metadata: authors: 0: author: Pkhikidze,Nino
count: Armenia
docna: 0: docna: Connectivity, Road Quality, and Jobs :
            Evidence from Armenia
docty: Policy Research Working Paper
theme: Rural Non-farm Income Generation,Rural Development,Private Sector Development,Rural Infrastructure and service delivery,Urban and Rural Development,Jobs
lndinstr: 0: lndinstr: Investment Project Financing
lndinstr_key: 1353855
abstracts: cdata!: Good road infrastructure decreases
            travel time and improves accessibility to urban areas.
            Improved rural-urban linkages could also affect rural
            employment through decreased time and travel costs. To study
            this link, the paper analyzes the impact of good quality
            roads on agricultural and non-agricultural jobs in Armenia,
            using different sets of data and different methodological
            approaches. To address endogeneity and reverse causality
            issues of road quality, the paper uses a historical
            instrumental variable obtained by digitizing historical
            roads which were mainly used for military purposes - from a
            military-topographic map of the Caucasus from 1903. The
            results show that a shorter distance to a good quality road
            has a statistically significant positive impact on overall
            non-agricultural employment for men and women, increasing
            the likelihood of cash-earning jobs for rural women and
            skilled manual and non-seasonal employment for rural men.
            People are more likely to work outside their villages and
            work for more hours if they have access to good quality
            roads. The results are robust from the analysis of
            Demographic and Health Survey as well as the Integrated
            Living Conditions Survey of Armenia.
display_title: Connectivity, Road Quality, and Jobs
            : Evidence from Armenia
listing_relative_url: /research/2024/07/34362953/connectivity-road-quality-jobs-evidence-armenia
EnvironmentalCategory: B
topicv3: Jobs m1503931 2670,Non-Agricultural Rural Employment m1503999 2697,Road Rehabilitation m1505360 4032
publishtoextweb_dt: 2024-07-10T00:00:00Z
dois: 10.1596/1813-9450-10847
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 43
author_keys: 0: author_key: 1816486
model_extractions: Policy Research Working Paper 10847 Connectivity, Road Quality, and Jobs Evidence from Armenia _Nino Pkhikidze_ Transport Global Practice July 2024
Policy Research Working Paper 10847

# **Abstract**

Good road infrastructure decreases travel time and improves accessibility to urban areas. Improved rural-urban linkages could also affect rural employment through decreased time and travel costs. To study this link, the paper analyzes the impact of good quality roads on agricultural and non-agricultural jobs in Armenia, using different sets of data and different methodological approaches. To address endogeneity and reverse causality issues of road quality, the paper uses a historical instrumental variable obtained by digitizing historical roads which were mainly used for military purposes—from a military-topographic map of the Caucasus from 1903. The results show that a shorter distance to a good quality road has a statistically significant positive impact on overall non-agricultural employment for men and women, increasing the likelihood of cash-earning jobs for rural women and skilled manual and non-seasonal employment for rural men. People are more likely to work outside their villages and work for more hours if they have access to good quality roads. The results are robust from the analysis of Demographic and Health Survey as well as the Integrated Living Conditions Survey of Armenia.

This paper is a product of the Transport Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at npkhikidze@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Connectivity, Road Quality, and Jobs: Evidence from Armenia Nino Pkhikidze<sup>_∗_</sup> Keywords: _Roads, infrastructure, rural employment, historical IV._ JEL classification: _H54, J60, C26, Q10, R40._

> _∗_ World Bank, 1818 H St. NW, Washington DC, 20433. npkhkidze@worldbank.org. I would like to thank Prof. Joachim von Braun and Alisher Mirzabaev for their guidance on this work. Gilles Duranton, Chiara Kofol, Lukas Kornher, and the participants of different seminars and workshops at the Center for Development Research (ZEF), the University of Bonn, Caucasus Resource Research Center (CRRC) Armenia, and the Development Impact Group (DIME) at the World Bank for their useful comments on the previous versions of this paper. I would also like to thank World Bank’s Armenia Country Office for their guidance and support. The findings, interpretations, and conclusions expressed in this paper are entirely of the author.
# **1 Introduction**

Access to jobs, particularly to better jobs, is crucial for economic development. Proximity to urban areas has always been considered an important aspect for rural households for accessing jobs, goods, and services, and it is widely studied in the literature. The studies show that living closer to urban areas increases non-farm employment and market oriented activities (Sheng et al., 2022; Deichmann et al., 2009; Fafchamps and Shilpi, 2005, 2003), improves the economic well-being and nutrition of rural households (Stifel and Minten, 2017; Sharma, 2016), and overall, positively affects spatial dimensions of development (Sharma, 2016). However, proximity alone cannot be a good measure of accessibility because it does not take into account road condition - which can have a large impact on travel time and costs to urban centers.

Poor transportation infrastructure restricts accessibility to markets and jobs for the rural population in low- and middle-income countries (World Bank, 2009). Improved roads tend to decrease travel time and travel costs, and promote mobility. Better transportation infrastructure might stimulate mobility of goods and labor, connecting people to jobs.

A large majority of households in low- and middle-income countries are still mainly employed in agriculture, often in self-subsistence farming. According to the accounts data, the average agricultural productivity gap is 3.5, meaning that value added per worker in agriculture is more than three times lower than the one of the non-agricultural sector. There are higher disparities in different income levels of the countries. In low-income countries labor productivity in non-agricultural employment is 4.5 times higher than in agricultural employment, it is 3.2 times higher in middle-income countries, and 2.2 higher in high-income countries (Gollin, 2023; Gollin et al., 2014). Structural transformation has been a key policy issue in low- and middle-income countries in recent decades. One of the ways to address this problem is to give rural households opportunities to be employed in more productive sectors. Proximity to cities and access to adequate road infrastructure could play an important role in increasing non-agricultural employment among rural people (Aggarwal, 2018; Asher and Novosad, 2020).

