Access to modern markets and the impacts of rural road

rehabilitation: Evidence from Nicaragua

∗

Javier Parada†

September, 2017

JOB MARKET PAPER

Abstract

The rehabilitation of a rural road connecting an isolated coastal area to the city of León in

western Nicaragua substantially improved the quality of the road’s surface. As a result, a significant

reduction in transportation costs and travel times was expected to improve rural households’

access to modern urban markets. The degree to which the resulting impacts on market prices for

locally-produced goods could effectively ameliorate poverty and income inequality in the coast as

a consequence of the project will depend on the heterogeneous distribution of the road’s benefits.

In the area of influence of the rehabilitated road, where fishing is the main source of income and

poverty is either high or severe, it was found that improving the road could have benefited the

poorest households by lowering the average cost of a basic basket of goods and by allowing fresh

fish caught in the coast to be sold in urban markets.

Keywords: Rural roads, Transportation, Market integration

JEL Codes: O12, O18

∗I would like to thank Michael Carter, Kevin Novan, Pierre Mérel, and Emilia Tjernström for providing me detailedcomments that significantly improved the quality of this paper.

†Javier Parada is a Ph.D. candidate in the Department of Agricultural and Resource Economics at the University ofCalifornia, Davis. E-mail: parada@ucdavis.edu. Website: http://javierparada.weebly.com/about-me.html

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1 Introduction

Improving the quality of Nicaragua’s rural road network is a vital foundation for rural development

and poverty alleviation. Following the effect of Hurricane Mitch in 1998 when the majority of bridges

and secondary roads in Nicaragua were heavily damaged or destroyed, donor agencies have identified

inadequate road infrastructure and its vulnerability to natural disasters as key hurdles holding back

Nicaragua’s economic growth. This paper studies how better rural roads reduce the costs of arranging

transactions between spatially separate locations and can have a significant effect on agricultural

market prices, consumption expenditures and poverty levels

1. In particular, it shows that in the area

of influence of a rehabilitated road in western Nicaragua where poverty is either high or severe, price

changes that result from a better connection to markets benefited poor households by lowering the

average cost of a basic basket of goods by -1.69%. It also shows that poor coastal households that rely

on fishing would benefit from the rehabilitated road allowing fresh fish caught in the coast to be sold

in urban markets where prices for fish are significantly higher.

The spatial arbitrage model provided in this paper uses a difference in difference in differences

(DDD) estimation strategy to measure the changes in markups that result from a rural road rehabili-

tation and estimates the magnitude these price changes would represent of a typical rural household’s

budget. A rural household’s typical basket of consumer goods includes both locally-produced agricul-

tural goods and manufactured goods brought in from an urban manufacturing center and sold at a

markup in the rural area. In this paper, price levels for locally-produced goods (e.g., crops, fish, or

eggs) and manufactured goods (e.g., toothpaste or laundry detergent) are taken from a store survey

conducted within a small region of western Nicaragua where the main rural road connecting a rural

area to the big city was significantly rehabilitated as part of a program conducted by the Millen-

nium Challenge Corporation (MCC). As a part of this program, the MCA-N Transportation Project

(2008-2009) had the goal of reducing transportation costs and improving rural communities’ access

to markets in the departments of León and Chinandega that benefit from the most fertile soil in the

country and proximity to the Pacific coast. This US $57.9 million investment allocated to the reha-

bilitation of 42 miles in two secondary roads, Somotillo-Cinco Pinos and León-Poneloya-Las Peñitas,

and one secondary trunk road, Villanueva-El Guasaule, was expected to decrease transportation costs

1Origin and destination surveys measuring traffic before and after upgrades in road quality show that rural roadrehabilitation projects facilitate farmers’ participation in markets and increase transportation of goods and services.However, few studies have examined the causal link between better rural roads and final welfare outcomes for agriculturalhouseholds. Jacoby and Minten (2009) is one of the few studies that makes this connection by concluding that ahypothetical rural road project in Madagascar that would reduce the transport costs of the most remote households byaround 75 USD/ton would raise their incomes by about 50%.

2

by US $3.2 million annually and significantly reduce travel time, benefiting more than 97,000 people

in its area of influence.

The road from León to Poneloya and Las Peñitas provides a simple scenario for this study because

it is the only direct access to these coastal communities

2. León is the second largest city in Nicaragua.

The fishing port of Poneloya and the coastal village of Las Peñitas are both beach communities that

are becoming popular tourist destinations.

3The road that connects these communities to León was

substantially rehabilitated at a cost of US $21 million, which amounts to 36% of the budget for

the MCA-N Transportation Project. Before the project the road’s 12.2-mile surface was paved but

in a very poor condition, as indicated by a baseline International Roughness Index (IRI) of 12.0,

which fell to 1.84 after the rehabilitation

4. The average running speed of vehicles was about 55 km/hr

resulting in increased operating costs and travel times. MCC’s investment helped strengthen pavement

structures, minor and major drainage structures, sidewalks and shoulders, significantly improving the

road’s quality and reliability, including during severe weather. The rehabilitation of the road was

performed on the existing route with no noticeable changes on its alignment.

Considerable attention has been given to the analysis of the spatial integration of markets in

the context of developing economies characterized by market fragmentation due to poor transport

infrastructure (Fackler & Goodwin, 2001). A spatial arbitrage model dictates that the prices of a

homogenous good at any two locations will differ by, at most, the cost of moving the good from

the region with the lower price to the region with the higher price. In this simple model, we would

expect better roads to lower transportation costs and narrow the gap between urban and rural prices

for homogeneous goods. This study suggests improved access from the city of León to Poneloya and

Las Peñitas benefited rural households moderately in the form of lower prices for some manufactured

goods. As it got easier and less costly to deliver manufactured goods to remote areas, the average

markup at which they were sold declined. Comparing prices from stores located close to Poneloya

and Las Peñitas to prices from stores in León before and after the road was rehabilitated showed that

the price of some manufactured goods – cooking oil, toilet paper, matchsticks, and toothpaste – that

travel from urban centers to rural areas significantly declined relative to urban prices.

On the other hand, changes in prices for locally-produced goods can hurt or benefit agricultural

2The direction of trade flows may not be as clear in a different area, for example, around Somotillo, a city locatedclose to the border with Honduras that has several links to other communities surrounding it.

3The region of western Nicaragua has a lot to offer to visitors. Landscapes across the region include volcanoes,beaches, mangrove forests, estuaries, agricultural fields, and historic towns. The coastal towns of Poneloya and LasPeñitas offer tourists the opportunity to enjoy the Pacific Ocean.

4IRI is the most commonly used road roughness index for evaluating and managing road systems.

3

households depending on whether they are net sellers or net buyers of those goods. The extent to

which price changes for locally-produced goods could ameliorate poverty and income inequality is an

empirical question that will depend on whether benefits are captured by the richest or the poorest

fraction of the population. Jacoby (2000) finds that the benefits of providing better road access to

markets in Nepal were not large enough or targeted efficiently enough to greatly reduce poverty and

income inequality. It is even feasible that some poor households will be negatively affected by better

roads even though it is common for rural road rehabilitation to be mistakingly described as a tide

that lifts all boats. For example, Casaburi, Glennerster, and Suri (2012) find that an improvement

in rural road quality led to a reduction in the prices of rice and cassava, two main staples produced

domestically in Sierra Leone, which would hurt net sellers and benefit net buyers in poor and remote

communities. In contrast, a price increase benefiting net sellers and hurting net buyers could result

from rural producers having easier access to urban markets at which they could charge higher prices

for their staples, meats and produce. This is the case for one good that clearly travels from the coast to

León: fresh fish. This paper shows that the local price of fish increased as a result of the intervention,

which benefited households whose main source of income is fishing. However, it could have certainly

hurt net buyers of fish.

This paper is organized as follows. Section 2 provides an introduction to the link between road

quality, economic growth and poverty reduction in Nicaragua. Section 3 provides a description of the

MCA-N Transportation Project and the dataset, and reviews Alevy’s (2014) impact evaluation of the

MCA-N Transportation Project. Section 4 presents this paper’s methodology for the impact evaluation

of a rural road rehabilitation and its estimation results. Section 5 provides a welfare analysis describing

how the resulting changes in prices have affected the cost of a typical consumption basket for a family

of six. Section 6 concludes and suggests some extensions to this analysis.

2 Rural Roads and Poverty Alleviation

Reliable trade linkages with other regions are a necessity for a rural economy to function efficiently.

Inputs need to be transported to production sites on time, and outputs must be moved to where

they can be used by consumers. The fragile and perishable nature of fruits, vegetables, livestock, and

grains, makes transporting these products in a timely manner essential to minimize post-harvest losses.

For these reasons, the improvement of rural roads and better access to modern markets are broadly

recognized as fundamental preconditions for rural farmers’ escape from subsistence poverty traps.

4

Nicaragua currently holds the lowest quality of road infrastructure in Central America. Nicaragua’s

road network totals 14,848 miles (23,897 km), of which only 14 percent is paved and only 25 percent

is in good or fair condition. Table 1 provides the total length of the road network by country and

includes the length of the paved and unpaved portions. It shows Nicaragua has the highest proportion

of unpaved roads among the seven Central American countries. The density of coverage, measured as

paved roads per capita, situates Nicaragua at 0.46 km/1,000 population, far below the Latin American

median of 1.51 km/1,000 population.

