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Workshop on Poverty Analysis, Using Census Data1

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1 Workshop on Poverty Analysis, Using Census Data1
Ben E. Aigbokhan,Ph.D Department of Economics Ambrose Alli University Ekpoma, Nigeria. UNSD Capacity Building Workshop For LISGIS Staff , Monrovia, Liberia 12-16 December 2011.

Objective of this season: To acquaint participatants with working definition of poverty and the justification for measuring poverty. Poverty may be defined on the basis of monetary dimension or non-monetary dimension: Either dimension views poverty as a manifestation of pronounced deprivation of well being. Well being is conventionally linked to command over consumption. The poor are therefore those who lack enough income or consumption to place them above some defined minimum level. This is the monetary dimension of poverty. The broader approach to well being focuses on the capabilities of individuals to function in society. Lack of capabilities is reflected in inadequate income, education, health, political freedom, or powerlessness. 1.2 Why Measure Poverty - To be able to identify the poor. - To be able to target appropriate interventions. - To be able to monitor and evaluate projects and policies targeted at the poor. - To be able to evaluate the effectiveness of institutions whose mandate is to help the poor.

2.1 Steps in Measuring Poverty Define an Indicator of Welfare. Most measures of welfare are based on household consumption expenditure or income. In developing countries preference is for current consumption rather than current income, for the following reasons: In the short run it reflects more accurately the resources that households control. Income tends to be understated (table 1). Over the long run, it reveals information about incomes at other dates; in the past and future. In poor countries, income is particularly difficult to measure accurately, especially in agriculture. Establish a minimum acceptable standard of the indicator to separate the poor from the non-poor. Generate a summary statistic or index to aggregate the information from the distribution of the welfare indicator relative to the poverty line. Comparisons across households at similar consumption level is meaningful when adjustments are made for differences in household size, age composition, the prices they face, the publicly provided goods to which they have access. When divided by the number of household members, this gives per capita measure of household consumption expenditure or income. When adjusted for age composition, and therefore household consumption needs, it gives adult equivalence scales. An adult equivalence scale typically measures the number of adult males to which that household is deemed to be equivalent. Each member of the household counts as some fraction of an adult male. Effectively, household size is the sum of these fractions. It is not measured in members but in numbers of adult equivalence. Both fall under the welfarist approach to measuring well being. A non-welfarist approach would focus on whether households have attained certain minimum levels of, say, nutrition or health. In this case, indicators would include caloric intake, proportion of income/expenditure devoted to food, housing conditions, child schooling, and infant mortality rate.

4 Income tends to be understated for the following reasons:
2.2 Sources of Data for Measuring Poverty - Household Sample surveys - National population census - Perception Assessment Interviews. Table 1: Income and Expenditure by per capita Expenditure Quintiles Vietnam 1992/1993. Lowest Lower-mid Middle Mid-upper Highest Overall Income/capita 494 694 956 1,191 2,190 1,105 Expenditure/capita 518 756 984 1,338 2,540 1,227 Food spending /capita 378 526 643 807 1,382 747 Food as % expenditure 73 70 65 60 54 61 Source: Haughton and Khandker (2009) P.23. Income tends to be understated for the following reasons: Recall problem. Intent to limit tax burden. Reluctance to report income from illegal sources. Some parts of income are difficult to calculate accurately. The understatement of both income and expenditure means that poverty rates are overstated. It also means that the estimates of income and expenditure that are based on sample survey data invariably fall short of the levels observed in national accounts data.

Objectives of the session: To acquaint participants with what a poverty line is and why is it needed. To enable participant develop the skill required for constructing poverty lines. 3.1 Defining a poverty line - The poverty line defines the level of consumption or income needed for a household to escape poverty. - The poverty line is obtained by specifying a consumption bundle considered adequate for basic consumption needs, then estimating the cost of those basic food and non food needs. - Another approach is to determine a food poverty line, based on some notion of the minimum caloric intake required to stay healthy and productive. The income required to purchase these basic needs food bundle is computed. - If the cost of basic nonfood needs is estimated, this added to the nonfood basic needs will equal the overall poverty line. 3.2 Approaches to Constructing a Poverty Line: Cost of basic needs approach: (i) One approach is to compute a poverty line for each household, adjusting it from household to household to take account of differences in the prices they face and their demographic composition. This gives different poverty line for each household. (ii) A second approach is to construct one per capita poverty line for all individuals, but to adjust per capita expenditure / income for differences in prices and composition. The adjusted per capita expenditure/income is then compared with the poverty line to determine whether the individual is living below the poverty line. This approach gives a single poverty line. (iii) A third approach is to construct poverty lines by region. Separate poverty line is constructed for each region, based on the prices prevailing in each region. Each household in a region is then assessed to be poor if its per capita expenditure / income is below the regional poverty line. (Table 2) - Over time, nominal poverty lines do change for a population; (i) Due to change in price over time, and (ii) If the real poverty threshold is revised over time (Table 2).