This paper analyzes the impact of road quality on agricultural and non-agricultural employment, studying the evidence from Armenia. Empirical studies on the relationship 2
between road infrastructure and labor outcomes often suffer from endogeneity and reverse causation problems. Road quality is usually not exogenous. Other than topographic characteristics, it is often driven by economic, social or political factors (Banerjee et al., 2012; Datta, 2012; Nguyen et al., 2011; Burgess et al., 2015). To address the endogeneity and reverse causation issues, this study uses a (historical) instrumental variable strategy. The instrumental variable has been obtained by georeferencing and digitizing a militarytopographic map of the Caucasus region printed by the Russian Empire in 1903. The study uses historical, primary military and post roads to instrument the existing road quality a century later.

The results show that a shorter distance to the nearest good quality road has statistically significant positive impact on overall non-agricultural employment, on skilled manual employment for rural men, and non-agricultural employment and cash earnings for rural women. People are more likely to work outside of their villages if they have access to good quality roads, and also tend to work for more hours. The analysis has been carried out on two different datasets, the Demographic and Health Survey and the Integrated Living Conditions Survey of Armenia, using different estimation methods. The results are similar and robust from both datasets.

This paper contributes to three groups of literature: (1) estimating impact of road infrastructure improvement, (2) examining rural employment and structural transformation, and (3) using historical setting for causal inference. There is a growing number of studies evaluating road construction or improvement programs in various countries, like in India (Aggarwal, 2018; Asher and Novosad, 2020; Bell and van Dillen, 2014; Duranton et al., 2014; Datta, 2012; Ghani et al., 2016; Herrera Dappe et al., 2021), Bangladesh (Khandker et al., 2009; Khandker and Koolwal, 2011), Papua New Guinea (Gibson and Rozelle, 2003; Wiegand et al., 2017), Ethiopia (Dercon et al., 2009), Indonesia (Gibson and Olivia, 2010; Gertler et al., 2022), Viet Nam (Mu and van de Walle, 2011), China (Banerjee et al., 2012; Wang et al., 2016; Fan and Chan-Kang, 2005; Faber, 2014), Georgia (Lokshin and Yemtsov, 2005), among others. Overall, improved connectivity reduces travel time and cost. The cost and time saving stimulates mobility, connecting rural areas to urban centers, people to markets and services. Easier mobility quite often is a key factor in trading, as a result reducing product prices (Donaldson, 2018; Andrabi and Kuehlwein, 3
2010; Aggarwal, 2018), it also reduces poverty (Khandker et al., 2009), and contributes in local market development (Mu and van de Walle, 2011) and welfare gains (Kebede, 2024).<sup>1</sup> The second area of contribution is to the literature on rural employment and structural transformation. Road improvement and travel time reduction contributes in easing access to jobs. High transportation costs lead to increased size of the agricultural workforce and employment in subsistence farming (Gollin and Rogerson, 2014; Adamopoulos, 2011). On the other hand, improved accessibility to jobs, through improved roads, benefits structural transformation. Asher and Novosad (2016) and Mu and van de Walle (2011) find that the rural roads programs in India and Viet Nam, respectively, increase wage labor participation and the share of households mainly relying on the service sector as their main source of income. Nakamura et al. (2020) show that improvement of rural roads in Ethiopia led to a 4.6 percentage point increase in women’s employment and 14.1 percentage point increase in youth employment. In India, Herrera Dappe et al. (2021) found that the construction of new all-weather rural roads resulted in a 9.5 percent increase in overall employment rates. This effect was more pronounced in villages located farther from urban centers. Improved rural roads have also been shown to increase household income by specializing in agriculture by using more fertilizer and newer technologies, and hiring more labor, as shown by Qin and Zhang (2016) and Shamdasani (2021) in the cases of Chinese and Indian rural roads, respectively.

And finally, the paper also contributes to the growing body of literature that use historical settings to account for endogeneity. Duranton and Turner (2012) use a historical highway plan of the US highway system to estimate a structural model of city growth and transportation, Baum-Snow et al. (2012) use Chinese rail and road networks from 1962 as a source of identifying variation in rail and road networks after 2000, Volpe Martincus et al. (2017) use historical Inca routes for Peru, Garcia-L ́opez et al. (2015) and Holl (2016) use Roman roads and the 1760 Bourbon postal routes as sources of exogenous variation of highway extension in Spain, and M ̈oller and Zierer (2018) rely on an 1890 plan of railroad network in Germany and 1937 map of planned autobahns to study regional employment.

> 1For an overview on the impacts of infrastructure improvements on various economic outcomes, please see Redding and Turner (2015).

4
This paper uses the historical military routes of Armenia during the Russian Empire times to account for the endogeneity of modern day road quality.

The research focuses on Armenia to fill the gap in the literature for several reasons. In Armenia the agricultural employment rate is still high, has reduced from 40.4% in 1991 to only 33.6% in 2016.<sup>2</sup> Moreover, most people engaged in agriculture in Armenia, like in many other low- and middle-income countries, are best characterized as engaging in subsistence or quasi-subsistence agriculture, meaning that they consume most of the goods they produce. Given this, identifying mechanisms of structural change is very important. In addition, Armenia, has been receiving funding for rehabilitating and improving existing deteriorated roads, as well as for building new ones. Therefore, it is policy relevant to study the impacts of these road projects. And lastly, the unique dataset on road quality and the historical setting of Armenia give an opportunity to identify impacts of road quality on rural employment outcomes.

# **2 Conceptual framework**

In regional research, the importance of transportation costs has long been recognized as one of the major factors of economic development. Von Th ̈unen’s land rent model and its subsequent modifications predict concentric circles of specialization in agriculture surrounding cities (Von Th ̈unen, 1826). Building on this, regional economic models predict that improving rural connectivity would facilitate moving from agricultural to nonagricultural employment. For example, the Alonso-Muth-Mills model predicts an urban perimeter beyond which agriculture would be the primary employment sector. The urban perimeter takes into account that urban wages, deduced with transportation costs, would be lower than agricultural wages (Brueckner, 1987). Therefore, if we consider road quality improvements as a source of decreasing commuting costs in a given location, we expect improved road quality to expand the urban perimeter.

