Food Insecurity through a fiscal lens

transcript

Jose CuestaPRMPR

April 22, 2011

Outline• “Fiscal Lens”: Work Program

• Food Security and Agricultural Spending in Bolivia

• Vulnerability index

• Agricultural Spending

• The Analysis

• Findings and Conclusions

• A Peek into Tariff Reform and Nutrition in Cote d’Ivoire

Work Program

• How public use of resources affect distributional outcomes?– How fiscal policies affect welfare distribution– How inequality affects policy decisions

• 3 pillars of work:– How traditional tools/techniques can deal with “non

traditional sectors”– Regional disparities and public spending/revenue – Policy decisions during the crisis

Work on food security• “Non traditional” sector, regional perspective

• Other examples are:• Nutritional effects of tariffs in Cote d’Ivoire• Unequal access to justice in Indonesia• Benefit incidence of road investments in Thailand• Central vs. local spending/revenue in Indonesia

• Bolivia study is part of an Agricultural PER

• How agricultural spending affects vulnerability to food insecurity in Bolivia?

Food Insecurity and Public Agricultural Spending in Bolivia

Policy Research Working Paper 5604http://siteresources.worldbank.org/INTPOVERTY/Resources/WPS5604.pdf

Jose Cuesta, Svetlana Edmeades & Lucia Madrigal

A simple story of success

19961997

19981999

20002001

20022003

20042005

20062007

Prefectura Municipio Central

1 2 3 4 50

Figure 2. Percentage of Municipalities by Vul-nerability Status

2003 2006 2007

Agricultural spending by administrative level

Food Security Definition in Bolivia • Based on “access to nutritious food” definition, that is:

“all people, at all times, have physical and economic access to sufficient, safe and nutritious food to meet their dietary needs, and food preferences for an active and healthy life”

• Bolivia follows this international definition around four dimensions:– Food availability– Access to food– Stable access to food– Safe use of food

• Definition of Vulnerability to Food also follows WFP’s VAM approach:– Risk exposure (e.g. natural disaster)– Capacity to address food insecurity (e.g., incomes, access to basic services)– Current situation as part of a historical trend (e.g. past malnutrition and poverty)

Food Security Policy in Bolivia• 2006 National Development Plan sets FS and sovereignty as

objectives of national production

• 2007 Agricultural Sector Strategy (Revolucion Rural, Agropecuaria y Forestal):– food security and sovereignty; – Increasing agricultural and forest production; and – sustainable management of natural resources

• Food Security Interventions (INE monitoring plan)– land redistribution; – promotion of food production and exports by state-owned enterprises– food security programs, including support to communities & small producers– nutritional programs (children, women) and school meals, among others.

FS Programs and Projects1. National Plan for Land Titling 2. National Plan for Land Distribution and Human Settlements 3. Planting the Right for Food (SEMBRAR)4. Creation of Rural Food Initiatives (CRIAR) 5. Organized Enterprises for Development (EMPODERAR) 6. Renewal of the Role of the State in Rural Food Businesses (RECREAR) 7. Development of territorial, integration and cross-sectoral production complexes 8. National Plan for Coca Development 9. Sustainable Use of Natural Resources (SUSTENTAR) 10. Conservation of Nature and Environmental Quality (CONSERVAR)11. Food Security Support Program (PASA) 12. Multisectoral Program of Zero Malnutrition 13. School Breakfast and Lunch Program

Source: Adapted from MVI Social (2010).

From programs to spendingArea (from AgPER) Category (for study) Type Current/Investment

Expenditure

Research, studies Research & Extension Restricted Current Technical assistance, seminars Research & Extension Restricted Current Water and irrigation Infrastructure Restricted Investment

Productive development Support & Development Restricted Current Assets and machinery Infrastructure Restricted Investment Seeds, fertilizer Support & Development Restricted Current Infrastructure Infrastructure Restricted Investment Plant & animal health support Support & Development Restricted Current Administration, regulation Administration & Procedures Restricted Current Development Support & Development Restricted Current Support Support & Development Restricted Current Roads and bridges Infrastructure Extended Investment Electricity infrastructure Infrastructure Extended Investment Warehousing and commercialization Support & Development Extended Current Risk management Administration & Procedures Extended Current Environmental management Administration & Procedures Extended Current Land organization Administration & Procedures Extended Current Organizational support Administration & Procedures Extended Current Education for Agriculture Research & Extension Extended Current