6 Example: Table 2: Summary of Poverty Lines for Cambodia: riels Per Person Per Day 1993/94SESC CSES Region Food Poverty Line Poverty Line Phnom Penh 1,185 1,578 1,737 2,470 Other Urban 995 1,264 1,583 2,093 Rural 881 1,117 1,379 1,777 Source: Haughton and Khandker (2009) P.42 3.3 Relative and Absolute Poverty Lines -Generally, the choice of poverty line depends largely on the intended use of the poverty rates. -Sometimes we are interested in identifying the poorest segment of the population for purposes of policy intervention. We may want to identify the poorest one-fifth or two- fifths or one- third. These are the relatively poor. -Poverty line may in such cases be defined as some percentage, say, two-thirds, of the mean income / expenditure. -Were the objective is to identify and target today’s poor, a relative poverty line is appropriate, if it is tailored to the overall level of development. -Absolute poverty line, on the other hand, is fixed in terms of the standard indicator being used, and fixed over the entire geographical space of the poverty comparison. The poverty line is set so that it represents the same purchasing power year after year, but this line may differ from region to region or country to country. -An absolute poverty line is essential if one in concerned to judge the effect of antipoverty policies over time. -Legitimate comparisons of poverty rates between one region and another, or between one country and another can only be made if the same absolute poverty line is used in both regions or countries. -The World Bank/UN needs absolute poverty lines to be able to compare poverty rates across countries: US$1/day, revised in 2008 to US$1.25 a day is commonly used ( Table 3). The poverty line refers to the poverty line used by the 15 poorest countries in their sample, converted to US dollars, using the most recent measures of purchasing power parity.

7 Table 3: $1/day Absolute Poverty Lines
Region % of Population Living on less than $1/day in 1998 % of population living on less than one-third of average national consumption for 1993 (in 1998) East Asia & Pacific 15.3 19.6 East Asia & Pacifie Excluding China 11.3 24.6 Europe & Central Asia 5.1 25.6 Latin America & the Caribbean 15.6 51.4 Middle East of North Africa 1.9 10.8 South Asia 40.0 40.2 Sub-saharan African 46.3 50.5 Total 24.0 32.1 Total excluding china 26.2 32.0 Table 4: Absolute and Relative Poverty Rates Country Year % of population living on less than $1/day % of population living on less than $1.25/day Indonesia, rural 2005 24 China, rural 26 Indonesia, urban 19 China, urban 2 Philipines 2006 23 India, rural 2004/05 44 Vietriam 2008 50 India, urban 36 22 Nigeria 2003 64 Source: Haughton and Khandker (2009) P.49

