For more information about the 6th PEP Research Network General Meeting, Please visit: www.pep-net.org

Connecting Rural Communities for Development : An Impact Evaluation of a Rural Roads Program in Peru

Paola Vargas

6th PEP Research Network General MeetingSheraton Lima Hotel, Paseo de la Republica 170

Lima, Peru June 14-16, 2007

CONNECTING RURAL COMMUNITIES FOR DEVELOPMENT: AN

IMPACT EVALUATION OF A RURAL ROADS PROGRAM IN PERU

Research Proposal presented to

PEP Research Network

By

Vanessa Cheng

Valerie Koechlin

Paola Vargas

February 28, 2007

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1. Abstract

Improved rural roads are increasingly showing potential for creating opportunities for

economic growth and poverty reduction in developing countries. Reduced transport costs

can lead to increased productivity and demand of labor for men and women in farm and

non-farm activities as well as larger investments in health and education for rural

households in most remote areas. In that sense, this intervention has the potential to

effectively reach a sector of the population that has been persistently marginalized from the

benefits of aggregate economic growth in Peru. Still, methodological constraints and data

limitations have made it difficult to measure the size of these benefits with precision. This

proposal attempts to contribute by evaluating a rural roads program in Peru using a quasi-

experimental approach that allows controlling for time-invariant unobserved characteristics

of villages and households. We propose to use a unique longitudinal dataset that enable us

to measure impacts on a wide variety of socio-economic, institutional and environmental

characteristics. We also plan to explore heterogeneity in the impacts by individual,

household and village characteristics, as well as conditioning factors for the realization of

benefits. Thirdly, knowing the time at which road maintenance started enables us to explore

about the time lags required for the benefits to show up. Thus, this study will not only be

able to tell the program whether they are having positive impacts but also help find ways to

improve their contribution to poverty alleviation by informing about the conditions that are

required for those positive effects to materialize for the neediest.

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2. Main Research Questions and Objectives

The Rural Roads Rehabilitation and Maintenance Program is run by the Ministry of

Transport and Communications (MTC) aims at improving transport conditions in rural

villages by supporting local governments and small and medium local enterprises to

manage and carry out, on a sustainable basis, the maintenance and upgrading of rural roads

in the poorest areas of Peru. Such sustainable interventions will integrate poorly accessible

zones to regional economic centers, reducing transport costs and raising the reliability of

vehicular access to expand markets for agricultural and non-farm products and enhancing a

more diversified set of employment opportunities for rural households. Improved

transportation will also reduce time to reach basic social services such as health, education

and justice1.

Additionally, economic review supports that there is a positive effect of the program

on rural households’ income (Ahmed y Hossain; 1990). However, the way in which this

income increase is assigned at households relates to higher value of real estates (Jacoby,

2000) and new opportunities in non farm activities2, more than an increase in the

productivity of the agricultural sector.

The effect on the productivity and agricultural income depends on the specific

circumstance and also on the possibility of complementarily interventions: like technical

support, credit access, among others (Biswanger, Khandker y Rosenzweig, 1993).

1 For example Levy (2004) provides evidence that girls’ school attendance is highly increased with an improved in transport conditions. For a review of several impact studies on access to health services see Porter (2002), Hettige (2006), Windle and Cramb (1996).

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The program includes the rehabilitation and maintenance of rural roads, connecting

primary and secondary roads; and non-motorized rural transport tracks. In the process; the

program also tries to promote institutional development by providing technical assistance to

local governments and small and medium local enterprises for improved planning and

management of rural roads and for the development of micro-enterprises formed by groups

of beneficiaries for road maintenance.

This proposal attempts to address at least the following research questions:

1) Does the Rural Roads Program (RRP) have a positive effect on rural households´

income, health, education, etc? Another important expected effect is the diversification

of income opportunities in terms of the reduction of the dependence on agriculture and

the increase of wage employment and non-farm independent economic activities.

2) Do some special groups benefit more from the rural roads program? In particular, to

what extent do poorest households, smaller communities, rural women, and other

especially marginalized groups, benefit from the enhanced economic environment

resulting from the RRP?