From definition to measurement of vulnerability

• Dependency rate• Life expectancy• Agricultural potential

(4 values on soil capacity)• Forestry potential (5 values)• Road density• Draught frequency• Frost days per year• Low weight at birth• Per capita household food

expenditures

• Urbanization rate• Rural population density (and

sq)• Prop. Institutionally attended

births• Schooling years• Log of per capita consumption• Under five malnutrition rate • Altitude • Rainfall• Flood Propensity (categorical 4

values)

From measurement to analysis VAM (Municipal Vulnerability Index)

1=very low, 2=low, 3=moderate, 4=high, 5=very high vulnerability

For 2003 each municipality is assigned a value 1 to 5 according to “agreed” vulnerability based on background study on vulnerability of 15 agro-ecological regions in the country (socioeconomic, demographic, ethnic and gender equity, climatic risks, subjective ability to respond to risks)

MNL of VAM2003 on 20 variables

MNL estimates allow 5 probabilities for VAMi=1…5 for each municipality

Estimated MNL on 10 variables for 2006 and 2007 (only those statistically significant in 2003)

Largest estimated prob(VAM=i) determines assigned VAM to each municipality

Some analytical caveats

A few obvious Original VAM 2003

Pragmatic selection of variables based on data considerations (availability and frequency)… too far?

Interdependence: Rural pop density and urbanization Low birth weight and institutional

delivery Floods, rainfall, draught

Not so obvious Inputs for and outcomes of FS

(e.g., malnutrition, urbanization)

Assumed same structural model between 2003 and 2007 (despite policies)

Little wiggle room for IV

A simple story of success… really?

19961997

19981999

20002001

20022003

20042005

20062007

Prefectura Municipio Central

1 2 3 4 50

Figure 2. Percentage of Municipalities by Vul-nerability Status

2003 2006 2007

Agricultural spending by administrative level

Simple perhaps, but not uniform0

Chuquisaca La Paz Cochabamba Oruro Potosi Tarija Santa Cruz Beni Pando

2003 2007

Figure 1. Vulnerability Status by Department

Chuquisaca La Paz Cochabamba Oruro Potosi Tarija Santa Cruz Beni Pando

2003 2007

Per Capita Agricultural Spending by Department (constant 2005 prices)

and not so simple

Current Investment

Distribution of Restricted Agricultural Spending

Table 1. Vulnerability Transition Matrix by Municipality 2006 2007

Low vulnerability High vulnerability Low vulnerability High vulnerability 2003 Low vulnerability 146 (45%) 14 (4%) 148 (45%) 12 (4%)

High vulnerability 35 (11%) 132 (40%) 54 (17%) 113 (35%)

Btw 15-20% of municipalities changed status For 1 that worsens, 3 improve

Substantive annual variations in investments

In fact, low correlation agr sp and VAM

.5 .6 .7 .8 .9 1low07

PC exp_2007 Fitted values

Prob VAM 1, 2, 3

.5 .6 .7 .8 .9high07

PC exp_2007 Fitted values

Prob VAM 4 or 5

Total PC AGR Expenditure and Prob of being in each VAM cat

Empirical strategy Probit of VAM ( high , low ) on pc capita agr spending

Several categories of spending considered Restricted vs. Extended Current vs. Investment Research & Extension; Infrastructure; Support & Development; Administrative

Regional dummies and region-clustered errors Single out level of spending from increases in spending Combine contemporaneous and lagged levels (not many IV

candidates) Robustness checks:

2006 data; ordered probit; total rather pc spending; Controls: CAPACITY, ELECTORAL. SOCIAL SPENDING variables

Findings

Positive impact of agr sp on high vam

Restricted significant

Investment drives results

Infrastructure significant, also R&D

Region specific effects

Table 3. Effects of Per Capita Agricultural Spending on Vulnerability to Food Insecurity 2007 (1) (2) (3) (4)

VARIABLES Total exp Restricted and extended definitions

Current and investment categories

By function

Total expenditure 0.5843***

(0.220)

Restricted expenditure 1.2889***

(0.489)

Extended expenditure 0.3106

(0.286)

Current expenditures 0.6495

(0.577)

Investment expenditures 0.5714**

(0.281)

Research and extension 8.9864**

(4.128)

Infrastructure 0.6776*

(0.352)

Support and development 0.0562

(0.991)

Administration and procedures

0.2547

(0.860)

Chuquisaca 0.3885*** 0.3702*** 0.3995*** 0.3799***

(0.108) (0.112) (0.107) (0.110)

