Does poor health affect productivity?

Define labor in terms of efficiency units 8 per time period, so that

“effective” labor L * for a farmer is (T - l)8 .f f

Let the efficiency per time-period of a laborer depend on his/her

afood consumption X , so thati

a8 = 8(X ), where 8N>0, 8O<0i i

The production function now, including hired labor, is

aQ = F(L *, L *, V, A)f h

In a competitive, full information market there would be a price

(wage) for each unit of efficiency labor, call it w*. Then the

budget constraint (ignoring the cash crop) is:

a a v m m a aw*8(T - l) + [p Q - w*L * - w*L * - p V] - p X - p X = 0f h

The marginal cost of consuming an additional unit of food is

a L*p [1 - (T - l)F 8N]

This is less than the market price per-unit of food:

Implications:

1. The poor will eat more per unit of income than the rich (lower

savings rate than the rich).

2. As long as hired labor is not a perfect substitute for family labor,

the poor (small) farmer will get less output per unit of land than the

rich (large-land) farmer.

Thus poverty is a cause of low savings and low productivity!

“Does Better Nutrition Raise Farm Productivity?” (Strauss, 1986)

How to test whether improved nutrition augments productivity?

Specify a production function, including food consumption of

workers as inputs

Challenges:

1. Measurement of individual food consumption (calories) of

family and hired workers (apportions total hh food

consumption by sex, age groups, assumes all hired workers

have average consumption in region)

2. Reverse causality: low productivity (income) causes low

food consumption (uses prices of output, wages, household

size as instruments)

Production function specification (Cobb-Douglas again):

a 1 2 a 3 a 3log Q = $ + $ (log L + log 8(X )) + $ (log L + log 8(X )) + $ log A + ,f f h h

and

a 1 a a 2 a a8(X ) = 1 + " ((X /x ) - 1) + " ((X /x ) - 1), i=f,hi i f i f 2

awhere x = sample mean consumptionf

Results (rural Sierra Leone farm households, 1974-75)

Problems

Does Better Nutrition Raise Wages?

it ait it itW = w*8(X , H )e

1 ait 2 it8() = exp(" X + " H )

Examples of H = height, body mass, biceps, gender

it 1 ait 2 it itln W = ln w* + " X + " H + ln e

Challenges:

1. Reverse causality: low wage causes low food consumption

(use prices of foods, non-labor income as instruments).

2. Are time wages the same as productivity?

3. Is it plausible that employers know calorie consumption of

workers?

Estimated Effects of Calorie Consumption on the log Wage,

by Payment Method:

Bukidnon, Philippines Harvest Workers over Four Seasons

All Harvest

Wages

Harvest Time

Wages

Harvest Piece-

Rate Wages

Calories consumed

(x10 )-3

.211

(2.36)

.0153

(0.22)

.438

(3.13)

Height 1.04

(2.37)

.040

(0.30)

.446

(1.41)

Male -.366

(2.37)

.285

(0.61)

-.357

(2.66)

N 327 291 136

Health and Schooling

Poor health, related to bad endowments - geography - often thought to be one reason for lack of

growth

One linkage - poor health among children reduces school attendance, performance

Examples: 1.3 billion people infected by hookworm and roundworm; whipworm affects 900

million people; schistosomiasis affects 200 million people

Educational impact of de-worming key issue

A. De-worming is relatively cheap - single-dose oral treatment reduces

infections by 99%, but need annual application because of reinfection

B. Children

1. Account for bulk of infections: 85-90% of all heavy schistosomiasis

infections in Eastern Kenya

2. Children most likely to spread disease, worm infections, because have

worst hygiene practices (less likely to use latrines)

3. Believed to affect schooling - anemia, malnutrition, illness

Relates to question: how much does geography matter? Poor health endowment: worms

What is evidence on effect of deworming on schooling outcomes?

Prior studies find little affect of de-worming treatment on school performance - test scores

Methodology seems correct: randomization of individual treatment of children

within schools

Two problems, however:

1. Neglects externalities: non-treated children are less likely to be infected infected

A. Understates effect of treatment for two reasons:

1. “Control” group (placebo) actually affected by the treatment:

difference between control-group and treatment-group outcomes

underestimates effectiveness of treatment on the treated

2. The reduction in the infections of the untreated - the externality - is

part of the benefits of the treatment and is ignored

2. The studies have not looked at a range of outcomes, including attendance in

school, enrollment test scores, promotion rates

New study (Miguel and Kremer, 2003): Primary School Deworming Project in Busia, Kenya

Methodology: Randomized phase-in of treatment at the school level, not individual.

75 schools randomly divided in three groups:

Group 1: free deworming in 1998 and 1999 (treatment in 1998 and 1999)

Group 2: free deworming in 1999 (control in 1998, treatment in 1999

Group 3: free deworming in 2001 (control in 1998 and 1999)

How infected were the children prior to treatment?

Survey of pupils: 92% had at least one helminth infection (understated -

why?)

Treatment: All students in school in treatment schools given drug for geohelminth

infection, except girls 13 and above by public health officials (nurses,

officers)

Also, health education on infection prevention

Treatment rates: 67% of eligible in 1998 (80% attendance); 57% in 1999

Results monitored: Survey one year after first round of treatment

Estimation strategy to take into account externalities:

Takes advantage of the fact that many close neighbors go to different schools: people in

proximity get different treatments

Children attending school or living nearby treatment schools have different exposure to

risk of infection: exposure is number of pupils in treatment school that are nearby, or

distance times treatment pupil density

Thus estimating equation is:

ijt 1 1it 2 2it d d dit d d dit i ijtY = a + $ T + $ T + E (( N ) + E (( N ) + u + eT

ijtwhere Y = outcome for student j in school i at time t

T = assigned treatment in year 1 or 2

ditN = total number of pupils in primary schools at distance d from school

i

ditN = number of pupils in treatment schools at distance d from school iT

(d = 1 is 1 kilometer, d=2 is 2 kilometers, etc.)

iu = school effect

1t d d ditSo, average treatment effect is $ + E (( N ), where N is the average number ofT’ T’

pupils in treatment schools located at d from the school

Results

58

Table 7: Deworming health externalities within and across schools, January to March 1999†

Any moderate-heavyhelminth infection, 1999

Moderate-heavyschistosomiasis infection,

1999

Moderate-heavygeohelminth infection, 1999

(1) (2) (3) (4) (5) (6) (7) (8) (9)Indicator for Group 1 (1998 Treatment) School -0.25***

(0.05)-0.12*

(0.07)-0.09(0.11)

-0.03(0.03)

-0.02(0.04)

-0.07(0.06)

-0.20***

(0.04)-0.11**

(0.05)-0.03(0.09)

Group 1 pupils within 3 km (per 1000 pupils) -0.26***

(0.09)-0.26***

(0.09)-0.11(0.13)

-0.12***

(0.04)-0.12***

(0.04)-0.11**

(0.05)-0.12*

(0.06)-0.12*

(0.07)-0.01(0.07)

Group 1 pupils within 3-6 km(per 1000 pupils)

-0.14**

(0.06)-0.13**

(0.06)-0.07(0.14)

-0.18***

(0.03)-0.18***

(0.03)-0.27***

(0.06)0.04

(0.06)0.04

(0.06)0.16

(0.10)Total pupils within 3 km (per 1000 pupils) 0.11***

(0.04)0.11***

(0.04)0.10**

(0.04)0.11***

(0.02)0.11***

(0.02)0.13***

(0.02)0.03

(0.03)0.04

(0.03)0.02

(0.03)Total pupils within 3-6 km (per 1000 pupils) 0.13**

(0.06)0.13**

(0.06)0.12*

(0.07)0.12***

(0.03)0.12***

(0.03)0.16***

(0.03)0.04

(0.04)0.04

(0.04)0.01

(0.04)Received first year of deworming treatment, whenoffered (1998 for Group 1, 1999 for Group 2)

-0.06*

(0.03)0.03**

(0.02)-0.04**

(0.02)(Group 1 Indicator) * Received treatment, when offered -0.14*

(0.07)-0.02(0.04)

-0.10***

(0.04)(Group 1 Indicator) * Group 1 pupils within 3 km (per1000 pupils)

-0.25*

(0.14)-0.04(0.07)

-0.18**

(0.08)(Group 1 Indicator) * Group 1 pupils within 3-6 km (per1000 pupils)

-0.09(0.13)

0.11(0.07)

-0.15(0.10)

Grade indicators, school assistance controls, districtexam score control

Yes Yes Yes Yes Yes Yes Yes Yes Yes

Number of observations 2328 2328 2328 2328 2328 2328 2328 2328 2328Mean of dependent variable 0.41 0.41 0.41 0.16 0.16 0.16 0.32 0.32 0.32

†Grade 3-8 pupils. Probit estimation, robust standard errors in parentheses. Disturbance terms are clustered within schools. Observations are weighted by totalschool population. Significantly different than zero at 99 (***), 95 (**), and 90 (*) percent confidence. The 1999 parasitological survey data are for Group 1 andGroup 2 schools. The pupil population data is from the 1998 School Questionnaire. The geohelminths are hookworm, roundworm, and whipworm. We use thenumber of girls less than 13 years old and all boys (the pupils eligible for deworming in the treatment schools) as the school population for all schools.

60

Table 9: School participation, direct effects and externalities†

Dependent variable: Average individual school participation, by yearOLS OLS OLS OLS OLS OLS IV-2SLS(1) (2) (3) (4)

May 98-March 99

(5)May 98-March 99

(6)May 98-March 99

(7)May 98-March 99

Moderate-heavy infection, early 1999 -0.028***

(0.010)-0.203*

(0.094)Treatment school (T) 0.051***

(0.022)First year as treatment school (T1) 0.062***

(0.015)0.060***

(0.015)0.062*

(0.022)0.056***

(0.020)Second year as treatment school (T2) 0.040*

(0.021)0.034*

(0.021)Treatment school pupils within 3 km(per 1000 pupils)

0.044**

(0.022)0.023

(0.036)Treatment school pupils within 3-6 km(per 1000 pupils)

-0.014(0.015)

-0.041(0.027)

Total pupils within 3 km(per 1000 pupils)

-0.033**

(0.013)-0.035*

(0.019)0.018

(0.021)0.021

(0.019)Total pupils within 3-6 km(per 1000 pupils)

-0.010(0.012)

0.022(0.027)

-0.010(0.012)

-0.021(0.015)

Indicator received first year of dewormingtreatment, when offered (1998 for Group 1,1999 for Group 2)

0.100***

(0.014)

(First year as treatment school Indicator)*(Received treatment, when offered)

-0.012(0.020)

1996 district exam score, school average 0.063***

(0.021)0.071***

(0.020)0.063***

(0.020)0.058

(0.032)0.091**

(0.038)0.021

(0.026)0.003

(0.023)Grade indicators, school assistance controls,and time controls Yes Yes Yes Yes Yes Yes YesR2 0.23 0.23 0.24 0.33 0.36 0.28 -Root MSE 0.273 0.272 0.272 0.223 0.219 0.150 0.073Number of observations 56487 56487 56487 18264 18264 2327 49

(schools)Mean of dependent variable 0.747 0.747 0.747 0.784 0.784 0.884 0.884

† The dependent variable is average individual school participation in each year of the program (Year 1 is May 1998to March 1999, and Year 2 is May 1999 to November 1999); disturbance terms are clustered within schools. Robuststandard errors in parentheses. Significantly different than zero at 99 (***), 95 (**), and 90 (*) percent confidence.Additional explanatory variables include an indicator variable for girls < 13 years and all boys, and the rate ofmoderate-heavy infections in geographic zone, by grade (zonal infection rates among grade 3 and 4 pupils are usedfor pupils in grades 4 and below and for pupils initially recorded as drop-outs as there is no parasitological data forpupils below grade 3; zonal infection rates among grade 5 and 6 pupils are used for pupils in grades 5 and 6, andsimilarly for grades 7 and 8). Participation is computed among all pupils enrolled at the start of the 1998 schoolyear. Pupils present during an unannounced NGO school visit are considered participants. Pupils had approximately3.8 attendance observations per year. Regressions 6 and 7 include pupils with parasitological information from early1999, restricting the sample to a random subset of Group 1 and Group 2 pupils. The number of treatment schoolpupils from May 1998 to March 1999 is the number of Group 1 pupils, and the number of treatment school pupilsafter March 1999 is the number of Group 1 and Group 2 pupils.