3) How long does it take for the impacts of the RRP to show up? Does the answer vary by

indicator?

4) Does the presence of other public programs or access to other infrastructure and

services make it more likely for the effects of the RRP to be positive and significant?

2 See for instance Hine y Riverson (2001), Jacoby (2000) and Escobal y Ponce (2002).

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5) Does the RRP have unintended negative effects such as accentuated land degradation,

child labor, crime and violence?

3. Scientific Contribution

The economic literature has been increasingly reporting mechanisms through which

improved roads can create opportunities for economic growth and poverty reduction

(Khandker, 2006). By reducing transportation costs, improved roads can increase

productivity and demand for labor in farm and non-farm activities thus leading to increase

income and consumption as well as household investments in health and education.

However, methodological constraints and data limitations have made it difficult to

measure the size of these benefits. Most of the previous studies have lacked panel

observations and appropriate control groups to correct for unobserved heterogeneity. Only

recent studies have started to use double-difference techniques (Khandker et. al., 2006; van

de Walle and Cratty, 2005). However, they have not been able to fully address the

heterogeneity of impacts across individuals, households and villages.

Another feature of this literature is that it seems to concentrate on experiences in

Asian countries. A noted exception is Escobal and Ponce (2002) who evaluated the

Peruvian Rural Roads Program by using a propensity score matching methodology with a

cross-section survey. Their results suggest that improved rural roads enhance income

opportunities for rural households, especially non-farm wage employment. However, these

income gains seem to be perceived as only temporary since household decide not to

increase consumption but rather increase savings through increments in livestock.

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This paper attempts to contribute to the related literature by evaluating a rural roads

program in Peru, using a quasi-experimental approach that allows controlling for time-

invariant unobserved characteristics. Access to three rounds of a specialized survey allows

constructing a unique dataset that enable us to measure impacts on a wide variety of socio-

economic, institutional and environmental characteristics. We are also able to explore

heterogeneity in the impacts by individual, household and village characteristics, as well as

conditioning factors for the realization of benefits. Finally, knowing the time at which road

maintenance started enables us to explore about the time lags required for the benefits to

show up.

4. Policy Relevance

The Rural Roads Program is a large program that has been operating in Peru since 1995.

The first phase of the RRP was carried out during 1995-2000 in 12 departments that ranked

highest in rural poverty. During that first phase, the project improved rural accessibility in

314 districts by rehabilitating about 11,200 kilometers of rural roads and key secondary

roads and about 3,000 km of paths for non-motorized transport3. A key important feature of

this program is that it has included a valuable impact evaluation component that, for

example, has funded the three rounds of data collection that are going to be used in this

study. In that sense, it is very important that such effort is rewarded with an impact

evaluation study that not only tells them if the program is having positive impacts but also

3 The system of district-level rural roads in Peru is estimated in 70,000 kilometers.

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helps them find ways to improve their contribution to poverty alleviation by informing

about the conditions that are required for those positive effects to materialize.

With still very few public programs having adequate evaluation components in

developing countries, it is important to publicize their usefulness not only for the continuity

of the program but also for the identification of the need to adjust some of its components

to increase the magnitude of the positive effect, or to make this effects reach the poorest,

excluded and most vulnerable. This is particularly useful in Peru as a new government has

just assumed and they are in need of knowing which programs to expand and which ones

they may explore to close.

5. Methodology

The study will use the double difference estimate to determine the impact of the rural roads

program on a wide variety of indicators at the level of the household and the localities

involved. The project will make use of three rounds of a specialized household and

community-level survey that includes a wide variety of socio-economic, institutional and

environmental indicators. For each round, a set of rural roads and non-motorized tracks to

be treated were randomly selected to be included in the evaluation study. For this randomly

selected treatment group, a control group was selected ex-ante. The control group was

selected so that they are similar to the treatment group in terms of observable variables such

as the longitude and type of road (rural road or non-motorized track), characteristics of the

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villages involved such as population size, access to basic public services and

infrastructure4.