Table 1: Road networks in Central America

Country Total length Paved % Unpaved % Year of data RAI

(km) (km) (km) (2004)

Nicaragua 23,897 3,346 14% 20,551 86% 2014 28%

Belize 2,870 488 17% 2,382 83% 2011 78%

Honduras 14,742 3,367 23% 11,375 77% 2012 40%

Costa Rica 39,018 10,133 26% 28,885 74% 2010 82%

Panama 15,137 6,351 42% 8,786 58% 2010 77%

Guatemala 17,332 7,483 43% 9,849 57% 2015 55%

El Salvador 6,918 3,247 47% 3,671 53% 2010 64%

One way of assessing the performance of a rural road network is to consider the level of accessibility

it offers to rural inhabitants. This measure is encapsulated in the World Bank’s Rural Accessibility

Index (RAI), which measures the proportion of the rural population within a two-kilometer walking

distance (approximately 20 minutes walking time) of an all-season road (a road that is drivable at

all times of the year within six hours after rain by the prevailing means of transport, often a pick-

up truck)

5. Table 1 shows Nicaragua’s RAI is the lowest in Central America meaning that access

to all-season roads is uncommon. Unpaved roads connecting remote rural areas in Nicaragua erode

easily and, in some cases, become impassible for weeks during the rainy season. Household question-

naires conducted for the 2005 Nicaraguan National Household Survey for the Measurement of Living

Standards provide additional evidence of this situation: 17% of rural households claim the main road

leading to their community is never accessible during the rainy season and 32% of rural households

claim the main road leading to their community is only accessible sometimes during the rainy season.

Another measure of road quality is the International Roughness Index (IRI), a measure of road

roughness that was also proposed by the World Bank in an effort to standardize the characterization of

road conditions globally. The IRI measures pavement roughness in wheel path in terms of the number

of meters per kilometer that a laser, mounted in a specialized van, jumps as it is driven along a road.

5According to this measure, thirty-one percent of the world’s rural population is isolated from a transport network.

5

In other words, the roughness of a road is defined as the variation in surface elevation that induces

vibrations in traversing vehicles. The lower the IRI number at given speed, the smoother the ride felt

by the road user. As mentioned previously, the IRI of road León-Poneloya-Las Peñitas went from 12.0

in the baseline to 1.84 after its rehabilitation, which can be described as a substantial improvement

(see Table 2).

Table 2: Range of roughness for unpaved roads

IRI (m/km) Road Description

1-4

Ride is comfortable at 120 km/h or more. Slight undulations

almost imperceptible. There are no potholes, corrugations,

noticeable depressions, or surface deformations.

4-7

Ride is comfortable between 100 and 120 km/h. Vehicle

occupants may feel slight vibrations or long-wave undulations.

Surface may exhibit occasional shallow depressions or potholes,

or rough patches and there may be several shallow potholes or

areas of surface raveling. Moderate corrugations or long

undulations may be noted.

7-9

Ride is comfortable between 70 and 90 km/h, but with quite

perceptible vibrations or other vehicle movements. Usually

encountered defects are: i) frequent moderate, uneven

depressions or patches; ii) occasional potholes, perhaps 1 to 3

every 50 meters; and iii) frequent rough corrugations or other

surface deformations.

9-11

Ride is reasonably comfortable only at speeds of 50-60 km/h,

with frequent vibrations and uncomfortable movements. Usually,

defects are severe, comprising long and uneven depressions at

frequent intervals, irregular patches (3 to 5 every 50 meters), and

frequent potholes (4 to 6 every 50 meters).

11-12

It is necessary to reduce speed to less than 50 km/h in order to

minimize discomfort. There are many deep potholes and/or

depressions (8-16 every 50 meters), and severe surface

disintegration.

Source: Pavement Interactive (2007)

A substantial decrease in road roughness along road León-Poneloya-Las Peñitas should be reflected

not only in decreased travel times and improved safety, but in lower transportation costs. Roughness

simply means how bumpy or smooth a road is, whether on a rural road or a deteriorated city street.

However, rough roads are more than just an uncomfortable ride. Rough roads increase the amount of

resistance a vehicle experiences as it travels and increased resistance translates into increased fuel con-

sumption. Roughness is also an indicator of the wear and tear on your vehicle. Akbarian et al. (2011)

use roughness and traffic data to show that rougher roads lead to greater fuel consumption, greenhouse

gas emissions, and user costs (oil, tires, maintenance parts and labor, and vehicle depreciation). With

6

an average truck speed of 20 kilometers per hour, inadequate infrastructure can drive transportation

costs in Nicaragua (averaging 0.17 USD per ton-kilometer) to twice that of comparable transportation

costs in the United States (ranging from 0.02 to 0.10 USD per ton-kilometer) (Osborne, Pachon, &

Araya, 2014).

For this reason, the main challenge for the road sector in Nicaragua is to improve the quality of

the existing roads rather than to increase the extension of its road network. The low quality of roads

in Nicaragua is aggravated by heavy traffic, geographic conditions and the high frequency of floods.

Heavy vehicles, such as trucks and other equipment used in agriculture, are harder on roads than cars.

For example, a fully loaded tractor-trailer is 20 times heavier than a passenger car, but its impact on

the roadway is disproportionately larger. Roads in rural areas with wet and mountainous terrain also

face substantially higher costs of maintenance than those in flat and arid terrain and must be fixed

frequently in order to be in fair condition. High rainfall accelerates the process of road deterioration,

requiring frequent and more intensive maintenance interventions. If water is allowed to stand on the

road surface after it rains, vehicles create potholes that eventually affect the cost of traveling and the

safety of all road users.

The competitiveness of poor rural farmers in Nicaragua is weakened by the high operating costs

of their vehicles, by the likely deterioration of their perishable produce, and by their vulnerability

to natural disasters. The effect of rough roads on competitiveness is likely to be associated with

limited market access and poverty among agricultural households. The links between remoteness,

rural road quality, and poverty reduction have been examined in numerous studies (Escobal & Ponce,

2002; Donaldson, 2010; Banerjee, Duflo, & Qian, 2012). However, as pointed out by Mu and Van de

Walle (2011) little hard evidence is available to document a causal relationship. This is a reflection

of the fact that benefits of rural roads are indirect and conditional on many other factors (OECD,

2008). Moreover, the location of a new road will hardly ever be randomized and endogeneity issues

need to be addressed when the location of new road investments may be influenced by factors (fertile

land, strategic locations) that are also believed to influence the outcomes from road interventions

6.

In Nicaragua, it is clear that poverty is now largely a rural problem

7and roads are rehabilitated in

6Shrestha (2012) overcomes this endogeneity by constructing an instrument for road networks based on a uniquegeographic feature that partly determines the placement of rural roads in Nepal. The cheaper cost of constructing anorth-south road relative to an east-west road to connect the district headquarters led to greater access for villages innorth-south hinterlands relative to those in east-west hinterlands. Shrestha (2012) uses this to develop an instrumentalvariable strategy and finds that the value of farmland appreciates by 0.25 percent when the travel time to the nearestroad decreases by 1 percent.

7

certain locations and not in others for reasons that tend to have a lot to do with the attributes of those

locations. This program is no exception. The MCC designed its rural road rehabilitation program in

this specific region because of its strategic location and economic potential.

3 Data

To support the national efforts to develop and rehabilitate the road network, Nicaragua has received

substantial financial support from a number of development partners. In 2005, the MCC signed a

US $175-million five-year compact (MCA-N) with the Government of Nicaragua to create an engine

for economic growth in the western part of the country in the departments of Chinandega and León.

The MCA-N consisted of three projects: 1) a Transportation Project that would decrease vehicle

operating costs and travel time; 2) a Rural Business Development Project that helped farmers develop

and implement a business plan built around a high-potential activity (e.g., producing beans, sesame,

cassava, vegetables, or milk); and 3) a Property Regularization Project that would eliminate the

institutional and regulatory barriers preventing productive investment in property in León.

Before a country becomes eligible for an MCC assistance program, the MCC’s Board examines its

commitment to democratic governance, investments in its people and economic freedom as measured

by different policy indicators. After the municipal elections of November, 2008, in response to a pattern

of actions by the Government of Nicaragua inconsistent with the MCC’s eligibility criteria, the MCC’s

Board terminated a portion of the MCA-N, reducing compact funding from US $175 million to US

$113.5 million. Funding was terminated for activities in the Property Regularization Project and for

activities in the Transportation Project, except the three roads that were already under contract. The

Rural Business Development Project was not affected. The Transportation Project ended up investing

US $57.9 million rehabilitating 42 miles of rural roads in 2008-2009 (See Table 3).

The MCC was specifically interested in being able to measure the Transportation Project’s impacts

through data collection and analysis, including traffic counts and an establishment survey that provides

data on the availability and prices of goods in the canasta básica (basic basket of goods that is used

in Nicaragua to track consumer prices). The establishment survey collected information from stores

regarding availability and prices of 53 goods: 23 food items, 15 household items, and 15 clothing

7Poverty in Nicaragua has declined in recent years, but it is still the second poorest country in Latin America afterHaiti. During the 2005-2009 period, the country saw a significant reduction in the poverty headcount of nearly 6percentage points (equivalent to around 230,000 fewer poor people), reaching a national rate of 42.5 percent in 2009.Meanwhile, extreme poverty fell from 17.2 to 14.6 percent between 2005 and 2009.

8

Table 3: MCA-N Transportation Project

Road Upgrade

Length

Average Annual IRI

Daily Traffic

Miles Baseline Endline Baseline Endline

(Kilometers) (Target) (Target)

Somotillo- 18.3 234 561 13.2 3.38

Cinco Pinos (29.4) (278) (3)

León- 12.2 1,103 1,462 12 1.84

Poneloya-Las Peñitas (19.6) (1,276) (3)

Villanueva- 11.2 1,413 1,961 12 1.76

El Guasaule (18) (1,580) (3.4)

items. Most of the surveyed establishments are small grocery stores. The rest are supermarkets or

distributors. Four distinct rounds of data collection were conducted for the establishment survey,

with two rounds both before and after the road rehabilitation.