The instrumental variables in regression 7 are the Group 1 (treatment) indicator variable, Treatment school pupilswithin 3 km, Treatment school pupils within 3-6 km, and the remaining explanatory variables. We use the number ofgirls less than 13 years old and all boys (the pupils eligible for deworming in the treatment schools) as the schoolpopulation for all schools.

Calculations:

1. What is the average spillover gain: the cross-school externality reduction in infection?

0-3 0-31 3-6 3-61( N + ( N = .26*454 + .14*802 = .23 (23 percentage points drop)T’ T’

2. What is the average spillover gain: the cross-school externality on enrollment?

2 percentage points increase in non-treatment schools

3. What is the direct effect of the treatment in treatment schools?

7.5 percentage point gain (one quarter drop in absenteeism)

4. What is the total increase in school years from treating one child?

1*.075 + .5*.075 + 1.5*.02 = .14 years

Based on the fact that:

1/3 of children in treated schools not treated

2/3 of all pupils not in treatment schools in 1998, 1/3 in 1999

What is the rate of return from treating one child?

1. Benefit:

A. From wage regression: one year gives a 7% increase in wage (males)

So 14 years gives .14*7 = 1% gain

B. Output per worker in Kenya = $570

C. Wages are 60% of output per worker

D. People work 40 years and wages do not increase

E. Use discount rate of 5%

2. Cost:

A. Opportunity cost of schooling: ½ of adult wage

B. $0.49 for administering the drug

Treating one child gives a net present value increase in lifetime wages of $30

Given externalities, subsidy is warranted but existence of infectious parasites does

not account for schooling or income deficit across Kenya and the developed world.

How Much Does Increasing Health Increase Development?

Observe: Low-Income Countries are less healthy - almost all measures of ill health negatively

correlated with per-worker GDP

Worker productivity lower in countries with low health

Why does this association not tell us the impact of health on development?

1. Low incomes may cause low health

2. Unmeasured factors causing ill health may also be limiting productivity

A. Poor delivery of health services may be due to bad governance, which also

affects ability to invest

3. Also, specific indicators of ill health may be proxying for more general health issues

A. Prevalence of malaria, or anemia may be correlated with other health

problems - so getting rid of malaria may not have a big impact

Two examples: Anemia (low levels of hemoglobin in blood due to worms, malaria, iron

deficiency in diet)

Low birthweight (low maternal nutrition)

Both have strong negative association with incomes by country

Available micro estimates indicating causal effects (from randomization) of:

A. Eliminating anemia

B. Eliminating the birthweight gap between rich and poor countries

Micro evidence:

Anemia: from randomized trials of iron supplementation (Shastry,

2002)

Birthweight: from differences in birthweight among identical twins

(Behrman and Rosenzweig, 2004)

Figure 1Mortality, Anemia, and Income per Capita

20

40

60

80

100

-4.0 -3.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5

Ln (Income per Worker relative to US)

Perc

enta

ge o

f Wom

en w

ho a

re n

ot A

nem

ic

400

500

600

700

800

900

1000

Adult Survival Rate

Table 1

Anemia Prevalence and Productivity Loss by Country

Country

Anemia Presence, Non-

pregnant Women

Average Productivity relative

to No Anemia: Randomized-

Design Estimate

India 76.7 .896

Tanzania 69.7 .914

Haiti 55.8 .942

Senegal 48.0 .954

Sri Lanka 45.1 .958

Algeria 36.7 .969

Brazil 29.7 .976

Japan 17.6 .988

United States 5.0 .997

From Kartini Shastry G.; Weil D.N., “How Much of Cross-country Income Variation is Explained

by Health?” Journal of the European Economic Association, Volume 1, Numbers 2-3, 1 April 2003,

pp. 387-396(10)

Figure 1

Low Birthweight and Log Per-worker GDP Around the World

Table 4

Percent Low-Birthweight Births and Mean Birthweight for Selected

Countries

Country % Low

birthweight

Mean

birthweight

Number

of births

Source

India 22.6 98.3

(24.5)a

8650 1998/99 DHS

Philippines 16.2 111.0

(26.3)

4337 1998 DHS

Pakistan 16.0 111.9

(28.8)

607 1991 DHS

Malaysia 14.3 108.3

(19.2)

3941 1976/77 MFLS

Senegal 11.2 110.9

(26.5)

2239 1997 DHS

Uganda 11.2 113.5

(28.2)

1889 1995 DHS

Brazil 9.1 114.3

(21.4)

4427 1996 DHS

Peru 9.0 114.6

(23.4)

10654 1996 DHS

United

States -

whites

8.9 118.2

(20.9)

1711 NLSY79, 1988

round

Standard errors in parentheses.a

Effects of birthweight on adult productivity are non-linear:

Greater impact at lower levels of birthweight

So aggregate effect depends on country-specific birthweight distribution

The formula for computing the percent earnings gain for a country j from closing the

birthweight gap between it and some target country is:

j i ij 1i j%earnings gain = G f á [birthweight gap ].

Examples: Malaysia (10-ounce gap) and India (20 ounce gap) relative to the US

Use no weights, Malaysia weights, India weights based on the birthweight distributions

Table 5

Within-MZ Estimates of Increasing Birthweight on the Ln Wage,

by Country-Specific Sample Weights

Weights No weights Malaysia weights India weights

Sample Full

sample

L.B. Full

sample

L.B. <50% Full

sample

L.B <50%

Birthweight

(ounces)

.00478

(2.41)a

.00643

(2.35)

.00413

(2.04)

.00612

(2.23)

.00502

(2.33)

.00430

(2.24)

.00736

(2.45)

.00452

(2.10)

N 812 404 812 404 632 812 404 616

Abolute value of t-ratio in parentheses.a

Figure 2

Distribution of the Absolute Value of Birthweight Differentials (oz.) Among identical Twins

Cooking with Biomass and Health: The Short- and Longer-run

Effects of Indoor Air Pollution

Facts

One half of the world’s population relies on biomass and coal as their primary source of

household energy, as much as 95% in Bangladesh

Exposure to indoor air pollution (IAP) from solid fuels is alleged to cause several diseases

including acute respiratory infections (ARI) (also chronic obstructive pulmonary disease

(COPD), asthma, cataracts and blindness, and low birth weight.)

ARI accounts for 6 percent of worldwide disease and mortality, mostly in the developing

countries.

ARI is also the most common cause of illness and mortality in children in the developing world

Acute lower respiratory infection accounts for 20 percent of the annual deaths of children

under five, with nearly all of these deaths occurring in the developing countries.

This paper investigates how and to what extent the division of household responsibilities and

ventilation, fuel and stove type affect both child and adult health in rural Bangladesh and India

Bangladesh: Households use biomass for cooking fuel; no substitution possibilities: only

variation in housing and in who cooks

India (national): Households use both biomass and clean fuels, different stove types

Average particulate concentrations of 300 ìg/m or higher are common in Bangladeshi3

households. [California indoor air quality standard = 50 ìg/m .]3

Empirical challenges:

1. Identification of exposure, fuel, stove, ventilation effects on adults and children when all are

endogenous (optimally chosen) - focus on choice of exposure, conditional on technology.

2. Identification of optimizing behavior: do households behave as if they are informed about the

health consequences of IAP?

Theory

A simple heuristic model to show how households seeking to minimize the burden of an unhealthy but

necessary activity will allocate the unhealthy task among its members.

Consider a separable household utility function of the form

where

m m1 m2 mJ H =(h h ,...,h ) is the set of health statuses of the J males in the household,

f f1 f2 fK H =(h ,h ,...,h ) is the set of health statuses of the K females in the household,

m m1 m2 mJ f f1 f2 fK X =(x x ,...,x ) and X =(x ,x ,...,x ) are sets containing the allocations of composite

consumption goods.

A single unit of time is allocated between a productive activity that is deleterious to health (“cooking”)

c a(t ) and productive employment (t , “agriculture”) that is not.

cOnly women devote time to the cooking activity and the total quantity of cooking time t that women

c1 c2 cmust provide is fixed, so that time spent cooking must be allocated such that t + t = t .

Health for women is produced with technology

where

fiì = the exogenous component of health (health endowment).

The productivity of time spent in agriculture is sensitive to health

cibut the productivity of time spent cooking, t , is not.

Households maximize the utility function subject to the health technologies, the productivity functions,

the time constraints, and the budget constraint

where

mi ai miv = the sum of non-earnings income and male earnings net of their consumption (3w t -p3x )

p = the price of consumption good x.

Implications:

Because health only affects income through agricultural work, then even if all women are

f1 f2 cidentical (ì =ì ) and t # 1 (household cooking time is less than the time available to any one

women), one woman may optimally do all of the cooking and the other woman will specialize in

agriculture if cooking requirements are sufficiently high.

The rationale is clear – any time spent in cooking by a woman reduces her health and

productivity in agriculture without affecting her productivity in cooking.

f1 f2Moreover, if one woman is innately less healthy than another (ì <ì ) she will always do more of

the cooking, while the other will specialize more in agriculture.

Thus, because the less healthy cook, the observed association between time exposed to an unhealthy

environment and ill-health will be an upward-biased measure of the effect of exogenously increasing

exposure.

In the Bangladesh, relative productivities and health endowments may not be the only factors that

allocate women to tasks, but also the identity of women in terms of their relationships to the household

head.

The anthropological literature suggests that mothers-in-law dominate daughters-in-law, mothers

dominate daughters, and elder brothers’ wives dominate younger brothers’ wives. A young wife

submissively follows the lead of her husband’s mother, and is rarely involved in decision-making.

This”social hierarchy” component of time allocation is exploited in the estimation strategy we adopt

(and is assessed).

