Pensions, Poverty and Pensions, Poverty and Household Investments in Household Investments in

BoliviaBolivia

Sebastian MartinezSebastian MartinezHuman Development NetworkHuman Development Network

The World BankThe World Bank

Perspectives on Impact Evaluation ConferencePerspectives on Impact Evaluation ConferenceCario, EgyptCario, EgyptApril 2009April 2009

Cash Transfers and PovertyCash Transfers and Poverty

Transfers have been shown to:Transfers have been shown to:• Increase current consumption Increase current consumption (Case and Deaton, 1998; Hoddinott et al, 2000;)(Case and Deaton, 1998; Hoddinott et al, 2000;)

• Improve human capital: health and education Improve human capital: health and education (Carvalho, 2001; Duflo, 2003; Gertler, 2004; Schultz, 2004)(Carvalho, 2001; Duflo, 2003; Gertler, 2004; Schultz, 2004)

Cash transfers may also help relax liquidity Cash transfers may also help relax liquidity constraints constraints (Sadoulet, de Janvry and Davis, 2001):(Sadoulet, de Janvry and Davis, 2001):

Investments in under-capitalized assets and opportunities Investments in under-capitalized assets and opportunities Multiplier effects Multiplier effects More income/consumption More income/consumption Reduce povertyReduce poverty

Impact of Cash Transfer in BoliviaImpact of Cash Transfer in Bolivia

Pension transfer to large group of poor householdsPension transfer to large group of poor households• Effect on household consumption & investmentEffect on household consumption & investment

Quasi-experimental evaluation:Quasi-experimental evaluation:• Pre- and post- data from policy shifts: available 1999-Pre- and post- data from policy shifts: available 1999-

2002, pensions paid as of 2001 2002, pensions paid as of 2001 • Known eligibility criteria: 65+Known eligibility criteria: 65+

Uses existing nationally representative household Uses existing nationally representative household datadata• External validity of resultsExternal validity of results• Cheap way to do an impact evaluation but….Cheap way to do an impact evaluation but….

Low power relative to primary data collection on target populationLow power relative to primary data collection on target population

Pensions to Poor Rural HouseholdsPensions to Poor Rural Households

Increased food consumption > transfer amountIncreased food consumption > transfer amount• Increased home production of meats & vegetablesIncreased home production of meats & vegetables

Evidence of increased investmentEvidence of increased investment• Increased expenditures on farm inputs Increased expenditures on farm inputs • Increased use of landIncreased use of land• Increased animal ownership Increased animal ownership

Results consistent with presence of liquidity Results consistent with presence of liquidity constraintsconstraints

Presentation Outline Presentation Outline

Country ContextCountry Context The InterventionThe Intervention Data SourcesData Sources Identification & EstimationIdentification & Estimation ResultsResults ConclusionsConclusions

Country Context - BoliviaCountry Context - Bolivia

BoliviaBolivia South AmericaSouth America

GDP PC (2001)GDP PC (2001) $945$945 $3930$3930Population below poverty Population below poverty lineline 63%63% 34%34%

Population below poverty Population below poverty line - Ruralline - Rural 80%80% 50%50%

Infant mortality per 1000 Infant mortality per 1000 live births (2000)live births (2000) 6262 2424

Life expectancy (2000)Life expectancy (2000) 6363 7272

Source: 2002 World Development Indicators; South America: Peru, Ecuador, Colombia, Venezuela, Chile, Argentina, Uruguay, Paraguay, Brazil; poverty line for average of available data 1990-2003: excludes Argentina, Uruguay and Venezuela (missing data)

Rural Bolivians Are..Rural Bolivians Are.. PoorPoor

• Less than $1 USD per day mean consumption per capitaLess than $1 USD per day mean consumption per capita• 35% of HHs with electricity35% of HHs with electricity• 72% of HHs with dirt floors72% of HHs with dirt floors

Have little access to formal creditHave little access to formal credit• Less than 2% have debt from formal lending institution Less than 2% have debt from formal lending institution

( mortgage, credit cards, micro-credit)( mortgage, credit cards, micro-credit) But they own landBut they own land

• Agrarian reform following 1952 revolutionAgrarian reform following 1952 revolution• 83% of HHs own land 83% of HHs own land • Median of 1 hectare under cultivationMedian of 1 hectare under cultivation• Average of 2.3 hectares under cultivationAverage of 2.3 hectares under cultivation