Cochabamba 0.0951 0.0825 0.1042 0.0782

(0.092) (0.091) (0.092) (0.092)

Oruro 0.1316 0.1024 0.1437 0.1226

(0.100) (0.100) (0.099) (0.100)

Potosí 0.3162*** 0.3089*** 0.3178*** 0.3080***

(0.096) (0.097) (0.096) (0.097)

Tarija -0.3516*** -0.3640*** -0.3485*** -0.3470***

(0.036) (0.032) (0.040) (0.035)

Santa Cruz -0.2899*** -0.2934*** -0.2830*** -0.2975***

(0.060) (0.058) (0.061) (0.057)

Beni -0.3164*** -0.3148*** -0.3124*** -0.3339***

(0.059) (0.057) (0.062) (0.053)

Pando -0.3711*** -0.3674*** -0.3750*** -0.3742***

(0.034) (0.032) (0.033) (0.033)

Observations 327 327 327 327

R2 0.217 0.223 0.217 0.226

*** p<0.01, ** p<0.05, * p<0.1

Findings

Restricted, significant

By function

(0.220)

(0.489)

(0.286)

(0.577)

(0.281)

(4.128)

(0.352)

(0.991)

0.2547

(0.860)

Chuquisaca 0.3885*** 0.3702*** 0.3995*** 0.3799***

(0.108) (0.112) (0.107) (0.110)

Cochabamba 0.0951 0.0825 0.1042 0.0782

(0.092) (0.091) (0.092) (0.092)

Oruro 0.1316 0.1024 0.1437 0.1226

(0.100) (0.100) (0.099) (0.100)

Potosí 0.3162*** 0.3089*** 0.3178*** 0.3080***

(0.096) (0.097) (0.096) (0.097)

Tarija -0.3516*** -0.3640*** -0.3485*** -0.3470***

(0.036) (0.032) (0.040) (0.035)

Santa Cruz -0.2899*** -0.2934*** -0.2830*** -0.2975***

(0.060) (0.058) (0.061) (0.057)

Beni -0.3164*** -0.3148*** -0.3124*** -0.3339***

(0.059) (0.057) (0.062) (0.053)

Pando -0.3711*** -0.3674*** -0.3750*** -0.3742***

(0.034) (0.032) (0.033) (0.033)

R2 0.217 0.223 0.217 0.226

*** p<0.01, ** p<0.05, * p<0.1

Findings

By function

(0.220)

(0.489)

(0.286)

(0.577)

(0.281)

(4.128)

(0.352)

(0.991)

0.2547

(0.860)

Chuquisaca 0.3885*** 0.3702*** 0.3995*** 0.3799***

(0.108) (0.112) (0.107) (0.110)

Cochabamba 0.0951 0.0825 0.1042 0.0782

(0.092) (0.091) (0.092) (0.092)

Oruro 0.1316 0.1024 0.1437 0.1226

(0.100) (0.100) (0.099) (0.100)

Potosí 0.3162*** 0.3089*** 0.3178*** 0.3080***

(0.096) (0.097) (0.096) (0.097)

Tarija -0.3516*** -0.3640*** -0.3485*** -0.3470***

(0.036) (0.032) (0.040) (0.035)

Santa Cruz -0.2899*** -0.2934*** -0.2830*** -0.2975***

(0.060) (0.058) (0.061) (0.057)

Beni -0.3164*** -0.3148*** -0.3124*** -0.3339***

(0.059) (0.057) (0.062) (0.053)

Pando -0.3711*** -0.3674*** -0.3750*** -0.3742***

(0.034) (0.032) (0.033) (0.033)

R2 0.217 0.223 0.217 0.226

*** p<0.01, ** p<0.05, * p<0.1

Findings

By function

(0.220)

(0.489)

(0.286)

(0.577)

(0.281)

(4.128)

(0.352)

(0.991)

0.2547

(0.860)

Chuquisaca 0.3885*** 0.3702*** 0.3995*** 0.3799***

(0.108) (0.112) (0.107) (0.110)

Cochabamba 0.0951 0.0825 0.1042 0.0782

(0.092) (0.091) (0.092) (0.092)

Oruro 0.1316 0.1024 0.1437 0.1226

(0.100) (0.100) (0.099) (0.100)

Potosí 0.3162*** 0.3089*** 0.3178*** 0.3080***

(0.096) (0.097) (0.096) (0.097)

Tarija -0.3516*** -0.3640*** -0.3485*** -0.3470***

(0.036) (0.032) (0.040) (0.035)