Bangladesh Data: 2000-2003 Nutrition Survey of Bangladesh

The survey sample has two components:

(1) a random sample of all households in 14 villages carried out in 2000, and

(2) a panel survey, consisting of the households of all surviving individuals included in the 1981-

82 Nutrition Survey of Bangladesh (Ahmed and Hassan, 1983) (originally sampled from the

same 14 villages) regardless of their residence during the interval 2000-2003, some of whom

were also included in the 14 village random sample frame of year 2000.

This data set provides multi-level (individual, household and village) survey information on health

status, activities, food consumption and resources for over 4000 men and women.

The panel component of the survey, by following individuals who departed from the original 14

villages, is characterized by very low (3%) household and individual attrition rates despite the

approximately 20-year interval between rounds.

The questionnaires elicited information on:

(i) detailed activities, including those for pay and not for pay, as well as the earnings from paid

activities, in a 24-hour period for every household member. Activities were coded into more than

150 categories. A key feature of this module is that activities are anchored around the five prayer

times observed during the day, salient features in the lives of the Muslim respondents.

(ii) detailed food consumption for every member of the household observed over the same 24-

hour period

(ii) home dimensions and construction materials including the location of the kitchen (outside or

not) as well as roof and wall material , and

(iii) 23 health symptoms for all household members, provided to a trained clinician by each

household respondent over age 10, and for children less than or equal to 10 by the relevant

mother. Of the 23 symptoms, three symptoms are respiratory-related (coughs, difficulty

breathing, with or without fever).

Table 1

Sample Characteristics

Variable Adults Aged 16+ Children 2-9

Respiratory symptoms .154

(.361)

.221

(.415)

Intestinal symptoms .0489

(.216)

.0694

(.254)

Age 36.0

(15.8)

6.09

(1.98)

Education (years) 3.51

(4.14)

.515

(.939)

Female .477

(.500)

.485

(.500)

Mother with children<5 .242

(.563)

-

Wife of head .278

(.448)

-

Daughter-in-law of head .0517

(.221)

-

Total hh expenditures (taka

per month)

4797

(4176)

4558

(4856)

Permeable roof .205

(.403)

.224

(.417)

Permeable walls .326

(.469)

.377

(.484)

Kitchen outdoors .262

(.440)

.292

(.455)

Number of individuals 4026 1365

Number of households 1198 780

Does cooking time cause respiratory illness symptoms among adults?

The equation estimated is given by

ij t ij A ij j z j X hj hij hij(9) h = á t + á A + Z á + X á + ì + ì + e ,

where

ijh is the incidence of any respiratory symptom for person i in household j;

ijt is the time spent cooking;

ijA is a set of person-specific attributes (age, sex, education);

jZ is a vector of household-level smoke-related factors that reflect ventilation (permeability of

walls and roof, and whether cooking is carried out outdoors);

jX is a vector of other household-level characteristics that may affect health, such as income;

t A z Xá , á , á and á are the corresponding vectors of coefficients.

Because cooking time may be correlated with unmeasured household and individual-specific health

variables, we also use a household fixed-effects procedure, which differences across women in the same

household

ij t ij A ij hij hij(10) Ä h = á Ä t + á Ä A + Ä ì + Ä e ,j j j j j

where Ä is the across-person difference operator.j

tBut estimates of á from (10) will not be consistent if:

(1) the within-household distribution of women’s chores is related to the differences in individual

health endowments (upward bias according to the model), or

ij(2) there is measurement error in the time exposed to smoke (t ) variable (bias to zero).

The net effect of the two sources of bias is of opposite direction and unknown a priori

Two strategies:

1. Instrumental variables applied to (10)

2. Within mother estimates: sweeps out individual endowment, identifies exposure effects by child age

Instrumental Variables

For instruments, variables are required that affect the allocation of cooking time across women in the

same household but do not, given a women’s time allocation, otherwise affect her health.

Households in rural Bangladesh contain sexually segregated spheres of influence in which gender-

specific hierarchies, based in large part on relationship to the head of household, operate to allocate

women to tasks based both on the gains from specialization and on rank in the household hierarchy.

Differences across women in their relationships to the household head are unlikely to directly affect

differences in respiratory health or be correlated with individual health endowments or productivity net

of age.

The instruments are dummy variables indicating whether the person is a wife of the head or a daughter-

in-law, the interaction of wife and daughter-in-law with the number of daughters-in-law, and the

interaction of daughter-in-law with the presence of any wife of the head in the household.

Diagnostics

Figure Y: Life-Cycle Status Changes: Proportion of Married Womenby Status Category and Age

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

20-29 30-39 40-49 50-59

Wife of HeadDaughter-in-law of HeadMother of HeadHead

Table 2: The Effects of Cooking Time on the Incidence of Respiratory

Symptoms, All Adults and Women Only,

by estimation Procedure

All Adults Aged 16+ Women Aged 16+

Estimation

procedure

RE FE-

HHold

FE-IV RE FE-

HHold

FE-IV

Cooking time

(x10 )-3 a

.152

(2.77)

.0871

(1.56)

.349

(2.72)

.159

(2.68)

.170

(2.51)

.455

(2.58)

Age .00107

(2.84)

.00113

(2.77)

.00146

(3.37)

.00069

(1.12)

.00178

(2.41)

.00284

(2.96)

Education -.0036

(2.09)

-.00106

(0.50)

-.00011

(0.05)

-.0028

(0.95)

.00465

(1.14)

.00892

(1.87)

Female .00093

(0.06)

.0157

(0.92)

-.046

(1.43)

- - -

Total expend

(x10 )-5

-.380

(1.88)

- - -.511

(1.78)

- -

Permeable

roof

-.0115

(0.66)

- - -.0384

(1.59)

- -

Permeable

walls

.00225

(0.15)

- - .0125

(0.59)

- -

Kitchen

outdoors

.015

(0.94)

- - .00948

(0.42)

- -

Number of

individuals

4590 4590 4590 2202 2202 2202

Number of

households

1371 1371 1371 1368 1368 1368

Endogenous variable. Absolute value of asymptotic t-statistic in parentheses.a

Diagnostics for Identification Strategy

1. Overidentification tests:

F=1.07 (p = 0.37) for adults and 0.99 (p = 0.42) for women Pass

but a) the tests may have no power

b) cooking time may be highly correlated with other health inputs: i.e., calorie

consumption

2. Estimate effect of cooking time on health symptoms not implicated as being affected by particulates.

i.e., intestinal symptoms if so, spurious relationship

Use same specification and identification strategy: should find no effect of cooking time on

intestinal symptoms if not spurious relationship

Also apply overidentification test

3. Include calorie consumption in both health equations: control for (endogenous and mis-measured)

calorie allocation

Apply overidentification tests, test cooking time effects net of calories

4. Assess anthropological literature observation: does household status have special relationship with

allocation of cooking?

Table 3

FE-IV Estimates: The Effects of Cooking Time on the Incidence of Intestinal Symptoms, All Adults and

Adult Women Only

Variable/Sample All Adults Women

Cooking time (x10 ) .0391-3 a

(0.49)

.0852

(0.76)

Age .000755

(2.80)

.000808

(1.32)

Education .00108

(0.80)

.000748

(0.25)

Female -.00226

(0.11)

-

Number of individuals 4590 2202

Number of households 1371 1368

Endogenous variable.a

But overidentification test fails: F = 1.81 (p = 0.10)

Test evidently has power - what is wrong?

Omitted input: calorie consumption

Table 4First-Stage FE-Household Estimates: The Determinants of Cooking

Time and Calories Consumed: All Adults

Cooking Time Calories

Variable (1) (2) (3) (4)

Age -1.33(10.2)

-.792(6.73)

-.178(0.20)

.129(0.14)

Education -3.43(4.72)

-2.13(3.35)

-10.7(2.22)

-9.18(1.90)

Female 256.3(72.2)

160.1(33.8)

-728.8(30.9)

-843.6(23.5)

Wife of head - 144.8(28.0)

- 129.0(3.28)

Daughter-in-law ofhead

- 197.6(5.47)

- -163.3(0.80)

Wife x number ofdaughters-in-law in thehousehold

- -67.3(10.2)

- 104.0(2.08)

Daughter-in-law xNumber of daughters-in-law

- -19.0(1.79)

- 10.3(0.13)

Daughter-in-law x Isthere wife of head?

- -35.4(1.58)

- 460.3(2.70)

R .640 .720 .168 .1682

F-statistic (5, 2647) - 171.5 - 5.67

Table 5

FE-IV Estimates: The Effects of Cooking Time and Calories on the

Incidence of Respiratory Symptoms, Body Weight and the Incidence of Intestinal Symptoms: All Adults

Dependent Variable Respiratory

Symptoms

Body Weight (kg) Intestinal Symptoms

Cooking time (x10 ) .392-3 a

(2.35)

11.5

(3.09)

-.00296

(0.02)

Calories consumed (x10 ) .00467-3 a

(0.04)

7.78

(2.92)

.170

(2.01)

Age .00156

(3.10)

.0217

(1.89)

.000787

(2.10)

Education .00205

(0.75)

.245

(3.98)

.00398

(1.97)

Female -.0671

(0.57)

-5.22

(2.07)

.128

(1.50)

Number of individuals 3878 3878 3878

Number of households 1371 1371 1371

Endogenous variable.a

Table 6

FE-IV Estimates: Do House Materials or Kitchen Location Ameliorate

the Effects of Cooking Time on the Incidence of Respiratory Symptoms?

All Adults and Women Only

Variable

All Adults Aged

16+

Women Aged 16+

Cooking time (x10 ) .418-3

(2.89)

.343

(1.59)

Cooking time x permeable roof

(x10 )-3

-.0857

(0.78)

.638

(1.47)

Cooking time x permeable walls

(x10 )-3

.0517

(0.52)

.0634

(0.16)

Cooking time x kitchen

outdoors (x10 )-3

-.0212

(0.20)

-.0183

(0.03)

Age .00168

(3.66)

.00336

(2.94)

Education .000749

(0.32)

.0122

(2.08)

Female -.0506

(1.46)

-

Test statistics, no house effects

÷ (3),( p-value)2

0.93 (.818) 2.43 (.488)

Number of individuals 4590 2202

Number of households 1371 1368

Table 7

The Effects of Mother’s Cooking Time on the Incidence of Respiratory

and Intestinal Symptoms of Her Children 2-9,

by Estimation Procedure

Respiratory Symptoms Intestinal Symptoms

Estimation

procedure

FE-HHold FE-

HHold

FE-

Mother

FE-

HHold

FE-

HHold

FE-

Mother

Mother’s

cooking time

(x10 )-3

.608

(1.93)

.524

(1.65)

- -.0909

(0.38)

-.102

(0.42)

-

Cooking time

x Child<5

(x10 )-3

- .311

(1.55)

.460

(2.19)

- .0415

(0.27)

.00858

(0.05)

Child age .0241

(0.52)

.119

(1.55)

.173

(2.18)

-.0114

(0.32)

.0012

(0.02)

.-.0121

(0.20)

Child age squ.