8This approach to data collection was

meant to reduce concerns that some random shock such as bad weather or a temporary transportation

difficulty would lead to an inaccurate conclusion about the conditions in specific establishments.

One of the most useful elements of this intervention is that communities along the cancelled road

rehabilitation projects were still surveyed and data from these non-treated areas was also collected.

Alevy’s (2014) independent impact evaluation commissioned by the MCC obtains the impact of the

Transportation Project by comparing store prices from areas where a rehabilitation took place to store

prices from areas where it did not relying on the use of panel data and propensity score matching, which

assigns heavier weights to control communities with similar characteristics to the treated communities.

The MCC expected communities living within the zone of influence of the road upgrades to benefit in

the form of lower prices and increased availability of consumer goods. Alevy’s (2014) analysis provided

evidence that the distribution of some perishable and fragile food items had improved as a result of the

Transportation Project, but that the overall effects of the project had been modest. Although there

was a slight increase in availability of consumer goods in treated communities, it was not found to be

statistically significant. The impact on the value of the basic basket of goods is found to be close to

zero in both urban and rural areas. In rural areas there is a small decline in its value (-0.97%) led by

an overall decline in the cost of food. Changes include a decline in food staples (-4.3%), a decline in

dairy and egg products (-20%), and modest increases in produce (2.6%) and meats (2.8%). In urban

8Two rounds of baseline surveys took place in August, 2008. The first round took place in August 11-16, 2008 andresulted in 209 completed surveys. The second round took place in August 25-30, 2008 and resulted in 200 surveyscompleted. Two rounds of surveys took place in the second half of 2010 after the road rehabilitation project wascompleted. The first round took place in August 30-September 3, 2010 and resulted in 224 completed surveys. Thesecond round took place in September 27-October 1, 2010 and resulted in 209 surveys completed. Therefore, the finaldataset of completed surveys contains 842 observations.

9

Figure 1: Map of rehabilitated road and surveyed establishments

areas there is a small increase in its value (0.91%) led by an increase in household goods (11%).

4 Spatial Arbitrage Model

This paper contributes a different approach on how to construct an appropriate comparison or control

group for communities receiving improved roads. Being such a large city, the prices for goods in León

are assumed to be exogenous and not to be affected by improved access to smaller towns like Poneloya

and Las Peñitas. Figure 1 displays the location of surveyed stores along the rural road from León

to Poneloya and Las Peñitas, which are only connected to León through this road and do not have

access to any other markets nearby. Meanwhile, there are several other routes leading to León. One,

in particular, from Santa Teresa to Las Brisas, was one of the roads where surveys were collected, but

the rehabilitation of the road did not take place. The surveys along road Santa Teresa-Las Brisas took

place in Chacaraseca and Loma Pelada. The location of these communities and the surveyed stores is

also displayed in Figure 1. A summary of the characteristics of these two roads is provided in Table 4.

10

Table 4: Baseline characteristics of the roads

Road León-Poneloya-Las Peñitas Santa Teresa-Las Brisas

Length 12.2 mi (19.6 km) 8.3 mi (13.4 km)

Baseline IRI 12.0 12-14

Material Pavement Pavement

Traffic 1,000 vehicles per day 200 vehicles per day

Average driving speed 45-55 km/h 45-55 km/h

Beneficiaries 10,000 inhabitants 4,000 inhabitants

Area of influence 145.12 square kilometers 95.64 square kilometers

Direction Starts in León and finishes at

the fishing port of Poneloya and

the coastal village of Las Peñitas

Starts in Santa Teresa in the

suburbs of León and finishes in

Las Brisas

Communities served Carlos Canales, Guanacastillo,

La Ceiba, La Gallina, Las

Delicias, La Pedrera, San

Roque, Miramar, Poneloya, and

Las Peñitas

Santa Teresa, Chacaraseca,

Santa Lucía, Loma Pelada, La

Concepción San José, Puerta de

Piedra, and Las Brisas.

Rehabilitation Provided the road with a new

durable riding surface which

guarantees circulation of

vehicles at higher speeds at any

time of the year.

Rehabilitation was cancelled

The model presented in this paper suggests a road of better quality resulting in lower transportation

costs would narrow the gap between urban and rural prices for consumer goods. Define PT and PC

as the vectors of prices corresponding to a rural area (T ) and a big city (C), which are connected by

a low-quality road. Markup ↵ is defined as the difference in prices between the rural area and the big

city:

↵ = PT � PC

A model of spatial price behavior states that the prices of a homogenous good at any two locations

will differ by, at most, the cost of moving the good from the region with the lower price i to the region

11

to the higher price j:

Pj � Pi rij

where transport cost, rij represents the cost of moving the good from location i to location j and

includes all relevant costs of arranging transactions between spatially separate locations. In equilib-

rium, markups for manufactured goods in a rural area must be less than or equal to the transaction

costs of transporting goods from the city and selling them in the rural area, ↵ = PT � PC rC,T .

Similarly, if markups are negative for locally-produced goods, this difference in prices must be lower

than the transaction costs of transporting goods from the the rural area and selling them in the city,

|↵| = |PT � PC | rT,C . Lower transaction costs rC,T and rT,C , led specifically by lower transporta-

tion costs due to improved road quality, would be expected, in equilibrium, to narrow the difference

between PT and PC . As it gets less costly to deliver manufactured goods like cooking oil, toilet paper,

matchsticks and toothpaste, to more remote areas, we would expect their prices to drop for rural

consumers relative to prices in León. Also, as rural producers gain easier access to urban markets

at which they could sell their locally-produced goods at higher prices, it would be possible to see an

increase in local prices for locally-produced goods as a result of a better connection to León.

Define P̄C as average prices in León and P̄T as average prices in the rural area including the five

surveyed communities connected to León by the road to Poneloya and Las Peñitas: Carlos Canales,

La Ceiba, San Roque, Poneloya, and Las Peñitas. The number of observations for each community

is described in detail in Table 5. Only four establishments were surveyed in La Ceiba and only two

were surveyed in San Roque

9. The dummy variable Li equals zero for León and equals one for all of

the communities along both roads. The treatment dummy variable Di equals one for the communities

on road León-Poneloya-Las Peñitas and equals zero for León and the communities on road Santa

Teresa-Las Brisas. The year dummy variable �t equals zero in 2008 and is equal to one in 2010.

9For many of the consumer goods, the number of observations in these communities is too low to conduct any analysis.

12

Table 5: Establishment surveys along León-Poneloya-Las Peñitas and Santa Teresa-Las Brisas

Community

Dummies Number of Observations

Totalsurveyed 2008 2010

Li Di establishments R1 R2 R1 R2

Poneloya 1 1 17 10 10 14 12 46

Las Peñitas 1 1 15 10 10 13 11 44

San Roque 1 1 2 1 2 2 2 7

La Ceiba 1 1 4 3 3 4 4 14

Carlos Canales 1 1 14 8 8 12 11 39

León 0 0 35 27 26 23 24 100

Chacaraseca 1 0 20 12 12 19 13 56

Loma Pelada 1 0 5 3 4 5 5 17

112 74 75 92 82 323

Table 6 provides the estimated average markups ↵̄ = P̄T �P̄C in 2008 before the road rehabilitation

took place. In 2008, most prices for consumer goods were found to be lower (22/37 goods) and less

volatile (23/37 goods) in León. The statistically-significant positive markup for thirteen goods confirms

that several goods are sold at a significantly higher price along the road to Poneloya and Las Peñitas.

These thirteen goods include six food items: beans, sugar, cooking oil, chicken, eggs, and potatoes.

They also include seven household items: detergent, toothpaste, matches, broom, toilet paper, sanitary

towels, and toothbrush. On average, these thirteen products are 13.77% more expensive outside of León

and range from 6% more expensive (chicken) to 21% more expensive (detergent). The only product that

was found to be significantly cheaper outside of León was fish, as indicated by a statistically-significant

negative markup. Table 7 provides the same statistics for 2010. Prices are given in córdobas, the official

currency of Nicaragua (1 córdoba = 0.05 US dollars approximately).

Estimating the difference in differences can deduce the impact of a policy change on the treated

population. The structure of this statistical technique implies that the treatment group and control

group are trending in the same way over time. In this case, this means markups would have stayed

constant had the road not been rehabilitated. The difference in differences is the measure of distance

between the unobserved outcome for average prices had the road not been rehabilitated and the actual

outcome. The measured differences in differences are presented in Table 8. In this table, it is simply

the difference between ↵̄ in 2008 and ↵̄ in 2010. These estimates indicate that the prices along the

road for cooking oil, toothpaste, matches and toilet paper could have seen a significant decrease from

being significantly more expensive initially compared to Léon.

