Arsenic Mitigation In Bangladesh A Household Labor Market Approach

1. Arsenic Mitigation In BangladeshA Household Labor Market Approach Richard T. Carson University of California, San Diego Phoebe Koundouri Athens University of Economics and Business Céline Nauges French Institute for Research in Agriculture & Toulouse School of Economics

2. Arsenic in Bangladesh Groundwater Wells • Rural Bangladesh areas originally relied on surface water • Walked long distances for water • Substantial bacterial and other contamination • Millions of shallow tube wells built starting in 1970’s • Fraction served by these wells accelerated over time • Now close to complete coverage (95%+) • Widespread arsenic contamination discovered in large scale survey of wells done by British Geological Survey (2001) • Our sample average arsenic concentration 62 μg/liter • Our sample range [0.3 to 421] • WHO standard 10 μg liter; Bangladesh standard 50 μg liter • 57 million people exposed to WHO standard or greater

3. Chronic Health Impacts of Arsenic • Short/Medium Term • Lethargy • Headaches/confusion • Skin problems: hypopigmentation & keratoses • Long term • Skin cancer • Internal cancers: bladder, liver, lungs • Arsenic builds up in body and is very slow to clear • Time from exposure to initial skin problems: 10+ years • Time from exposure to more serious problems: 20+ years

4. Existing Work • Large amount of epidemiology focused on: • Body burden • Skin problems • Various types of cancer • Public health/economics work focused on: • Information/incentive for identifying/switching water sources • Public health/economic work focused on: • Medical cost of illness/WTP to avoid adverse effects

5. Specific Issues This Paper Examines Concerning Impact of Arsenic on Labor Supply • Relationship between arsenic exposure and household labor hours supplied in rural Bangladesh • Substitution patterns involving household labor supply associated with arsenic exposure • Interaction between labor hours supplied, arsenic and three main types of assets: land, physical capital, and human capital

6. Key to econometric identification strategy • Arsenic contamination function of geological conditions • Use data from time period before widespread knowledge of arsenic contamination in specific well • Spatially merge data from the British Geological Survey (BGS, 2001) of groundwater wells (done in mid 1998-late 1999) with Bangladesh government’s Household Income and Expenditure Survey done in 2000. • Merge takes place at thana level—5th order subdistrict small enough that BGS levels highly correlated with actual exposure but large enough that households ~independent • 220 thanas each with 20 sampled households

7. Household Labor Supply • Labor hours recorded for any type of remunerated work • Each household member • Paid in money or in-kind/household farm or firm • Hours “worked” at home not recorded • Approach taken • Add together labor hours supplied by each house member • Use household demographic characteristics as regressors • Number of member in each sex/age category • Other household demographics variables

8. Choice of Modeling Framework • Nature of Dependent Variable • Non-negative by definition with upper-end of hours worked for households with large number of members quite large but finite • Suggests survival model with hours worked as “time” variable • Spike at/near zero and “ties” for popular hours worked suggest semi-parametric Cox Proportional Hazard Model • Hazard function h(t) = f(t)/S(t), where S(t)= 1 – F(t) • Covariates assumed to proportionately shift h(t) • h(HHWi | Xi, ASi) = h0(HHW)exp(αASi + βXi), where • HHWi is household hours work, Xi demographic composition of household, ASi is arsenic level • Β larger than 1 indicates downward shift in HHW

9. Cox Proportional Hazard Model • Allows arbitrary (non-parametric) baseline hazard • Hazard function h(t) = f(t)/S(t), where S(t)= 1 – F(t) • Covariates assumed to proportionately shift h(t) • Basic model: • h(HHWi | Xi, ASi) = h0(HHW)exp(αASi + βXi), where • HHWi is household hours work, Xi demographic composition of household, ASi is arsenic level • Coefficient of 1 for [α, β] indicates no shift in baseline h(t) • Smaller than 1 indicates upward shift in HHW (more hours) • Larger than 1 indicates downward shift in HHW (less hours) • If indicator variable, then coefficient - 1 is percentage shift

10. Base Model (Arsenic Excluded)Pattern of Demographic Results • Increase in HHW for females 6-25, particularly pronounced for 16-25 age group • No significant deviation from 1 for older females • Significant reversal from younger females though • Increase in HHW for males of all ages starting with [6-10] • HHW roughly constant from 16-55, enormously significant • Islamic households work substantially fewer hours • Conditional on this effect, older females work more • Quadratic with acres, linear HHW down, quadratic up • HHW goes up with assets, down with Max house educ.

11. Adding Arsenic • Linear term only 1.0108 (t=6.45) • Quadratic specification • Linear 1.0226 (t=4.59) • Quadratic 0.9996 (t=-2.50) • HHW is decreasing in arsenic but at a slowly decreasing rate • Turning point is at ~300 μg (3% of data beyond that point) and at ~580 μg in specification with interactions

12. Adding Interactions with Arsenic • Smaller households work proportionately less • Females, particularly older associated with lower HHW • Prime age males associated with higher HHW • Older head, more assets, HHW goes up • More acres, HHW goes down

13. Predicted Effect on HHW • Reducing arsenic level to zero • Increase HHW by 7.9% • Reducing arsenic to WHO standard (10 μg) • Increase HHW by 6.5% • Reduce arsenic to Bangladesh standard (50 μg) • Increase HHW by 3.6% • Impact on median household substantially smaller because arsenic exposure highly skewed

14. Summary • For poor in many places, labor hours main asset • Bangladesh in 2000 ideal for examining the impact of large scale low level chronic health problems induced by exogenous and unknown arsenic exposure • Estimated effect large, 7.9% reduction HHW • Compensation mechanisms • Given overall arsenic reduction, more work by prime age males, less by females • Physical assets decrease arsenic related loss in HHW • Land assets increase arsenic related loss in HHW