Santa Cruz -0.2899*** -0.2934*** -0.2830*** -0.2975***

(0.060) (0.058) (0.061) (0.057)

Beni -0.3164*** -0.3148*** -0.3124*** -0.3339***

(0.059) (0.057) (0.062) (0.053)

Pando -0.3711*** -0.3674*** -0.3750*** -0.3742***

(0.034) (0.032) (0.033) (0.033)

R2 0.217 0.223 0.217 0.226

*** p<0.01, ** p<0.05, * p<0.1

Findings

Restricted significant

By function

(0.220)

(0.489)

(0.286)

(0.577)

(0.281)

(4.128)

(0.352)

(0.991)

0.2547

(0.860)

Chuquisaca 0.3885*** 0.3702*** 0.3995*** 0.3799***

(0.108) (0.112) (0.107) (0.110)

Cochabamba 0.0951 0.0825 0.1042 0.0782

(0.092) (0.091) (0.092) (0.092)

Oruro 0.1316 0.1024 0.1437 0.1226

(0.100) (0.100) (0.099) (0.100)

Potosí 0.3162*** 0.3089*** 0.3178*** 0.3080***

(0.096) (0.097) (0.096) (0.097)

Tarija -0.3516*** -0.3640*** -0.3485*** -0.3470***

(0.036) (0.032) (0.040) (0.035)

Santa Cruz -0.2899*** -0.2934*** -0.2830*** -0.2975***

(0.060) (0.058) (0.061) (0.057)

Beni -0.3164*** -0.3148*** -0.3124*** -0.3339***

(0.059) (0.057) (0.062) (0.053)

Pando -0.3711*** -0.3674*** -0.3750*** -0.3742***

(0.034) (0.032) (0.033) (0.033)

R2 0.217 0.223 0.217 0.226

*** p<0.01, ** p<0.05, * p<0.1

Findings

Restricted signficant

By function

(0.220)

(0.489)

(0.286)

(0.577)

(0.281)

(4.128)

(0.352)

(0.991)

0.2547

(0.860)

Chuquisaca 0.3885*** 0.3702*** 0.3995*** 0.3799***

(0.108) (0.112) (0.107) (0.110)

Cochabamba 0.0951 0.0825 0.1042 0.0782

(0.092) (0.091) (0.092) (0.092)

Oruro 0.1316 0.1024 0.1437 0.1226

(0.100) (0.100) (0.099) (0.100)

Potosí 0.3162*** 0.3089*** 0.3178*** 0.3080***

(0.096) (0.097) (0.096) (0.097)

Tarija -0.3516*** -0.3640*** -0.3485*** -0.3470***

(0.036) (0.032) (0.040) (0.035)

Santa Cruz -0.2899*** -0.2934*** -0.2830*** -0.2975***

(0.060) (0.058) (0.061) (0.057)

Beni -0.3164*** -0.3148*** -0.3124*** -0.3339***

(0.059) (0.057) (0.062) (0.053)

Pando -0.3711*** -0.3674*** -0.3750*** -0.3742***

(0.034) (0.032) (0.033) (0.033)

R2 0.217 0.223 0.217 0.226

*** p<0.01, ** p<0.05, * p<0.1

Findings

As before, positive impact of agr sp on high vam

Now both Restricted and Extended are significant

Investment keeps driving results

Infrastructure significant and so R&E (now negative!)

Region specific effects as before

Table 5. Effects of 2003 Per Capita Agricultural Spending on 2007 Vulnerability to Food Insecurity (1) (2) (3) (4)

VARIABLES Total exp Restricted and extended

definitions

By function

(0.338)

Restricted expenditure 1.2450**

(0.562)

Extended expenditure 0.6782**

(0.313)

Current expenditures -0.0250

(0.564)

Investment expenditures 1.3207***

(0.491)

Research and extension -11.1869**

(4.844)

Infrastructure 1.3973***

(0.531)

(1.723)

Administration and procedures -0.9973

(1.365)

Chuquisaca 0.3503*** 0.3273*** 0.3411*** 0.3331***

(0.115) (0.117) (0.117) (0.121)

Cochabamba 0.1067 0.1136 0.1066 0.1141

(0.093) (0.092) (0.094) (0.094)

Oruro 0.1036 0.1256 0.1224 0.1206

(0.102) (0.103) (0.103) (0.103)

Potosí 0.3239*** 0.3096*** 0.3300*** 0.3192***

(0.095) (0.096) (0.095) (0.095)