Starting from Lewis (1954) model, researchers have argued that labor market imperfections prevent people employed in the agricultural sector from relocating towards

> 2Source: International Labor Organization (ILO), Key Indicators of the Labor Market `https://www. ilo.org/ilostat` . Accessed April, 2019. Note: The recently updated ILO methodology somewhat differs with the one of Armenia’s Statistical Committee.

5
higher productivity sectors.<sup>3</sup> Since labor productivity is 4.5 and 3.2 times higher in nonagricultural employment than in agriculture in low- and middle-income countries, respectively (Gollin, 2023; Gollin et al., 2014), it is important to study the transition channels from agricultural to non-agricultural employment. A multi-sector multi-region model developed by Gollin and Rogerson (2014) shows that higher transport costs increase the size of agricultural workforce and self-subsistence farming. Deichmann et al. (2009) studies rural-urban linkages in Bangladesh and finds that households living closer to urban centers are more likely to be employed or self-employed in non-farm sector, and Asher and Novosad (2020) find that newly paved rural roads in India increase non-farm employment, however slightly, by stimulating easier access to outside-village labor markets.

There are two channels linking road infrastructure and rural employment. The first channel is through agricultural productivity. Improved roads decreasing transportation costs might decrease the costs of agricultural inputs, as shown in (Aggarwal, 2018), help individuals to move to non-agricultural employment by increasing agricultural productivity. For example, Sotelo (2015) estimates on average a 14% increase in agricultural productivity because of paving existing roads, by increasing access to inputs and increasing output prices. Shamdasani (2021) shows that rural roads program in India increased the mobility of agricultural workers by the integration of village labor markets across space.

The second channel works through decreased job search and commuting costs. Decreased transportation costs likely decrease costs of job search or commuting to a job, reducing barriers to working outside the village. It is an important issue, considering possible underemployment in the agricultural sector. The studies show that agricultural work is less productive than non-agricultural work. However, by looking at actual reported hours worked in different sectors in several Sub-Saharan African countries, McCullough (2017) has shown that the productivity gap is not as high as it was previously considered. She observes that agricultural workers are working far fewer hours than non-agricultural workers, and therefore they can be considered underemployed. The underemployment might be due to the seasonality of agricultural work, as some seasons require far more

> 3The literature has also suggested that barriers to the reallocation of labor could result from insurance networks that discourage movement out of rural areas (Munshi and Rosenzweig, 2016), and informational frictions (Banerjee and Newman, 1998), among others.

6
work than others. On the other hand, if farmers have limited access to farm inputs, they might need to work more hours in agricultural sector.

Improved accessibility to jobs could help farmers to take non-agricultural work during the agricultural off-season. Commuting might be particularly hard not only due to long distance to urban areas but also due to poor road quality and poor transportation infrastructure. Improved roads and transportation infrastructure, therefore, are expected to increase the probability of household members engaging in work outside their village.

# **3 Background**

The paper focuses on Armenia to study the relationship between road quality and rural employment. Armenia is a lower-middle-income country (GDP per capita USD 3,917 in 2016 (constant 2010 US$)) with a population of 2.9 million people.<sup>4</sup> The country has quite a well-developed road network, Figure 1 shows that most of the settlements in the country are connected to roads. However, the quality of roads is still a matter of concern. The railroad network is not very well developed in the country mainly due to the rough terrain. Therefore, road network is vital for passenger and freight transportation (World Bank, 2017).

The classified road network of Armenia is 7,700 km long, from which around 1,400 km are interstate roads, 2,520 km are regional roads, and 3,780 km are local roads. Most of the roads were built in 1960s and 1970s, and have deteriorated since independence in 1991 due to poor maintenance (World Bank, 2017). Since the early 2000s the country has received funding from different international organizations, such as the Millennium Challenge Corporation (MCC), the World Bank (WB), and the Asian Development Bank (ADB) to rehabilitate the lifeline roads<sup>5</sup> in the country and expand the major interstate road into a highway, connecting the south to the north of the country. Lifeline roads network serves as a vital link in the country, connecting 960 communities and approximately 1.1 million rural residents - 37 percent of the total population - with one another and

> 4Source: Databank, the World Bank `https://data.worldbank.org/indicator` Accessed: June 2019.

> 5The roads connecting villages to major roads are often called “lifeline roads” in Armenia (World Bank, 2017).

7
with urban centers throughout the country (Mathematica, 2015). There have been several ongoing projects since then: the Rural Roads Rehabilitation Project, which was started by the MCC and taken over and expanded by the World Bank as Lifeline Road Network Improvement Project (LRNIP), and two projects by the ADB: North-South Highway and Rural Roads Sector Project. The projects have been improving road quality and ruralurban connectivity. The Lifeline Road Improvement Project was financed by the World Bank and initiated in 2009. It involved the rehabilitation of 446 kilometers of lifeline roads, approximately 11 percent of the entire Lifeline Road Network of the country.

A large share of Armenians are employed in agriculture. Structural transformation has been slow: as of 2016, 33.6% of Armenians were working in agriculture, just 6.8 percentage points lower than in 1991.<sup>6</sup> Agriculture accounted for 16.4% of GDP in 2016.<sup>7</sup> Like in most low- and middle-income countries, the majority of people employed in agriculture in Armenia are rural individuals employed in self-subsistence or semi-self-subsistence farming. Therefore, it is crucial to understand the link between improved road quality and rural employment, and what role improved roads could play in structural transformation.

Finally, the historical setting and available information for Armenia makes it possible to employ instrumental variable strategy to study the causal impacts of road quality. In particular, the paper uses historical roads from the times of the Russian Empire as a conditionally exogenous source of variation in quality of transport infrastructure.

> 6Source: International Labor Organization (ILO), Key Indicators of the Labor Market `https://www. ilo.org/ilostat` . Accessed April 2019.

> 7Source: Statistical Committee of the Republic of Armenia, ArmStatBank `http://armstatbank.am/ pxweb/hy/ArmStatBank/?rxid=602c2fcf-531f-4ed9-b9ad-42a1c546a1b6` Accessed April 2019.