8 3.4 Objective Poverty Lines The Cost of Basic Needs method
This method requires composing a consumption bundle that is deemed to be adequate, with both food and nonfood components, and then estimate the cost of the bundle for each subgroup. The process is as follows: Pick a nutritional requirement for good health. The standard value widely used, which has been proposed by the FAO, is 2,100 calories per person per day. Estimate the cost of meeting this food energy requirement, using a diet that reflects the habits of households close to the poverty line. Let this food component be Zf. Add a nonfood component, Znf. Sum up both to derive the basic needs poverty line, Zbn = Zf + Znf Table 5: Illustration of construction of cost of food component of poverty line for Nigeria. Table 5.1: Monthly per capital food poverty line-national urban average, 1996 Food items Monthly Consumption (kg) Calories Per kg Total calories Per month Unit required Prices N/kg 1996 Food Expenditure Poverty line Cassava 2.64 3510 9,266.4 103.30 272.71 Beans 2.46 3,420 8,413.2 134.77 313.53 Rice 2.20 364.0 8,008.0 123.06 270.73 Maize 2.28 3.570 8,139.6 44.95 102.49 Millet 0.38 3,330 1,265.4 32.00 12.16 Yam 3.29 1,235 4,063.2 143.72 472.84 Meat 1.63 2,500 4,075.0 129.87 211.69 Fish(dried) 1.90 2,890 5,491.0 360.43 684.82 Eggs 0.44 1,400 616.0 42.111 18.53 Palm oil 0.52 8,750 4,550 186.15 96.80 Tomatoes 2.58 220 567.6 131.01 338.01 Pepper 0.87 940 8718 28.37 24.68 Vegetables 1.28 250 320 35.58 45.54 Fruits 0.34 430 146.2 24.8 8.43 Onions 0.57 410 233.7 47.26 26.94 Sugar 0.22 4,000 880.0 43.23 9.51 56,853.1 2,928.93

9 Table 5.2: Food poverty lines
1985/86 1992/93 1996/97 National 776.27 723.77 1,169.18 Urban 808.34 742.06 1,278.25 Rural 742.68 737.96 1,179.76 Gender Male-headed 770.52 833.93 1,237.59 Female-headed 760.18 711.82 1,154.23 Regions:Northeast 736.64 795.16 1,169.72 Northwest 827.64 1,181.95 1,169.92 North central 795.55 725.77 1,149.25 Southeast 800.93 807.85 1,379.39 Southwest 764.69 739.18 1,235.88 South south 781.31 824.29 1,156.61 Source: Aigbokhan (2000) p. 15. 3.5 Subjective Poverty Lines - This approach relies on self-rated or self-assessment poverty lines. It involves asking people simple questions such as; (i) What income level do you personally consider to be absolutely minimal; that is, below which you could not make ends meet? (ii) Do you consider your current consumption to be adequate to make ends meet? The answers provided, e.g. in (i) could be plotted, and a line fitted through the points to obtain a subjective poverty line, z*. Example: x Z* Y (actual income) subjective minimum income

Objectives of the session: having obtained a satisfactory measure of wellbeing and a poverty line, this session is to enable participants understand how to construct summary statistical measures of the extent of poverty. There are a number of such summary measures. 4.1 Headcount Index - This is the most widely used measure of poverty. It measures the proportion of the population that is counted as poor P0 = NP ……………………… (1) N E.g if 60 people out of a sample population of 300 are poor, then P0 = 60/300 = 0.2 = 20%. Equation (1) is better written as P0 = I I (yi < Z) ………………. (2) Yi is household income / expenditure, Z is the poverty line. If yi is less than Z, the household would be counted as poor. The headcount index does not capture the intersity of poverty, for example, if a poor household were to give to a very poor household, headcount index would remain unchanged even though overall poverty would have lessened. The index does not indicate how poor the poor really are, and hence does not change if people below the poverty line become poorer. 4.2 Poverty Gap Index - This index measures the extent to which households fall below the poverty line (the poverty gaps) as a proportion of the poverty line. - The sum of these poverty gaps gives the minimum cost of eliminating poverty, if transfers were perfectly targeted. - More specifically, poverty gap, Gi, is defined as the poverty line (Z) less actual income (yi) for poor households Gi = (Z-Yi) x I (Yi < Z) …………………… (3) The poverty gap index, PI, may be written as PI = I Gi ………………. (4) N Z The measure is the mean proportionate poverty gap index in the population, where nonpoor have zero poverty gap. Example: Calculating the Poverty Gap Index Assume Z = $125