Tarija -0.2877*** -0.3020*** -0.2832*** -0.2883***

(0.086) (0.074) (0.090) (0.088)

Santa Cruz -0.2919*** -0.2965*** -0.2893*** -0.2816***

(0.062) (0.061) (0.063) (0.064)

Beni -0.3139*** -0.3130*** -0.3063*** -0.3046***

(0.068) (0.063) (0.072) (0.072)

Pando -0.3353*** -0.3765*** -0.3251*** -0.3303***

(0.064) (0.034) (0.074) (0.071)

Pseudo R2 0.223 0.218 0.227 0.235

*** p<0.01, ** p<0.05, * p<0.1

Findings

definitions

By function

(0.338)

(0.562)

(0.313)

(0.564)

(0.491)

(4.844)

(0.531)

(1.723)

(1.365)

Chuquisaca 0.3503*** 0.3273*** 0.3411*** 0.3331***

(0.115) (0.117) (0.117) (0.121)

Cochabamba 0.1067 0.1136 0.1066 0.1141

(0.093) (0.092) (0.094) (0.094)

Oruro 0.1036 0.1256 0.1224 0.1206

(0.102) (0.103) (0.103) (0.103)

Potosí 0.3239*** 0.3096*** 0.3300*** 0.3192***

(0.095) (0.096) (0.095) (0.095)

Tarija -0.2877*** -0.3020*** -0.2832*** -0.2883***

(0.086) (0.074) (0.090) (0.088)

Santa Cruz -0.2919*** -0.2965*** -0.2893*** -0.2816***

(0.062) (0.061) (0.063) (0.064)

Beni -0.3139*** -0.3130*** -0.3063*** -0.3046***

(0.068) (0.063) (0.072) (0.072)

Pando -0.3353*** -0.3765*** -0.3251*** -0.3303***

(0.064) (0.034) (0.074) (0.071)

Pseudo R2 0.223 0.218 0.227 0.235

*** p<0.01, ** p<0.05, * p<0.1

Findings

definitions

By function

(0.338)

(0.562)

(0.313)

(0.564)

(0.491)

(4.844)

(0.531)

(1.723)

(1.365)

Chuquisaca 0.3503*** 0.3273*** 0.3411*** 0.3331***

(0.115) (0.117) (0.117) (0.121)

Cochabamba 0.1067 0.1136 0.1066 0.1141

(0.093) (0.092) (0.094) (0.094)

Oruro 0.1036 0.1256 0.1224 0.1206

(0.102) (0.103) (0.103) (0.103)

Potosí 0.3239*** 0.3096*** 0.3300*** 0.3192***

(0.095) (0.096) (0.095) (0.095)

Tarija -0.2877*** -0.3020*** -0.2832*** -0.2883***

(0.086) (0.074) (0.090) (0.088)

Santa Cruz -0.2919*** -0.2965*** -0.2893*** -0.2816***

(0.062) (0.061) (0.063) (0.064)

Beni -0.3139*** -0.3130*** -0.3063*** -0.3046***

(0.068) (0.063) (0.072) (0.072)

Pando -0.3353*** -0.3765*** -0.3251*** -0.3303***

(0.064) (0.034) (0.074) (0.071)

Pseudo R2 0.223 0.218 0.227 0.235

*** p<0.01, ** p<0.05, * p<0.1

Findings

definitions

By function

(0.338)

(0.562)

(0.313)

(0.564)

(0.491)

(4.844)

(0.531)

(1.723)

(1.365)

Chuquisaca 0.3503*** 0.3273*** 0.3411*** 0.3331***

(0.115) (0.117) (0.117) (0.121)

Cochabamba 0.1067 0.1136 0.1066 0.1141

(0.093) (0.092) (0.094) (0.094)

Oruro 0.1036 0.1256 0.1224 0.1206

(0.102) (0.103) (0.103) (0.103)

Potosí 0.3239*** 0.3096*** 0.3300*** 0.3192***

(0.095) (0.096) (0.095) (0.095)

Tarija -0.2877*** -0.3020*** -0.2832*** -0.2883***

(0.086) (0.074) (0.090) (0.088)

Santa Cruz -0.2919*** -0.2965*** -0.2893*** -0.2816***

(0.062) (0.061) (0.063) (0.064)

Beni -0.3139*** -0.3130*** -0.3063*** -0.3046***

(0.068) (0.063) (0.072) (0.072)