(x10 )-1

-.0404

(1.06)

-.105

(1.86)

-.143

(2.47)

.00461

(0.16)

.00395

(0.09)

.00633

(0.14)

Child female -.0595

(1.93)

-.0594

(1.93)

-.0541

(1.72)

-.0142

(0.60)

-.0141

(0.60)

-.0303

(1.28)

Mother’s

education

.0149

(0.59)

.0152

(0.61)

- .0319

(1.67)

.0320

(1.67)

-

Number of

children

1365 1365 1365 1365 1365 1365

Number of

mothers

889 889 889 889 889 889

India REDS 1999

Sample of 7,474 rural households in 17 states of India

7,015 households with at least one ever-married woman aged 15-60 (9,208 with children)

Advantages:

1. Variation across households in fuel use and stove type (5 categories)

2. Information on venting - house materials, windows, chimneys

3. Information on health symptoms for all children residing in each household

4. Detailed daily (by ½ hour) time use data for all women aged 15-60

5. Information on principal activities of all household members

Disadvantages:

1. No information on health symptoms for adults

2. Categories of time use lump together cooking, cleaning and child care

Thus, analysis of effects of maternal smoke exposure on her youngest children’s health as

mediated by fuel, stove type, ventilation

Figure 1Rural India, 1999: Household Distribution of Stoves

0

5

10

15

20

25

30

35

40

45

50

TraditionalChulah

OtherTraditional

Stove

Improved(Smokeless)

Chulah

Gas/KeroseneStove

Biogas Stove

Figure 2Rural India, 1999:

Mean Per-Household Expenditures (Including Imputed Self-Collected) on Fuel (Rupees)

0

100

200

300

400

500

600

700

800

900

Firewood Dung Charcoal Soft Coke Kerosene Gas

Figure 3Rural India, 1999:

Percentage of Children with Respiratory Illness in the Past Year, by Age and Stove Type

0

5

10

15

20

25

30

Age 2-4 Age 5-9

Traditional StoveSmokeless Stove

0

10

20

30

40

50

60

70

80

Chimney Kitchen Window

Traditional StoveSmokeless Stove

Figure 4Rural India, 1999:

Percentage of Households with Chimneys and Kitchen Windows, by Stove Type

Table 1

Conditional Logit Estimates: Effects of Maternal Household Work Specialization,

Cooking Fuel and Venting on the Incidence of Respiratory Symptoms for her Children 2-9

Variable/estimation

procedure

FE-

Household

FE-

Household

FE-Mother FE-Mother FE-Mother

Mother at home .444

(1.18)

-.261

(0.34)

- - -

Mother at home x traditional

biomass stove

- .947

(1.07)

- - -

Mother at home x child aged

2-4

-.0446

(0.24)

-.289

(1.36)

-.279

(1.24)

-.279

(1.24)

-.279

(1.24)

Mother at home x child aged

2-4 x biomass stove

- .435

(2.13)

.423

(1.95)

.549

(2.36)

.509

(1.79)

Mother at home x child aged

2-4 x biomass stove x

chimney

- - - -.485

(1.38)

-.437

(1.15)

Mother at home x child aged

2-4 x biomass stove x

kitchen window

- - - - -.0411

(0.12)

Mother at home x child aged

2-4 x biomass stove x thatch

roof

- - - - .163

(0.48)

Child aged 2-4 -.0906

(0.46)

-.0796

(0.40)

-.0859

(0.42)

-.0886

(0.43)

-.0868

(0.42)

Age of child -.00727

(2.57)

-.00727

(2.56)

-.00805

(2.72)

-.00812

(2.74)

-.00808

(2.72)

Age of mother .357

(2.40)

.369

(2.53)

- - -

Age of mother squared -.00530

(2.33)

-.00548

(2.44)

- - -

Number of mothers 4468 4468 2431 2431 2431

Number of observations 2503 2503 2070 2070 2070

Absolute value of robust t-statistics in parentheses.

Table 2

Conditional Logit Estimates: Effects of Maternal Household Work Specialization,

Cooking Fuel and Venting on the Incidence of Intestinal Symptoms for her Children 2-9

Variable/estimation

procedure

FE-

Household

FE-

Household

FE-Mother FE-Mother FE-Mother

Mother at home -.593

(0.69)

12.6

(11.2)

- - -

Mother at home x traditional

biomass stove

- -13.8

(9.86)

- - -

Mother at home x child aged

2-4

-.462

(1.11)

-.567

(1.16)

-.263

(0.50)

-.263

(0.50)

-.262

(0.50)

Mother at home x child aged

2-4 x biomass stove

- .154

(0.36)

-.00616

(0.01)

-.0270

(0.06)

-.152

(0.24)

Mother at home x child aged

2-4 x biomass stove x

chimney

- - - .221

(0.23)

-.102

(0.10)

Mother at home x child aged

2-4 x biomass stove x

kitchen window

.446

(0.61)

Mother at home x child aged

2-4 x biomass stove x thatch

roof

-.0216

(0.03)

Child aged 2-4 .346

(0.75)

.360

(0.78)

.398

(0.84)

.400

(0.85)

.389

(0.82)

Age of child -.0136

(2.05)

-.0136

(2.05)

-.00809

(1.18)

-.00804

(1.17)

-.00831

(1.20)

Age of mother .237

(1.18)

.247

(1.21)

- - -

Age of mother squared -.00326

(1.08)

-.00333

(1.07)

- - -

Number of mothers 4468 4468 2431 2431 2431

Number of observations 596 596 454 454 454

Absolute value of robust t-statistics in parentheses.

Are there longer-term health effects of childhood smoke exposure?

1. Can construct for each mother a history of her status in the household based on the retrospective

(a) marriage histories of all immediate family members of the head and (b) household division

histories in the 1999 India data.

2. Given 1, know the status of any child’s mother at any age of the child.

3. With a predicting equation based on the mother’s status, can predict at each age of the child

whether the mother is the principal homemaker in the household.

4. Issues:

1. Is the relationship between maternal status and work assignments stable over time?

A. Use 1999 and 1982 rounds to estimate the predicting equation, test if

stable structure.

2. Predicting equation comes from the 1999 and 1982 rounds of data using all women,

while the child health equation are estimated using the 1999 round for mothers with

children aged 6-10, so need to correct standard errors of the coefficients to take this into

account.

B. Use Multiple Imputation method, with 1000 bootstrap iterations.

Table V

Within-Household Estimates of the Determinants of Specialization in

Household Care (India), by Survey Year

Variable 1999 1982 Combinedb

Relationship of woman

to heada

Mother .193

(5.10)

.146

(1.61)

.154

(4.11)

Sister-in-law .117

(3.83)

.0610

(1.60)

.105

(3.74)

Daughter-in-law .0320

(1.17)

.0235

(0.66)

.0669

(3.31)

Oldest son daughter-

in-law

-.0250

(1.53)

-.0208

(1.06)

-.0307

(2.01)

Age -.0197

(4.90)

-.0284

(4.46)

-.0139

(4.90)

Age squared .000256

(4.84)

.000394

(3.97)

.000182

(4.84)

Year of survey - - .0163

(26.8)

Number of households 6,733 3,948 10,681

Number of women 9,208 5,539 14,747

Left out categories: Wife of head and heada

Test statistic for equality of coefficients across survey years,b

F(5,6496)=1.26

0.65

0.66

0.67

0.68

0.69

0.7

0.71

0.72

0.73

0.74

18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 600

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

Est. Probability of Mother Workingas HomemakerProportion of Children Born

Figure X: (Lowes-Smoothed) Life-Cycle Probability of Mother Working as a HomemakerAnd Proportion of Children Born by Mother’s Age

Table X

Within-Household Estimates of Early Childhood Exposure Effects

on the Health Symptoms of Children Aged 5-9 in Households Using

Biofuels

Symptom Respiratory Intestinal

Estimation method

FE-

HH

FE-

HH

FE-

HH

FE-

HH

FE-HH

Maternal exposure - first

two years post-conception

- 1.54

(3.18)

1.63

(2.01)

1.81

(1.85)

.299

(0.60)

Maternal exposure -

prenatal

1.42

(1.31)

- - - -

Maternal exposure - first

year post-birth of child

1.70

(1.42)

- - - -

Maternal exposure - 2nd

year post-birth of child

-1.06

(0.91)

-.992

(0.81)

-1.09

(0.71)

-1.13

(0.73)

-.614

(0.95)

Maternal exposure - child

ages 3-5

-.296

(0.65)

-.300

(0.66)

-1.04

(0.62)

-0.939

(0.56)

.445

(0.78)

Maternal exposure - pre-

conception of child

- - - -.602

(0.49)

-

Maternal exposure now .0193

(0.20)

.0197

(0.82)

.0212

(0.22)

.0219

(0.22)

-.0785

(1.64)

Maternal age now .0937

(1.88)

.0938

(1.88)

.0924

(1.86)

.0888

(1.86)

-.0102

(1.11)

Maternal age squared now

(x10 )-2

-.122

(1.83)

-.122

(1.83)

-.120

(1.81)

-.117

(1.82)

.0122

(1.18)

Child’s age now (months

x 10 )-2

.198

(0.82)

.197

(0.82)

.193

(0.79)

.184

(0.77)

.0729

(1.42)

Child’s birth order .0650

(1.11)

.0647

(1.11)

.0647

(1.11)

.0652

(1.11)

.0419

(2.97)

Child is girl (x10 ) .917-2

(0.33)

.916

(0.33)

.930

(0.34)

.847

(0.31)

1.66

(1.45)

Number of dynasties 1072 1072 1072 1072 1072

Number of households 1550 1550 1550 1550 1550

Number of mothers 1675 1675 1675 1675 1675

Number of children 2209 2209 2209 2209 2209

Table XII

Within-Mother Estimates of Early Childhood Exposure Effects

on the Health Symptoms of Children Aged 5-9 in Households Using

Biofuels

Symptom Respiratory Intestinal

Estimation method

FE-

Mom

FE-

Mom

FE-

Mom

FE-

Mom

FE-

Mom

Maternal exposure - first

two years post-conception

- 1.35

(2.15)

1.41

(1.67)

1.60

(1.72)

.303

(0.46)

Maternal exposure -

prenatal

.741

(0.61)

- - - -

Maternal exposure - first

year post-birth of child

2.10

(1.54)

- - - -

Maternal exposure - 2nd

year post-birth of child

-1.70

(1.33)

-1.36

(1.03)

-1.60

(0.85)

-1.73

(0.92)

-.734

(1.10)

Maternal exposure - child

ages 3-5

-.443

(0.72)

-.470

(0.77)

-1.62

(0.71)

-1.57

(0.68)

.641

(0.93)

Maternal exposure - pre-

conception of child

- - - -.972

(0.71)

-

Child’s age (months) .0009

30

(0.22)

.00079

9

(0.19)

.00049

8

(0.11)

.0001

05

(0.024

)

.00116

(0.92)

Child’s birth order .0690

(0.82)

.0642

(0.76)

.0652

(1.11)

.0651

(0.76)

.0500

(2.12)

Child is girl (x10 ) .121-2

(0.04)

.036

(0.01)

.847

(0.31)

.152

(0.05)

2.11

(1.68)

Number of dynasties 1072 1072 1072 1072 1072

Number of households 1550 1550 1550 1550 1550

Number of mothers 1675 1675 1675 1675 1675

Number of children 2209 2209 2209 2209 2209

But, do the less healthy household members cook?