13

Table 6: Difference in means in 2008

Price (Córdobas) P̄T S.D. N P̄C S.D. N ↵̄ ↵/P̄C%

Basic Foods

Rice (lb) 10.25 0.69 36 10.71 1.29 12 -0.46 -4.28%

Beans (lb) 15.60 0.99 15 13.73 1.27 11 1.87*** 13.64%

Sugar (lb) 5.95 0.24 30 5.38 0.32 10 0.58*** 10.70%

Cooking oil (l) 38.50 2.18 36 34.42 1.24 12 4.08*** 11.86%

Tortilla 1.08 0.19 12 1.50 0.58 4 -0.42 -27.78%

Pinolillo 3.03 2.61 45 6.07 6.17 10 -3.04 -50.03%

Pasta (400 g) 7.38 1.36 29 7.05 0.96 11 0.33 4.74%

Meat, Poultry, Fish

Beef (lb) 38.50 4.95 2 37.00 1.18 11 1.5 4.05%

Pork (lb) 34.00 1.73 3 36.00 1.22 5 -2 -5.56%

Chicken (lb) 23.87 0.97 23 22.50 0.87 5 1.37** 6.09%

Fish (lb) 18.67 1.15 3 23.50 3.51 8 -4.83*** -20.57%

Eggs (dozen) 33.00 3.04 42 29.40 1.95 5 3.6*** 12.24%

Produce

Tomato 1.70 0.98 32 1.60 0.49 12 0.1 6.17%

Yellow onion 2.06 0.88 26 1.79 0.58 12 0.27 14.85%

Potatoes 11.42 0.95 26 10.08 1.08 12 1.34*** 13.29%

Chiltoma 1.02 0.31 30 1.17 0.44 12 -0.15 -12.86%

Green plantain 3.00 0.56 25 3.25 0.75 12 -0.25 -7.69%

Orange 1.67 0.52 6 1.78 0.38 10 -0.11 -6.10%

Cabbage 11.29 4.27 17 12.67 3.70 12 -1.37 -10.84%

Household Goods

Laundry Soap 12.25 2.07 24 11.41 1.07 11 0.84 7.37%

Detergent 1.92 0.44 26 1.58 0.38 6 0.34* 21.46%

Toothpaste 22.76 2.82 25 20.40 1.35 10 2.36*** 11.57%

Matches 1.09 0.22 41 0.98 0.08 11 0.11** 11.06%

Broom 32.88 4.19 8 28.82 3.57 11 4.06** 14.08%

Toilet paper 8.33 1.25 46 7.05 1.17 10 1.28*** 18.10%

Bathroom soap 10.37 1.07 41 10.50 0.67 12 -0.13 -1.28%

Sanitary towels 11.81 1.67 32 10.08 0.47 12 1.73*** 17.15%

Deodorant 41.23 7.17 13 40.00 4.45 11 1.23 3.08%

Toothbrush 9.03 1.34 34 7.67 0.62 12 1.36*** 17.77%

Clothing

Short shirt (Men) 77.50 45.96 2 103.75 38.16 4 -26.25 -25.30%

Underpants (Men) 18.33 5.16 6 16.75 5.68 4 1.58 9.45%

Socks (Men) 12.29 2.06 7 11.25 2.50 4 1.04 9.21%

Short shirt (Women) 75.00 1 112.50 32.83 6 -37.5 -33.33%

Underpants (Women) 19.17 4.92 6 16.50 1.73 4 2.67 16.16%

Brassier (Women) 24.17 3.76 6 24.50 1.00 4 -0.33 -1.36%

Underpants (Kids) 9.71 0.76 7 10.80 4.02 5 -1.09 -10.05%

Socks (Kids) 10.17 0.41 6 11.40 2.19 5 -1.23 -10.82%

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

14

Table 7: Difference in means in 2010

Price (Córdobas) P̄T S.D. N P̄C S.D. N ↵̄ ↵/P̄C%

Basic Foods

Rice (lb) 10.05 0.55 41 9.77 0.60 13 0.28 2.86%

Beans (lb) 13.84 3.17 19 13.17 3.97 9 0.68 5.13%

Sugar (lb) 6.95 1.53 42 7.61 4.12 14 -0.66 -8.69%

Cooking oil (l) 30.05 2.32 38 27.68 1.03 14 2.37*** 8.58%

Tortilla 1.07 0.26 15 1.00 4 0.07 6.67%

Pinolillo 2.93 0.21 48 11.79 16.62 12 -8.86* -75.18%

Pasta (400 g) 9.18 1.23 37 7.71 1.19 14 1.46*** 18.94%

Meat, Poultry, Fish

Beef (lb) 40.00 5 36.00 0.93 8 4 11.11%

Pork (lb) 34.50 2.12 2 39.00 1.10 6 -4.5 -11.54%

Chicken (lb) 22.32 1.54 31 20.00 1 2.32*** 11.61%

Fish (lb) 26.60 13.79 5 25.00 2 1.6 6.40%

Eggs (dozen) 30.55 9.35 47 28.00 3.46 5 2.55 9.12%

Produce

Tomato 1.73 0.59 35 2.11 0.65 9 -0.38 -18.12%

Yellow onion 3.21 1.10 36 3.22 1.97 9 -0.01 -0.43%

Potatoes 10.28 1.05 32 8.89 1.05 9 1.39*** 15.66%

Chiltoma 1.88 0.65 26 1.67 0.50 9 0.22 13.08%

Green plantain 2.99 0.81 34 2.61 0.65 9 0.37 14.33%

Orange 1.58 0.49 6 1.50 0.71 5 0.08 5.56%

Cabbage 15.13 4.73 8 24.44 6.35 9 -9.32*** -38.13%

Household Goods

Laundry Soap 13.51 2.18 42 12.95 1.26 13 0.56 4.36%

Detergent 2.50 1.71 44 1.96 0.43 14 0.54* 27.64%

Toothpaste 22.11 4.09 37 22.79 3.62 14 -0.68 -2.97%

Matches 1.15 0.28 43 1.60 1.94 13 -0.45 -27.98%

Broom 32.87 3.83 15 30.63 2.07 8 2.24* 7.32%

Toilet paper 9.78 0.77 45 9.55 1.87 14 0.22 2.35%

Bathroom soap 12.91 1.32 38 12.04 0.69 14 0.87*** 7.25%

Sanitary towels 14.05 1.30 39 12.75 1.76 14 1.3** 10.21%

Deodorant 46.18 5.13 20 41.23 3.22 13 4.94*** 11.99%

Toothbrush 9.57 1.09 35 8.12 1.36 13 1.46*** 17.94%

Clothing

Short shirt (Men) 165.00 7.07 2 160.00 21.60 4 5 3.13%

Underpants (Men) 22.00 6.71 5 19.75 10.96 2 2.25 11.39%

Socks (Men) 12.75 3.65 8 13.50 2.12 2 -0.75 -5.56%

Short shirt (Women) 137.00 38.99 5 161.25 34.73 4 -24.25 -15.04%

Underpants (Women) 19.38 7.86 12 17.50 2.50 3 1.88 10.71%

Brassier (Women) 26.00 3.16 10 25.00 3 1 4.00%

Underpants (Kids) 11.00 3.25 12 10.83 1.44 3 0.17 1.54%

Socks (Kids) 12.20 2.97 10 10.00 3 2.2** 22.00%

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

15

Table 8: Difference-in-differences estimation results

↵̄ = P̄T � P̄C Diff-in-diff Significant baseline Change in

Price (Córdobas) 2008 2010 markup markup

Basic Foods

Rice (lb) -0.46 0.28 0.74**

Beans (lb) 1.87*** 0.68 -1.20 + ↓Sugar (lb) 0.58*** -0.66 -1.24 + ↓

Cooking oil (l) 4.08*** 2.37*** -1.71* + ↓Tortilla -0.42 0.07 0.48**

Pinolillo -3.04 -8.86* -5.83**

Pasta (400 g) 0.33 1.46*** 1.13*

Meat, Poultry, Fish

Beef (lb) 1.5 4.00 2.5*

Pork (lb) -2.0 -4.50 -2.50

Chicken (lb) 1.37** 2.32*** 0.95 + ↑Fish (lb) -4.83*** 1.60 6.43 - ↓

Eggs (dozen) 3.6*** 2.55 -1.05 + ↓Produce

Tomato 0.1 -0.38 -0.48

Yellow onion 0.27 -0.01 -0.28

Potatoes 1.34*** 1.39*** 0.05 + ↑Chiltoma -0.15 0.22 0.37

Green plantain -0.25 0.37 0.62*

Orange -0.11 0.08 0.19

Cabbage -1.37 -9.32*** -7.95**

Household Goods

Laundry Soap 0.84 0.56 -0.28

Detergent 0.34* 0.54* 0.20 + ↑Toothpaste 2.36*** -0.68 -3.04* + ↓Matches 0.11** -0.45 -0.56* + ↓Broom 4.06** 2.24* -1.82 + ↓

Toilet paper 1.28*** 0.22 -1.05* + ↓Bathroom soap -0.13 0.87*** 1.01**

Sanitary towels 1.73*** 1.3** -0.43 + ↓Deodorant 1.23 4.94*** 3.71

Toothbrush 1.36*** 1.46*** 0.09 + ↑Clothing

Short shirt (Men) -26.25 5.00 31.25

Underpants (Men) 1.58 2.25 0.67

Socks (Men) 1.04 -0.75 -1.79

Short shirt (Women) -37.5 -24.25 13.25

Underpants (Women) 2.67 1.88 -0.79

Brassier (Women) -0.33 1.00 1.33

Underpants (Kids) -1.09 0.17 1.25

Socks (Kids) -1.23 2.2** 3.43

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

16

Markups did not increase significantly for any of the thirteen goods that were significantly more

expensive outside of León initially. Results show that there is a significant decrease in markup for

cooking oil, toothpaste, matches, and toilet paper. The price behavior of these manufactured goods

after the intervention resembles the model presented in Figure 2. These goods were significantly more

expensive in the rural area in the baseline. In the endline, their prices moved significantly closer to the

prices in León. The markup for cooking oil used to be 4.08 in 2008 and fell to 2.37 in 2010. However,

the price of cooking oil in 2010 is still significantly higher (8.58%) in the rural area. The markup for

toothpaste used to be 2.36 in 2008 and fell to -0.68 in 2010. This markup is no longer significant,

which means that the price of toothpaste in the rural area is no longer significantly different from the

one in León. The markup for matches used to be 0.11 in 2008 and fell to -0.45 in 2010. Similarly, the

markup for toilet paper fell from 1.28 in 2008 to 0.22 in 2010 and it is no longer significant.

Figure 2: Model scenarios for manufactured goods

Price trend in the big city

Price trend in the rural area

t0 t1

p p

t0 t1

pT

pC

pT

pC

17

The price behavior of fish is different from Figure 2. Table 6 shows there is a significant negative

markup for fish in 2008. In León it was sold for 23.5 córdobas per pound on average and along the road

it was sold for 18.67 córdobas per pound on average. This markup was no longer significant in 2010.