Source: MECOVI 1999-2002

Presentation Outline Presentation Outline

Country ContextCountry Context

The InterventionThe Intervention Data SourcesData Sources Identification & EstimationIdentification & Estimation ResultsResults ConclusionsConclusions

Intervention - BONOSOLIntervention - BONOSOL

Established by 1996 pension reform to:Established by 1996 pension reform to:1.1. Provide pension coverage for majority of Provide pension coverage for majority of

seniors outside the old pension systemseniors outside the old pension system

2.2. Distribute proceeds from partial privatization of state Distribute proceeds from partial privatization of state owned companies (1.7 billion USD)owned companies (1.7 billion USD)

3.3. Reduce povertyReduce poverty

Annuity of $248 to ALL Bolivians 65 and older Annuity of $248 to ALL Bolivians 65 and older • 40% of annual minimum salary40% of annual minimum salary

• 85% of per-capita income for extreme poor85% of per-capita income for extreme poor

BONOSOL HistoryBONOSOL History

YearYear GovernmentGovernment BONOSOLBONOSOL

19971997 MNR – Sanchez de LozadaMNR – Sanchez de Lozada $248$248

1998 – 20001998 – 2000 ADN – BanzerADN – Banzer $0 $0 (Program (Program Suspended)Suspended)

20012001 ADN – BanzerADN – Banzer $120$120

20022002 ADN – QuirogaADN – Quiroga $120$120

20032003 MNR – S. de L.MNR – S. de L. $248$248

20042004 MNR – MesaMNR – Mesa $248$248

$120 USD$120 USD

Equivalent to:Equivalent to:• 33% of annual rural per capita consumption33% of annual rural per capita consumption• 47% of rural per capita food consumption 47% of rural per capita food consumption • 48 Chickens 48 Chickens • 17 sheep17 sheep• 7 pigs7 pigs• 5 Llamas5 Llamas• 1 Cow/Oxen1 Cow/Oxen

Presentation Outline Presentation Outline

Country ContextCountry Context The InterventionThe Intervention

Data SourcesData Sources Identification & EstimationIdentification & Estimation ResultsResults ConclusionsConclusions

Data: MECOVIData: MECOVIYearYear PeriodPeriod Eligible Eligible

HHHHBONOSOLBONOSOL Pay Pay

DatesDatesSurvey Survey DatesDates

Sample Size Sample Size (HH)(HH)

19991999 NoNo

TreatmentTreatment

65 +65 + $0$0 NANA 11-1211-12 2,4992,499

20002000 NoNo

TreatmentTreatment

65 +65 + $0$0 NANA 11-1211-12 3,9173,917

20012001 TreatmentTreatment 66 +66 + $840Bs $840Bs ($120USD)($120USD)

1-41-4 11-1211-12 4,9494,949

20022002 TreatmentTreatment 66 +66 + $840Bs $840Bs ($120USD)($120USD)

1-61-6 11-1211-12 5,1725,172

Presentation Outline Presentation Outline

Country ContextCountry Context The InterventionThe Intervention Data SourcesData Sources

Identification & EstimationIdentification & Estimation ResultsResults ConclusionsConclusions

IdentificationIdentification

Regression Discontinuity:Regression Discontinuity: • compare consumption of eligible & ineligible HHs compare consumption of eligible & ineligible HHs

above and below 65 year eligibility thresholdabove and below 65 year eligibility threshold

• in pre- and post-treatment periodsin pre- and post-treatment periods

Estimate effect of BONOSOL on consumption:Estimate effect of BONOSOL on consumption:

Report robust SE, clustered at primary sampling unitReport robust SE, clustered at primary sampling unit

4

1 21 1 3

*N L

it it it t t n n l itt n l

C Eligible Treatment Eligible Time Age X

CovariatesCovariates

Include controls for:Include controls for:• Education of oldest memberEducation of oldest member• Gender of oldest memberGender of oldest member• Ethnicity (language) of oldest memberEthnicity (language) of oldest member• Household SizeHousehold Size• Age/gender composition Age/gender composition • RuralRural• Regional fixed effects (department)Regional fixed effects (department)

Results robust to exclusion of covariatesResults robust to exclusion of covariates

Analysis SampleAnalysis Sample

Start with 16,537 HHsStart with 16,537 HHs Drop households with:Drop households with:

• Oldest household member < 45 years or >80 Oldest household member < 45 years or >80 years years 4,032 Households 4,032 Households