A basic regression-based DD estimate can be obtained from the following regression:

ijtTCj

ADt

TCj

ADtijt DDDDY εβββ +⋅⋅+⋅+⋅= 321 (1)

where ijtY denotes the value of an indicator of interest for household i that resides in village

j at period t (t=0 is the baseline; t=1 is the follow-up survey). TCD is a categorical variable

that takes value one if the household resides in a treated village and zero if it resides in a

control village. ADD is a categorical variable that takes value one if the observation is from

the follow-up survey and zero if it comes from the baseline. In that setting, 3β is the DD

estimator of the impact of the program on variable Y 5. It is often referred as an average

effect because it refers to all beneficiaries without distinction.

If we identify systematic differences between the treatment and control groups in

observable variables, we would need to include some controls in expression (1) to check

the robustness of our DD estimate. Furthermore, we cannot assure that there are non-

observables that can establish systematic differences between treatment and control groups,

but the double-difference (DD) estimate can help control for any time-invariant systematic

non-observable difference by including village fixed effects. Thus, a full version of the

average DD estimate can be obtained from the following expression:

4 In addition, control roads were also required not to have any intersection with a treated road or track. 5 In case of dichotomic variables, the estimation of the marginal effects is somewhat more complicated. See Karlan and Valdivia (2006).

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ijtjTCj

ADt

TCj

ADtjijt DDDDXY ευβββα ++⋅⋅+⋅+⋅+⋅= 321 (2)

where X is another dichotomic variable that takes value 1 if the village has the

characteristic of interest or concern. jυ denotes the vector of community fixed effects.

Finally, ijε denotes the error term which is assumed to be independent across villages but

not necessarily within them6.

We expect heterogeneous effects depending on the characteristics of the roads and the

beneficiary villages. The associated econometric analysis will use the following

formulation:

ijtjjTCj

ADt

TCj

ADt

jTCj

TCjj

ADt

ADtjijt

XDDDD

XDDXDDXY

ευγβ

γβγβα

++⋅⋅⋅+⋅⋅

+⋅⋅+⋅+⋅⋅+⋅+⋅=

33

2211 (3)

In that case, 3β comes to be the DD estimator of the program´s impact for those

households that reside in a village that does not have the characteristic of interest X, and

33 γβ + is the one for those that do have it.

6. Data Requirements and Sources

This proposal pretends to use three rounds (2000, 2004 and 2006) of a specialized

household and community-level survey that includes a wide variety of socio-economic,

6 Thus, we use the Huber-White covariance matrix estimator to obtain the standard error of our coefficients of interest.

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institutional and environmental indicators7. The survey questionnaires were the same for

the three rounds, and they all were applied in the fourth quarter of the corresponding years,

so that consistent comparisons are allowed. The household survey includes information

about the characteristics of the dwelling, health and education of all household members,

farm and non-farm entrepreneurial activities, commercialization channels, etc8. The

community-level survey is applied to key local informants and includes information about

the characteristics of the villages in terms of access to public infrastructure and basic

services, distance to nearest markets, and other key public facilities. It also includes

characteristics of the roads such as the time required to travel from the initial to the final

point of the road by the different means, number of public transportation units that use the

roads, the number of months the road remain closed over the past year, number of car

accidents, maintenance and operation costs for public transportation units offering services

in the road. Also, number of students in primary and secondary schools, number of health

services offered by public health facilities, judiciary and police crime records, use of

associated roads, among many other variables.

For each round, a sample of rural roads and non-motorized tracks to be treated were

randomly selected to be included in the evaluation study9. For this randomly selected

treatment group, a control group was selected ex-ante. The control group was selected so

that they are similar to the treatment group in terms of observable variables such as the

7 The first two rounds of the survey were applied by the firm Cuánto that won the corresponding call made by the Rural Roads Program for the evaluation of the program. The third round is currently being applied by GRADE as they won the last call. 8 See Table 1 and Table 2 below for a list of the main indicators available in all survey rounds.