Pando -0.3353*** -0.3765*** -0.3251*** -0.3303***

(0.064) (0.034) (0.074) (0.071)

Pseudo R2 0.223 0.218 0.227 0.235

*** p<0.01, ** p<0.05, * p<0.1

Findings

definitions

By function

(0.338)

(0.562)

(0.313)

(0.564)

(0.491)

(4.844)

(0.531)

(1.723)

(1.365)

Chuquisaca 0.3503*** 0.3273*** 0.3411*** 0.3331***

(0.115) (0.117) (0.117) (0.121)

Cochabamba 0.1067 0.1136 0.1066 0.1141

(0.093) (0.092) (0.094) (0.094)

Oruro 0.1036 0.1256 0.1224 0.1206

(0.102) (0.103) (0.103) (0.103)

Potosí 0.3239*** 0.3096*** 0.3300*** 0.3192***

(0.095) (0.096) (0.095) (0.095)

Tarija -0.2877*** -0.3020*** -0.2832*** -0.2883***

(0.086) (0.074) (0.090) (0.088)

Santa Cruz -0.2919*** -0.2965*** -0.2893*** -0.2816***

(0.062) (0.061) (0.063) (0.064)

Beni -0.3139*** -0.3130*** -0.3063*** -0.3046***

(0.068) (0.063) (0.072) (0.072)

Pando -0.3353*** -0.3765*** -0.3251*** -0.3303***

(0.064) (0.034) (0.074) (0.071)

Pseudo R2 0.223 0.218 0.227 0.235

*** p<0.01, ** p<0.05, * p<0.1

Findings

definitions

By function

(0.338)

(0.562)

(0.313)

(0.564)

(0.491)

(4.844)

(0.531)

(1.723)

(1.365)

Chuquisaca 0.3503*** 0.3273*** 0.3411*** 0.3331***

(0.115) (0.117) (0.117) (0.121)

Cochabamba 0.1067 0.1136 0.1066 0.1141

(0.093) (0.092) (0.094) (0.094)

Oruro 0.1036 0.1256 0.1224 0.1206

(0.102) (0.103) (0.103) (0.103)

Potosí 0.3239*** 0.3096*** 0.3300*** 0.3192***

(0.095) (0.096) (0.095) (0.095)

Tarija -0.2877*** -0.3020*** -0.2832*** -0.2883***

(0.086) (0.074) (0.090) (0.088)

Santa Cruz -0.2919*** -0.2965*** -0.2893*** -0.2816***

(0.062) (0.061) (0.063) (0.064)

Beni -0.3139*** -0.3130*** -0.3063*** -0.3046***

(0.068) (0.063) (0.072) (0.072)

Pando -0.3353*** -0.3765*** -0.3251*** -0.3303***

(0.064) (0.034) (0.074) (0.071)

Pseudo R2 0.223 0.218 0.227 0.235

*** p<0.01, ** p<0.05, * p<0.1

Findings

Very much the same results as before in terms of signs and significance

Incremental spending is significant and negative but negligible in magnitude

Effects of Initial and Incremental Per Capita Spending on Vulnerability (1) (2) (3) (4) (5)

VARIABLES Total exp Total exp (quadratic form for change)

Restricted and extended definitions

By function

Total expenditure 2003 0.9950*** 0.9659***

(0.344) (0.338)

Restricted expenditure 2003 0.9361*

(0.533)

Extended expenditure 2003 1.0241**

(0.465)

Current expenditures 2003 0.0598

(0.562)

Investment expenditures 2003 1.2893***

(0.494)

Research and extension 2003 -10.5311*

(6.056)

Infrastructure 2003 1.3837**

(0.544)

Support and development 2003 1.7249

(1.746)

Administration and procedures 2003 -0.8587

(1.351)

Change in per capita agricultural spending 06-07 (“incremental “ effect)

-0.0001** 0.0001 -0.0001** -0.0001* -0.0001**

(0.000) (0.000) (0.000) (0.000) (0.000)

Change squared in per capita agricultural spending 06-07 (“incremental” effect)

-0.0000

(0.000)

Chuquisaca 0.3492*** 0.3479*** 0.3507*** 0.3411*** 0.3322***

(0.117) (0.118) (0.114) (0.119) (0.122)

Cochabamba 0.1234 0.1135 0.1235 0.1231 0.1304

(0.095) (0.094) (0.095) (0.095) (0.096)

Oruro 0.1238 0.1136 0.1245 0.1383 0.1262

(0.104) (0.103) (0.104) (0.104) (0.104)