8
<!-- Start of picture text --> Sheps ro Oe> < OOP<br>ra 0 oo) ° 5 ~~ . 7<br>Y chy 8 4 a7 { Da|<br>> Bred bv \ J<br>Legend M . | Pe LSS<br>© Settlements [J Aragatsotn [| Shirak<br>Roads [| Ararat [| syunik ,<br>[__] Armenia_Border_poly | | Armavir [| Tavush on ed<br>ArmeniaMajor_LakesMarzes {||__| GegharkunikKotayk [| VayotsYerevan DzorCi R,<br>0 15 30 60 90 1 20<br>es Kn a x<br><!-- End of picture text -->
<!-- Start of picture text --> Regional road quality Village road quality<br>100% 100%<br>90% 90%<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br>TMGood mAverage m Poor TMGood Average mM Poor<br><!-- End of picture text -->
in Armenia. The data has been collected by the National Statistical Service and the Ministry of Health of the Republic of Armenia and is co-funded by the United States Agency for International Development (USAID). The main goal of the survey is to collect demographic and health indicators, particularly from women of reproductive age, and in some questions from men as well. In total, 2015-2016 survey has collected data on 6,116 women and 2,755 men of the age range of 15 to 49. The data is representative on the national and rural-urban areas. The 2015-2016 survey has a total of 313 clusters, of which 121 are rural.

One of the advantages of the DHS survey is that the program collects the GPS location data of surveyed clusters. In order to protect the confidentiality of the respondents, the locations have been displaced. Each urban cluster has been displaced from the actual location up to 2 kilometers, and each rural cluster up to 10 kilometers.<sup>10</sup> The survey also provides a wide range of geospatial covariates, very useful for the purpose of the study.

|Occupation|Men|Women|Total|
|---|---|---|---|
|Not working|27%|62%|50%|
|Professional/technical|9%|25%|17%|
|Clerical|1%|2%|1%|
|Sales|6%|9%|8%|
|Agricultural|26%|45%|36%|
|Services|16%|6%|11%|
|Skilled manual|30%|6%|18%|
|Unskilled manual|9%|7%|8%|
|Other|4%|0%|2%|
|Total|1,233|2,571|3,804|
|Working|904|981|1,885|

Table 1: Rural employment by gender (DHS) Rural respondents report lower labor activity. 43% of DHS respondents report living in rural areas, of which half indicate that they have not worked in last 12 months. The difference is very high between men and women. More than double the women report not working than men - 62% and 27% respectively.<sup>11</sup> Table 1 reports rural employment indicators. From those working, women are overwhelmingly employed in agriculture, 45%

> 10For more information please see Mayala et al. (2018).

> 11Unfortunately, limited employment variables do not fully allow the calculation of the proportion of unemployed people versus the ones who do not work for other reasons.

11
<!-- Start of picture text -->
- ; ><br>eh7 eo °<br>fC<br>Ne,m \>aA5 »,|<br>« ae =<br>A eed I )<br>Legend US y<br>aN Faye o &<br>© DHS clusters<br>@ — a rs Wy oa,<br>= Marz centers % ae PY<br>Road condition by segment vr ~ > a<br>Segment condition t<br>———— VeryGood good oes , Aa<br>Fair id ° .<br>—— Poor \<br>—— Very Poor nn re?<br>© Boarder of Armenia<br>; | Major_Lakes<br>0 15 30 60 90 120<br>esKi<br><!-- End of picture text -->
the International Roughness Index (IRI): very good, good, fair, poor, and very poor.<sup>13</sup> The data shows that the road quality substantially differs between different regions (Marzes). For example, as Figure 8 shows, Yerevan - the region of the capital city has only 4% of its roads in poor or very poor quality, the reported road quality is better in in Kotayk and Ararat Marzes - surrounding Yerevan - more than 85% of primary and feeder roads are in good or very good condition, while Lori and Shirak regions have only around one-fifth of the primary and feeder roads in good condition.

# **5 Methodology**

## **5.1 Empirical strategy**

The impacts of infrastructure are often challenging to capture. This paper studies the questions using several datasets and estimation methods.

The first method studies the association between regional road quality and different employment outcomes. It uses the Integrated Living Conditions Survey - 10 years of repeated cross-sectional data - to answer the research questions. The survey has collected rich data on various employment outcomes, which is not usually the case in other surveys, making it possible to look at more variables of interest.<sup>14</sup> The first part of the analysis studies the association of reported road quality leading to towns and markets, and binary employment outcomes: whether a person is employed in non-agricultural sector, in seasonal employment, and whether at least one member of the household works outside the village. The second part of the analysis studies the relationship between hours worked, agricultural employment, and road quality.

The second method utilizes the opportunity of GPS location information collected by the surveys to answer the research questions. It combines the georeferenced data on quality of roads in Armenia collected by the World Bank, matching the geographic coordinates of household cluster locations collected by the DHS. The combined dataset with geographic locations provides an opportunity to calculate the proximity to good quality roads from

> 13Road quality categories: Very good (IRI range between 1.0 and 2.0), Good (2.0 - 4.0), Fair (4.0 - 6.0), Poor (6.0 - 10.0), Very poor (10.0 - 16.0).

> 14The questionnaires were slightly modified over the period of 10 years, therefore, the variables have been adjusted accordingly to ensure comparability over the years.

13
each cluster where the household lives, and then estimate the impact of the distance on various employment outcomes. In addition, the availability of georeferenced information allows for the possibility to control for additional geospatial variables which could also be influencing employment outcomes. The idea of quality of roads and employment probability is captured in a simple reduced form specification: The outcome variable _Yi_ of an individual _i_ living in settlement _s_ shows the probability of being employed in the non-agricultural sector, skilled manual employment, seasonal employment, or likelihood of getting cash earnings. The main independent variable is _LogDistGRs_ - log distance to the nearest good/very good quality road. The additional controls include individual level characteristics _Iis_ , household level characteristics _His_ , a vector of settlement (cluster) level geospatial covariates _Ss_ , and region fixed-effects _Rs_ .

