metadata: authors: 0: author: Fratto,Chiara
1: author: Giannone,Elisa
count: West Bank and Gaza
docna: 0: docna: Market Access and Development of the ICT
            Sector in the West Bank
docty: Policy Research Working Paper
subtopic: Information Technology,International Trade and Trade Rules,Construction Industry,Common Carriers Industry,Food & Beverage Industry,Plastics & Rubber Industry,Business Cycles and Stabilization Policies,General Manufacturing,Pulp & Paper Industry,Textiles, Apparel & Leather Industry,Transport Services
teratopic: Macroeconomics and Economic Growth,Industry,Information and Communication Technologies,International Economics and Trade,Transport
abstracts: cdata!: Despite the popular view that, with the
            advent of digital technologies, the world has become flat,
            geography still plays a key role in the economy. COVID-19
            has brought even more awareness of this. This paper studies
            the impact of security measures that have reduced the
            mobility of people and goods on the industrial composition
            of the West Bank economy, and, in particular, on the
            development of the information and communications technology
            sector. The paper identifies different channels through
            which changes in market access due to the introduction of
            mobility restrictions can differentially affect industries.
            A newly designed survey instrument is used to identify and
            disentangle these mechanisms separately. This novel data set
            contains information on more than 500 establishments that
            are representative of different sectors of the economy,
            including information and communications technology,
            manufacturing, and retail trade. The analysis finds that the
            mobility restrictions involved a reallocation of resources
            toward the information and communications technology sector,
            as the industry was relatively less affected by the
            restrictions. Yet, the key role of the input-output
            linkages, strong dependence on local clients and suppliers,
            and deep interconnectedness of the information and
            communications technology sector with other domestic
            industries severely limit the extent to which the sector can
            benefit from the reduction in opportunity cost. An
            industrial strategy focusing on the harmonious development
            of the entire economy is advised, rather than targeting a
            specific industry in isolation from the rest.
geo_reg_key: 119229,517191
repnme: repnme: Market Access and Development of the ICT
            Sector in the West Bank
display_title: Market Access and Development of the
            ICT Sector in the West Bank
listing_relative_url: /research/2020/10/32465827/market-access-development-ict-sector-west-bank
topicv3: Manufacturing Sectors m1326521 1281,Information and Communication Technologies
            m1326624 133,Transport m1326661 135,Trade m1326924 698,Coronavirus (COVID-19) m1939478 4828
publishtoextweb_dt: 2020-10-06T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Industry,Information and Communication Technologies,International Economics and Trade,Transport
origu: Office of the Chief Economist (MNACE)
no_of_pages: 61
author_keys: 0: author_key: 1690392
1: author_key: 1338131
model_extractions: Policy Research Working Paper 9426 Market Access and Development of the ICT Sector in the West Bank _Chiara Fratto Elisa Giannone_ Middle East and North Africa Region Office of the Chief Economist October 2020
Policy Research Working Paper 9426

# **Abstract**

Despite the popular view that, with the advent of digital technologies, the world has become flat, geography still plays a key role in the economy. COVID-19 has brought even more awareness of this. This paper studies the impact of security measures that have reduced the mobility of people and goods on the industrial composition of the West Bank economy, and, in particular, on the development of the information and communications technology sector. The paper identifies different channels through which changes in market access due to the introduction of mobility restrictions can differentially affect industries. A newly designed survey instrument is used to identify and disentangle these mechanisms separately. This novel data set contains information on more than 500 establishments that are representative of different sectors of the economy, including information and communications technology, manufacturing, and retail trade. The analysis finds that the mobility restrictions involved a reallocation of resources toward the information and communications technology sector, as the industry was relatively less affected by the restrictions. Yet, the key role of the input-output linkages, strong dependence on local clients and suppliers, and deep interconnectedness of the information and communications technology sector with other domestic industries severely limit the extent to which the sector can benefit from the reduction in opportunity cost. An industrial strategy focusing on the harmonious development of the entire economy is advised, rather than targeting a specific industry in isolation from the rest.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region. 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 authors may be contacted at cfratto@imf.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
# Market Access and Development of the ICT Sector in the West Bank