Fish in León was sold for 25 córdobas per pound on average and along the road it was sold for 26.60

córdobas per pound on average. A positive difference in differences indicates that the rehabilitation

of the road from León to the coast could have benefited urban consumers and hurt rural consumers

by narrowing the gap between urban and rural prices. Note that the changes in prices suggest the

narrowing of the gap was due more to a rise in the price of fish in the rural area rather than a fall in

the price of fish in León.

Changes in prices at the stores located along the rehabilitated road León-Poneloya-Las Peñitas can

be attributed more certainly to the road improvement if this same effect cannot be found along the

non-rehabilitated road Santa Teresa-Las Brisas. For that reason, the price levels collected along road

Santa Teresa-Las Brisas were integrated into the analysis through a second model. Model 1 is defined

as:

Pigtr = �0 + �1Di + �2�t + �3Di�t + uigtr (1)

where the dependent variable of interest, Pigtr, is the price at establishment i of consumer good g

where t indicates year 2008 or 2010 and r indicates survey round one or two. Assuming that the error

term uigtr satisfies the assumptions of a linear regression model with E [uigtr] = 0 and V ar [uigtr] = �2g

the resulting values of these regressions for each individual good are shown in Table 13. The sign of

�1 is an indicator of whether markup is positive or negative for good i. The year trend, �2, is always

positive for household goods and does not follow a clear trend for the rest of the goods. The difference-

in-differences estimate is given by �3. If E [Pigtr|Di = 0, t = 2010] � E [Pigtr|Di = 0, t = 2008] = �2

and E [Pigtr|Di = 1, t = 2010]� E [Pigtr|Di = 1, t = 2008] = �2 + �3, then:

E [Pigtr|Di = 1, t = 2010]� E [Pigtr|Di = 1, t = 2008]

� [E [Pigtr|Di = 0, t = 2010]� E [Pigtr|Di = 0, t = 2008]] = �3

Model 2, which integrates data collected along road Santa Teresa-Las Brisas, is defined as:

Pigtr = �0 + �1Li + �2�t + �3Li�t + �4Di + �5Di�t + uigtr (2)

18

where the dummy variable Li equals zero for León and is equal to one for all of the communi-

ties along both roads and the treatment dummy Di is equal to one for the communities on road

León-Poneloya-Las Peñitas and equals zero for León and the communities on road Santa Teresa-Las

Brisas. The resulting values of these regressions for each individual good are shown in Table 14. The

difference in difference in differences (DDD) estimate is given by �5. If E [Pigtr|Di = 0, t = 2010] �

E [Pigtr|Di = 0, t = 2008] = �2 + �3Li and E [Pigtr|Di = 1, t = 2010] � E [Pigtr|Di = 1, t = 2008] =

�2 + �3Li + �5, then:

E [Pigtr|Di = 1, t = 2010]� E [Pigtr|Di = 1, t = 2008]

� [E [Pigtr|Di = 0, t = 2010]� E [Pigtr|Di = 0, t = 2008]] = �5

The relative magnitudes of these two models are presented in Table 9. The measures for Model 1,

�1

�0

and

�3

�0+�1+�2, give us the baseline markup and the percent change the difference in differences would

represent of the unobserved scenario. The measures for Model 2,

�4

�0+�1and

�5

�0+�1+�2+�3+�4, also give

us the baseline markup and the percent change the difference in differences would represent, but these

are compared to the price trend along road Santa Teresa-Las Brisas. Most of the percent changes in

prices for household goods from Model 1 are negative, but a definition of what a consumption basket

looks like for a typical family is required to measure the magnitude of the effect that these price changes

would have on consumers. The clear negative magnitude and statistical significance of differences in

differences from Model 1 for cooking oil (-5.4%*), toothpaste (-12.1%**), matches (-32.5%**), and

toilet paper (-9.7%**) are not as clear for Model 2. Note that Model 2 could not be estimated for fish

because the price of fish was not collected along road Santa Teresa-Las Brisas probably because there

was no fresh fish available in the area. The difference in differences for fish can be interpreted as a

31.9% increase in price compared to the unobserved scenario had the road not been rehabilitated.

19

Table 9: Relative changes in price levels

Model 1 Model 2

Product �0�1

�0

�3

�0+�1+�2�0

�4

�0+�1

�5

�0+�1+�2+�3+�4

Basic Foods

Rice (lb) 10.7*** -.043** .079** 10.7*** -0.027 .069*

Beans (lb) 13.7*** .136* -0.08 13.7*** 0.106 -0.01

Sugar (lb) 5.38*** 0.107 -0.151 5.38*** -0.029 0.019

Cooking oil (l) 34.4*** .119*** -.054* 34.4*** -0.021 .074*

Tortilla 1.50*** -.278*** 0.829 1.50*** 0.083 -.326***

Pinolillo 6.07*** -.500** -.666*** 6.07*** 0.19 -0.154

Pasta (400 g) 7.05*** 0.047 .140* 7.05*** -0.02 0.063

Meat, Poultry, Fish

Beef (lb) 37.0*** 0.041 .067* 37.0*** .100** -0.041

Pork (lb) 36.0*** -.056** -.068* 36.0*** 0 -0.014

Chicken (lb) 22.5*** .061** 0.045 22.5*** 0.038 -0.008

Fish (lb) 23.5*** -0.206 0.319 23.5*** 0 0

Eggs (dozen) 29.4*** 0.122 -0.033 29.4*** 0.112 -0.11

Produce

Tomato 1.60*** 0.062 -0.218 1.60*** 0.103 0.042

Yellow onion 1.79*** 0.148 -0.08 1.79*** 0.076 .413*

Potatoes 10.1*** .133*** 0.005 10.1*** 0.038 -0.031

Chiltoma 1.17*** -0.129 0.243 1.17*** 0.043 0.131

Green plantain 3.25*** -0.077 0.264 3.25*** -0.047 -0.061

Orange 1.77*** -0.061 0.138 1.77*** 0.429 -0.269

Cabbage 12.7*** -0.108 -.344*** 12.7*** 0.167 -.373***

Household Goods

Laundry Soap 11.4*** 0.074 -0.02 11.4*** -0.017 0.022

Detergent 1.58*** 0.215 0.088 1.58*** 0.163 -0.064

Toothpaste 20.4*** .116* -.121** 20.4*** 0.07 -0.056

Matches .977*** 0.111 -.325** .977*** 0.04 0.008

Broom 28.8*** .141** -0.052 28.8*** -0.004 -0.015

Toilet paper 7.05*** .181*** -.097** 7.05*** 0.026 -0.046

Bathroom soap 10.5*** -0.013 .085* 10.5*** -.065** 0.057

Sanitary towels 10.1*** .171*** -0.03 10.1*** -0.016 .087**

Deodorant 40.0*** 0.031 0.087 40.0*** 0.031 0.089

Toothbrush 7.67*** .178*** 0.01 7.67*** 0.01 0.072

Clothing

Short sleeve shirt (Men) 104*** -0.253 0.234 104*** 0 0

Underpants (Men) 16.8*** 0.095 0.031 16.8*** -0.083 0.192

Socks (Men) 11.2*** 0.092 -0.123 11.2*** -.353*** 1.12

Short sleeve shirt (Women) 112*** -0.333 0.107 112*** -0.118 0

Underpants (Women) 16.5*** 0.162 -0.039 16.5*** 0.278 -0.213

Brassier (Women) 24.5*** -0.014 0.054 24.5*** 0.082 -0.076

Underpants (Kids) 10.8*** -0.101 0.128 10.8*** -0.089 -0.031

Socks (Kids) 11.4*** -0.108 0.392 11.4*** -0.135 0.408

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

20

5 Welfare Analysis

The consumption basket is designed following the Nicaraguan National Information and Development

Institute’s (INIDE) definition of canasta básica, which includes 53 goods in three main categories (food,

household goods, and clothing). Nationally-representative average prices of goods are available from

INIDE to calculate the value of this basket at a national level. The average value for this consumption

basket along road León-Poneloya-Las Peñitas, including 37 out of 53 goods for which there were

enough observations in the establishment survey, is shown in Table 10. This 37-good consumption

basket at a national level had a value of $4,735.45 córdobas in August 2008 and 4,690.96 córdobas

in September 2010. Data from the establishment survey indicates that the basket’s value would have

been approximately 20% lower in León ($3,658.90 córdobas in August 2008 and $3,813.76 córdobas in

September 2010). Although the nationally-representative data indicates the value of this consumption

basket fell over time, the value of the consumption basket in León increased by 4.23% over time. Prices

from surveys collected along the road to Poneloya and Las Peñitas also provide evidence that such a

basket would have been approximately 1.5% more expensive along the road than in León: $3,719.90

córdobas in August 2008 and $3,871.64 76 córdobas in September 2010. The value of the basket along

the road increased by 4.08% over time.

10

The welfare effect of the rehabilitated road is obtained by measuring the value of the difference

in difference in differences (DDD) estimates in a typical monthly consumption basket. The monthly

quantities consumed by an average family of six (4 adults and 2 children) as defined by INIDE are

displayed in Tables 15, 16, and 17. The resulting changes in córdobas for Model 1 are obtained

by multiplying these monthly quantities times �3, and finding the total change in value for each

subcategory of goods,

Pi �3iQi. As a result of the road rehabilitation project, Model 1 resulted in

the cost of the monthly food basket decreasing by 25.56 córdobas (1.17 USD), the cost of the monthly

basket of household goods decreasing by 12.74 córdobas (0.59 USD) and the cost of the monthly

basket of clothing increasing by 34.86 córdobas (1.62 USD). The resulting changes in córdobas for

Model 2 are obtained by multiplying these monthly quantities times �5, and finding the total change

in value for each subcategory of goods,

Pi �5iQi. Model 2 resulted in the cost of the monthly food

basket decreasing by 81.66 córdobas (3.80 USD), the cost of the monthly basket of household goods

increasing by 5.02 córdobas (0.23 USD) and the cost of the monthly basket of clothing increasing by

10According to Nicaragua’s Central Bank, annual inflation was approximately 3.7% in 2008-2009 and 5.5% in 2009-2010. The exchange rate (USD/NIO) was approximately 16.73 in 2005, 19.49 in August 2008, and 21.57 in September2010.