# **5.2 Identification strategy**

The question of endogeneity usually arises while studying the economic impacts of infrastructure. Initial conditions are likely to determine where are new roads be built or which old roads receive maintenance (Banerjee et al., 2012; Datta, 2012). Roads might be built and kept in good condition in areas with high economic potential. Reverse causality might also be in play: areas with non-agricultural employment potential might require better roads, or better roads might lead to higher non-agricultural employment. So the question could be summed up as follows: do areas with better roads show better nonagricultural employment outcomes because of good road quality, or are better roads attracted by (potentially) higher non-agricultural employment? For example, if areas with good quality roads show higher non-agricultural employment, it could be because the roads were built and well-maintained in areas with higher economic potential. Therefore, simple correlation between road quality and non-agricultural employment would overestimate the impact of road quality.

Another concern on road quality placement is political or ethnic favoritism. Burgess et al. (2015) show evidence of mis-targeted infrastructure projects in Kenya due to ethnic 14
favoritism, Nguyen et al. (2011) show nepotism of public officials in their communes in Viet Nam, mis-targeting infrastructural projects. Studying the long time span from 1960 to 2010, Jedwab and Storeygard (2017) show that cities around the leader’s place of origin in Sub-Saharan Africa were growing faster than other cities because the leaders favored their cities of origin to target road infrastructure projects. Hodler and Raschky (2014) measured regional favoritism from outer space: they found that subnational regions, where current political leaders were born, have more intense nighttime lights.

This paper uses an instrumental variable (IV) strategy to account for endogeneity.<sup>15</sup> The IV is based on a map of historical road networks of Armenia obtained from a Militarytopographic map of the Caucasus region - shown on Figure 9 - prepared under the Russian Empire in 1903. The argument of exogeneity of the historical setting of roads can be motivated by several reasons. During the beginning of the 20th century Armenia was under the rule of the Russian Empire. The southernmost state of the empire, situated on the border of Ottoman and Persian empires, Armenia was an important territory for military defense. The roads maintained by the Russian Empire were mainly used to transport armies. Even though the roads could be also used for trade and economic reasons, we can argue that the government of the Russian Empire would invest in building, rehabilitating and maintaining the roads necessary for military reasons. The second argument is that, since the historical roads were mapped before the industrialization of the region, when the large majority of people were employed in agriculture, we can argue that the roads would not have been built and maintained to promote non-agricultural employment. It is very unlikely that any decisions made in different settings far back in history, motivated mainly by non-economic reasons, could have anticipated rural employment development a century later.<sup>16</sup>

> 15The literature using similar method includes: Duranton and Turner (2012), Baum-Snow et al. (2012), Volpe Martincus et al. (2017), Garcia-L ́opez et al. (2015), Holl (2016) and M ̈oller and Zierer (2018), among others.

> 16In the section of robustness checks the paper also addresses the factor of mine locations in the region, and shows that it is highly unlikely that mines would have influenced the historical primary road placement.

15
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endogenous regressor _LogDistGRs_ but independent from the error _εs_ . We can argue that old main roads would have been maintained to transport armies, and later on these roads would also have higher quality.

# **6 Results**

The results of the two separate datasets and methods are summarized in different subsections below.

## **6.1 ILCS**

First, I show the results from the analysis of the repeated cross-sectional data of Integrated Living Conditions Survey (ILCS). The survey has collected information on perception of respondents on road quality in the region and locally - within the village. The road quality indicators include “good”, “average”, and “poor” (“good” is a reference category in the following results tables).

Table 2 shows the marginal effects of the Probit regression. It shows the probability of a respondent being employed in non-agricultural sector (columns (1) and (2)), doing seasonal work (columns (3) and (4)), and whether there is anyone from the household working outside of village (column (5)). Since the distance variables were not included in the questionnaires in 2007 and 2008, the regression is done with and without the distance variables. The first column shows a clear negative association between the household living in a region with average or poor road quality and non-agricultural employment. Individuals reporting poor road quality are 5.4 percentage points less likely to be working in non-agricultural sector than the individuals reporting good quality roads in their regions. Controlling for the distance to the nearest market, kindergarten, and health center lowers the marginal effects, but still holds statistically significant 3.5 percentage points lower probability of working in non-agricultural sector (column (2)). As expected, average roads show lower marginal effects; individuals reporting quality of roads in their region leading to towns and markets as average, are 1.9 percentage points less likely to work in nonagricultural sector than the individuals reporting good quality roads in their regions. The marginal effect changes slightly to 1.4 percentage points (column (2)) when distance 17
variables are included (hence, excluding 2007-2008 datasets because of the lack of the distance variables).

Columns (3) and (4) report the probability of working in seasonal employment given different regional road quality leading to towns and markets. Respondents who report average and poor road quality in their regions are, respectively, 1,5 and 5.2 percentage points less likely to work in seasonal employment. Considering that 66% of the respondents employed in agriculture report working whole year round and only 1,3% of them or 370 people report having a second job, this could mean that people living in areas with poor roads are less likely to take seasonal jobs during low agricultural seasons. Lastly, column (5) shows that households are less likely to report any household member working outside of the village if they report having poor or average road quality leading to towns and markets.<sup>17</sup> Table 3 reports the relationship between hours worked, agricultural employment, and reported road quality. The first column shows that people who work in agriculture tend to work less, estimated at 16.5 less hours worked the week before the survey, holding all other variables constant and controlling for the survey month. Descriptive statistics show that non-agricultural workers had worked on average 23 hours the week before the interview, while the non-agricultural workers had worked on average 41 hours. This result is in line with the study done by McCullough (2017). By analyzing the Living Standards Measurement Study Integrated Surveys on Agriculture (LSMS-ISA) for four African countries - Ethiopia, Malawi, Tanzania and Uganda - she found that each agricultural worker was working on average 700 hours per year compared to the 1850 hours per non-agricultural worker. Our estimates are higher than those of McCullough (2017) but still show significantly large differences between the working hours of agricultural and non-agricultural workers. Column (2) shows that individuals reporting average or poor quality of roads leading to towns and markets, work slightly less than people reporting having good regional road quality. Column (3) shows how the inclusion of the interaction terms affects the results. Respondents working in agriculture who report average quality of regional roads are estimated to have worked 1.7 hours less the previous week than people reporting good roads. Interestingly, people employed in agriculture who report poor regional roads

> 17Column (5) reports the analysis at the household level.