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longitude and type of road (rural road or non-motorized track), characteristics of the

villages involved such as population size, access to basic public services and infrastructure,

altitude. In addition, control roads were also required not to have any intersection with a

treated road or track to minimize the probability that benefits on treated villages spill over

the control villages. Table 3 shows the pre-treatment means for treatment and control

groups for many observable variables, showing there are almost not statistically significant

differences between these two groups.

Treated and control roads are associated to villages by defining the origin and the end

of the road. In the case of small roads or tracks (less than 20 kms.), 6 households were

randomly selected within the initial and end villages. In the case of large roads, an extra

intermediate village is included in the sample. In the 2000 round, a baseline was established

with 2,000 households residing in villages associated to a sample of the roads that were

going to be treated during the 2001-02 period. In the 2004 round, the survey was applied to

the panel of households from the first round, plus 2,457 households associated to the

villages associated to the villages that were going to intervene during the period 2004-05.

Finally, in the 2006 round, we are following the households from the two previous rounds

plus 1,743 new households associated to the program´s intervention plan for 2007.

7. Dissemination Strategy

The hope of those involved with this study is that its results will help identify and

implement some adjustments to the program that can help their benefits to reach the

9 Random selection was muti-staged and stratified by department.

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poorest. To achieve this goal, the results of the study must be made available and

distributed to a wide and diverse audience of local academics, policy-makers, and

development practitioners. To target as many audiences as possible, the research team will

produce a technical working paper as well as a less technical presentation of findings upon

completion of the data analysis stage.

Upon completion of the study, a technical paper detailing the double-difference

estimates of the impact of the RRP on the various variables considered as key indicators for

the program, including socio-economic conditions as well as institutional and

environmental variables. This paper will be first published as a working paper in GRADE.

Availability on the web will allow researchers over the world to reach the information. This

version will be developed into a paper that would be submitted for publication in a peer-

reviewed journal, and the policy recommendations would be extended beyond the local

environment to the worldwide development community.

Moreover, GRADE’s institutional visibility will greatly increase the circulation of the

results within Peru. As one of the most widely-respected development think-tank in the

country, GRADE has links to numerous government ministries and a number of local

NGOs. Therefore, a dissemination paper focusing on policy recommendations will be

drafted and then published in GRADE’s bulletin Análisis y Propuestas, which is widely

distributed among local politicians, policy makers and development practitioners.

Finally, we expect to take advantage of the accumulation of impact evaluation

projects PEP has funded with GRADE researchers. In case we get the funding for this

proposal, we will have three projects that evaluate the impact of anti-poverty programs

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using different methodological approaches. The first project was directed by Martín

Valdivia on “Business Development Services for Female Microfinance Clients in Peru: A

Randomized Impact Evaluation”. The second project is being directed by Miguel Jaramillo

on “Do the Poorest among the Poor Benefit Less from Active Labor Market Programs?

Evidence from PROJOVEN”. In that sense, we propose to use that accumulation of

knowledge on impact evaluation studies within GRADE to raise awareness among local

policy makers and the local research community about the need of rigorous evaluations of

anti-poverty programs to learn systematically about what really works for the poor. Thus,

besides the regular dissemination article in Análisis y Propuestas for each project, we

propose to generate one that focuses on the lessons learnt from the three projects from a

methodological and conceptual standpoint, trying to make a case for the local public and

private sector to raise the standards on the evaluation of anti-poverty programs. That article

will be followed by a special conference in which we will try to make the case, maybe with

the help of other experienced researchers with international recognition in the area,

including those from PEP.

8. Ethical, Social, Gender, or Environmental Issues or Risks

The design of the project used the opinions of potential beneficiaries expressed through

focus groups organized in several rural communities (see Ford and Menendez, 2005). The

World Bank and IADB made sure to organize separate focus groups for men and women so

that women were able to express their opinions independently. This explicit inclusion of

women generated a key adjustment in the design of the program, to include funding for the

rehabilitations and maintenance of non-motorized transport tracks, which are the ones

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women used most often ignored by road upgrading programs. Focus groups after the

intervention have also received special attention and they suggest that a large proportion of

women see the program benefit them in terms of enabling them to travel farther and more

safely, and has also enable them to increase their income. The impact evaluation proposed

here will pay special attention to the effect of the program on women´s employment,

education and health, to see whether the program benefits them more or less than it does to

men.