11 Square Poverty Gap Index (P2)
Expenditure for each household Poverty gap index (PI) Expenditure in country 100 110 150 160 Poverty Gap 25 15 Gi/Z 0.20 0.12 0.08(=0.32/4) The index does not reflect changes in inequality among the poor. 4.3 Poverty Severity (Squared Poverty Gap) Index - The measure is a weighted average of poverty gaps as a proportion of the poverty gap. - The weights are the proportionate poverty gaps themselves. That is, where the poverty gap is, say 10%, of the poverty line, it is given a weight of 10%; for a poverty gap of 50%, it is given a weight of 50% - By squaring the poverty gap index, the measure implicitly puts more weight on observations that fall well below the poverty line. Example: Calculating Squared Poverty Gap Index Assume Z = $125 Square Poverty Gap Index (P2) Expenditure in the country 100 110 150 160 Poverty Gap 25 15 G/Z 0.20 0.12 (G/Z)2 0.04 0.0144 0.0136(=0.0544/4) -The three indexes of poverty form what has been termed a family of poverty measures proposed by Foster, Greer, and Thorbecke (1984), which is written as: Pα = I (Yi) α ………………. (6) N Z Where α is a measure of the sensitivity of the index to poverty when the parameter α = 0, P0 is simply the headcount index; when α = I, the index is poverty gap, and when α = 2, the index is poverty severity index. 4.4 Checking for Robustness of Poverty Lines - There are four main reasons why measures of poverty may not be robust. (i) Sampling error, which is due to the fact that poverty rate for the entire population of a country is based on sample data which captures only a modest number of household. (ii) Measurement error, which is due say to underreporting. This tends to overstate the degree of poverty. Unknown differences in needs between households at similar levels of income / consumption, even when adult equivalence scale is used. Uncertainty and imprecision due to choice of poverty line.

Objective of the session: To expose participants to what a poverty profile is and why it is useful, particularly in mapping the various dimensions of poverty. 5.1 What is a Poverty Profile? - A poverty profile sets out the major dimensions of poverty. - It shows how the pattern of poverty varies by demographic characteristics and geographical location of individuals or households. - Thus, a poverty profile is useful for comprehensive poverty comparison across sub- groups of a country. Box 2: Key Questions to Guide in Preparing a Poverty Profile. -Does poverty vary widely between different areas in the country? -Are the most populated area also the areas where most of the poor live? -How is income poverty correlated with gender, age, urban and rural, racial or ethnic characteristics? -What are the main sources of income for the poor? -On what sectors do the poor depend for their livelihoods? -What products or services-tradables and montradables-do the poor sell? A tradable good is one that is, or easily might be, imported or exported. The prices of such goods are influenced by changes in the world price and the exchange rate. -To what extent are the rural poor engaged in agriculture? In off-farm employment? -How large a factor is unemployment? Underemployment? -What are the important goods in the consumption basket of the poor? How large are the shares of tradables and nontradable? -How is income poverty linked to malnutrition or educational outcomes? -What are the fertility characteristics of the poor? -To what public services do the poor have access? What is the quality of these services? -How important are private costs of education and health for the poor? -Can the poor access formal or informal credit market? -What assets-land, housing, and financial-do the poor own? Do properly rights over such assets exist? -How secure is their access to, and tenure over, natural resources?

13 Contribution of sectoral change (x 100)
5.2 Poverty Profile Presentation There are two main ways of presenting a poverty profile. By poverty status: Poor versus nonpoor, or by expenditure per capita quintile. Then summarize the incidence of characteristics, e.g. educational level of each group. By some characteristics, e.g. age, educational level, occupation of household head, region of residence. Then report the poverty rate for each components. Examples on Nigeria and Indonesia 5.3 Poverty comparisons over Time To enable comparison of poverty oven time, based on more than one round of household survey, certain steps are necessary. Use data from highly comparable questionnaires with respect to sampling frame and definitions of income or consumption; otherwise, adjust the definition of income por consumption aggregates over time to ensure that similar definitions are used. Adjust for price changes over time. Use temporal or regional price indexes to ensure similar definition of the poverty line over time and across regions to measure real income or expenditure over time. Principal Sector of Employment Population Share, 1984 (percent) Contribution of sectoral change (x 100) Headcount Index Poverty gap index Poverty Severity Index (po)(percent) (p1) (percent) (p2), Farming Self-employed Laborer 45.0 9.0 49.8 11.2 54.6 14.8 57.4 16.5 Industry Urban Rural 2.6 3.3 4.1 0.8 2.8 3.2 0.4 3.1 0.3 2.7 2.2 construction trade 5.4 6.6 3.8 7.2 3.6 1.6 5.6 1.4 4.7 Transport services Total sector effect (including omitted sectors) Contribution of population shifts Interaction effects 6.5 5.8 n.a. 1.0 2.9 89.3 13.2 -2.6 2.4 93.8 10.4 -4.3 0.9 2.0 95.1 9.4 -4.5 Total 100.0