Who is less healthy?

Use individual health “endowment” measured from 1982 survey round estimated in Pitt et al. (AER,1990)

lower endowment = lower weight-for-height net of (endogenous) calorie intake and energyexpenditure

Predicted contemporaneous individual calorie consumption, activity choice in 1982

But also, strong predictor of mortality by 2002!

Does the health endowment affect cooking time in 2002, among those still alive?

Table A

Logit and Conditional Logit Estimates:

The Effects of the 1982 Health Endowment on the Probability of Death by

2002,

Panel Sample with Endowments

Estimator Logit

Conditional

Logit:

Village FE

Conditional

Logit:

HH FE

Age in 1982 .0653

(12.8)

.0655

(13.6)

.0589

(10.1)

1982 health endowment

(estimated in 1988 study)

-1.33

(2.95)

-1.45

(3.07))

-1.44

(2.45)

Female -.0459

(0.27)

-.0408

(0.23)

-.149

(0.73)

Household head literate -

1982

-.355

(2.01)

-.324

(1.66)

-

Land owned - 1982 .000108

(0.35)

.0001

(0.02)

-

Household income - 1982

(x10 )-3

-.108

(0.76)

-.130

(0.83)

-

Number of individuals 1539 1539 727

Table 9

Who Cooks? Within-Household Determinants of Cooking Time, All Adults

and Women Only: Panel Sample with Endowments

All Adults Aged

16+

Women Aged 16+

Age -.596

(2.01)

-2.82

(2.14)

Education -.615

(0.40)

-6.04

(0.96)

1982 health endowment

(estimated in 1988 study)

-60.9

(2.77)

-140.1

(1.85)

Female 167.7

(13.9)

-

Mother with children<5 -11.78

(0.51)

65.6

(0.98)

Mother with children<10 42.93

(3.11)

12.31

(0.29)

Wife of head 77.2

(5.11)

86.9

(2.17)

Daughter-in-law of head -3.44

(0.01)

-155.6

(0.31)

Wife x number of daughters-in-

law in hh

-26.7

(2.18)

-5.96

(0.13)

Daughter-in-law x number of

daughters-in-law

81.73

(0.47)

160.48

(0.51)

Daughter-in-law x Is there a wife

of head

-247.14

(0.92)

-152.9

(0.29)

Number of individuals 922 371

Number of households 449 291

Conclusions

(1) Proximity to stoves causally and adversely affects the respiratory health of women and the young

children they closely supervise.

(2) Households appear to be aware of and attempt to mitigate the health effects of cooking with

biomass fuels in their time allocation decisions, including effects on young children:

A. Women with lower endowed health have greater exposure to smoke and B. Women with

very young children have lower exposure to pollutants.

(3) Conventional estimates of the impact of smoke inhalation are underestimated substantially due to

measurement error,

(4) But, given (2)A., using more accurate measures of exposure may lead to overestimates of

exposure health effects

(5) Improving ventilation by increasing the permeability of roofs or walls does not ameliorate the

effects of smoke exposure on respiratory health. However, changing fuels and stove venting do.

Education

Key question: Why is schooling low in low-income countries?

a. Barriers to investment (supply):

No schools

Bad schools

Low income

Credit constraints

Ignorance

b. Low payoffs (thus low demand for the schooled and thus for schooling):

Due to bad institutions other than schools

What are the returns to schooling?

Are the returns always high? What do they depend on?

Do people respond to changing returns despite barriers?

How do you measure the returns to schooling?

Does schooling matter for growth?

Some aggregate facts

Economic Growth Schooling

The growth rates of the OECD countries have

been relatively stable for over 100 years.

Schooling has expanded substantially in the

OECD.

There has been divergence in the average per

worker output between the leading countries and

the lagging countries in absolute and relative

terms, 1960-95:

sd(log GDP): .93 6 1.13

There has been convergence in the levels of

education across countries, 1960-95:

sd (log Ed): .94 6 .56

There has been a substantial and pervasive

deceleration of growth, especially in the

developing countries, since the late 1970s.

Schooling is nearly universally much higher,

and growing as fast, as before the growth

deceleration.

Source: Lant Pritchett, “Does learning to add up add up? The returns to schooling in aggregate

data,” Harvard University, 2003

Does more spending on schools pay off?

Some aggregate facts

Change in NAEP Test Scores and Real School Expenditures, 1970-94

Country % Change in Math and Science

Score

% Change in Real

Expenditures per Pupil

Japan -1.9 103.1

Australia -2.3 269.8

United States 0 33.1

United Kingdom -8.2 76.7

Source: L. Woessman (2002), Schooling and the Quality of Human Capital, Berlin: Spinger,

Tables 3.3, 3.4.

Micro Studies of Schooling Returns

Many studies use earnings of salary/wage workers: compare earnings by schooling

Three problems:

1. Most workers in low-income countries are not wage workers

Rural India, 1999: 45% of primary activities of men aged 25-55 not in wage or

salary work (61% in 1982)

2. Wages may not reflect productivity: e.g., artificially inflated government salaries:

Egypt, 1998: government employs 70% of university graduates, 63% of those

with intermediate schooling +

Cote d’Ivoire, 1988: government employs 50% of anyone with schooling above

primary schooling

3. Private earnings may understate returns: externalities

Does More Schooling Raise Farm Productivity?

Why not repeat the same exercise as Strauss adding the schooling of

workers to the set of inputs to answer this question?

aQ = F(L *, L *, V, E, A)f h

What question does the estimate answer? The mp of schooling,

Eholding constant all inputs (“worker” effect): F

Is this the full contribution of schooling to output? No, ignores

education effects on choice of inputs, allocation of inputs

Use again the household model, with all markets in operation

Simplified profit (value-added) function - one variable (V) input:

vMax B = pF(V, E, A) - p v

v vFONC: dB/dv = (pF - p ) = 0

Suppose choice is not perfect, and E contributes to better choice:

full marginal contribution of schooling =

v v vdB/dE = (pF - p )dV/dE + pF > 0

The first term is the “allocative” effect of schooling

Thus, obtain total contribution of schooling from estimating the profit

function B=B(E, A) to get expression above

Under what circumstances is the allocative effect going to be most

important?

Examples:

1. No learning; no return: Philippine harvest workers (worker effect)

2. Is it novelty?

1. Lessons from the contraceptive revolution

a. new = less challenging

2. Lessons from the green revolution

a. new= more challenging

b. challenging technology: whether to adopt, how to use (LBD in agriculture)

Schooling and the Green Revolution

Two questions:

1. Did the flow of challenging new technologies increase the

return to schooling?

a. Which level of schooling?

b. For whom?

2. Did investment in schooling respond to the change in the

return to schooling?

a. By whom?

b. Did school availability play a role?

Answers exploit the spatial variation in the suitability of new

seeds:

a. HYV seeds more sensitive to water, fertilizer than

traditional seed varieties.

c. HYV seeds only suitable to particular regions, given

a.

Thus there is variation in growth potential across areas of India due

to soil, weather that differentially affect the returns to learning

skills

LBD and Learning Externalities in Agriculture: Adopting and Using New HYV Seeds

1. The Indian “Green Revolution”

1. Development of High-Yielding Varieties (HYV) of (hybrid) wheat, rice, corn outside of India in

mid-1960's and imported to India.

Policy example of market openness and market interference: substantial public

investment - in local crop improvements

2. Characteristics of “revolution”

A. Continuous development of new seed varieties for original crops and new crops (e.g.,

sorghum, cotton). Continuing new challenges for farmers every year - whether and what to

adopt, how to use.

B. HYV seeds more sensitive to water, fertilizer than traditional seed varieties.

C. HYV seeds only suitable to particular regions, given B.

D. Because of the above, enormous growth in crop yields on average, but uneven across

regions and across farmers.

A second green revolution - for Africa? - GMO’s, but...

Figure 4HYV-Crop Productivity Growth by State:1961-81

1. Did users of new seeds with more schooling benefit more?

Use the HYV-conditional (on HYV seed use) profit function A :h

t t t t, t t t t(1) B =A (H , S , A w , p , 2 , :, , ,) = h

t t t t t t t t t t t t t t t t t t t t t t max [H [q(2 ,S ,L ,F ; A , :, , ) - w L - p F ] + (8-H )[q'(2' ,S ,L' ,F' ; A , :, ,' ) - w L' -p F' ]

t t t t L , L' , F , F'

where

tH = HYV seed use

t tw and p = the prices of labor and fertilizer, respectively

8 = the total amount of land cultivated

primes (‘) denote old (traditional) technology values

What is the difference between the contributions of schooling to output under the traditional

and new technologies?

t t t t t t t(2) M A /MH MS = Mq /MS - Mq' /MS 2 h

2. But, the estimation of the conditional profit function may underestimate total contribution

A method for estimating the total contribution of schooling to profitability under

technological change is to estimate the unconditional or meta-profit function A :m

t t t t t t t t t t t t t t(3) B = A ( S , A , w , p , 2 , :, , ) = max A (H , S , A , w , p , 2 , :, , ).m h

t H

t t1. The total effect of schooling on profits is MA /MS - the effects of schooling on both them

profitability of HYVs and the level of adoption of HYVs.

t t t2. Identifies the effects of technology on the returns to schooling, MA /MS M2 .2 m

tBut, how do you estimate the level of technology 2 ?

Exploit characteristics of green revolution:

1. Area-specific variation in productivity growth after the green revolution:

After the onset of technological change, technology grows in each district at

different rates, depending on the area-specific endowments.

2. No area-specific variation in technology before the green revolution

Approximation to the profit function:

1. For any farmer i in area j in the pre-growth period 0:

ij0 k kij0 s ij0 L j0 F j0 ij , ij0(4) B ( ) = E$ A + $ S + $ w + $ p + : + $ , ,

where

L t tA = vector of farm assets, $ = -MA /Mw = labor demand (duality!)m

2. After the green revolution begins the structure of the profit function changes and becomes

differentiated across areas: the meta-profit function (5) for district j at time t becomes:

ijt jt k k kjjt kijt s s jt ijt L L jt jt F F jt jt ij (5) B ( ) = 2 + E($ + " 2 )A + ($ + " 2 )S + ($ + " 2 )w + ($ + " 2 )p + :

, , jt ijt + ($ + " 2 ), ,

where

jt j02 = the area-specific level of the technology at time t (2 = 0)

k jt" = the differential contribution of a fixed or variable factor k to profits by 2

t ijt S e.g., if the return to schooling (MA /MS ) increases with technology, (" > 0) m

Problem: variation across areas in returns to assets and schooling could be due to other factors

- fixed attributes of the soil, weather that have independent effects on profits

Solution: look at changes in profits for the same farmer:

Subtracting (4) from (5) yields:

ijt jt k kijt k jt kijt s ijt s jt ijt L jt L jt jt F jt(6) )B = J + E$ )A + E" J A + E$ )S + E" J S + E$ )w + E" J w + E$ )p

F jt t , ijt , jt ijt + E" J p + E$ ), + E" J , ,

where

ijt ijt ij0 jt jt jt 0)B = B - B , J = )2 = 2 (because 2 =0) = area-specific technology change

identifies:

s1. The pre-green revolution return to schooling: $

S2. The change in the return to schooling after the onset of the green revolution: "

jt3. The area-specific J : i.e., where technological change was more and less rapid

jt jt(identification from assumptions: 2 varies across areas, effect of 2 only differs by input or asset)

NCAER ARIS-REDS Sample Villages

Table 1

Determinants of HYV Adoption by 1971:

Farm Households in HYV-Using Districts

Variable Means

(S.D.)