### Chiara Fratto<sup>_∗_</sup> and Elisa Giannone<sup>_†‡_</sup>

Keywords: ICT, market access, input-output, mobility restrictions, West Bank JEL codes: O18, F1, O25, O33, R1

## **1 Introduction**

Despite the popular view that, with the advent of digital technologies, the world has become flat (Friedman (2005)), geography still plays a key role in the economy. We commute every day, and merchandise gets shipped every day. People and goods travel constantly and costs are not trivial. This is true at a large scale for shipments along the global supply chains as well as at a smaller scale for daily commutes of workers. Geography has become even more important in the COVID-19 era, with teleworking, travel restrictions, lockdowns, and the disruption of the global supply chain (Papanikolaou and Schmidt (2020), Dingel and Neiman (2020)).

Some industries are affected by spatial concerns more than others (Kolko (2010)). Distance can be an impediment to access international markets or a protectionist device to nurture local firms (Krishna and Krueger (1995)). So, this begs the question: in a world with scarce resources, would one dollar be better invested towards shortening distances, such

> _∗_ Chiara Fratto (corresponding author), cfratto@imf.org, International Monetary Fund, 700 19th St NW, Washington, DC 20431.

> _†_ Elisa Giannone, Pennsylvania State University, State College, PA 16801.

> _‡_ The authors acknowledge the generous funding of PEDL-CEPR Exploratory Grant and the World Bank. The authors wish to thank all the participants of the 2019 World Bank’s workshop on the digital economy in MENA for their valuable feedback, and in particular Daniel Lederman, Christina Wood, Bob Rikjers, and Ana Paula Cusolito. We are grateful to conference and seminar participants for their helpful comments and suggestions, including Fernando Alvarez, Faisal Awartani, Ufuk Akcigit, Chris Blattman, Jonathan Dingel, Richard Hornbeck, Erik Hurst, Rula Kher, Eliana La Ferrara, Omar Omran, Alessandra Voena. We thank Elia Pietro Boe for his excellent research assistance, and Alpha International for Research Polling, and Informatics for its technical assistance. The views expressed in the paper are those of the authors and do not necessarily represent the views of the IMF, its Executive Board, or IMF management. The views expressed in the paper do not necessarily presented the views of the Palestinian Central Bureau of Statistics.

1
as building a road, or towards the development of industries that can naturally overcome distance, such as the information and communication technology (ICT) sector? The ICT sector is a promise for a “productivity miracle” (Bloom et al. (2012)). Focusing on goods sold virtually like software development, application development, and website design, the ICT sector is able to absorb the supply of a high skilled labor force in the area while being less susceptible to mobility restrictions. In addition, ICT has an important role in accounting for aggregate productivity (Draca et al. (2006)). However, as much as we think of ICT as being able to overcome physical barriers, the sector is still deeply affected by them. ICT heavily relies on other industries for its inputs, and an important part of the sector is non-tradable, linked to face-to-face interaction with clients, suppliers, and among employees.<sup>1</sup> The West Bank, a territory interspersed with checkpoints and other mobility restrictions, is an almost ideal place to address these questions. Since 2000, mobility restrictions resulted in a dramatic increase in travel times and transportation costs (figure 1). In the meantime, the Palestinian ICT sector has witnessed a significant expansion, partially driven by the outpouring of foreign investment and foreign aid. Palestinian programmers are employed either directly or via outsourcing by Israeli companies. The largest, Mellanox, employs 125 – Palestinians in the West Bank and 25 in Gaza. Other Israel-based ICT firms Cisco, Microsoft, Intel, Sun-Disk - employ Palestinian workers, attracted by the large pool of cheap, high-skilled labor.<sup>2</sup> The Palestinian ICT sector has focused on the production of intangible products that require high human capital and low physical capital. This is the case, for instance, of Yamsafer, an online booking website and one of the most successful ICT startups in the West Bank. Established in 2011 thanks to venture capital money, it successfully raised $3.5 million in its second investment round in June 2015. Donors have often seen the Palestinian ICT as the golden goose, the solution to (physical) barriers and obstacles Palestinians face, and the hope to bring the economy into the new century.<sup>3</sup> In 2012, the New York Times wrote “Compared with other industries that the anemic West Bank economy might look to develop, the information and communications technology sector has an advantage: it is much less affected by impediments to movement, like the barriers, checkpoints and permit requirements that Israel imposes on the territory in the name of security.”<sup>4</sup> As recently as June 15, 2020, the World Bank made the same bet, promising to invest $15 million in the Palestinian sector,<sup>5</sup> arguing that “the outlook for the Palestinian IT sector is promising if the right actions are taken.”<sup>6</sup> We investigate how and to what extent the Palestinian ICT sector can overcome the barriers to the movement of people and goods, and how this has affected the industrial

> 1In addition, innovation and technology are important determinants themselves of spatial agglomeration (Davis and Dingel (2019)).

