [check] Mental Health x x x [check] Marital History and Marriage Related Transfers x x x [check] Shocks and Coping Strategies x x x x Household Assets x x x x Household Food Security x x x x Security x x x x Subjective Welfare x x x x Business and Enterprises x x x x Transfer/Assistance from Programme and Individuals x x x x PPHS (2010) Modules Male Female Household Roster [check] [check] Education [check] [check] Agriculture [check] x Non-Farm Enterprises [check] x Employment [check] [check] Migration [check] x Consumption [check] [check] Credit [check] x Livestock Ownership x [check] Housing x [check] Health x [check] Dowry and Inheritance x x Mental Health x x Marital History and Marriage Related Transfers x x Shocks and Coping Strategies x [check] Household Assets x [check] Household Food Security x [check] Security [check] [check] Subjective Welfare [check] [check] Business and Enterprises [check] x Transfer/Assistance from Programme and Individuals [check] x Table 4 Sample Attrition Rates of Panel Households--Rural (%) 2001-2004 2001-2010 2004-2010 Pakistan 14.1 19.6 24.9 Punjab 12.9 17.1 23.8 Sindh 15.7 18.3 26.2 KPK. -- 16.1 -- Balochistan -- 33.2 -- Source: Authors' computations based on PRHS 2001 and PPHS 2010 micro-datasets. Table 5 Determinants of Attrition through Logit Regression Correlates (2001/02) Model 1 Model 2 Model 3 Log per capita consumption -0.286 * -0.342 * -0.353 * Log household size -0.257 * -0.177 *** Households with 1 or 2 family members only (yes=l) 0.416 *** Age of head of household (years) Age-square of head of household Female headed households (yes=l) Literacy of the head (literate=l) Livestock owned (yes=l) land owned (yes=l) Provinces (Punjab as ref.) Sindh KPK Balochistan Constant 0.580 1.458 ** 1.36 ** LR chi-square 11.93(1) 19.35(2) 21.63(3) Log likelihood -1353.789 -1350.079 -1348.941 Observations 2,714 2,714 2,714 Correlates (2001/02) Model 4 Model 5 Log per capita consumption -0.214 ** -0.152 *** Log household size -0.014 0.056 Households with 1 or 2 family members only (yes=l) 0.426 *** 0.353 Age of head of household (years) 0.001 0.003 Age-square of head of household 0.000 0.000 Female headed households (yes=l) 0.378 0.493 *** Literacy of the head (literate=l) -0.138 0.010 Livestock owned (yes=l) -0.443 * -0.451 * land owned (yes=l) -0.280 * -0.377 * Sindh -0.009 KPK -0.021 Balochistan 0.910 * Constant 0.926 0.222 LR chi-square 53.71 (9) 102.63 (12) Log likelihood -1332.229 -1307.268 Observations 2,711 2,711 Source: Authors' computations based on PRHS 2001 and PPHS 2010 micro-datasets. Note: *** P<0.01; ** P<0.05, * P<0.10. Table 6 Household Expenditure: OLS Regression Model 2001-2010 Full Sample Variables Coefficients St. Error Age (years) -0.001 0.004 [Age.sup.2] 0.000 0.000 Literacy (literate=l) 0.196 * 0.023 Family Size -0.032 * 0.003 Land Ownership (yes=l) 0.255 * 0.023 Livestock 0.142 * 0.025 Own House (yes=l) -0.104 ** 0.047 Constant 6.838 * 0.105 F-stat 56.46 R-square 0.1305 Observations 2.642 Always in' (Non-attrition) t-difference Variables Coefficients St. Error test Age (years) 0.001 0.004 -0.500 [Age.sup.2] 0.000 0.000 0.000 Literacy (literate=l) 0.190 * 0.025 0.251 Family Size -0.036 * 0.003 1.333 Land Ownership (yes=l) 0.252 * 0.025 0.125 Livestock 0.133 * 0.028 0.341 Own House (yes=l) -0.134 ** 0.055 0.592 Constant 6.870 * 0.117 -0.290 F-stat 47.66 -- R-square 0.1367 -- Observations 2.115 -- Source: Authors' computations based on PRHS 2001 and PPHS 2010 micro-datasets. *** P<0.01; ** P<0.05, * PO.10. Table 7 Correlates of Poverty: Logistic Regression Model 2001-2010 Full Sample Correlates Coefficients St. Error Age (years) 0.025 0.019 [Age.sup.2] 0.000 *** 0.000 Literacy (literate=l) -0.545 * 0.102 Family Size 0.093 * 0.011 Land Ownership (yes=l) -0.827 * 0.102 Livestock (yes=l) -0.592 * 0.105 Own House (yes=l) 0.538 ** 0.210 Constant -1.817 * 0.483 LR chi-square 206.39 Log likelihood -1374.198 Observations 2,642 Always in'(Non- attritors) t-difference Correlates Coefficients St. Error test Age (years) 0.022 0.022 0.147 [Age.sup.2] 0.000 0.000 0.000 Literacy (literate=l) -0.504 * 0.117 -0.376 Family Size 0.108 * 0.013 -1.257 Land Ownership (yes=l) -0.840 * 0.116 0.120 Livestock (yes=l) -0.504 * 0.122 -0.780 Own House (yes=l) 0.639 ** 0.263 -0.430 Constant -1.994 * 0.568 0.339 LR chi-square 160.22 -- Log likelihood -1058.706 -- Observations 2,115 -- Source: Authors' computations based on PRHS 2001 and PPHS 2010 micro-datasets. *** P<0.01; ** P<0.05; * P<0.1.
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|Title Annotation:||Data Note|
|Author:||Durr-e-Nayab; Arif, G.M.|
|Publication:||Pakistan Development Review|
|Date:||Jun 22, 2014|
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