18
are estimated to work slightly more than agricultural employees who report good regional roads. They are estimated to have worked 3.5 hours more the week before. These results suggests that while people employed in agriculture indeed work for fewer hours, they work more if they have poor connectivity with towns and markets. The reasons could be that people with inadequate access have difficulties accessing agricultural inputs and extension services, therefore are less productive and need to do more manual work.

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Table 2: Marginal effects - probability of positive outcome.

||Non-agricu<br>employmen|ltural<br>t|Seasonal e|mployment|Job outside village|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Poor reg. road quality|-0.054***|-0.035***|-0.054***|-0.052***|-0.028*|
||(0.006)|(0.008)|(0.007)|(0.009)|(0.015)|
|Average reg. road quality|-0.019***|-0.014**|-0.015***|-0.015**|-0.033***|
||(0.005)|(0.006)|(0.006)|(0.007)|(0.011)|
|Log distance to market||-0.013***||-0.023***|-0.015***|
|||(0.003)||(0.003)|(0.005)|
|Log distance to kindergarten||-0.008***||-0.009***|-0.007**|
|||(0.002)||(0.002)|(0.003)|
|Log distance health center||-0.006**||0.012***|-0.006|
|||(0.003)||(0.003)|(0.005)|
|Individual controls|Yes|Yes|Yes|Yes|Yes|
|Household controls|Yes|Yes|Yes|Yes|Yes|
|Region-year FE|Yes|Yes|Yes|Yes|Yes|
|Survey month FE|Yes|Yes|Yes|Yes|Yes|
|Observations|49,443|37,245|49,442|37,244|7,228|

Individual controls include: _gender, age, age squared, household head, marital status, education categories_ . Household controls include: _family size, number of children, family member abroad as an immigrant._ Robust standard errors in parentheses clustered on household level. ***p _<_ 0.01, **p _<_ 0.05, *p _<_ 0.1 20
Table 3: OLS estimates on number of hours worked in previous week

||(1)|(2)|(3)|
|---|---|---|---|
|Agriculture|-16.51***||-16.06***|
||(0.220)||(0.368)|
|Average reg. road quality||-0.995***|0.444|
|||(0.242)|(0.379)|
|Poor reg. road quality||-0.653**|-2.902***|
|||(0.318)|(0.561)|
|Agriculture x Average reg. road quality|||-1.717***|
||||(0.438)|
|Agriculture x Poor reg. road quality|||3.533***|
||||(0.638)|
|Individual controls|Yes|Yes|Yes|
|Household controls|Yes|Yes|Yes|
|Geographic controls|Yes|Yes|Yes|
|Region-year FE|Yes|Yes|Yes|
|Survey month FE|Yes|Yes|Yes|
|Const.|14.47|0.87|14.95|
|Observations|37,245|37,245|37,245|
|R-squared|0.372|0.213|0.375|

Individual controls include: _gender, age, age squared, hh head, marital status, education categories_ . HH controls include: _family size, number of children, family member abroad as an immigrant._ Geographic controls include: _log distance to market, log distance to kindergarten, log distance to health center_ . Robust standard errors in parentheses clustered on household level. ***p _<_ 0.01, **p _<_ 0.05, *p _<_ 0.1 21
# **6.2 DHS**

This section reports the results of the analyses performed on the combination of DHS survey and the road survey. I mapped the GPS locations of DHS survey clusters together with the road survey vector dataset and other geographic raster data layers.

Table 4 shows the estimates of the first-stage regression - following the equation (2). The dependent variable is the endogenous variable _LogDistGRs_ - log of distance to the nearest road in good condition. It is instrumented with _LogDistRoads_ 1903 _s_ - log distance to the nearest primary military and postal road in 1903.

Table 4: First-stage regression.

||Coef.|s.e|
|---|---|---|
||Logdist. good road||
|Logdist. Road 1903|0.423***|(0.0252)|
|Logdist. regional center|-0.316***|(0.0446)|
|Logdist. capital city|0.612***|(0.0846)|
|Altitude|0.00101***|(0.0001)|
|Slope|-0.0716***|(0.0199)|
|Population Density 2015|0.00438***|(0.00025)|
|Nightlights Composite|-1.652***|(0.0971)|
|Aridity|-0.00689|(0.00665)|
|Individual controls|Yes||
|Household controls|Yes||
|Region dummies|Yes||
|Observations|3,801||
|R-squared|0.350||
|First stage F-stat|186.38||

Individual controls include: age, age squared, gender, years of education, individual weights. Household controls include: owns land usable for agriculture, wealth index, number of children of age 5 and lower, number of family members. Robust standard errors in parentheses. ***p _<_ 0.01, **p _<_ 0.05, *p _<_ 0.1 Table 4 shows that _LogDistRoads_ 1903 _s_ has a strong, statistically significant correlation with the endogenous variable _LogDistGRs_ . The First-stage F-statistics also prove that _LogDistRoads_ 1903 _s_ is a strong instrument.

The coefficients in Table 5 represent the average marginal effects on the probability 22
of non-agricultural employment and skilled manual employment. The marginal effects are estimated on the whole sample as well as on the samples of only women and only men. Column (1) shows the average marginal effects of the probability to be employed in the non-agricultural sector. The relationship between the distance to the nearest road in good condition and non-agricultural employment, as expected, is negative: 1 unit increase in log distance to the nearest good quality road, or approximately 2.7 fold increase (e _≈_ 2.71828) decreases the probability of being employed in non-agricultural sector by 5.7 percentage points. The effects are consistent for women and men, showing, respectively, -5.8 and-6.2 percentage points likelihood of being employed in the non-agricultural sector.