14 Table 5.2 Sectoral Poverty Profile for Indonesia, 1987
Principal Sector of Employment Population Share (percent) Headcount Index (po) Poverty gap index (p1) Poverty severity Farming Self-employed Laborer Industry Urban Rural construction trade Transport services Other total 41.1 8.6 3.0 3.4 4.3 6.3 7.6 4.1 7.3 6.7 100.0 3.11 38.1 8.1 19.4 17.4 5.0 14.7 10.7 4.2 11.6 17. 21.7 6.42 7.62 1.26 3.00 2.92 0.71 2.42 1.53 0.61 1.84 3.55 4.22 1.97 2.21 0.32 0.76 0.80 0.17 0.34 0.14 0.49 1.03 1.24 Source: Huppi and Ravallion, 1991  Table 5.3: Distribution of Poverty by Age and Gender of Household Head in Cambodia, 1999 Head count Index (Po) Poverty gap Index (P1) Poverty severity c Index (P2), x100 Index (percent) Contribution To total index Share of Total Population 35.9 100.0 6.5 2.0 Age of head 18-30 years 36.7 10.7 5.6 9.1 1.4 7.5 10.5 31-35 years 35.4 10.9 5.4 9.2 1.6 8.8 11.1 36-40 years 43.6 21.2 8.0 21.6 2.7 23.3 17.5 41-45 years 40.3 15.7 7.3 15.8 2.2 15.3 14.0 46-50 years 36.5 14.4 7.7 16.9 2.4 14.2 51-60 years 28.3 5.3 16.3 1.7 16.8 20.0 61 and above 32.0 11.3 1.8 27.7 Male 36.4 84.4 6.6 84.2 2.1 85.1 83.3 Female 33.6 6.1 14.9 16.7

Objective of the session: To enable participants identify main causes of poverty and understand how regression technique could be used to identify the immediate main causes of poverty, for purposes of identifying necessary policy interventions. 6.1 Causes of Poverty The main causes of poverty may be classified by certain characteristics; Household and individual characteristics, the most important of these are; Demographic characteristics, such as household size, age structure, gender of head, dependency ratio. Economic, such as employment status, occupation, hours worked, property owned. Social, such as health and nutritional status, shelter, education of head. Regional level characteristics, which include availability of infrastructure, proximity to markets, quality of governance, property rights and their enforcement, and vulnerability to weather conditions. 6.2 Analyzing the Determinants of Poverty - The most widely used technique to identify the contribution of different variables or indicators to poverty is regression analysis. - There are two main types of analysis; (i) Attempts to explain the level of expenditure per capita (an indicator for living standard or welfare) as a function of a variety of variables or indicators, typically those discussed in 6.1 above. (ii) Attempts to explain whether the household is poor, using a probit or logit regression. In this case, the independent variables are as in 6.1, but the dependent variable is binary, taking the value of 1 if the household or individual is poor and zero otherwise. - A regression estimate shows how closely each independent variable is related to the dependent variable, holding all other influences constant. A typical multiple regression equation as applied in poverty analysis is Log (Yi) =a0+a1Xi1+a2Xi2+a3Xi3+…..+anXin Z

16 6.3: Interpreting Regression Results on Determinants of Poverty
Where z is the poverty line, yi is per capita expenditure or income, Xi are respective explanatory variables, and aj are the coefficients to be estimated. -Level of significance: As a rule of thumb, t-statistic of less than 2, p-value of 0.05 and above, suggests that estimated coefficient is not statistically significant from zero. 6.3: Interpreting Regression Results on Determinants of Poverty Examples: Table 6.1: Determinant of household welfare in Ivory Coast, 1993 Dept. variable: lag(per capital) Urban Coeff: t-statistic Rural Educ. Level of most Educated male Elementary Junior secondary Senior secondary University Educated female Value of selected house Hold assets Home Business assets Savings Hectares of agric. Land Cocoa trees Coffee trees Distance to nearest paved road Distance to nearest market Unskilled wage