> 2Senor and Singer (2011) describes the role of startups and technological innovation in Israel.

> 3See The Portland Trust (2013) for an analysis of the IT sector.

> 4New York Times, July 29, 2012. West Banks’s Emerging Silicon Valley Evades Issues of Borders.

> 5World Bank, Press Release, June 15, 2020

> 6World Bank senior private sector specialist Iulia Cojocaru, according to The Jerusalem post, June 15, 2020.

2
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linkages (Donaldson and Hornbeck (2016)). Although all industries would benefit from the removal of mobility restrictions, part of the development of the ICT sector is attributable to a reallocation of resources away from manufacturing and retail trade, which are more affected by the mobility restrictions. And yet, the ICT sector still suffers from the introduction of mobility restrictions, impaired by low domestic demand. Most of the ICT firms interviewed do not export abroad, but rather produce for the domestic market. Palestinians spend only 9% of their income in ICT goods and services;<sup>8</sup> and ICT constitutes only a small fraction of the material inputs used by other industries.<sup>9</sup> The result is an anemic growth, even in the presence of a favorable shift in the opportunity costs to invest in the industry. Input-output linkages are an extremely important mechanism: in their absence, mobility restrictions have almost no impact on the economy.

No single industry lives in isolation. Industrial policies supporting more competitive sectors increases productivity growth (Aghion et al. (2015)). In these times of automation, policy makers often propose industrial policies and employment protection programs focused on promoting a specific industry. However, the relevance of input-output linkages suggests a scope for designing industrial plans that focus on the harmonious development of all industries, rather than focusing on a single one.<sup>10</sup> Industry-specific projects that do not acknowledge the interconnectedness of the industries are bound to fail. To get the most out of the development of the ICT sector, countries also need to work on improving the overall competitiveness (The World Bank, 2016). The COVID-19 outbreak and the resulting disruption of the global supply chain also point at the importance of the domestic market for the resilience of industries.

Although the paper does not deal with agglomerations, our results speak to the geographical clustering of ICT firms. The industry’s sensitivity to mobility barriers could be a contributing factor to explaining why ICT firms are clustered in or near high-populationdensity centers, rather than in rural areas. In this sense, the industry might be similar to older industries, such as manufacturing.

The rest of the paper develops as follows. Section 2 describes the institutional background. Section 3 reports the data used for the project, including details from the survey. Section 4 develops the model. Section 5 describes the mechanisms through which changes in market access affect the industrial composition of the economy and how they are linked to the model. Section 6 presents the results from the survey and the calibration. In section 7, we perform a number of counterfactual exercises. In section 8, we describe the role of input-output linkages. Section 9 concludes.

# **2 Institutional Background**

The West Bank is a patch of land of the size of Delaware. It has an area of 5,655 km<sup>2</sup> (2,183 sq mi). The largest economic sectors are manufacturing, wholesale and retail trade. It is

> 8PCBS data.

> 9With the exception of services.

> 10Ellison et al. (2010) point out their key role in explaining firm agglomeration.

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organized in 11 districts: Bethlehem, Hebron, Jenin, Jericho, Jerusalem, Nablus, Qalqiliya, Ramallah and Al-Bireh, Salfit, Tubas, and Tulkarm. While the district capitals host the majority of the population, a large number of built-up areas are evenly distributed in the entire area (figure 2). The population lives along the mountain ridge that covers the eastern part of the territory, while the Jordan Valley, on the west, is less populated and largely under the Israeli control. The West Bank has a population of approximately 2.9 million people as of 2017. The 1949 Armistice Line to the West and the Jordan River to the east mark the borders of the West Bank. The 1949 Armistice Line, also known as the Green Line, is the end result of the 1948 Arab-Israeli war (UNOCHA).