21

10.35 córdobas (0.48 USD).

Table 10: Value of monthly consumption basket

For a family of six (4 adults and two children)

Group Category 2008 2010

Nicaragua Food 4,134.12 3,992.41

Household 407.11 476.33

Clothing 194.22 222.23

Total 4,735.45 4,690.96

(242.98 USD) (217.44 USD)

León Food 2,881.06 2,871.90

Household 518.62 605.85

Clothing 259.22 336.02

Total 3,658.90 3,813.76

(187.74 USD) (176.78 USD)

Carlos Canales, Food 2,926.79 2,892.31

La Ceiba, Household 572.42 646.89

San Roque, Clothing 220.69 332.43

Poneloya, and Total 3,719.90 3,871.64

Las Peñitas (190.87 USD) (179.46 USD)

Table 11 summarizes what the percent changes in value would be for Model 1 and Model 2 by

subcategories of goods. These results suggest the rehabilitation of road León-Poneloya-Las Peñitas

had a significant effect in reducing the value of the consumption basket by -0.09% comparing to León

(Model 1) and by -1.69% comparing to road Santa Teresa-Las Brisas (Model 2). The results from

Model 1 are similar to Alevy’s (2014) in the sense that the impact on the value of the whole basket of

consumer goods is found to be close to zero. However, such an aggregate measure does not allow to

see what is happening to the value of the basket for each subcategory of goods. For example, Table

15 shows that the value of the food basket has decreased as a whole, but that the value of the meat,

poultry, and fish basket has increased, mostly driven by the increase in the price of fish. Alevy’s (2014)

analysis supports the idea that the price of perishable and fragile items in the area of influence of the

rehabilitated roads has decreased as a result of the intervention, but the estimation of Model 1 has not

led to strong evidence that this has been the case in the area of influence of the road León-Poneloya-Las

Peñitas. In fact, the results from Model 1 seem to support the opposite idea. Price decreases are most

consistent for storable consumer goods like cooking oil, toilet paper, matches, and toothpaste. The

results from Model 2 are more consistent with Alevy’s (2014) hypothesis of an expected decrease in

the value of perishable goods.

22

Table 11: Percentage changes in value of consumption basket

Category

Model 1

Predicted (1) Change (2) (2) / (1)Pi [�0i + �1i + �2i]Qi

Pi �3iQi

Food 1,492.85 -87.05 -5.83%

Meat 1,058.70 65.64 6.20%

Produce 364.92 -4.16 -1.14%

Household 659.52 -12.74 -1.93%

Clothing 297.37 34.86 11.72%

Total 3,873.35 -3.44 -0.09%

Category

Model 2

Predicted (1) Change (2) (2) / (1)Pi [�0i + �1i + �2i + �3i + �4i]Qi

Pi �5iQi

Food 1,400.52 5.71 0.41%

Meat 1,168.35 -43.88 -3.76%

Produce 404.43 -43.69 -10.80%

Household 641.68 5.02 0.78%

Clothing 321.87 10.35 3.22%

Total 3,936.85 -66.48 -1.69%

These price changes resulting from rural road rehabilitation could have decreased poverty and

inequality if they benefited the poorest households in the coast. This seems true among poor households

whose main source of income is fishing. Comparing to León, estimates from Model 1 lead to conclude

that improving the road benefited fishing households by lowering the average cost of a basic basket of

manufactured goods by -1.93% and the average cost of the whole canasta básica by -0.09%, and by

allowing fresh fish caught in the coast to be sold at a 31.88% higher price. Comparing to the value of

the canasta básica along road Santa Teresa-Las Brisas, Model 2 resulted in a monthly 1.69% decrease

in value for this basket of goods, which may represent significant benefits considering that levels of

poverty are either high or severe in the area of influence of the rehabilitated road. The population of

Poneloya and Las Peñitas is less than 3,000 people, but the MCC originally estimated that this road

would benefit 10,000 inhabitants. Even though the surveyed communities have small populations,

there are many non-surveyed communities around them.

11

Household surveys from the 2005 Nicaraguan National Household Survey for the Measurement of

Living Standards indicate how low rural household expenditures are on food, especially among the

extreme poor households. These levels are summarized in Table 12. The INIDE defines its food basket

as an ideal combination of food in appropriate and sufficient quantities to meet the energy and protein

11The Department of León has 175,00 people (35,000 households) and 38,000 (8,000 households) of them live in therural areas outside of the city of León.

23

Table 12: Monthly rural household expenditures on food (Córdobas)

Average, (Standard Deviation), Number of Observations

Income

Municipality of León Nicaragua

level

Extreme poor 720.50 542.32

(296.03) (386.25)

10 825

Poor 1,245.50 826.63

(911.92) (570.02)

14 1351

Non-poor 1,420.48 1,278.13

(1,032.78) (957.18)

17 1174

Total 1,190.00 914.84

(891.86) (756.42)

41 3350

Source: 2005 Living Standards Measurement Survey

needs of a household, which explains why its value of 2,926.79 córdobas in 2008 and 2,892.31 córdobas

in 2010 is so different from the extreme poor’s expenditures in rural Nicaragua, which average 720.50

córdobas (43 USD approximately). A fall in the value of a basic food basket in the range of [-2.75%,

-0.88%] as a result of the rehabilitation of the rural road such as indicated by the estimated results

from Model 1 and Model 2 would be a significant channel for extremely poor households to benefit. In

addition, if the rehabilitation of the rural road allows these extremely poor households to sell their fish

or other locally-produced goods at a higher price they would experience an unambiguous improvement

in their food expenditure-to-income ratio.

24

6 Conclusion

Rural roads play a central role in the economic and social development of rural communities, but despite

years of investment from development agencies in the rehabilitation of rural roads, little is known

about the degree to which they reduce transport costs, generate new market activity, and affect input

and output prices. Their effectiveness as poverty and inequality alleviation instruments is also under

question because of the lack of evidence regarding the heterogeneous distribution of their benefits. This

paper suggests the degree to which the resulting impacts on market prices could effectively ameliorate

poverty and income inequality will depend on whether these price changes benefit the richest or the

poorest fraction of the population according to their status as net sellers or net buyers of traded goods.

In the area of influence of the rehabilitated road under study where poverty is either high or severe, it

was shown that price changes that result from a better connection to markets would benefit net sellers

of fish. The clear trade flows between León and the coast allowed to assume poor households whose

main source of income is fishing would have benefited from the rural road rehabilitation lowering the

average cost of a basic basket of manufactured goods and allowing fresh fish caught in the coast to be

sold at higher prices in urban markets.

This paper contributes a methodological approach on how to construct an appropriate comparison

or control group for communities receiving improved rural road infrastructure. Evaluating several

projects as a whole can lead an impact evaluation to ignore a lot of what is happening within the area

of influence of each project. By providing a model of spatial arbitrage and analyzing a single road’s

area of influence individually, the model presented in this paper suggests that the relevant comparison

group for the rehabilitated road is the urban area of León and the area of influence of a similar road that

was not rehabilitated. By comparing prices from stores located in rural areas along the rehabilitated

road to the prices from stores in León it was shown that the price of some storable goods — cooking

oil, toilet paper, matchsticks and toothpaste — that travel from urban centers to rural areas declined

relative to urban prices. It was also shown that the price of one good that travels from rural areas

to urban areas — fresh fish caught in the coast — increased. These findings suggest that as it gets

easier and less costly to deliver consumer goods to more remote areas, we would expect the prices of

these goods to drop for rural consumers and as rural producers have easier access to urban markets at

which they could charge higher prices, local prices for their products could increase.

In short, better roads should help narrow the gap between urban and rural prices. The model

this paper has adopted predicts that lower transportation costs should be associated with decreases

25

in prices in rural markets for manufactured goods due to reduced transaction costs. However, this

analysis will be extended by analyzing how the competition-enhancing effect of an improved road

could be reducing the middlemen’s market power and would also be able to explain these smaller gaps.

A reasonable scenario is that in which an improvement in transportation infrastructure intensifies

competition between traders in the rural area reducing their oligopolistic power. The lack of selling

opportunities for isolated farmers could initially allow intermediaries to reduce purchase volumes and

farm prices below the competitive levels. As the rural area’s connection to other markets is improved,

the wholesalers, processors, and other intermediaries in these markets enjoying significant market

power may not be able anymore to buy farm produce at such a low price or sell manufactured goods

at such a high price.

Further research in this area will also lead to incorporating the benefits of decreased uncertainty

in transportation due to better rural road quality into the modeling of households’ behavior. Trans-

portation costs may be extremely volatile, for example, when heavy rains during the monsoon season

in some regions of the world might even make some villages inaccessible. The vulnerability of poor

isolated households to weather shocks suggests they would benefit, not only from a reduction in the

average transportation costs of purchasing inputs and selling outputs, but also from a reduction in the

uncertainty of these costs.

26

References

Akbarian, M., Gregory, J., Ulm, F., & Greene, S. (2011). Where the rubber meets the road: Es-

timating the impact of deflection-induced pavement-vehicle interaction on fuel consumption.

Massachusetts Institute of Technology .

Alevy, J. E. (2014). Impacts of the MCC transportation project in Nicaragua (Tech. Rep.). Millennium

Challenge Corporation.