Improving the connection of rural communities to markets has some environmental

risks as households may respond to the increase in profitability by increasing the intensity

in the utilization of scarce natural resources such as land, eroding the soils and affecting

their future productivity. The study will also monitor the intensity in the use of land in

agricultural activities.

9. Key references

Ahmed, R. y Hossain, M. (1990): "Developmental Impact of Rural Infrastructure in Bangladesh". Washington D.C.: International food Policy Research Institute.

Bertrand, Marianne; E. Duflo; S. Mullainathan (2004). “How Much Should We Trust Differences-in-Differences Estimates?”. Quarterly Journal of Economics 119 (1): 249-275, February.

Binswanger, H. P.; S. R. Khandker; M. R. Rosenzweig (1993). “How infrastructure and financial institutions affect agricultural output and investment in India”. Journal of Development Economics 41: 337-366.

Escobal, Javier; Máximo Torero (2005). “Measuring the Impact of Asset Complementarities: The Case of Rural Peru”. Cuadernos de Economía, Vol. 42, pp 137-164, May.

Escobal, Javier; Carmen Ponce (2002). “The Benefits of Rural Roads: Enhancing Income Opportunities for the Rural Poor”. GRADE Working Paper # 40, Lima, November.

Fort, Lucia, Aurelio Menéndez (2002). “Making Rural Roads Work for Both Women and Men: The Example of Peru’s Rural Roads Program”. The World Bank, Promising Approaches to Engendering Development.

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Fort, Ricardo; Fernando Aragón (2006). “Impacto de los Caminos Rurales sobre las Estrategias de Obtención de Ingresos de los Hogares”. En Iguiñiz, Escobal y Degregori (eds.) SEPIA XI, Perú: El Problema Agrario en Debate, Lima.

Gannon, C.; Z. Liu (1997). “Poverty and Transport”. The World Bank, INU/TWU Series Transport Publications. TWU-30, Washington, D.C.

Hettige, Hemamala (2006). “When do Rural Roads Benefit the Poor and How?: An In-Depth Analysis Based on Case Studies”. Asian Development Bank, Operations Evaluation Department, Philippines.

Hine, J. L. and J. D. N. Riverson (2001). “The Impact of Feeder Road Investment on Accessibility and Agricultural Development in Ghana”. In The Impact of Road Investment on Poverty Alleviation.

Instituto Cuánto (2004). “Evaluación Económica, Social, Ambiental e Institucional del Provias Rural Fase I”. Informe Final, November.

……….. (2000). “Evaluación económica, social, ambiental e institucional del Programa Caminos Rurales”. Informe Final.

Jacoby, H. C. (2000). “Access to markets and the benefits of rural roads”. Economic Journal 110 (465): 713-737.

Karlan, Dean; Martín Valdivia (2006). “Teaching Entrepreneurship: Impact of Business Training on Microfinance Clients and Institutions”. Yale University, Economic Growth Center Discussion Paper # 941, July

Khandker, Shahidur R.; Zaid Bakht; Gayatri B. Koolwal (2006). “The Poverty Impact of Rural Roads: Evidence from Bangladesh”. World Bank Policy Research Working Paper # 3875, April.

Levy, Hernan (2004). “Rural Roads and Poverty Alleviation in Morocco”. Banco Mundial, Manuscrito preparado para la conferencia Scaling Up Poverty Reduction: A Global Learning Process, Shanghai, May 25-27.

Liu, Z. (2000). Economic analysis of a rural basic access road project: the case of Andhra Pradesh, India.: Infrastructure Notes: Transport Sector, (Transport No. RT-5). The World Bank.

Lucas, K., Davis, T., & Rikard, K. (1996). Agriculture transport assistance program: impact study. Dar es Salaam: Project Number 621-0166. USAID/Tanzania.

