NTA by SES (NTASES, N project) Sang Hyop Lee University of Hawii at - - PowerPoint PPT Presentation

NTA by SES (NTASES, N project)

Sang‐Hyop Lee University of Hawii at Manoa East‐West Center November 12, 2014 NTA 10, Beijing, PRC

Outline

• What is NTA by SES?
• How to measure?
• What are the major challenges?
• See some results

SES?

• Gender + time use
• Income/consumption level
• (Parents’) education
• Urban vs. rural (+ time use?)
• Region
• Formal vs. informal sector
• Extended household vs. nuclear household
• Ethnicity/race/immigrants
• Marital status

NTA and SES

• What: Disaggregate NTA by household or

individual characteristics

• Why: SES is an important analitical dimension

in many countries

– SES is highly associated with many other social, economic and demographic variables – Large heterogeneity in public and private flows within age groups – Intergenerational transmission of inequality

• How: Estimate NTA by SES

How to do it?

Step 1. Just construct NTA a) And allocate values based on individual characteristics (if you can) b) And allocate values based on household characteristics (if you can) E.g. 1) gender E.g. 2) household head education, rural vs. urban E.g. 3) income, consumption

Annual consumption by education of household head (Chile, 2007)

Household characteristics? (disaggregation of data)

1) Total household income (labor earnings, self‐ employment income, business revenue, asset income, transfer income) 2) Per capita household income 3) Equivalence‐scale‐based per capita household income/consumption 4) Per capita or total household consumption

Macro control problem

• If you cannot assign individual or household

characteristics, then you need separate macro control by SES e.g, tax profiles and government asset allocation—can still assign based on micro survey data share

Data issues

• Constructing NTA requires individual or

household micro‐survey data sets

• A good survey data set has the properties of:

– Extent: It has the variables of interest at a certain level of detail – Reliability: The variables are measured without error – Validity: The data set is representative

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Reliability: Measurement error

• Non response rate by SES (representative)
• Response error

– Respondents do not know what is required – Incentive to understate/overstate – Recall bias: Related to period of survey – Coding error: Using wrong/different reporting units

• Reporting error: Heaping or outliers
• Discrepancy between aggregate value and

individual value

Brazil: What funds consumption?

Among elderly of any SES and children of low SES = public transfers

Source: Turra & Queiroz

Labor income profiles by residence and per capita household income (China, 2009)

5000 10000 15000 20000 25000 30000 35000 40000 45000 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 RMB Age Urban Rural 10000 20000 30000 40000 50000 60000 70000 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 RMB Age quartile 1 quartile 2 quartile 3 quartile 4

Source: Shen and Lee (2014)

Labor income profiles by formal vs. informal sector (India, 2004–05)

Source: Narayana (2014)

Public education profiles by residence and per capita household income (China, 2009)

0.0 500.0 1000.0 1500.0 2000.0 2500.0 3000.0 3500.0 4000.0 4500.0 5000.0 4 8 12 16 20 24 28 32 36 40 Age Urban Rural 1000 2000 3000 4000 5000 6000 5 10 15 20 25 30 35 40 RMB Age quartile 1 quartile 2 quartile 3 quartile 4

Source: Shen and Lee (2014)

Public health profiles by residence and per capita household income (China, 2009)

500 1000 1500 2000 2500 3000 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 RMB Age Urban Rural 500 1000 1500 2000 2500 3000 3500 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 RMB Age quartile 1 quartile 2 quartile 3 quartile 4

Source: Shen and Lee (2014)

Pension benefit profiles by residence and per capita hh income (China, 2009)

2000 4000 6000 8000 10000 12000 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 RMB Age Urban Rural 2000 4000 6000 8000 10000 12000 14000 16000 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 RMB Age quartile 1 quartile 2 quartile 3 quartile 4

Source: Shen and Lee (2014)