1 Labor Market Information Systems and Data Analysis Kathleen Beegle Development Economics Research Group And Living Standards Measurement Study (LSMS) group World Bank April 7, 2009

2 Data for labor analysis: what can you do and how can you do it?  Focus on quantitative analysis Qualitative analysis is another method of analysis which is not part of this discussion When under-taking new quantitative data collection (and even when doing analysis of secondary analysis), you would probably always do some qualitative analysis.  Focus on data needs for analytical work

3 Labor data  Many sources of data for labor market analysis.  How/if these data can be used will depend on several factors: Who is eligible to be included?  census v. survey v. administrative records Who reports information?  Household head reporting for all individual members  Firm manager reporting on individual staff What information is reported?  Unpaid family labor  Women’s domestic work How often is the data collected? Can it be merged/combined with other data?  LFS combined with rainfall data

4 Types of data  Population and Housing census In theory, all residents of the country with limited information (age, sex, education, migration, “main activity”) Every 10 years Often source for sampling frame for household surveys Difficult to get unit-record data but means are often readily available

5 Types of data  Household survey data Topical surveys  Household Budget Surveys (HBS), Income and Expenditure Surveys (IES)  Labor Force Surveys (LFS)  ILO SIMPOC surveys (Statistical Information and Monitoring Programme on Child Labour part of IPECL)  Demographic and Health Surveys Integrated Household Surveys (LSMS, FLS) include income & non-income dimensions of living standards  

6 Types of data  Administrative data (records) From companies or from governments (local, regional, national)  Firm/enterprise surveys  rru.worldbank.org/EnterpriseSurveys/  Rural investment climate surveys Non-labor data is also relevant…examples:  Price data (to deflate nominal values)  Infrastructure information (access to markets)

7 Data producers  National statistical office policies of access to unit-record data vary  Multi-laterals: ILO, World Bank, IDB Not systematically public  Researchers Not systematically public  How to find data: not so easy! IHHSN  WB’s DDP (e.g. Africa Household Survey Data bank)

8 A “simple” labor question may embed many demands on data  Single topic surveys may lack breadth of topics (eg: measure of poverty status)  LSMS surveys may lack depth (eg: willingness to co-pay for health insurance, pension contributions for civil servants)  Administrative data: little background information on respondents (eg: education level)

9 Unemployment & Poverty Nicaragua, 1993

10 UNE and Poverty: what data would you need?  “ILO” definition of unemployment Did you work (for at least 1 hour) in the last 7 days  Does this include working as unpaid family labor on the hh farm?  Does this include the wife who worked 2 hours in the hh’s non-farm business? If no, do you have a regular job (on leave/sick) to which you will return? If no, have you searched for work in the past 4 weeks?  3+ questions, asked of all household members.  In low-income countries, you find few who qualify as unemployed

11 UNE and Poverty: what data would you need?  Poverty status of household (detailed consumption module)  Sufficient sample sizes in each region to generate reliable unemployment statistics

12 Employment and Poverty Indicators Sample Means Poor below food poverty line Between food poverty line and poverty line NonpoorTotal Household members who have worked during the past week (%) Number of jobs per household member during the past week Average monthly wage from primary occupation 65,94899,706103,43898,401 Children under 15 who were employed during the past week (%) Cambodia 1997

13 Employment and Poverty: What data would you need?  Poverty status of household  Work status of individual household members, including children under 15 (often missing)  Information on working and number of jobs wrt to some time period – last week, month year  Wage data imputed wages for self-employed? In-kind value of wage payments (housing, food) Difficult to annualize  CPI to convert price data from nominal to real values (spatial and temporal price data)

14 Private Rates of Return to Schooling by Level of Education Education Level AllMalesFemales Public Sector Private Sector Primary (v. less than primary) 134*21-27*23 Secondary (v. primary) Vocational (v. primary) 4556** University (v. secondary) ** * Not statistically significant **Not enough observations Vietnam 1992/93

15 RTE: What data would you need?  Sector of work (maybe including second or thirds jobs?)  Wages (real values)  Education level

16 Time Spent on Activities Ghana

17 Time Use: What data would you need?  Reported time use across distinct sub-categories of activities asked of every individual member of the household Fetching water and collecting firewood:  does this include waiting time?  does it include walking to the source What if farming is combined with child- care?

18 What data would you need?  MDG 3: Share of women in wage employment in the nonagricultural sector  Impact of credit access on entry into self-employment  Ex-post impact of minimum wage legislation  Ex-ante impact of proposed changes changes to pension system

19 Data analysis of labor issues: Challenges  What if? Posing hypothetical situations to respondents (willingness to pay/contingent valuation) Reliability of complicated questions on tradeoffs today with future returns  Consistency in definitions across time and space  Relevance of international definitions Unemployment in SSA v. ILO definition  Impact: Identifying appropriate control groups

20 Data analysis of labor issues: challenges  Seasonality Means are deceiving How to measure labor bottlenecks?  Rare events: Difficult to measure/assess rare events in large- scale LSMS-type surveys Impact of HIV/AIDS on absenteeism LM outcomes for disabled Children involved in dangerous work

21 Malawi Time Use 2004

22 Conclusions  Lots of data, but (usually) no one source has it all  Search for your data: good literature review may reveal some ideal data  Be creative. combine data across sources (LSMS with administrative data)  Be realistic about what you can and can’t answer  Pay attention to the details of your data source

23 Web Source of Information on Household Surveys with Labor Data  LFS  LSMS  DHS  MICs  IES/HBS europa.eu.int/estatref/info/sdds/en/hbs/hbs_base.htm  CWIQ

24  rvey.list?p_lang=en