In 2000, a political uprising, known as the Second Intifada, broke out. During the same period, Israel started the establishment of permanent facilities to control the mobility of the Palestinian people. These mobility restrictions, with few changes, are still in effect today. Israel also started the construction of a wall separating the West Bank from Israel. Although sections of the wall are still under construction today, development has significantly slowed since the end of the Intifada.<sup>11</sup> The mobility restrictions have two main functions. First, they deter attacks on the Israeli population in the West Bank. According to the United Nations,<sup>12</sup> _“the IDF states that the reason for the closure regime [...] is to reduce attacks on Israelis by Palestinian militants by limiting their ability to move freely by vehicle.”_ As a matter of fact, 25% of the obstacles in 2007 were in close proximity to an Israeli settlement or an Israeli military base. Second, mobility restrictions also regulate the traffic flow along the major highways in the West Bank. These restrictions allow the enforcement of roads to create Israeli-limited traffic, and the control of Palestinian IDs and permits within the West Bank.

# **2.1 What is the ICT sector in the West Bank?**

In the last decade, the Palestinian ICT sector has witnessed a large development.<sup>13</sup> The Palestinian Information Technology Association (PITA) represents the most vibrant part of the sector, and estimates that ICT represents 6% of the Palestinian GDP.<sup>14</sup> According to the PCBS, 4,862 people were employed in the ICT in 2017. Leaders, established in 2004, supports local organizations delivering services on private-sector development, job creation, innovation and entrepreneurship. Since 2011, Sadara Ventures provided early investments to a number of start-ups. The year 2017 saw the termination of the works to build Rawabi, a new city designed with the goal of attracting high tech in the West Bank. In June 2020, the World Bank approved a US$15 million grant to sustain the Palestinian youth through the ICT sector.<sup>15</sup> While there are almost 1,000 ICT companies, the majority are repairers, retailers, and

> 11The figure on construction progress is available in the appendix.

> 12UNOCHA (2007)

> 13In this paper, whenever possible, we use a definition based on the “OECD Guide to Measuring the Information Society” (2011). See table 1 for a description of the ICT sector based on such a definition.

> 14PITA Newsletter 10, 2020

> 15World Bank, Press Release, June 15,2020

7
Internet cafes, small businesses serving local clients. Wireless and wired telecommunication activities employ the largest share of workers, although this sector is highly concentrated. When designing our survey, we paid particular attention to the coverage of businesses other than retailers and repairers. These are businesses operating in computer consultancy, computer programming, data processing, hosting and related activities, lively businesses, often small, at the frontier of technology.

While the spatial distribution of establishments matches the spatial distribution of residents (figures 2 and 3), Palestinian ICT is concentrated in the largest towns (figure 4). Based on the responses to our survey, among the major factors for the location choice of ICT firms are proximity to clients, to the labor force, to suppliers and to funding sources.<sup>16</sup> ICT firms are relatively less concerned about high rents and wages, typical of large towns.

> 16Retailers and repairers are excluded.

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Table 1: The ICT sector in the West Bank

||ISIC|industry|Estab.|Employed|
|---|---|---|---|---|
|Repair of computers and peripheral equipment|9511|S|244|325|
|Repair of communication equipment|9512|S|183|340|
|Other telecommunications activities|6190|J|160|282|
|Wired telecommunications activities|6110|J|96|1,337|
|Wireless telecommunications activities|6120|J|88|1,888|
|Computer programming activities|6201|J|79|957|
|Radio broadcasting|6010|J|47|454|
|Television programming and broadcasting<br>activities|6020|J|25|351|
|Publishing of newspapers, journals and peri-|5813|J|22|244|
|odicals|||||
|Other information technology and computer<br>service activities|6209|J|18|112|
|Motion picture, video and television pro-|5911|J|18|87|
|gramme production activities|||||
|Data processing, hosting and related activities|6311|J|13|101|
|News agency activities|6391|J|9|57|
|Computer consultancy and computer facili-<br>ties management activities|6202|J|8|132|
|Publishing of books|5811|J|6|22|
|Sound recording and music publishing activities|5920|J|5|15|
|Other Publishing activities|5819|J|4|10|
|Satellite telecommunications activities|6130|J|3|6|
|Other information service activities n.e.c.|6399|J|3|22|
|Manufacture of measuring, testing, navigat-|2651|C|1|1|
|ing and control equipment|||||
|Manufacture of communication equipment|2630|C|1|0|
|Motion picture, video and television pro-|5912|J|1|6|
|gramme post-production activities|||||
|Web portals|6312|J|1|1|
|Total, excluding maintenance and trade|||520|4,197|
|Total|||947|4,862|