Potosí 0.3438*** 0.3485*** 0.3444*** 0.3485*** 0.3385***

(0.097) (0.099) (0.097) (0.096) (0.097)

Tarija -0.2784*** -0.2794*** -0.2779*** -0.2739*** -0.2799***

(0.090) (0.078) (0.090) (0.094) (0.094)

Santa Cruz -0.2895*** -0.2823*** -0.2892*** -0.2874*** -0.2803***

(0.062) (0.061) (0.063) (0.063) (0.064)

Findings

By function

(0.344) (0.338)

(0.533)

(0.465)

(0.562)

(0.494)

(6.056)

(0.544)

(1.746)

(1.351)

-0.0001** 0.0001 -0.0001** -0.0001* -0.0001**

(0.000) (0.000) (0.000) (0.000) (0.000)

-0.0000

(0.000)

Chuquisaca 0.3492*** 0.3479*** 0.3507*** 0.3411*** 0.3322***

(0.117) (0.118) (0.114) (0.119) (0.122)

Cochabamba 0.1234 0.1135 0.1235 0.1231 0.1304

(0.095) (0.094) (0.095) (0.095) (0.096)

Oruro 0.1238 0.1136 0.1245 0.1383 0.1262

(0.104) (0.103) (0.104) (0.104) (0.104)

Potosí 0.3438*** 0.3485*** 0.3444*** 0.3485*** 0.3385***

(0.097) (0.099) (0.097) (0.096) (0.097)

Tarija -0.2784*** -0.2794*** -0.2779*** -0.2739*** -0.2799***

(0.090) (0.078) (0.090) (0.094) (0.094)

Santa Cruz -0.2895*** -0.2823*** -0.2892*** -0.2874*** -0.2803***

(0.062) (0.061) (0.063) (0.063) (0.064)

Findings

By function

(0.344) (0.338)

(0.533)

(0.465)

(0.562)

(0.494)

(6.056)

(0.544)

(1.746)

(1.351)

-0.0001** 0.0001 -0.0001** -0.0001* -0.0001**

(0.000) (0.000) (0.000) (0.000) (0.000)

-0.0000

(0.000)

Chuquisaca 0.3492*** 0.3479*** 0.3507*** 0.3411*** 0.3322***

(0.117) (0.118) (0.114) (0.119) (0.122)

Cochabamba 0.1234 0.1135 0.1235 0.1231 0.1304

(0.095) (0.094) (0.095) (0.095) (0.096)

Oruro 0.1238 0.1136 0.1245 0.1383 0.1262

(0.104) (0.103) (0.104) (0.104) (0.104)

Potosí 0.3438*** 0.3485*** 0.3444*** 0.3485*** 0.3385***

(0.097) (0.099) (0.097) (0.096) (0.097)

Tarija -0.2784*** -0.2794*** -0.2779*** -0.2739*** -0.2799***

(0.090) (0.078) (0.090) (0.094) (0.094)

Santa Cruz -0.2895*** -0.2823*** -0.2892*** -0.2874*** -0.2803***

(0.062) (0.061) (0.063) (0.063) (0.064)

Findings

Civil servants per capita not significant

% of budget executed over approved or % own resources invested significant but negligible

Political party in office (different from central government) not significant

Per capita social spending not significant

Table A3. Effects of Per Capita Agricultural Spending with Observed Controls, 2007 Variables (1) (2) (3) (4) (5) (6) (7) (8)

Total expenditure 0.5399** 0.5776*** 0.6113* 0.3190* 0.5842*** 0.5839*

(0.217) (0.221) (0.328) (0.178) (0.221) (0.325)

(0.778)

Extended expenditure -0.0856

(0.522)

Current expenditure 0.8600

(0.964)

Investment expenditure 0.5380

(0.408)

Civil servants pc, 2006 2.0963 0.1927 -0.8149 0.6524

(11.532) (23.856) (22.781) (23.946)

Political party in office, 2006

0.0683 0.0782 0.0937 0.0792

(0.063) (0.078) (0.080) (0.078)

% budget executed over approved (2006)

-0.0003* -0.0003* -0.0003* -0.0003*

(0.000) (0.000) (0.000) (0.000)

% of investment financed by own resources (2006)

-0.0000***

(0.000)

Per capita social spending, 2006

-0.0000 -0.0001 -0.0001 -0.0002

(0.000) (0.000) (0.000) (0.000)