Next, the study estimates the average marginal effects of the probability of being employed in skilled manual employment given distance to the nearest good quality road. The results are reported in columns (4), (5), and (6). Overall, one unit increase in log distance (or approximately 2.7 fold increase) decreases the probability of having skilled manual job by 5.1 percentage points. When the estimations are performed on men and women separately we see that average marginal effects for women are still negative but lose statistical significance, while the coefficient for men still holds at statistically significant at negative 6.6 percentage points. This could be explained by the distribution of men and women in skilled employment shown in Figure 7. Only 6% of surveyed working women are employed in skilled manual employment, compared to 31% of men.

Table 6 shows the average marginal effects of the probability of being employed in seasonal employment, and the probability of receiving cash earnings. Overall, living further from a road in good condition is negatively associated with seasonal employment, 1 log increase in distance from a good road decreases the probability of working seasonally overall by 5.5 percentage points. These findings are in line with the results of the analysis done on the ILCS data, presented in Table 2. When marginal effects are calculated separately for women and men, the coefficient of the average marginal effect of women having seasonal jobs becomes positive but statistically not significant. However, the marginal effect of the probability of seasonal employment on men is statistically highly significant - one unit increase in log distance decreases the probability of having a seasonal job by 10.7 percentage points. Unfortunately, limitations of the employment data in DHS surveys do not allow me to study deeply the seasonality outcomes. The descriptive statistics show 23
that almost half (47%) of agricultural workers regard their employment as non-seasonal, working whole year round. The results could indicate that going further from a good quality road increases the probability that people regard their agricultural employment as non-seasonal. This could be because of lower probability of getting off-season employment.

Lastly, the study estimates the probability of getting cash earnings given distance to a road in good condition. The results suggest that one unit increase in log distance to a good quality road decreases the probability of getting cash earnings by 5.6 percentage points. When average marginal effects are estimated separately for men and women, effect on men is low and statistically not significant. On the other hand, being close to good quality road seems very important for women - 1 log - or approximately 2.7 fold increase in distance to a good quality road - decreases the probability of getting cash earnings by 9.3 percentage points.

Overall, shorter distance to good quality road seems to be decreasing agricultural employment, increasing skilled manual and seasonal employment, and seems to provide outside village job opportunities. Proximity to good quality roads is particularly important for women - living closer to a road in good condition increases their likelihood to be employed in non-agricultural sector and get cash earnings for their work.

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Table 5: Marginal effects of distance to roads in good condition on non-agricultural and skilled manual employment

||No|n-agricultural em|ployment|Sk|illed manual emp|loyment|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
||All|Women|Men|All|Women|Men|
|Logdist. good road|-0.057**|-0.058*|-0.062**|-0.051**|-0.027|-0.066**|
||(0.024)|(0.033)|(0.029)|(0.021)|(0.029)|(0.029)|
|Age|0.022**|-0.003|0.036***|0.006|-0.001|0.019|
||(0.010)|(0.013)|(0.013)|(0.009)|(0.008)|(0.015)|
|Gender (woman=1)|-0.177***|||-0.198***|||
||(0.019)|||(0.018)|||
|Education in years|0.045***|0.062***|0.019***|-0.019***|-0.017***|-0.018***|
||(0.005)|(0.007)|(0.007)|(0.004)|(0.005)|(0.007)|
|Number of children under 5|0.029*|-0.025|0.066***|0.004|0.020|-0.015|
||(0.016)|(0.023)|(0.022)|(0.014)|(0.014)|(0.023)|
|Owns agricultural land|-0.171***|-0.216***|-0.093*|-0.016|0.008|-0.041|
||(0.043)|(0.064)|(0.052)|(0.030)|(0.042)|(0.050)|
|Other individual controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Geographical controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Region dummies|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|1,885|981|904|1,885|968|904|

Geographical controls include: Distance to Marz center, Distance to Yerevan, Altitude, Slope, Population density, Nightlights composite, Aridity. Additional individual and household controls include: Age squared, individual weights, HH wealth categories, family size. Robust standard errors in parentheses * _p <_ 0 _._ 1, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01
Table 6: Marginal effects of distance to roads in good condition on seasonal employment and cash earnings

|||Seasonal employ|ment||Cash earning|s|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
||All|Women|Men|All|Women|Men|
|Logdist. good road|-0.055**|0.010|-0.107***|-0.056**|-0.093***|-0.016|
||(0.024)|(0.038)|(0.025)|(0.024)|(0.029)|(0.033)|
|Age|-0.035***|-0.032**|-0.034**|0.030***|-0.011|0.065***|
||(0.010)|(0.015)|(0.013)|(0.010)|(0.013)|(0.014)|
|Gender (woman=1)|-0.063***|||-0.245***|||
||(0.020)|||(0.020)|||
|Education in years|-0.034***|-0.038***|-0.024***|0.066***|0.073***|0.035***|
||(0.005)|(0.007)|(0.006)|(0.005)|(0.008)|(0.007)|
|Number of children under 5|-0.032*|-0.019|-0.033|0.017|-0.042*|0.046**|
||(0.017)|(0.025)|(0.022)|(0.017)|(0.022)|(0.024)|
|Owns agricultural land|0.015|-0.090|0.039|-0.171***|-0.239***|-0.083|
||(0.040)|(0.063)|(0.050)|(0.044)|(0.059)|(0.056)|
|Other individual controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Geographical controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Region dummies|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|1,869|966|890|1,884|981|903|

Geographical controls include: Distance to Marz center, Distance to Yerevan, Altitude, Slope, Population density, Nightlights composite, Aridity. Additional individual and HH controls include: Age squared, individual weights, HH wealth categories, family size. Robust standard errors in parentheses * _p <_ 0 _._ 1, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01
<!-- Start of picture text --> Non-agricultural employment (All) ———_o——_-<br>Non-agricultural employment (Women)<br>Non-agricultural employment (Men) —o>————_<br>Skilled manual employment (All) ——_<br>Skilled manual employment (Women)<br>Skilled manual employment (Men) ————_o——<br>Seasonal employment (All) —o—_<br>Seasonal employment (Women)<br>Seasonal employment (Men) | ———-®———<br>Cash earnings (All) —>—_<br>Cash earnings (Women) ~————®————<br>Cash earnings (Men)<br>-.15 -.1 -.05 0 .05 1<br><!-- End of picture text -->
might swing highly, and second, multicollinearity reduces the precision of the estimated coefficients, weakening statistical power. These problems are evident from the regression results once the incomplete roads dataset is integrated in the analysis: while coefficients keep the same sign, the coefficient value and statistical power fluctuate highly.