MacDonald, Charles L. (2001) “Ecuador: Programa de Infraestructura Rural de Transporte, valuación Económica de los Proyectos de Rehabilitación de Caminos”. Development Ideas, INC.

Oré, María Teresa (2001). “El impacto socio-cultural del programa caminos de herradura 1995-2000”. Thw World Bank.

Porter, G. (2002). “Living in a walking world: rural mobility and social equity issues in Sub-Saharan Africa”. World Development 30 (2): 285.300.

van de Walle, D. (2002). “Choosing Rural Road Investments to Help Reduce Poverty”. World Development 30 (4): 575-589, April.

Windle, J.; R. A. Cramb (1996). “Roads, remoteness and rural development: social impacts of rural roads in upland areas of Sarawak Malaysia”. Department of Agriculture, University of Queensland, Agricultural Economics Discussion Paper 3/96, Brisbane, Australia.

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Table 1: Key indicators available in household and community-level surveys Indicators Description of variables Source a/ Transport

Travel time time in minutes needed to go from the initial to the final point of the road CLS Traffic intensity average number of public and private transportation units using the road, and frequency of

public units CLS Cost of public transportation ticket prices for transporting people and cargo CLS Usability of the road number of months the road was CLSosed over the past 12 months CLS

Access to health and education Schooling maximum level of schooling attained by each individual HLS school attendance proportion of children currently attending school HLS School accessibility means of transport used to go to school and travel time HLS School availability in the locality Number of schools available in the locality, by level CLS Illness number of days individuals were sick/disabled, incidence of diarrhea among children HLS Anthropometric measures /a Height and weight for children under five HLS Use of health care /b number of ill individuals that consulted with doctors HLS Pregnancies with birth control consultancies, institutional births over the last two years HLS Accessibility to health care means of transport used to go to the nearest health facility and travel time HLS Availability of health facilities Number of health facilities available in the locality, by level CLS

Access to other services public telephone Availability of public phone in the locality CLS internet Availability of internet in the locality CLS radio Availability of radio in the locality CLS TV signal Availability of public TV signal in the locality CLS

Income and employment Income total montHLSy labor income, by individual and household HLS Diversification proportion of income coming from agricultural, livestock and non-agricultural activities HLS wages average agricultural and non-agricultural wages for unskilled labor in the locality HLS time use time dedicated to domestic activities, by age and gender HLS

(continue …)

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Table 2: Key indicators available in household and community-level surveys (… continue) Indicators Description of variables Source Productive activities

Agricultural land Size of the plots owned and managed by household members HLS Land use intensity land cultivated by household members HLS productivity yields of main products and value added per hectare HLS Livestock Number of heads by type of animal HLS Productive assets number and value of key equipment and machinery HLS trade proportion of production destined to the local and regional markets HLS market accesibility means of transport used to go to the main market (local fair) and travel time HLS Access to agricultural services number of households with access to credit and technical assistance HLS

Expenditures and poverty household expenditures total per capita monthly expenditures HLS poverty rate proportion of households with expenditures under the poverty and extreme poverty lines HLS Unmet basic needs proportion of households without at least one of the basic needs unmet (treated water, sewage, type of

roof, children in school age not attending school, large dependency ratio) HLS Social capital

Migration number of permanent and temporary migrants and immigrants HLS Social organizations number of social organizations in the locality CLS Presence of public programs number of public programs that operated in the locality over the past two years, and number of

beneficiaries in the locality HLS Participation number of households with individuals that are active members of local social organizations HLS

Opinion of the program performance of the program perception of the quality of rehabilitation and maintenance of roads CLS impact perception of the types of benefits brought by the rehabilitation and maintenance of the road HLS / CLS distribution of benefits

proportion of households that report having benefited with the rehabilitation and maintenance of road HLS CLS - community-level survey; HLS - household level survey /a only for rounds 2000 and 2004 /b only for round 2006

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Table 3: Pre-treatment differences for cohorts 2000 and 2004 Variables (%) Control Treatment Difference T-stat 2000 cohort Age