Probability Ever Adopted

(Probit)

Household Schooling:

Primary Highest .493 .524

(.500) (8.55)

Secondary Highest .213 .140

(.410) (1.89)

Household Owned land

(acres)

10.5 .0159

(12.5) (6.40)

Village Agricultural

Extension

.560 .162

(.496) (3.04)

Village Primary Highest .955 .012

(.207) (0.09)

IADP .222 .340

(.416) (5.29)

Constant -- -.726

(5.57)

N 2532 2532

Absolute values of t-ratios in parentheses.a

Table 3

Estimates of HYV Use on Farm Profits , by Farmer Schooling Level

1969-71

Variable\Est. meth. FE-IV FE-IVa a

HYV acreage 722 -10100

(0.65) (3.53)c

HYV×schooling -- 7650

(3.07)

HYV×proportion land

irrigated

-- 6130

(2.54)

Farm equipment 4.21 2.37

(2.51) (1.16)

Irrigation assets .768 .273

(1.73) (0.54)

Other farming assets 5.40 8.21

(2.69) (3.30)

Adverse weather in

village

-369 -477

(3.39) (3.61)

N 1517 1517

Farmers in areas with some HYV use (74 districts) that cultivate in crop years 1970 and 71. All variables excepta

weather are instrumented.

Absolute values of asymptotic t-ratios in parentheses.c

Table 4

Non-Linear FE-IV Estimates: Conditional Profit Function

with District-Specific Growth Intercepts, 1971 - 1982

Variable Coefficient NL-FE-IV

Primary schooling: $ 368

(2.35)b

" .556

(2.55)

Irrigation Assets $ .139

(4.20)

" .000133

(3.11)

Irrigated area (acres): $ 169

(9.06)

" -.102

(2.62)

Unirrigated area (acres): $ 67.3

(5.80)

" -.180

(3.16)

Value of farm machinery: $ .101

(3.16)

" -.0000525

(1.63)

Value of animal stock: $ .434

(6.59)

" -.000164

(3.59)

Male wage rate, Rs. per day: $ 33.97

(0.37)

" -.116

(6.34)

N 1788

Does the return to schooling matter in determining schooling demand?

jt1. Did school enrollment increase in locations where J was higher?

Look at change in school enrollment as a function of level and change in

technology

2. How do we know school enrollments would not have increased anyway?

jta. Implication for whom J was most relevant: farmers and farm households

Decision-makers have a reason to increase schooling; not landless

b. If just general area-specific increase in the demand for schooling would see all

households increase school enrollment

3. If boys become farm managers, should we expect to see no increase in girls schooling?

What is the return to girl’s schooling and what determines it?

How do we measure value of girl’s schooling if women are not employed for

wages?

Table 5

Agricultural Productivity Growth and School Enrollment Rates:

Children Aged 5-14a

Sample Means (S.D) IV-Fixed Effects Coeffs.

Variable

1971

Level

71-82

Change (1) (2)

Yield (Rs.) per

hectare (x10 )3

3.090

(.918)

.927

(.671)

.158

(4.28)b

.141

(3.49)

Yield growth

(x10 )3

.394

(.451)

-.0491

(.568)

.225

(1.97)

.348

(2.61)

Yield growth x

nonfarm

household (x10 )3

-- -- -- -.704

(2.26)

Yield level x

nonfarm

household (x10 )3

-- -- -- .0434

(0.85)

School built in

village

.944

(.231)

.085

(.280)

.572

(2.89)

.622

(2.92)

Male wage rate

(Rs. per day)

2.58

(1.13)

.452

(1.16)

-.102

(3.02)

-.105

(2.96)

Wealth (x10 ) .0137716

(.02310)

.001454

(.00224)

1.51

(1.32)

1.34

(1.12)

Sample size=847 households. Data sources: ARIS, REDS, Vannemana

and Barnes.

Absolute values of asymptotic Huber t-ratios in parentheses. Allb

variables are instrumented.

Change in HYV-Crop Productivity and School Enrollment in Sample Districts: 1971-82

Globalization of the Bombay Economy and Schooling Choice

For more than 100 years, Bombay was an industrial city, blue-collar jobs dominated by (lower)

sub-caste networks providing job referrals

Transformation starting in mid-1980's, accelerating in 1990's to prominence of trade, corporate,

and financial sectors

One important consequence of openness: English is principal medium of exchange in

globalized world today; English skill valuable in

the new economy

Key schooling choice in Bombay: whether to take instruction in English or local language

Consequences of choice if choose English medium schooling:

1. Fluency in English and thus potential employability in sectors where English is useful

2. Ability to continue education in tertiary schools, in which the instruction medium is

English

What happened to the returns to English between the 1980's and 1990's?

Based on retrospective earnings histories: small rise in returns to years of schooling

large rise in returns to English

The short-run effects of the structural shift is likely to have increased inequality

A. Rate of English-medium schooling among the upper-caste adults in 2000 is 15 times

higher than in the lower castes

B. Lower demand for blue-collar jobs dominated by the lower castes

Longer-run effects: creation of incentives for schooling in English

Did the changes in returns to skill alter the schooling choices among the urban, lower-caste

poor?

Theoretical barriers:

1. Conventional wisdom: income, credit are barrier to skill upgrading, and English-

medium schools have higher fees and income did not rise much for lower castes

2. Parents of lower caste poor children much less likely to have been schooled in

English, so children are at a disadvantage in English medium schools (how to help

with homework)

If these barriers are relatively unimportant, should observe:

1. Shift to English-medium schools

2. Convergence in English-medium enrollment rates across castes

Specific Research Questions:

1. Why are enrollment rates in English-medium schools rising?

A. Increase in demand for English? Why?

B. Increase in demand for better schools, which happen to be

English-medium? Are English-medium schools higher

quality?

2. Why are enrollment rates of boys in lower-castes not catching up?

A. Lower incomes? tuition in English-medium schools higher

(949 vs. 2,176 rupees in 2001)

B. Lower pre-school human capital (endowments)?

C. Discrimination?

D. Caste-based - institutional - explanation

1. Caste is an inherited trait

2. Castes are closed groups: little or no inter-

marriage across jatis

3. Castes tend to specialize in occupations

4. Castes provide services of network: credit,

insurance, job referrals

Outline

1. Describe caste model: role of employment networks and network externalities

2. Test implications of the model

A. School choice: Does caste play a role in choice of English-medium schools?

B. School selectivity: What are the effects of caste and rise in return to English on the

quality of students in English- and Marathi-medium schools

3. Examine alternative explanations for caste effects on school choice

A. Income, parental schooling

B. Occupation of parents determines children’s occupation and thus school choice

C. Unmeasured caste quality and preferences

4. Test whether quality differences between schools: school characteristics and test scores

Caste-Based Competitive Employment Model (Free Mobility)

Set-up

1. Two types of jobs:

A. Blue-collar (“high-referral” sector with network externality):

a. inability to discern productivity so that employers pay expected productivity

b. W ijB = Pj

B, where PjB = proportion of persons in jati j in blue-collar jobs

B. Professional:

a. W ijP = Tij2, where Tij = ability of individual i in jati j; 2 = returns to ability

b. English necessary, so 2 = return to English

2. Three ability-types of workers, equally distributed across jatis:

PL, PM, PH = proportions low, medium, high ability in each jati

3. Each individual lives two periods; chooses schooling type - Marathi or English - in the first

period based on expected occupation he/she will be in second period to maximize net

expected return (Marathi education less expensive)

Proposition 1 (static economy): 3 equilibria possible for each jati; jatis can differ persistently

in schooling choices, with no change in 2

Example: pre-reform 2<1; TL =0; TM = 1/2; TH = 1

Three equilibria: (i) Everyone in jati chooses Marathi schooling; W jB = 1 > WHj

P = 2

(ii) Only high-ability types choose English schooling

sustainable when 2/2 < PL + PM < 2

(iii) Only low-ability types choose Marathi schooling

sustainable when PL < 2/2

Proposition 2 (dynamic economy): all jatis converge to equilibrium 3 sequentially

Post-reform 2 $ 1

At 2 = 1: all equilibrium -1 jatis move to equilibrium 2, as high-ability types

switch to English schools and professional jobs

For 2 $ 2(PL + PM): all jatis move to equilibrium 3

Dynamic implications for school selectivity:

1. Students in English-medium schools always higher ability than those in Marathi

schools.

2. Average ability declines in Marathi schools as 2 increases.

3. Average ability declines in Marathi schools as 2 increases more among the jatis

concentrated in the blue-collar jobs.

4. Changes in the average ability in English-medium schools is ambiguous:

Example: Initially, (2=1), average ability in English schools rise (because only

high-ability types switch)

At higher levels of 2 (2 $ 2(PL + PM)), average ability in English schools decline.

Optimal and Sub-optimal Caste Restrictions on Mobility

Assume jati maximizes average wage W of it members

Thus, in equilibrium 1, W1 = 1 for all values of 2

Examples:

A. for 2 = 1 and free mobility, jatis in equilibrium 1 move to second equilibrium,

then

W2 = (PL + PM)2 + PH

but, W2 < W1: jati welfare declines, creating incentive for caste-based

restrictions on mobility (preserve integrity of network)

Social restrictions on mobility welfare-enhancing and efficient

B. for 2 $ 1 + PL + PM and free mobility, W2 > W1, jati welfare increases

At some point, social restrictions on mobility reduce jati welfare and

efficiency

Empirical question: do we see caste-based restrictions on mobility in blue-collar jatis,

and thus non convergence, due to network externalities in the labor market?

Data: 2002 Dadar, Mumbai Student Survey

1. Random sample of 4700 student records for students residing in the 29 schools in Dadar

A. Enrolled in grades 1 through 10 in fall 2001 or

B. Enrolled in grade 10 over the period, from 1982-1991.

Thus, covers enrollment decisions over the period 1982-2001

2. In-home interviews of parents of students completed February 2002

A. Information on parents, grandparents, siblings

B. Information on schools attended, scores on secondary-school-leaving exams for

students; earnings histories, schools attended and how found job for parents;

parental and sibling occupation, remittances and transfers; and sub-caste (jati). There

are 59 sub-castes represented.

3. Survey of the school principals in the 29 schools

A. Medium of instruction, class sizes, teacher qualifications, average test scores of

students, facilities.