Banerjee, A., Duflo, E., & Qian, N. (2012). On the road: Access to transportation infrastructure and

economic growth in China. Working Paper, National Bureau of Economic Research.

Casaburi, L., Glennerster, R., & Suri, T. (2012). Rural roads and intermediated trade: Regression

discontinuity evidence from Sierra Leone.

Donaldson, D. (2010). Railroads of the Raj: Estimating the impact of transportation infrastructure.

National Bureau of Economic Research.

Escobal, J., & Ponce, C. (2002). The benefits of rural roads: Enhancing income opportunities for the

rural poor. Working Paper(40).

Fackler, P. L., & Goodwin, B. K. (2001). Chapter 17 spatial price analysis. In Marketing, distribution

and consumers (Vols. 1, Part B, p. 971 - 1024). Elsevier.

Jacoby, H. (2000). Access to markets and the benefits of rural roads. The Economic Journal , 110 (465),

713–737.

Jacoby, H., & Minten, B. (2009). On measuring the benefits of lower transport costs. Journal of

Development Economics, 89 (1), 28–38.

Mu, R., & Van de Walle, D. (2011). Rural roads and local market development in Vietnam. The

Journal of Development Studies, 47 (5), 709–734.

OECD. (2008). General study of the impact of rural roads in Nicaragua (Tech. Rep.). Organisation

for Economic Co-operation and Development.

Osborne, T., Pachon, M. C., & Araya, G. E. (2014). What drives the high price of road freight

transport in Central America? World Bank Policy Research Working Paper(6844).

Shrestha, S. A. (2012). Access to the North-South roads and farm profits in rural Nepal. Working

Paper .

27

Estimation results

Model 1: León-Poneloya-Las Peñitas

Pigtr = �0 + �1Di + �2�t + �3Di�t + uigtr

The treatment dummy Di equals zero for León and is equal to one for the rest of the communities

located along road León-Poneloya-Las Peñitas. The year dummy �t equals zero in 2008 and is equal

to one in 2010.

Model 2: León-Poneloya-Las Peñitas and Santa Teresa-Las Brisas

Pigtr = �0 + �1Li + �2�t + �3Li�t + �4Di + �5Di�t + uigtr

The dummy Li equals zero for León and is equal to one for all of the communities located along both

roads. The treatment dummy Di is equal to one for the communities located along road León-Poneloya-

Las Peñitas and equals zero for León and the communities located along road Santa Teresa-Las Brisas.

The year dummy �t equals zero in 2008 and is equal to one in 2010.

28

Table 13: Model 1 estimation results

Price of product �0 �1 �2 �3 N

Basic Foods

Rice (lb) 10.7*** -.458* -.939*** .738** 102

(0.21) (0.24) (0.29) (0.33)

Beans (lb) 13.7*** 1.87* -0.561 -1.2 54

(0.78) (1.03) (1.17) (1.47)

Sugar (lb) 5.38*** 0.575 2.24*** -1.24 96

(0.59) (0.68) (0.77) (0.89)

Cooking oil (l) 34.4*** 4.08*** -6.74*** -1.71* 100

(0.59) (0.68) (0.80) (0.93)

Tortilla (lb) 1.50*** -.417** -.500** .483** 35

(0.14) (0.16) (0.20) (0.22)

Pinolillo (lb) 6.07*** -3.04 5.72** -5.83** 115

(1.82) (2.01) (2.47) (2.74)

Pasta (lb) 7.05*** 0.334 0.669 1.13* 91

(0.37) (0.44) (0.50) (0.59)

Meat, Poultry, Fish

Beef (lb) 37.0*** 1.5 -0.999 2.50* 26

(0.43) (1.09) (0.66) (1.36)

Pork (lb) 36.0*** -2.00* 3.00*** -2.5 16

(0.61) (1.00) (0.83) (1.50)

Chicken (lb) 22.5*** 1.37** -2.50* 0.953 60

(0.58) (0.64) (1.42) (1.47)

Fish (lb) 23.5*** -4.83 1.5 6.43 18

(2.75) (5.27) (6.16) (8.39)

Eggs (dozen) 29.4*** 3.6 -1.4 -1.05 99

(3.07) (3.24) (4.34) (4.57)

Produce

Tomato (lb) 1.60*** 0.099 0.507 -0.481 88

(0.22) (0.26) (0.33) (0.38)

Yellow onion (lb) 1.79*** 0.266 1.43*** -0.28 83

(0.32) (0.39) (0.49) (0.57)

Potatoes (lb) 10.1*** 1.34*** -1.19*** 0.053 79

(0.30) (0.36) (0.45) (0.53)

Chiltoma (lb) 1.17*** -0.15 .500** 0.368 77

(0.14) (0.17) (0.22) (0.25)

Green plantain (lb) 3.25*** -0.25 -.639** .624* 80

(0.21) (0.25) (0.32) (0.37)

Orange (lb) 1.77*** -0.108 -0.275 0.192 27

(0.16) (0.26) (0.28) (0.40)

Cabbage (lb) 12.7*** -1.37 11.8*** -7.95*** 46

(1.35) (1.77) (2.07) (2.88)

Continues in the next page

29

Price of product �0 �1 �2 �3 N

Household Goods

Laundry Soap 11.4*** 0.841 1.54* -0.276 90

(0.59) (0.71) (0.80) (0.94)

Detergent 1.58*** 0.34 0.377 0.202 90

(0.51) (0.56) (0.61) (0.68)

Toothpaste 20.4*** 2.36* 2.39* -3.04* 86

(1.09) (1.29) (1.43) (1.69)

Matches .977*** 0.108 .621** -.555* 108

(0.21) (0.24) (0.29) (0.32)

Broom 28.8*** 4.06** 1.81 -1.82 42

(1.08) (1.66) (1.66) (2.28)

Toilet paper 7.05*** 1.28*** 2.50*** -1.05* 115

(0.37) (0.41) (0.49) (0.55)

Bathroom soap 10.5*** -0.134 1.54*** 1.01** 105

(0.32) (0.36) (0.43) (0.50)

Sanitary towels 10.1*** 1.73*** 2.67*** -0.428 97

(0.42) (0.49) (0.57) (0.66)

Deodorant 40.0*** 1.23 1.23 3.71 57

(1.57) (2.13) (2.13) (2.83)

Toothbrush 7.67*** 1.36*** 0.449 0.093 94

(0.34) (0.40) (0.47) (0.55)

Continues in the next page

30

Price of product �0 �1 �2 �3 N

Clothing (Men)

Short sleeve shirt 104*** -26.2 56.2** 31.2 12

(15.70) (27.30) (22.30) (38.60)

Underpants 16.8*** 1.58 3 0.667 17

(3.19) (4.12) (5.53) (6.75)

Socks 11.2*** 1.04 2.25 -1.79 21

(1.45) (1.81) (2.50) (2.92)

Clothing (Women)

Short sleeve shirt 112*** -37.5 48.8* 13.2 16

(14.50) (38.30) (22.90) (45.10)

Underpants 16.5*** 2.67 0.999 -0.792 25

(3.13) (4.04) (4.78) (5.71)

Brassier 24.5*** -0.333 0.5 1.33 23

(1.47) (1.90) (2.24) (2.71)

Clothing (Kids)

Underpants 10.8*** -1.09 0.033 1.25 27

(1.28) (1.68) (2.09) (2.49)

Socks 11.4*** -1.23 -1.4 3.43 24

(1.00) (1.35) (1.63) (2.00)

31

Table 14: Model 2 estimation results

Price of product �0 �1 �2 �3 �4 �5 N

Basic Foods

Rice (lb) 10.7*** -0.173 -.939*** 0.091 -0.286 .647* 140

(0.24) (0.33) (0.34) (0.44) (0.27) (0.34)

Beans (lb) 13.7*** 0.384 -0.561 -1.05 1.49 -0.147 69

(0.80) (1.20) (1.20) (1.84) (1.12) (1.68)

Sugar (lb) 5.38*** 0.75 2.24*** -1.36 -0.175 0.127 134

(0.54) (0.68) (0.70) (0.90) (0.53) (0.69)

Cooking oil (l) 34.4*** 4.90*** -6.74*** -3.77** -0.818 2.06* 143

(0.84) (1.04) (1.14) (1.45) (0.79) (1.12)

Tortilla (lb) 1.50*** -.500** -.500** .999*** 0.083 -.517* 42

(0.15) (0.23) (0.22) (0.32) (0.20) (0.26)

Pinolillo (lb) 6.07*** -3.52* 5.72*** -5.29** 0.483 -0.535 158

(1.56) (1.91) (2.11) (2.59) (1.32) (1.82)

Pasta (lb) 7.05*** 0.482 0.669 0.585 -0.148 0.543 125

(0.35) (0.45) (0.47) (0.62) (0.35) (0.50)

Meat, Poultry, Fish

Beef (lb) 37.0*** -2 -1 4.20** 3.50** -1.7 33

(0.51) (1.30) (0.79) (1.62) (1.69) (2.00)

Pork (lb) 36.0*** -2.00* 3.00*** -2 0 -0.5 18

(0.59) (0.96) (0.80) (1.44) - (1.32)

Chicken (lb) 22.5*** 0.5 -2.50* 1.14 0.87 -0.183 77

(0.56) (0.76) (1.38) (1.52) (0.58) (0.73)

Fish (lb) 23.5*** -4.83 1.5 6.43 0 0 18

(2.75) (5.27) (6.16) (8.39) - -

Eggs (dozen) 29.4*** 0.284 -1.4 2.72 3.32* -3.76 141

(3.08) (3.46) (4.35) (4.85) (1.90) (2.59)

Produce

Tomato (lb) 1.60*** -0.06 .507* -0.551 0.159 0.07 128

(0.19) (0.25) (0.29) (0.36) (0.20) (0.27)