• Top and bottom 1% of consumption outliersTop and bottom 1% of consumption outliers• Exclude households with more than one Exclude households with more than one

beneficiary (for now) beneficiary (for now) 0.46% of sample 0.46% of sample Final analysis sample of 11,614 Final analysis sample of 11,614

householdshouseholds

Presentation Outline Presentation Outline

Country ContextCountry Context The InterventionThe Intervention Data SourcesData Sources Identification & EstimationIdentification & Estimation

ResultsResults ConclusionsConclusions

100

120

140

160

180

200

220

240

Con

sum

ptio

n P

er C

apita

45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79Age of Oldest HH Member

Treatment Year Non-Treatment Year

Figure 1.2b: Rural Consumption Per Capita - Fan regression

Table 4 & 5: Food Consumption Dependent variable is value of monthly household food consumption ($Bs)

Model 1

Model 3 Rural

Model 5 Land

Total Home Total Home Total Home

Eligible*TREATMENT 73.99** 23.83* 17.11 -3.74 35.56 -10.60 (19.99) (10.41) (23.89) (8.58) (24.60) (9.62) Eligible*TREATMENT*Rural 128.71** 63.02** (37.86) (22.65) Eligible*TREATMENT*Land 85.35* 77.44** (35.99) (19.76) Observations 11614 11614 11614 11614 11614 11614 R-squared 0.49 0.34 0.49 0.34 0.49 0.35 F test: TREATMENT = 65.2 0.19 15.80 6.59 0.08 3.58 0.01 Prob > F 0.66 0.00 0.01 0.78 0.06 0.93 Mean Dep Var 733.77 106.51 733.77 106.51 733.77 106.51 SD 556.09 256.82 556.09 256.82 556.09 256.82 Notes: Linear regression. Robust standard errors clustered at the primary sampling unit level in parentheses (+ significant at 10%; * significant at 5%; ** significant at 1%).

Table 4 & 5 (continued): Food Consumption - Multiplier Dependent variable is value of monthly household food consumption ($Bs)

Multiplier

Total Total

Monthly Transfer Amount*TREATMENT 1.13** 0.26 (0.31) (0.37) Monthly Transfer Amount*TREATMENT *Rural 1.97** (0.58) Observations 11614 11614 R-squared 0.49 0.49 F test: TREATMENT = 1 0.19 6.58 Prob > F 0.66 0.01 Mean Dep Var 733.77 733.77 SD 556.09 556.09 Notes: Linear regression. Robust standard errors clustered at the primary sampling unit level in parentheses (+ significant at 10%; * significant at 5%; ** significant at 1%).

Table 6 & 7: Food Consumption by Type (Total and Home Produced) Dependent variable is value of monthly food consumption by food group ($Bs)

Notes: Linear regression. Robust standard errors clustered at the primary sampling unit level in parentheses (+ significant at 10%; * significant at 5%; ** significant at 1%).

Cereals Meats Dairy Vegetables Fruit

Total Home Total Home Total Home Total Home Total Home

Base Regression Eligible*TREATMENT 12.30* 1.04 25.94** 13.47* 16.39** 8.80* 7.18 -0.73 5.46+ 0.59 (4.86) (3.29) (9.26) (5.58) (4.95) (3.45) (4.41) (2.96) (2.98) (1.55) Rural Eligible*TREATMENT*Rural

25.03** 10.90+ 42.76* 27.38* 23.69* 17.90* 17.19* 7.91 11.33* 1.84

(9.00) (6.24) (18.02) (12.65) (9.34) (7.20) (8.27) (6.02) (5.64) (3.44) Eligible*TREATMENT 1.16 -3.57 6.72 1.40 6.16 1.04 -0.52 -4.39+ 0.71 0.07 (4.87) (2.91) (11.09) (4.37) (6.03) (3.07) (5.05) (2.39) (4.24) (1.40) Landed Eligible*TREATMENT*Land

21.19* 16.75** 23.94 28.95* 15.80+ 16.29* 14.77+ 16.06** 6.14 3.08

(8.82) (5.83) (17.22) (11.70) (8.61) (6.56) (7.90) (5.52) (5.10) (2.98) Eligible*TREATMENT 2.75 -6.54* 15.25 0.56 9.12 1.54 0.58 -7.69** 2.56 -0.84 (4.97) (3.14) (11.55) (4.78) (6.34) (3.32) (5.15) (2.24) (4.16) (1.37) Observations 11614 11614 11614 11614 11614 11614 11614 11614 11614 11614 R-squared (Base) 0.41 0.19 0.32 0.19 0.25 0.17 0.23 0.13 0.23 0.13 Mean Dep Var 167.75 25.10 217.47 30.40 88.87 17.91 131.93 24.74 55.63 7.67 SD 142.89 75.53 231.75 132.17 112.15 66.31 119.60 80.50 74.84 41.57