_Notes:_ In the column “industry,” we follow the ISIC classifications for the sections: C is manufacturing, J is information and communication, and S is other service activities. _Sources:_ PCBS, authors’ calculations.

11
# **3 Description of the Survey and Other Data**

## **3.1 Survey Instrument**

The goal of the survey is to gain understanding of aspects of a business connected to spatial aspects. In particular, we are interested in learning in what dimensions the ICT sector differs from the other sectors of the economy. The survey captures measures of firm performance, capital accumulation, trade, and workforce, as well as the perception of growth and the challenges encountered.

The sample frame used is the universe of private sector establishments operating in the West Bank based on the Palestinian Central Bureau of Statistics (PCBS) Establishment Census 2017. The sample was selected using stratified cluster sampling. Strata are defined according to the following variables: North and South of the West Bank, large and small villages, large and small establishments (below and above 10 employees), and industry subgroups (ICT, manufacturing, wholesale and retail trade, and other industries).<sup>1718</sup> Within a stratum, clusters were identified as establishments belonging to the same stratum and located in the same town. A sample of 10% of the establishments was selected in each cluster. Firms within the cluster have been identified using a random walk approach. The sample size has been chosen as to obtain unbiased estimates with 90% confidence intervals at 10% precision for the whole universe, as well as for the following industry sub-groups: IT, manufacturing, and wholesale and retail trade. The survey was conducted via face-to-face interviews and followed by call-back interviews to guarantee high quality of the data, in two phases, from 28/03/2019-05/06/2019 and from 22/12/2019-07/01/2020.<sup>19</sup> More information on the methodology is provided in the appendix.

A total of 533 interviews were completed. For comparison, the World Bank 2019 Enterprise Survey for West Bank and Gaza consisted of 365 interviews, 205 of which were in the West Bank. Given our main focus on ICT, this industry is over-represented in the survey (table 2), representing 23.6% of the sample or 126 interviews and 14% of the existing establishments. The remaining 407 interviews cover the manufacturing sector (87 interviews), the retail and wholesale trade sector (202 interviews) and other industries (118 interviews).

As in the population of reference, almost half of the ICT firms interviewed are retailers or repairers (table 3), while the other half consists of firms conducting activities such as computer programming, telecommunications, information services, and other ICT related activities.<sup>20</sup>

> 17Sectors excluded from the analysis are: agriculture, mining and quarrying, electricity and water supply, public administration and defense, compulsory social security, activities of households as employers, undifferentiated goods- and services-producing activities of households for own use, activities of extraterritorial organizations and bodies. Retailers of ICT products are excluded from the wholesale and retail trade. Repairers of ICT products are excluded from other industries.

> 18We will refer to the wholesale and retail trade sector as retail.

> 19In the second phase, call-backs and 65 additional interviews were conducted.

20Businesses conducting maintenance and repair often also sell computers and other hardware components as secondary economic activity.

12
Table 2: Summary Statistics for the Survey

||N|Survey<br>%|Po<br>N|pulation<br>%|% of<br>population|
|---|---|---|---|---|---|
|ICT|126|23.6%|894|0.05%|14.1%|
|Manufacturing|87|16.3%|15,110|14.21%|0.57%|
|Retail Trade|202|37.9%|54,618|51.36%|0.37%|
|Other Industries|118|22.1%|51,091|48.04%|0.23%|
|Total|533|100%|106,338|100%|0.50%|

_Notes:_ the population refers to the number of establishments according to the 2017 census. The last column is the number of interviews in the survey sample divided the number of establishments in the population. _Sources:_ PCBS, authors’ survey.

# **3.2 Other Data**

Jointly with the survey, auxiliary data is used in this paper.