[0-9] 25.1 27.1 -2.0 -1.81 ** [10-18] 24.5 23.8 0.6 0.61 [19-36] 25.3 24.2 1.1 1.07 More than 37 years old 25.1 24.9 0.2 0.17

Education (with 3 years or more) No level 11.7 12.8 -1.1 -1.27 Pre - school 5.5 6.4 -0.9 -1.46 * Primary 49.4 48.5 0.9 0.74 Secundary 26.9 26.4 0.5 0.46 Superior 6.5 6.0 0.5 0.80

Household variables % of households with access to water 72.1 70.7 1.4 0.56 Female household head 9.6 10.4 -0.8 -0.49 Head with indigenous mother tongue 47.3 51.8 -4.5 -1.63 * Agricultural Income 1957.3 1933.6 23.6 0.15 2004 cohort Age

[0-8] 26.9 28.0 -1.1 -1.30 * [9-18] 24.5 23.9 0.6 0.69 [19-35] 23.7 24.1 -0.4 -0.55 More than 36 years old 25.0 24.0 1.0 1.20

Education (with 3 years or more) No level 13.3 13.4 -0.1 -0.07 Pre - school 8.2 8.8 -0.7 -1.23 Primary 50.3 50.4 0.0 -0.01 Secundary 24.9 24.4 0.5 0.54 Superior 3.3 3.0 0.3 0.79

% of chidren and adolescents attending school Ages [6-11] 93.9 92.9 1.0 0.98 Ages [6-11] / Female 94.8 91.9 2.9 1.97 ** Ages [6-11] / Male 93.0 93.9 -0.9 -0.59 Ages [12-18] 85.7 81.4 4.3 2.44 *** Ages [12-18] / Female 81.8 79.5 2.2 0.80 Ages [12-18] / Male 88.8 83.1 5.7 2.54 ***

Household variables % of households with access to water 48.6 50.4 -1.8 -0.88 Female household head 11.0 11.1 -0.1 -0.09 Head with indigenous mother tongue 61.4 59.4 1.9 0.98 Per Capita Expenditure (monthly) 87.0 94.6 -7.6 -2.11 ** Per Capita Income (monthly) 94.9 96.3 -1.4 -0.27 Poverty

Extreme 51.3 49.4 1.8 0.92 No extreme 30.6 30.5 0.0 0.03

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Village level variables Nº of medical attention in health services 1530.6 1516.9 13.7 0.03 Nº of primary schools 1.0 1.2 -0.1 -1.08 Nº of secundary schools 0.4 0.5 -0.1 -1.39 *

10. List of Team Members:

This project counts with three team members:

i) Vanessa Cheng

Age: 24 (under 30)

Sex: Female

Prior Training/Experience:

1) GRADE. Research Assistant in the Rural Development area. Participated in the project

Socio-economic, Institutional and Environmental Impact Evaluation of the Peruvian

Rural Roads Program, for the Ministry of Transport and Communications. In charge of

constructing the control group for the 2006 survey and participated in the statistical

analysis.

Presidencia del Consejo de Ministros (PCM). Technical Assistant to the Program for

the Development of Productive Partnerships in Rural Sierra

GRADE. Research Assistant in the Rural Development area. Participated in the project

“Characterization of the Targeted Population for the project Development Strategy for

the Rural Sierra, for the World Bank and the PCM. In charge of econometric analysis

and the development of poverty maps.

ii) Valerie Koechlin

Age: 24 (under 30)

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Sex: Female

Prior Training/Experience:

GRADE. Research Assistant in the Rural Development area. Participated in the project

Socio-economic, Institutional and Environmental Impact Evaluation of the Peruvian

Rural Roads Program, for the Ministry of Transport and Communications. In charge of

monitoring application of survey fieldwork and data entry, and participated in the

statistical analysis.