Table 8: Farm Expenditures and Sales (Sub-sample of rural households with crop production) Dependent variable is value of YEARLY agricultural expenses and sales ($Bs) Model 1

Total Farm

Model 2 Seed

Model 3 Fertilizer

Model 4 Pesticide

Model 5 Animal

Feed

Model 6 Plow

Rental

Model 7 Land Use (hectares)

Base Regression Eligible*TREATMENT 159.08+ 44.35* 16.21 14.10 6.23 2.66 0.49 (87.38) (19.68) (19.15) (10.46) (15.79) (1.99) (0.36) Observations 3310 3310 3310 3310 3310 3310 3464 R-squared 0.18 0.10 0.10 0.10 0.09 0.07 0.25 Mean Dep Var 751.20 110.62 111.75 49.93 67.66 7.06 2.47 SD 1234.71 227.94 248.54 143.98 199.22 24.43 4.41

Notes: Linear regression. Robust standard errors clustered at the primary sampling unit level in parentheses (+ significant at 10%; * significant at 5%; ** significant at 1%).

Table 9: Animal Ownership (Sub-sample of rural households with animal production) Dependent variable is number of animals owned Goats Llamas Chickens Base Regression Eligible*TREATMENT 0.32 1.33+ 2.04 (1.24) (0.78) (1.58) Landed Beneficiary Eligible*TREATMENT 4.13* 1.13 -0.64 (2.09) (2.52) (3.19) Eligible*TREATMENT*Land -4.29+ 0.01 3.07 (2.54) (2.75) (3.30) Observations 4232 4232 4232 R-squared 0.19 0.16 0.37 Mean Dep Var 4.05 2.49 13.29 SD 13.86 13.24 25.90

Notes: Linear regression. Robust standard errors clustered at the primary sampling unit level in parentheses (+ significant at 10%; * significant at 5%; ** significant at 1%).

Table 10: Nonfood Consumption Dependent variable is value of monthly household non-food consumption ($Bs)

Model 1 Non Food

Cons

Model 2 Medical –

Doctor Visits

Model 3 Medical - Pharmaceuticals

Model 4 Medical - Hospitali

zation

Model 5 transfers

Model 6 tobacco

Model 7 alcohol

Base Regression Eligible*TREATMENT 48.94* 3.81** 3.89** 1.01 -3.82 0.78 2.01 (24.46) (1.44) (1.48) (0.83) (3.59) (0.51) (1.29) Rural Eligible*TREATMENT 64.31+ 4.34* 4.05+ 1.27 1.12 1.09+ 3.79* (35.93) (2.18) (2.10) (1.25) (4.85) (0.63) (1.82) Eligible*TREATMENT* Rural

-34.15 -0.87 0.27 -0.29 -11.40+ -0.67 -4.54*

(34.47) (2.28) (2.58) (1.62) (6.17) (0.94) (2.14) Observations 11423 11423 11423 11423 11423 11423 11423 R-squared (Base) 0.29 0.05 0.08 0.02 0.04 0.10 0.12 Mean Dep Var 366.11 7.67 11.74 2.50 12.89 2.72 13.75 SD 581.66 26.15 28.32 16.09 79.87 9.97 30.39

Notes: Linear regression. Robust standard errors clustered at the primary sampling unit level in parentheses (+ significant at 10%; * significant at 5%; ** significant at 1%).

Presentation Outline Presentation Outline

Country ContextCountry Context The InterventionThe Intervention Data SourcesData Sources Identification & EstimationIdentification & Estimation ResultsResults

ConclusionsConclusions

ConclusionConclusion BONOSOL Cash Transfer:BONOSOL Cash Transfer:

• Evidence of multipliers: Increase in food Evidence of multipliers: Increase in food consumption > value of transferconsumption > value of transfer

• Effect driven by poor rural & landed households :Effect driven by poor rural & landed households : Increase home produced food consumptionIncrease home produced food consumption Evidence of investments in farm inputs & animal stockEvidence of investments in farm inputs & animal stock

• Consistent with story that HHs use transfer to Consistent with story that HHs use transfer to overcome liquidity constraints on productive overcome liquidity constraints on productive activities, boosting consumption through activities, boosting consumption through investmentsinvestments