Yellow onion (lb) 1.79*** 0.12 1.43*** -1.22** 0.146 .937** 124

(0.28) (0.37) (0.43) (0.53) (0.30) (0.40)

Potatoes (lb) 10.1*** .917** -1.19** 0.382 0.423 -0.329 113

(0.32) (0.41) (0.48) (0.61) (0.34) (0.48)

Chiltoma (lb) 1.17*** -0.192 .500** 0.15 0.042 0.218 117

(0.13) (0.17) (0.20) (0.25) (0.13) (0.19)

Green plantain (lb) 3.25*** -0.103 -.639** .817** -0.147 -0.193 117

(0.20) (0.27) (0.31) (0.39) (0.22) (0.30)

Orange (lb) 1.77*** -0.608 -0.275 0.775 0.5 -0.583 33

(0.18) (0.37) (0.31) (0.56) (0.40) (0.57)

Cabbage (lb) 12.7*** -2.98 11.8*** 1.04 1.61 -8.99** 63

(1.71) (2.48) (2.62) (3.99) (2.30) (3.95)

Continues in the next page

32

Price of product �0 �1 �2 �3 �4 �5 N

Household Goods

Laundry Soap 11.4*** 1.05 1.54* -0.571 -0.212 0.295 124

(0.59) (0.80) (0.80) (1.06) (0.67) (0.85)

Detergent 1.58*** 0.071 0.377 0.373 0.269 -0.171 125

(0.46) (0.55) (0.55) (0.67) (0.38) (0.48)

Toothpaste 20.4*** 0.873 2.39* -1.72 1.49 -1.32 113

(1.06) (1.46) (1.38) (1.90) (1.21) (1.57)

Matches .977*** 0.066 .621** -.565* 0.042 0.009 156

(0.18) (0.22) (0.24) (0.29) (0.15) (0.21)

Broom 28.8*** 4.18** 1.81 -1.31 -0.125 -0.508 54

(1.09) (1.83) (1.67) (2.67) (1.95) (2.61)

Toilet paper 7.05*** 1.07** 2.50*** -0.581 0.207 -0.471 160

(0.36) (0.44) (0.48) (0.59) (0.30) (0.42)

Bathroom soap 10.5*** 0.591 1.54*** 0.315 -.725* 0.692 133

(0.32) (0.46) (0.43) (0.60) (0.37) (0.49)

Sanitary towels 10.1*** 1.92*** 2.67*** -1.55** -0.188 1.13** 138

(0.39) (0.50) (0.53) (0.68) (0.39) (0.53)

Deodorant 40.0*** 0 1.23 -0.049 1.23 3.76 68

(1.88) - (2.55) (2.55) (2.55) (3.46)

Toothbrush 7.67*** 1.27** 0.449 -0.553 0.092 0.646 122

(0.37) (0.49) (0.52) (0.71) (0.39) (0.58)

Continues in the next page

33

Price of product �0 �1 �2 �3 �4 �5 N

Clothing (Men)

Short sleeve shirt 104*** -26.2 56.2** 31.2 0 0 12

(15.70) (27.30) (22.30) (38.60) - -

Underpants 16.8*** 3.25 3 -2.88 -1.67 3.54 30

(3.67) (4.93) (6.36) (7.62) (4.45) (6.11)

Socks 11.2*** 7.75*** 2.25 -8.54** -6.71*** 6.75** 30

(1.35) (2.34) (2.34) (3.19) (2.17) (2.58)

Clothing (Women)

Short sleeve shirt 112*** -27.5 48.8* 13.2 -10 0 17

(14.50) (38.30) (22.90) (45.10) (50.10) -

Underpants 16.5*** -1.5 0.999 4.45 4.17 -5.25 39

(2.94) (4.50) (4.50) (5.91) (4.16) (4.84)

Brassier 24.5*** -2.17 0.5 3.47 1.83 -2.13 36

(2.27) (3.47) (3.47) (4.58) (3.21) (3.80)

Clothing (Kids)

Underpants 10.8*** -0.133 0.033 1.6 -0.952 -0.348 43

(1.76) (2.38) (2.87) (3.51) (2.18) (2.76)

Socks 11.4*** 0.35 -1.4 -0.1 -1.58 3.53** 36

(0.89) (1.34) (1.46) (1.91) (1.29) (1.60)

34

Table

15:

Change

in

cost

offood

basket

For

afam

ily

ofsix

(4

adults

and

2children)

Category

Product

Unit

Monthly

Quantity

Model1

Model2

�3

�3iQ

i�3i

�0i+�1i+�2i

�5

�5iQ

i�5

�0+�1+�2+�3+�4

Basic

Foods

Rice

Pound

38

.738**

28.04

7.93%

.647*

24.59

6.89%

Beans

Pound

34

-1.2

-40.80

-8.00%

-0.147

-5.00

-1.05%

Sugar

Pound

30

-1.24

-37.20

-15.13%

0.127

3.81

1.86%

Cooking

Oil

Liter

7-1.71*

-11.97

-5.39%

2.06*

14.42

7.36%

Tortilla

Pound

57

.483**

27.53

82.85%

-.517*

-29.47

-32.68%

Pinolillo

Pound

10

-5.83**

-58.30

-66.63%

-0.535

-5.35

-15.45%

Pasta

Pound

51.13*

5.65

14.03%

0.543

2.72

6.29%

Subtotal

-87.05

-5.83%

Subtotal

5.71

0.41%

Meat,Poultry,Fish

Beef

Pound

82.50*

20.00

6.67%

-1.7

-13.60

-4.08%

Pork

Pound

5-2.5

-12.50

-6.76%

-0.5

-2.50

-1.43%

Chicken

Pound

80.953

7.62

4.46%

-0.183

-1.46

-0.81%

Fish

Pound

96.43

57.87

31.88%

00.00

0.00%

Eggs

Dozen

7-1.05

-7.35

-3.32%

-3.76

-26.32

-10.95%

Subtotal

65.64

6.20%

Subtotal

-43.88

-3.76%

Produce

Tom

ato

Pound

14

-0.481

-6.73

-21.80%

0.07

0.98

4.23%

Onion

Pound

8-0.28

-2.24

-8.03%

.937**

7.50

41.35%

Potatoes

Pound

15

0.053

0.80

0.52%

-0.329

-4.94

-3.09%

Chiltom

aPound

30.368

1.10

24.21%

0.218

0.65

13.05%

Green

Plantain

Pound

16

.624*

9.98

26.43%

-0.193

-3.09

-6.07%

Orange

Pound

46

0.192

8.83

13.84%

-0.583

-26.82

-26.97%

Cabbage

Pound

2-7.95***

-15.90

-34.37%

-8.99**

-17.98

-37.19%

Subtotal

-4.16

-1.14%

Subtotal

-43.69

-10.80%

TotalFood

Basket

-25.56

-0.88%

-81.86

-2.75%

35

Table

16:

Change

in

cost

ofhousehold

goods’basket

For

afam

ily

ofsix

(4

adults

and

2children)

Category

Product

Unit

Monthly

Quantity

Model1

Model2

�3

�3iQ

i�3i

�0i+�1i+�2i

�5

�5iQ

i�5

�0+�1+�2+�3+�4

Household

Goods

Laundry

Soap

Unit

12.55

-0.276

-3.46

-2.00%

0.295

3.70

2.23%

Detergent

40

gram

bag

27.97

0.202

5.65

8.79%

-0.171

-4.78

-6.40%

Toothpaste

115

gram

s2.13

-3.04*

-6.48

-12.09%

-1.32

-2.81

-5.63%

Matches

40-pack

10.87

-.555*

-6.03

-32.53%

0.009

0.10

0.79%

Broom

Unit

1.22

-1.82

-2.22

-5.25%

-0.508

-0.62

-1.52%

Toilet

Paper

Rolls

10.71

-1.05*

-11.25

-9.70%

-0.471

-5.04

-4.60%

Bathroom

Soap

Unit

4.67

1.01**

4.72

8.48%

0.692

3.23

5.66%

Sanitary

Tow

els

10-pack

2.21

-0.428

-0.95

-2.95%

1.13**

2.50

8.72%

Deodorant

Unit

1.90

3.71

7.05

8.74%

3.76

7.14

8.87%

Toothbrush

Unit

2.49

0.093

0.23

0.98%

0.646

1.61

7.24%

TotalH

ousehold

Goods

Basket

-12.74

-1.93%

5.02

0.78%

36

Table

17:

Change

in

cost

ofclothing

basket

For

afam

ily

ofsix

(4

adults

and

2children)

Category

Product

Unit

Monthly

Quantity

Model1

Model2

�3

�3iQ

i�3i

�0i+�1i+�2i

�5

�5iQ

i�5

�0+�1+�2+�3+�4

Clothing

(M

en)

Shirt

Unit

0.66

31.2

20.59

23.28%

00.00

0.00%

Underpants

Unit

1.57

0.667

1.05

3.12%

3.54

5.56

19.14%

Socks

Pair

1.32

-1.79

-2.36

-12.35%

6.75**

8.91

113.45%

Clothing

(W

om

en)

Shirt

Unit

0.66

13.2

8.71

10.71%

00.00

0.00%

Underpants

Unit

1.23

-0.792

-0.97

-3.93%

-5.25

-6.46

-21.32%

Brassiere

Unit

0.97

1.33

1.29

5.39%

-2.13

-2.07

-7.57%

Clothing

(K

ids)

Underpants

Unit

1.43

1.25

1.79

12.83%

-0.348

-0.50

-3.07%

Socks

Pair

1.39

3.43

4.77

39.11%

3.53**

4.91

40.72%

TotalC

lothing

Basket

34.86

11.72%

10.35

3.22%

37