**Maps and Geographic unit.** The primary geographic unit is the locality or town. The PCBS defined locality as _“a permanently inhabited place, which has an independent municipal administration, or a permanently inhabited, separated place, not included within the formal boundaries of another locality.”_<sup>21</sup> The West Bank is partitioned in 443 villages as of 2007. The shapefiles on the location of the Palestinian buildups and other spatial information has been kindly provided by UNOCHA.

**Population and Establishment Census.** Access to the Establishment Census has been negotiated by the author during several visits to the West Bank. The two Census datasets contain data on population, employment, and establishment. The data used from the Establishment Census include jobs in non-farming private establishments, disaggregated by economic sector (ISIC4) and village. The analysis is restricted to establishments in operation at the moment of the data collection, with national or foreign ownership. Activities not classified as establishments or jobs in the public sector are not included in the job count.<sup>22</sup>

**Mobility restrictions.** The data on the mobility restrictions, the Separation Wall, and the location of settlements and military areas come from the United Nations Office for the Coordination of Humanitarian Affairs (UNOCHA). Data are available between 2003 and 2017 at a yearly frequency with gaps. UNOCHA also kindly provided geo-referenced data on the road network. Each road segment is classified as regional, primary, secondary, tertiary, residential, or track. UNOCHA also collects data on the road regime, and it classifies roads as either restricted for Israeli cars or unrestricted. The Applied Research Institute Jerusalem (ARIJ) provided information on the location of mobility restrictions at a yearly frequency since 1995.

> 21PCBS Population Housing and Establishment Census 2007.

> 22The data exclude clusters (industry grouping * village) where the number of establishments was one or two. Moreover, between the two time periods, the ISIC classification changed. Some sectors have been merged, others have been disjointed. Economic sectors are reclassified based on the ISIC Revision 4.

13
Table 3: Main economic activity of ICT firms interviewed

||ISIC 4|interviews|
|---|---|---|
|Computer programming, consultancy and related activities|62|29|
|Telecommunications|61|15|
|Information service activities|63|6|
|Architectural and engineering activities; technical testing and analysis|71|6|
|Civil engineering|42|2|
|Publishing activities|58|1|
|Motion picture, video and television programme production, sound<br>recording and music publishing activities|59|1|
|Total, excluding maintenance and trade||60|
|Repair of computers and personal and household goods|95|56|
|Retail trade, except of motor vehicles and motorcycles|47|9|
|Wholesale trade, except of motor vehicles and motorcycles|46|1|
|Total||126|

_Sources:_ Authors’ survey.

**Travel times.** A key ingredient of the analysis is the estimate of travel times. We borrow this part of the analysis from Fratto (2019). Travel times are computed using the shortest route between the centroid of two locations using ArcGIS. Table 4 summarizes the assumptions on the travel times and delays at each obstacle. Vehicles cannot pass through roadblocks, earthmounds, barrier gates, and across the Separation Wall. Due to an absence of information on road gates, they are assumed to be open.

For the checkpoints, there are three independent sources. First, Eklund and Martensson (2012) conducted an extensive survey in 2009 in which they asked questions on the shortest delays at a checkpoint (ranging from 0 to 90 minutes, with responses concentrated between 0 and 30 minutes) and the longest delays (between 0 and 1,080 minutes, with responses concentrated between 0 and 360 minutes). They find an average shortest delay of 15 minutes. Van Der Weide et al. (2018) provide a similar average delay using information from the UN. The PCBS also conducted a survey on the topic in 2006 and found that the average shortest delay was 12 minutes and 35 seconds. In this paper, it is assumed that crossing a checkpoint entails a delay of 15 minutes. The road network is held constant. This is consistent with the evaluations from IEG (2010) that argue there has been little progress between 2000 and 2009 on the development of the Palestinian road network.

14
Table 4: Assumptions on travel times

|Average traveling times by type of road||
|---|---|
|regional|60kmh|
|primary|50kmh|
|tertiary|30kmh|
|residential|20kmh|
|track|10kmh|
|Israeli only|0kmh|
|Roadblocks, earthmounds, barrier gates,<br>separation wall (planned and under construction)|infnite|
|Checkpoints|15min|

_Notes:_ Source for average travel times by type of road: Van Der Weide et al. (2018). Source for checkpoint travel time: PCBS, Eklund and Martensson (2012), and Van Der Weide et al. (2018).