Regional dummies yes yes yes Yes yes yes yes yes

Population yes yes yes Yes yes yes yes yes

Observations 300 327 234 314 327 227 227 227

R2 0.214 0.22 0.237 0.27 0.217 0.225 0.235 0.225

*** p<0.01, ** p<0.05, * p<0.1

Findings

(0.217) (0.221) (0.328) (0.178) (0.221) (0.325)

(0.778)

(0.522)

(0.964)

(0.408)

(11.532) (23.856) (22.781) (23.946)

0.0683 0.0782 0.0937 0.0792

(0.063) (0.078) (0.080) (0.078)

-0.0003* -0.0003* -0.0003* -0.0003*

(0.000) (0.000) (0.000) (0.000)

-0.0000***

(0.000)

-0.0000 -0.0001 -0.0001 -0.0002

(0.000) (0.000) (0.000) (0.000)

Observations 300 327 234 314 327 227 227 227

R2 0.214 0.22 0.237 0.27 0.217 0.225 0.235 0.225

*** p<0.01, ** p<0.05, * p<0.1

Findings

(0.217) (0.221) (0.328) (0.178) (0.221) (0.325)

(0.778)

(0.522)

(0.964)

(0.408)

(11.532) (23.856) (22.781) (23.946)

0.0683 0.0782 0.0937 0.0792

(0.063) (0.078) (0.080) (0.078)

-0.0003* -0.0003* -0.0003* -0.0003*

(0.000) (0.000) (0.000) (0.000)

-0.0000***

(0.000)

-0.0000 -0.0001 -0.0001 -0.0002

(0.000) (0.000) (0.000) (0.000)

Observations 300 327 234 314 327 227 227 227

R2 0.214 0.22 0.237 0.27 0.217 0.225 0.235 0.225

*** p<0.01, ** p<0.05, * p<0.1

Conclusions• 2 facts: large increases in agr spending and lower

vulnerability to food insecurity in Bolivia

• The link between the two facts is no so clear:• Looking at levels of spending, it seems that agr spending goes where is

most needed

• A larger effect is observed when we allow for time to elapse

• When incorporating incremental spending, the sign implies some effectiveness in reducing high vulnerability

• But its magnitude shows a very small effect when significant

Two final thoughts

• These results are in line with previous evidence of decentralization leading to increased spending and investments where needed most but with only –at best– partial success (Faguet, Inchauste)

• 2 methodological caveats:– Proper account of (desirable) endogeneity– Further refinement of Food Security categories

Distributional effects of tariff reforms in Cote d’Ivoire

(on going)

Stefania Lovo, Jose Cuesta & Hassan Zaman

Tariff reform and nutrition in Cote d’Ivoire• CdI immersed in two trade integration processes working in

opposite directions:– Interim Economic Partnership Agreement with EU: elimination of

tariffs for 80% of the EU imports

– Common External Tariff with ECOWAS: potential increase of tariffs for rice, flour, edible oil and alcoholic beverages

• Tariff reforms will affect prices for critical foodstuffs, differently across commodities and across households

• How tariff reform in C d’Ivoire might affect nutritional outcomes across socioeconomic groups?

Several “housekeeping” issues• Aggregate 115 food categories in 2008 ENV into manageable

categories 9 categories: imported rice, flour, vegetable oils, sugar, alcoholic drinks, cereals, vegetables and fruits, meats and fish, milk & dairy, other.

• Convert reported consumption units into metric units Use standard conversion factors (Smith and Subandoro 2007, USDA) and econometric techniques (using household expenditure, reported q, regional metric prices)

• Convert consumption of foodstuffs into calories and proteins Use standard tables by FAO

Empirical strategy• After conversions done,

• Estimate a QUAIDS food demand system to obtain own and cross price elasticities

• Simulation of price changes across food ‘groups’ affected by tariffs on consumption and, ultimately, nutritional intakes

• Comparison across groups

A few additional issues• No population weights in the survey

• However, rich information available to exposure to conflict and intra-household allocation

• Analyze effects by

– Poor / Non Poor:

– N/S;

– male vs female dominant hhs;

– farmers vs non farmers;

Looking forward• Relatively low nutritional effects hypothesized

– Relatively low tariffs of some critical foodstuff (for example rice) may lead to small tariff changes

– The opposite effects of two diverging trade processes may lead to small net nutritional impacts

– A first look at the sources of calories and nutrients for P and NP suggest that there are not substantive difference in their sources

Many thanks!!

www.worldbank.org/poverty

Food Insecurity through a fiscal lens

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