B. English is the medium of instruction in 10 schools; Marati (local language) in 9.

Table 1Secondary Student Quality and School Quality in Dadar, by School

Language of Instruction

School type EnglishMedium

MarathiMedium Difference

Student exam results (1998-2001)

Percent passed 92.3(6.19)

52.5(24.6)

39.8(t=6.33)

Percent first class amongpassed

36.4(5.38)

24.7(13.8)

11.7(t=3.02)

Percent distinction amongpassed

25.3(12.3)

7.16(7.77)

18.2(t=4.00)

School characteristics

Student-faculty ratio 36.7(7.60)

35.8(8.96)

0.956(t=0.28)

Class size 61.9(3.69)

62.3(3.16)

-378(t=0.08)

Students per desk 2.40(0.316)

2.36(0.479)

0.039(t=0.23)

Proportion of teacherswith B.Ed.

0.725(0.221)

0.701(0.203)

0.024(t=0.28)

Proportion of teacherswith higher degree

0.0786(0.0925)

0.0971(0.147)

-0.0185(t=0.36)

Computers per student 0.0174(0.0138)

0.0176(0.0192)

-0.0002(t=0.03)

Number of schools 10 18 28

Enrollment per school 1528 1029

Percent of Men Receiving Job Referrals and Speaking English, by Occupation

Table 1

Occupational Distribution (%), by Caste: Mumbai Men

Occupation Low Castes Middle Castes High Castes

No work 2.71 2.76 0.97

Unskilled

manual

10.9 7.69 4.38

Skilled manual 16.8 13.4 10.4

Organized blue

collar

22.1 18.5 2.80

Clerical 27.3 35.5 20.7

Professional 8.25 8.5 42.9

Business 7.70 8.86 15.2

Petty trade 3.93 4.24 2.56

Farming 0.33 0.48 0.12

Number 1806 1885 821

Table 2

Occupational Distribution (%), by Caste: Mumbai Women

Occupation Low Castes Middle Castes High Castes

No work 79.7 80.5 49.1

Unskilled

manual

6.06 3.24 1.18

Skilled manual 1.81 1.60 3.17

Organized blue

collar

0.90 1.03 0.35

Clerical 6.38 7.88 23.4

Professional 3.46 4.53 20.3

Business 0.90 0.51 1.88

Petty trade 0.80 0.72 0.59

Farming 0 0 0

Number 1881 1942 851

Table 3

Determinants of the Choice of English-Medium Schooling, by Gender

Sample Boys Girls All

Variable/estimation

procedure OLS

FE-

occup. OLS

FE-

occup. FE-caste

Jati-level job

assistance

-.378

(2.55)

-.334

(2.21)

.116

(0.69)

.169

(1.00)

-

Jati-level job

assistance x boy

- - - - -.404

(5.59)

Age (cohort) -.0090

(4.51)

-.0112

(6.83)

-.0099

(5.17)

-.012

(5.34)

-.00992

(6.64)

English medium

schooling - father

.234

(7.13)

.208

(5.33)

.309

(12.0)

.285

(10.1)

.246

(11.9)

English medium

schooling - mother

.211

(7.38)

.175

(6.01)

.263

(5.98)

.240

(6.60)

.232

(7.52)

Years of schooling

- father

.0222

(5.63)

.0193

(5.33)

.0199

(6.64)

.0158

(4.85)

.0209

(8.85)

Years of schooling

- mother

.0242

(7.21)

.0193

(6.38)

.0262

(8.75)

.0222

(6.84)

.0244

(9.96)

Father’s income

(x10-5)

.566

(1.21)

.271

(0.84)

.818

(2.78)

.601

(3.16)

.557

(1.76)

Boy - - - - .253

(6.13)

N 2240 2240 2046 2046 4286

Table 4

Determinants of the Choice of English-Medium Schooling for Boys, by

Time Period

1992-2001 1982-1991 1982-2001

Jati-level job assistance -.394

(2.19)

-348

(1.63)

-

Jati-level job assistance

- 1995-2000

- - -.438

(2.22)

Jati-level job assistance

- 1990-1994

- - -.439

(2.32)

Jati-level job assistance

- 1980-1989

- - -.315

(1.50)

Age (cohort) - 1995-

2000

- - -.00065

(0.05)

Age (cohort) - 1990-

1994

- - -.0177

(2.56)

Age (cohort) - 1980-

1989

- - -.00317

(1.14)

1995-2000 - - .121

(0.62)

1990-1994 - - .27`

(1.67)

Age (cohort) -.0160

(4.48)

-.00294

(1.04)

-

N 1209 1031 2240

Table 5

Change in School Selectivity by Caste-type, Post-1990 Period:

Student’s Father’s Schooling

Sample Boys in Marathi-Medium

School

Boys in English-Medium

School

Variable/Estim

ation procedure

OLS FE-Caste OLS FE-Caste

Age (cohort) .708

(3.29)

.548

(3.51)

-.331

(2.10)

-.392

(2.49)

Age x caste-

level job

assistance

-1.43

(3.88)

-1.14

(4.53)

.706

(2.27)

.806

(2.54)

Caste-level job

assistance

6.54

(1.58)

- -15.2

(4.17)

-

Constant 7.63

(3.06)

- 20.4

(10.9)

-

If They Build Schools, Will They Come? The Indonesia INPRES Program

Mobilization of oil revenues to finance a school building program in 1973

1. Between 1973-74 and 1978-79, 61,807 new schools built - fastest primary school

building program in the history of the world

2. Recruited and trained new teachers, but number grew at a slower pace (school quality?)

3. Suppressed primary school fees (so not just a school building program)

4. Schools built where non-enrollments rates high - placement was non-random

What was the impact - on schooling attainment and earnings?

Approaches:

A. Compare schooling attainment for those in areas with intense school construction

program and less intense program when they started school (aged 2 through 6 in

1974)

But, school construction placed in low demand areas - could get

negative effect!

B. Compare difference between children exposed differentially to the new schools

with older children from the same areas - difference in difference. Cross-area

differences for older children not due to the INPRES.

Based on Table 1, Duflo (2001)

Table 3 results from Duflo (2001):

Comparing across areas with low and high building intensity:

1. Among children exposed to the program, educational attainment highest in low-

exposure areas (program placement bias)

2. Among children not exposed from same areas (born before the program was in

place) educational attainment highest in low construction areas

3. Difference between the differences is positive

A. School attainment went up by .12 years = .13 years from adding one school

per 1,000 children (not stat. significant)

B. Wages up by 2.6% = 2.9% years from adding one school per 1,000

children (not stat. significant)

C. Implied return to schooling is B/A = 2.9%/.13 = 22%! (but not stat.

significant)

Regression analysis exploits variation in building intensity (not just categories), so more

precise results

Regression estimates:

1. Adding one school per 1,000 children increases schooling attainment by .12 -.19 years

(stat. significant) based on whole sample

2. Adding one school per 1,000 children increases wages by1.5-2.7%

But, only 45% of earners work for wages!

3. Including the self-employed (imputing incomes based on occupations and earnings

from another source), the return on schooling is 3.5% (compared with 6.8-10.6% for

wage earners)

Demand-side Intervention: The Mexican Progresa Program

Alternative direct (non-growth) poverty-reducing programs:

A. Means-tested transfer program: provide income grants to

poor people (condition on income)

1. Creates disincentive to work or upgrade skill

2. Effect on school enrollment of children -

income effect

3. Non targeted with respect to schooling - single

men included

B. Means-tested transfer program to women with children

(condition on income and fertility)

1. Same as 1 above

2. Same as 2 above

3. Encourage larger families, reduce schooling -

subsidy to numbers of children, not “quality”

C. Price supports for agricultural commodities

1. No work disincentives

2. Raises income and wages of child workers

3. Benefits largest farmers most

4. Hurts net consumers

D. Progresa Program: promised transfers over three years

that condition on pre-program income only and children

enrolled and attending school

1. Avoids disincentive work on work - post-program or

current income does not affect income transfer

2. Creates subsidy to child schooling

Progresa design and administration

1. Identify set of poor rural communities also unlikely to

benefit from NAFTA - 495 identified

2. Based on 1997 census, identify poor households in poor

communities - 2/3 of households “poor”

3. Randomly phase-in: randomly select 314 of 495 to receive

the program for first two years, then remainder receive

program in third year (181 “controls”)

4. Grants

A. Eligible households with children enrolled in grades

3-9 with 85% or better attendance record get three-year

grant

B. Amount of grant

1. How does grant compare to school costs?

2. What are school costs?

A. Tuition (=0), books, travel: direct costs

B. Foregone earnings: opportunity costs

39

Table 1

Monthly Payments for Progresa Program Eligible Familiesfor Children who attend at least 85 Percent of Daysa

Educational Levels of StudentsEligible for Payments July - December 1998b

Primary School - both sexes3rd Year4th Year5th Year6th Year

7080105135

Secondary School 1st Year Males

Females2nd Year Males

Females3rd Year Males

Females

200210210235225255

Source: Progresa Staff

a Excluding those days for which medical or parent excuses were obtained,accumulated over the last two months.

b Corresponds to school year first-term, September to December, 1998.

60

Table A-2

All Children in October 1997 Household Censusof All 500 Progresa Evaluation Villages

AgeProportion (Samples Size)

In Paid Labor ForceAverage Monthly Wage

Pesos (20 Days)

Female Male Female Male

8 .003 (1751) .006 (1888) 178 353

9 .004 (1686) .007 (1699) 99 350

10 .008 (1802) .014 (1920) 184 373

11 .007 (1782) .021 (1745) 607 346

12 .022 (1710) .053 (1898) 387 420

13 .040 (1674) .098 (1737) 467 413

14 .066 (1612) .187 (1721) 538 482

15 .115 (1604) .305 (1706) 584 593

16 .151 (1518 .438 (1564) 637 599

Thus, grants equal about 2/3 of child wage and 44% of male adult

agricultural wage (635 pesos per months)

Evaluation: difference in differences (Schultz, 2001)

1tS = eligible household in Progresa village: gets

transfer

2tS = eligible household in non-Progresa village

(control): no transfer

t = program in place, t-1 = pre-program year

1. Compare difference in outcomes:

t 1t 2tD1 = S - S

But assumes program placement is random (supposed to

be in this case!)

2. Compare differences over time in both places:

t 1t 2t 1t-1 2t-1DD1 = (S - S ) - (S - S )

Did the difference in schooling change in treatment and

control villages among the eligible households after the

program was in place?

Results

1. Impact - how much did schooling increase? Other effects?

2. What was the rate of return on the program?

41

Source: Estimated by the author based on the two pre-program rounds of the survey for only children who are matched in all five rounds or the PanelSample.

Table 3Differences Between Enrollment Rates Between Progresa and Non-Progresa Poor Children and Over Time.