# **Location of mines**

Location of historical roads might be influenced by other factors than geography and military strategies. For instance, roads could have been built in areas with greater economic potential. During the period, which the historical map is depicting, the main non-agricultural sector with economic potential in Armenia was mining. Armenia has a long history of metal mining and is accounting a large part of the economy. According to the UN Comtrade, metals and ore concentrates accounted for more than half of Armenia’s exports in 2017.<sup>18</sup> Copper mining first started in the Alaverdi area of Lori Marz in 1770s. Later, it started in Kapan in Syunik Marz in 1840s and by the mid-20th century the Kajaran copper-molybdenum mine in Syunik Marz started production. Kajaran mine is the largest operating mine in Armenia, accounting for 60% of the total mining turnover in the country (World Bank, 2016). In spite of the historical importance of the mining sector in Armenia, it is unlikely that by the end of the 19th century primary road placement would have been influenced by mine locations. There are two main reasons to argue this. First, most of the historical mines have been operated in Lori (in north Armenia) and Syunik (in south Armenia) regions of the country. In both regions, as 6 shows, large mines are located very far from the historical routes. However, they are connected by railway lines. The only mine which seems to be on the primary historical road route is the Ararat mine. However, the operation of the Ararat mine had been relatively small until 1975. In addition, it is not an active mine but rather a processing plant.<sup>19</sup>

> 18UN Comtrade Database `https://comtrade.un.org/data/` (Reviewed: December 2020).

> 19Source: State Committee of Real Estate Cadastre of Armenia.

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<!-- Start of picture text --> \<br>Le<br>0<br>Lp eS Pn Coa<br>Ww<br>: Bs Y 4, — SS =<br>C ba: rr, D>} \<br>ee Bei s= »<br>Se<br>~N<br>x}<br>Legend _—Pe eeSF<br>A Mines - In operation © Road condition by segment<br>A Mines - Small operation Segment condition .<br>Nc@y<br>—— Roads in 1903 —— Very good ><br>—+— Railroads —— Good<br>HE Major_Lakes Fair ce<br>=<br>C Boarder of Armenia ——— Poor<br>—— Very Poor<br>0 15 30 60 90 120<br>a ees Km<br><!-- End of picture text -->
years of repeated cross-sectional Integrated Living Conditions Survey of individuals from 2007 to 2016, where respondents answered a set of questions about local infrastructure quality. The second method uses the Demographic and Health Survey from 2015-2016. Since DHS allows for approximate household location identification, I match the locations with a unique road quality dataset and use topographic control variables. In order to address the problem of endogeneity and reverse causation, the study uses an instrumental variable strategy based on historical military and postal routes in Armenia from a 1903 map, when Armenia was part of the Russian Empire.

The analysis with both datasets and methods shows that road quality is positively associated with non-agricultural employment in rural areas. Households living further from good quality roads are more likely to be employed in agriculture and less likely to be employed in seasonal employment. The DHS analysis shows negative association between distance to good quality roads and employment outcomes. People are 5.7 percentage points less likely to work in the non-agricultural sector and 5.1 percentage points less likely to be engaged in skilled manual employment with 1 log increase in distance (or approximately 2.7 fold increase in distance). Women are particularly affected in the type of pay they receive for work. The results show that 1 log increase in distance decreases the probability of getting cash earnings for work by 9.3 percentage points. These results are particularly interesting in the prism of women’s empowerment, where financial independence is one of the key factors.

The result on seasonal employment is particularly interesting for its unexpected opposite sign. The analysis from both datasets shows a positive association of seasonal employment with better quality roads. The analysis on ILCS shows that people reporting poor road quality leading to towns and markets are 5.2 percentage points less likely to engage in seasonal work. The analysis on DHS shows 5.5 percentage points less likelihood of seasonal employment with one unit increase in log distance from a good quality road (approximately 2.7 fold increase). These results could be explained by the outcome that people living nearby improved roads are more likely to work outside the village. Better quality road infrastructure stimulates mobility and commuting through two main channels: increased productivity, and reduced job search and commuting costs. Therefore, people living closer to better roads might be more likely to find additional jobs during less 30
active agricultural seasons.

The paper is in line with the literature on agricultural productivity in terms of hours worked. Similar to McCullough (2017), this paper finds that agricultural workers work on average fewer hours than non-agricultural workers, 16.5 hours less per week. The results are very policy relevant. People living further away from good quality roads tend to be underemployed (working fewer hours), are less likely to work outside of village, and less likely to find seasonal jobs during inactive agricultural seasons. People who report average road quality in their region tend to work fewer hours in agricultural jobs than people living closer to good quality roads; however, respondents reporting poor quality roads work more hours in agriculture. This result could be related to access to agricultural inputs and productivity. People with average quality roads still have relatively adequate access to agricultural inputs, while poor quality roads hinder access to inputs, requiring more manual work.

While many low- and middle-income countries are investing heavily in new road construction projects, keeping local roads in good condition should also be their priority. The literature shows that having higher maintenance expenditure rather than new infrastructure investment can lead to a positive impact on output (Rioja, 2003 _a_ , _b_ ).

Access to better quality road infrastructure is positively associated with rural employment. The results are very policy relevant in the sense of structural transformation, providing non-agricultural jobs and new or additional work opportunities to people living in rural areas.

31