17 Lecture 7: Poverty Analysis based on Census data
6.2: OLS Regression of determinant of economic welfare in Nigeria, 1996: dependent variables: LnPCE Constant Age of head Age2 Sex of head Edu. Of head Household size Size2 Adj. R2 Middle Belt Rural 8.73 (57.68) -0.002 (0.33) (0.19) -0.64** (13.29) 0.03 (1.46) -0.31** (22.23) 0.01** (13.18) 0.31 Urban 7.49 (22.63) 0.02 (0.99) (0.89) -0.29** (3.15) 0.17** (6.11) -0.25** (13.49) 0.006** (7.63) 0.32 North-East (42.63) 0.01* (1.59) * (1.70) -0.12 0.09* (3.61) -0.32** (20.10) 0.29 7.26 (11.99) 0.01 (0.51) (0.69) 0.14 (1.14) 0.19** (4.44) -0.26** (6.64) 0.009** (3.75) 0.35 North-West 7.60 (41.58) -0.02** (2.62) 0.0002* (1.18) 0.14** (4.10) (16.65) (9.40) 0.28 8.63 (12.82) -0.05* (1.77) 0.0004 (1.45) 0.12 (0.58) 0.05 (0.92) -0.20** (5.81) 0.004** (2.38) 0.38 South-East 8.10 (36.35) (0.63) (0.55) -0.16** (3.55) 0.12** (4.81) -0.35** (18.10) (10.34) 7.92 (11.39) (1.01) (1.11) 0.06 (0.24) 0.18** -0.58** (7.46) 0.03** (4.76) 0.49 Source: Aigbokhan (2000) Lecture 7: Poverty Analysis based on Census data Objective of session: To acquaint participants with basic methodologies useful in computing and analyzing poverty estimates, based on national population census data.

18 7.1 Poverty Mapping Governments are often interested in more detailed poverty rates for relatively small geographical units. Often in a given region there are typically wide divergence in standards of living Detailed poverty maps cannot be generated from household survey data alone. If one tries to use survey data to measure poverty in each district or region, such estimates would be based on just a few observations, and would as such be imprecise. One solution is to use population census data. Census data are more available for all households and can provide reliable estimates at highly disaggregated levels, such as small municipalities, towns and villages. Census do not contain the income or consumption information necessary to yield reliable indicators of welfare or standard of living, such as poverty and inequality measures. 7.2 Methodology One approach involves estimations of measures of poverty based on the access and quality of service. One such measure is the Living Conditions Index, (LCI). This index: Summarizes access and quality of household source of water Source of drinking water Type of toilet facility The main method of garbage collection. The extent of crowding in the household (number of people divided by the number of bedrooms in the dwelling). This approach involves classifying responses into Level 1 ( high quality) through Level 5 ( low quality). Then assign a number of points to each level, say, 100,75,50,25, and 0 to the respective level. Let us specify h000=y, to denote the relevant question number in the census questionnaire, where y denotes the code for the answer; e.g: water supply: level 1=if h17=1/h17=4; level 2= if h17=3/ h17=5; level =if h17=6/h17=7; level4= if h17=2; level 5 =if h17=8/h17=9. Crowding Index: level 1= one person to a bedroom, level 2= 1 or 2 persons, Level 3 = 2 or 3 persons, Level 4 = 3 or 4 persons, Level 5= 4 persons or more. For each household in the census, the value of LCI is constructed as the simple sum of points across the six variables. The lower the sum, the poorer the household. The house-specific index is then averaged by enumeration district, villages or region to yield a picture of the relative quality of service by geographical location. Another measure is the Enumeration District Marginality Index (E DMI) Variables included are: Proportion of adults in the district/ county who have either had no education at all or did not complete primary schooling. Proportion of adults ( 15 years plus) in the primary sector. Proportion of children (6-14 years) who do not attend school full time. Proportion of dwellings without piped-water. Proportion of dwellings without W.C. linked to sewer. Proportion of dwellings without electricity supply. Proportion of dwellings who do not have garbage collection service for refuse disposal. Average number of family members per bedroom. After constructing these variables at the district/county level, the EDMI is constructed using the method of “principal component”, a statistical technique for forming new variables which are linear combination of the original variables. STATA is a useful software The new variables are referred to as the principal components. The first principal component accounts for the maximum variance in the data, the second principal component accounts for the maximum variance that has not been accounted for by the first principal component, and so on. The EDMI is the first principal component which is the weighed sum of the variables considered. High values of the EDMI denote more marginal ED. Examples of the application of both measures to Guyana