(Significance Levels in Parentheses Beneath Differences)b

Year ofSchooling

Completed inPrevious Year

Pre-Program Difference of PoorProgresa - Non-Progresa

D1

Post-Program Difference of PoorProgresa - Non-Progresa

D1

Post-Preprogram Difference in Differences

DD1

All Female Male All Female Male All Female Male

0.009

(.351).010

(.433).007

(.615)-.002(.854)

-.010(.564)

.006(.742)

-.011(.482)

-.021(.353)

-.001(.969)

1.001

(.410)-.009(.816)

.010(.376)

.022(.008)

.007(.418)

.036(.002)

.020(.136)

.016(.652)

.025(.070)

2-.004(.276)

-.013(.386)

.006(.506)

.020(.009)

.018(.796)

.021(.001)

.023(.226)

.031(.693)

.015(.030)

3.015

(.278).025

(.162).005

(.882).032

(.008).013

(.679).049

(.001).017

(.219)-.012(.508)

.044(.014)

4.008(.500

-.016(.836)

.030(.266)

.041(.001)

.038(.261)

.044(.001)

.033(.053)

.055(.335)

.013(.064)

5.015

(.129).005

(.544).025

(.125).047

(.001).055

(.232).041

(.000).032

(.146).050

(.647).017

(.077)

6.024

(.345).048

(.433)-.019(.002)

.111(.002)

.148(.001)

.065(.317)

.087(.004)

.100(.070)

.085(.005)

7-.012(.894)

-.005(.854)

-.015(.958)

.013(.147)

.025(.533)

.003(.006)

.025(.378)

.030(.583)

.018(.062)

8-.030(.913)

-.051(.932)

-.016(.836)

.001(.162)

.015(.575)

-.010(.100)

.031(.347)

.066(.687)

.006(.235)

9 or More

.103(.534)

.327(.001)

-.156(.006)

.066(.317)

.111(.042)

.026(.813)

-.037(.914)

-.216(.044)

.182(.020)

Notes: a For definition of D1 and DD1, see Figures 1 and 2 and text

46

Table 7

Cumulative Expected Enrollment Years for Birth Cohort of Poor Children who Enroll and Complete Grade 1

Grade Completed

PreprogramRounds 1 and 2

Post-Program Rounds 3, 4, and 5

Difference inDifferences

ProgresaNon-

Progresa ProgresaNon-

Progresa DI DDI

1 .977 .975 .975 .953 .022 .020

2 .936 .938 .939 .899 .040 .042

3 .896 .884 .904 .837 .067 .041

4 .856 .838 .866 .768 .098 .080

5 .816 .786 .825 .695 .130 .100

6 .464 .428 .511 .352 .159 .121

7 .436 .407 .484 .330 .154 .125

8 .414 .399 .450 .306 .144 .129

Expected TotalYears Enrolledfor Both Sexes

6.80 6.66 6.95 6.14 .81 .66

Years EnrolledFemales 6.66 6.62 6.95 6.19 .76 .72

Years EnrolledMales 6.93 6.72 6.96 6.11 .85 .64

1. Impact: elasticity of enrollment to school cost = -.2

50% reduction in opportunity costs

10% increase in schooling attainment (.66/6.8)

2. Rate of return calculation = 8%

Equate discounted Progresa grant costs to discounted stream of additional earnings over

lifetime of child - but what are additional earnings?

Assumptions:

1. All children end schooling at age 16

2. All children migrate to urban areas

Too few wage earners in rural areas - self-employed again!

3. Urban wages 12% higher for each additional grade completed (Census estimates)

4. Rural migrants in urban areas earn 20% less than natives (Census estimates)

5. Children start work at 18 and work until they are 65

The Progresa program evaluation ran for a short period - can we do

better in anticipating its long run effects?

But in the longer run, fertility might change - and too little time to

estimate impact of the actual program.

What about alternative programs - creating prizes for schooling

accomplishments? Would these have been more effective?

How can we know from this one, short-term experiment?

Estimate a structural model - obtain estimates of fundamental parameters

(preferences, technology, constraints) and use them to carry out policy

experiments of any type.

But how do we know the model is a good one - what is validation?

Todd-Wolpin study:

1. Estimate dynamic, structural model using pre-program data

(baseline plus control).

2. Assess model by comparing it predictions for Progresa effects

on schooling with actual effects from the randomized evaluation

— A valid structural model can be used to eval-

uate counterfactual policies.

∗ consider variations in program parameters:amounts paid, program structure, changes

outside scope of program

∗ consider impacts of long-run programs. Al-most always, social experiments are short-lived. If they seem to work, they have to be

extended to the control group. If they seemnot to work, they are abandoned. You can’t

usually use randomized evaluations of longterm programs (n.b., this does not mean

you can’t use these methods to look at long-

run impacts).

— the model makes it possible to consider mech-anisms through which the impact occurs. e.g.,

to interpret the externalities in the worms pa-per, Miguel and Kremer had to have a model.

• Why not?

— Answers only sensible if the model is right.

— pre-testing bias: there are approximately an

infinite number of auxilary assumptions that

need to be made on the shapes of preferences,

technologies, distributions of unobserved ran-

dom variables. A clever researcher can choose

these to match the data quite nicely.

• This is why Todd and Wolpin is interesting. Theyestimate the structural model using the control

group and pre-intervention treatment group, then

compare predictions with the information from

the randomized evaluation. (Some danger here).

The Todd-Wolpin Model

The problem: before the intervention, and for the con-

trol group, there is no variation in cost of schooling.

How to identify impact of subsidy?

Use variation in wages. Suppose for example:

u = C + (α+ ε)s

s = 1 if kid in school.

C = y + w(1− s)

ε˜N(0, σ2). Hence s = 1 iff

y + α+ ε > y +w

ε > w − α

So

Pr(s = 1) = 1−Φ(w − α

σ)

where Φ is the standard normal density. So as long

as we observe all the wage offers and the schooling

choices, we can identify α and σ. Then we also know

the impact of a subsidy on schooling choices.

Now the real model:

• Discrete time, chose to have a kid next period,choose school for kids 6− 15, choose work (kids> 12) (alternative, sit at home).

• parents income is given exogenously. no saving,

no borrowing.

• p(t) indicator of becoming pregnant→ n(t+1) =

1. (they differentiate between b(t+ 1) and g(t+

1).) Stock of kids is N(t+1) = N(t)+n(t+1).

A child born to a women when she is τ years old

is t − τ in t. s(t, τ) = 1 if that kid is in school

at t.

• c(t, τ) = 1 if a year of school is completed at t.

So the stock of schooling of a kid born at τ is

S(t, τ) = S(t − 1, τ) + c(t − 1, τ). Probability

of completing a year of schooling once started is

πc(t− 1, τ , S(t− 1)|s(t− 1) = 1, µc) where µc isa family fixed effect.

• h(t, τ) = 1 if the kid works

• There is a specific utility function given in theappendix. The general idea is:

U(t) = U(C(t), p(t), n(t), s(t), S(t); zs, εp, εl, µN, µS, µl)

where z is distance to school, ε are random shocks

to prefs over pregnancy and having a kid at home,

and the µ are permanent diffs across households

in prefs for number of kids, schooling of kids, and

home time of kids (these can all vary by gender).

•C(t) = yp(t) +

Xnyo(t, τn)h(t, τn)

where

yp(t) = yp(ap(t), zc, εyp(t);µyp)

a is ages, z is distance from city.

yo(t, τ) = yo(t− τ, gender, zc, εyo(t), µyo)

note that the transitory and permanent shocks do

not depend on the identity of the kid, so they are

common to all the kids in the household. Also

note that there is no change relative wages of

different aged kids over time.

• Now distributional assumptions: there are five εshocks (four we’ve written down: 2 in prefs, one

in kid income, one in parent income ... TW break

up one of the pref shocks into two by gender).

These are jointly serially uncorrelated. Joint dis-

tribution is f(ε(t))

• The five µ’s are distributed according to g(µ).

They assume a discrete distribution, which defines

a number of family types.

• At t, chose over the K(t) discrete alternatives (ofschooling, birth and work ... there can be lots of

these) to

maxK

ETXτ=t

δτ−tU(t)|Ω(t) ≡ V (Ω(t), t)

At any given set of parameter values, solve this

backwards, numerically to give a value conditional

on Ω(t) for each choice in K(t). Then pick the

best, and move on back to τ :

V (Ω(t), t) = maxk∈K V k(Ω(t), t)

where

V k(Ω(t), t) = Uk+δE(V (Ω(t+1), t+1)|dk(t) = 1,Ω(t))

• Problem: K is huge. So they make some re-

strictions which seem sensible (e.g., can’t send an

older child to school if younger one not in school).

• Problem: Ω also huge. Use Keane and Wolpin

to approximate it as a smooth function of a few

of the points in Ω.

That’s how you solve the model for given parameter

values. For example,

yp(t) =3X

j=1

γjI(type = j)+γ4ap(t)−γ5ap(t)2+γ6zc+εyp(t)

we now know how to calculate V k(Ω(t), t) for given

values of γ. How do we get estimates of γ?

There is data on: the choice of k, success/failure of

kid to complete grade, wage of kids working, parental

income. Let O(t) be that vector, and O that vector

over all periods. For a given household, we can de-

duce Pr(O|Ω(t = 0), µ) (Ω is the observable part of

the state space at zero). Since we can’t observe µ,

they integrate it out:

L = ΠNn=1Σ

3j=1 Pr(O|Ω(t = 0), type = j)

∗ Pr(type = j|Ω(t = 0)(note 3 types). Now cycle back and forth.

One year ahead predictions:

•

Long run effects

Then lots of experiments with different subsidy pro-

grams.

Household Sharing and the Effects of a School Lunch Program

What is an effective program to increase food intake of school-age children?

Subsidize food - but increases food consumption of everyone, not just children, expensive

School lunch program - targets relevant population

Will school lunch program actually increase food intake of school-age children?

1. Theory

a. Assume lunch is inframarginal = is not so large that exceeds total food intake for

the day provided by parents before the program

b. No parental adjustment, substitution - child receives added food provided at school

value of added food intake of child = value of school lunch

c. Parents egalitarian - will share free lunch with other household members - how?

with complete sharing, effect on child’s consumption = household income

effect (value of the lunch) < value of school lunch

2. Evidence - how would you test?

a. Compare consumption of children across program and non-program schools

but, program placement effect - target poor families who eat less in general

b. Compare children in same school with lunch program on school days and non-school

days

but activities may be different on non-school days (work in field, consume more)

c. Compare school-day and non-school-day consumption for children in program and

non-program schools: difference in difference approach

assumes that daily activity differential not different across children in the program

and non-program schools

3. Study: Jacoby, Economic Journal, January 2002

Used a sample of 3,200 children aged 6-12 in Metropolitan Cebu (Cebu Island,

Philippines)

Interviewed on different days and asked about all food consumed in the previous day

Previous day was a school day for 1/2 the children - Sunday, holiday,

15% of the children attended a school with a lunch program = 1/5 daily calories

Results:

1. No reduction in food consumed at home! total calorie increase = school lunch

calories

Problems:

1. Substitution of other goods - work harder, less clothing

2. Substitution occurred on other days

3. School lunch poor substitute for food child likes - but still an increase in calories,

if not utility