Research experience at Inter-American Development Bank, Washington D.C., in the

project “International Remittances and Income Inequality: an empirical investigation”

(IADB WP-571, October 2006). This paper was presented in the 11th Annual Meeting

of the Latin American Economic Association (LACEA), ITAM, Mexico, November 2-

4, 2006.

iii) Paola Vargas

Age: 22 (under 30)

Sex: Female

Prior Training/Experience:

Research experience with education and poverty issues at university. The bachelor

paper assessed the determinants of child labor by evaluating the luxury and the

substitution axiom in households’ time allocation.

Research experience at GRADE since 2005, in Health, Poverty, Nutrition and Human

Development Area. Expertise in database management (household surveys, census,

among others) and in econometric tools such as linear regression, probit models,

difference in difference methodologies, decomposition of outcome differentials, etc.

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GRADE. Research Assistant in the Poverty and Equity area. Participated in the project

“Business Development Services for Female Microfinance Clients in Peru: A

Randomized Impact Evaluation”, for the PEP Research Network. In charge of

developing econometric analysis.

11. Research Capacity Development & Division of Labor

This project will be developed by three young female researchers under the advice of a

male senior researcher, Martín Valdivia. Paola Vargas will serve as the coordinator of the

project. Martín Valdivia will serve in an advisory role throughout all phases of the project.

However, his direct input will be counted on more heavily in the latter stages.

The three young female researchers have recently graduated as economists and have

been working as research assistants in GRADE. So, this experience will be the first one in

which they will be in charge of a research project. Paola Vargas has already had some

exposure in impact evaluation studies, but for Vanessa Cheng and Valerie Koechlin this

project will significantly enhance their knowledge of the different methodological

approaches associated to impact evaluation of poverty alleviation programs.

The division of labor between all project members will be as follows:

1. Identification of Control groups (Vanessa Cheng - VCh)

2. Monitoring and Administration of the second follow-up survey (VCh & Valerie

Koechlin - VK)

3. Processing of three rounds of survey database (VCh, VK & Paola Vargas – PV)

4. Analysis of all Databases (VCh, VK, PV)

5. Analysis and Discussion of Results. (PV & & Martín Valdivia - MV)

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6. Evaluation of Policy Recommendations. (VCh, VK, PV & MV)

7. Preparation of Final Document. (VCh, VK, PV & MV)

A timeline for the progress of all project work is detailed below by month:

Work Timeline Months

1 2 3 4 5 6 7 8 9 10

1. Identification of control groups x

2. Follow-Up Survey x x x x

3. Processing of Follow-Up Survey Data x x

4. Analysis of All Databases x x

5. Analysis and Discussion of Results x x

6. Evaluation of Policy Recommendations x x

7. Preparation of Final Document x x

12. Past, Current, or Pending Projects in Related Areas Involving Team Members

2) Project Name: Evaluation of the Social, Economic, Institutional and Environmental

Impacts of the Peruvian Rural Roads Program

- Funding Institution: Ministry of Transport and Communications

- List of team members involved: Vanessa Cheng, Valerie Koechlin, Martin Valdivia

- Status: Ongoing

3) Project Name: Business Development Services for Female Microfinance Clients in

Peru: A Randomized Impact Evaluation

- Funding Institution: PEP Research Network

- List of Team Members Involved: Paola Vargas, Martín Valdivia

- Status: Past

4) Project Name: Child Health, Poverty and the Role of Social Policies in Peru

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-Funding Institution: IADB Regional Research Network

-List of Team Members Involved: Martín Valdivia

-Status: Past

5) Project Name: Mobilizing Microsavings in Peru: Empowering the Poor Through their

Financial Agents and Local Assets Markets

-Funding Institution: Ford Foundation

-List of Team Members Involved: Martín Valdivia

-Status: Past

6) Project Name: A Dynamic Analysis of Household Decision-Making in Peru: Changes

in Household Structure, Female Labor Participation, Human Capital and its Returns

-Funding Institution: IADB Regional Research Network

-List of Team Members Involved: Martín Valdivia

-Status: Past

7) Project Name: How Well do Mother-Child Public Health Programs Reach the Poor in

Peru

-Funding Institution: World Bank

-List of Team Members Involved: Martín Valdivia

-Status: Past.