19 Table 1: A comparism of the ranking of rural and urban areas based on the LCI and the EDMI (means weighted by the number of households in the ED) Poverty score Based on LC11 based on EDMI Rural 341 0.333 Urban 426 -0.782 Table 2: A comparism of the ranking of regions based on the LCI and EDMI (means weighted by the number of households in the ED) Based on LCI Rank (poorest on top less poor at bottom) Based on EDMI Region 8 162 1 Region 1 2.125 Region9 184 2 Region 9 2.049 207 3 1.982 Region 7 259 4 1.023 Region 2 278 5 0.583 Region 3 352 6 Region 5 0.303 355 7 0.234 Region 10 364 8 Region 6 0.188 373 9 Region 4 -0.137

20 Useful to examine the relation between poverty and inequality.
7.3 Combining Household Survey and Census Data. An alternative approach is to combine survey data with census data to create more detailed poverty maps. The basic idea is to use more detailed survey data to construct household or individual level indicators of welfare as a function of variables that are common to both survey and census data.. In(yί)X1ί β+Uί………………….(1) The second step is to use estimates from equation (1) along with census data on the Xi variables, to get predicted values of Yi for every household in the country. These predicted values can then be used to measure poverty to a more disaggregated level. Total poverty gap (TPG) in each district/ county can be estimated as TPGi =P1i x Zi x Ni……………(2) Where P1i is poverty gap in location i, Zi is poverty line in location i, Ni is total population in location i. The share (Wi ) of the total poverty gap of location i among k locations within a county is calculated as: ∑TPG  i-ikWi = TPGi (3) Inequality measures: Useful to examine the relation between poverty and inequality. Correlation estimation between FTG poverty indices and inequality indices: May be positive or negative in survey data. More likely to be negative in census data. This is because being a larger population than sample data, the larger the proportion of the population below the poverty line, the less scope there is for variance in the per capita consumption measure, and therefore the less inequality likely to be. 7.4 Exercises 1. Identify questions from National Census Questionnaire to construct the following variables: EDMI VARIABLES -Proportion of households in the district who do not have electricity supply (H08-H09) -Proportion of household who do not have piped water supply (H07) -Proportion of households who do not have W.C. toilet system (H10) -Proportion of households who do not have basic durable goods (Q1-Q8) -Proportion of adults (15 years and above) who have either no education at all or did not complete primary education (P20) -Proportion of adults who work in agriculture sector (P22, A01-A13) -Proportion of adults who work in the informal sector (P22) -proportion of children (6-14 years) who do not attend school full time (P19) -Proportion of adults who were unemployed in the past 7 days (P21) -Proportion of adults who are paid unskilled worker (P24) -Proportion of households with child death in the past 12 months (P34-P37)

21 -Proportion of dwellings built with mud/stick/reed/bamboo/thatch/mat/grass ( H04-H06)
-Average number of household members per bedroom (H03) LCI VARIABLES -Number and quality of rooms the household occupy (H03-H06) -Main source of water supply : L1 Pipe or pump indoors;L2 Pipe or pump outdoors/Public tap; L3 Close well or protected spring;L4 Open well;L5 River/Lake/Spring/Vendors (H07) -Mai n source of fuel used for lighting/cooking : L1 Electricity own generator, L2 Electricity power supply, L3 Gas/Kerosine, L4 Palm oil lamp/charcoal, L5 wood (H08-H09) -Type of human waste disposal system used by household: L1Flush toilet for household alone, L2 Flush toilet shared with other households, L3 Covered pit latrine, L4 Open pit latrine, L5 Bush/Beach (H10) -Distance from nearest facilities (H11-H13): L1 Less than 20 mins, L mins, L mins, L mins, L5 80 mins and more (H11-H13) -Construction materials of housing unit (H04-H06): Outer walls—L1 cement blocks, L2 Mud blocks, L3 zinc and iron, L4 mud and sticks, L5 bamboo/reed/grass/mat Roof---L1 concrete/tiles, L2 Asbestos, L3 Zinc, L4 Tapaulin, L5 Bamboo/thatch. Floor---L1 Tiles, L2 cement, L3 mud, L4 wood, L5 others.


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