# **4 Model**

Consider a static model with _J_ sectors and _O_ localities. Workers live and work in the same location. We allow for intersectoral and interregional trade linkages. We allow industries to differ across a number of dimensions: location-specific exogenous productivity and production technology, consumption shares, elasticity to trade costs. Workers can freely move between locations and between industries.

## **4.1 Households**

Workers have Cobb-Douglas preferences over consumption goods, with shares _αs_ . Preferences are homothetic of degree 1, so<sup>�</sup> _j_<sup>_αj_= 1.</sup> Utility over a consumption basket in location _n_ is given by: where _c_<sup>_j_</sup> _n_<sup>istheconsumptionoffinalgood,boughtatprices</sup><sup>_P j_</sup> _n_<sup>.</sup> Workers supply inelastically one unit of labor and they receive wage _wn_ regardless of the industry in which they are employed. We do not have information on the trade imbalances between locations in the West Bank. As a result, we assume trade to be always balanced. Land is rented locally and receipts _rn_ are distributed equally across the workers in location _n_ . Total local income is: where _Ln_ is the number of people living in _n_ , and _Hn_ is the amount of land available.

15
# **4.2 Final Goods**

Final goods are non-tradable, and they are used for consumption and as material inputs into the production of intermediate goods.

Final goods are produced using varieties of intermediate goods _Yn_<sup>_j_(</sup><sup>_z_),indexedbypro-</sup> ductivity _z_ , according to the following production function: where _φ_<sup>_j_</sup> _i_<sup>(</sup><sup>_zj_)isthejointdensityofthevectorofprobabilitydraws</sup><sup>_zj_=(</sup><sup>_z_</sup> 1<sup>_j, z_</sup> 2<sup>_j, ...z_</sup> _O_<sup>_j_),asso-</sup> ciated with the probability distribution of an Extreme Value Type I. Producers of the final good then maximize profits subject to the cost of the intermediate goods: <u>1</u> 1 _−ηj_ where _Pn_<sup>_j_(</sup><sup>_zj_)isthepriceoftheintermediategoodsand</sup><sup>_P j_</sup> _n_<sup>=</sup> _Pn_<sup>_j_(</sup><sup>_zj_)(1</sup><sup>_−ηj_)</sup><sup>_φj_</sup> _i_<sup>(</sup><sup>_zj_)</sup><sup>_dz_</sup> . �� � There is free entry in the market for final goods implying zero profits.

# **4.3 Intermediate Goods**

There is a representative firm in each region _i_ and sector _j_ producing a continuum of varieties of intermediate goods that differ in their idiosyncratic productivity level, _zi_<sup>_j_.</sup> _i_<sup>.</sup> Firms use a Cobb-Douglas technology to produce intermediate goods from labor, land, and material inputs, taking as given the rental rate of land, _ri_ , the wage rate, _wi_ , the price of final goods, _Pi_<sup>_j_,andproductivity,</sup><sup>_z_</sup> _i_<sup>_j_.Theproductionfunctionforavarietyofgood</sup><sup>_j_in</sup> location _n_ associated with productivity level _zi_<sup>_j_is:23</sup> where _Mi_<sup>_jk_(</sup><sup>_z_</sup> _i_<sup>_j_)isthedemandoffinalgoods</sup><sup>_k_usedasmaterialinputsintheproductionin</sup> location _n_ industry _j_ . Similarly, _L_<sup>_j_</sup> _i_<sup>(</sup><sup>_z_</sup> _i_<sup>_j_) and</sup><sup>_H_</sup> _i_<sup>_j_(</sup><sup>_z_</sup> _i_<sup>_j_) are respectively the demand for labor and</sup> land. We maintain the assumption of constant returns to scale in the production function, i.e.<sup>�</sup> _k_<sup>_γ_</sup> _n_<sup>_jk_= 1</sup><sup>_−γ_</sup> _n_<sup>_j_.Moreover,</sup><sup>_β_</sup> _n_<sup>_j∈_(0</sup><sup>_,_1).</sup> Following Eaton and Kortum (2002), the productivity shock is modeled after a Frechet distribution with shape parameter _θ_<sup>_j_</sup> and scale parameter _A_<sup>_j_</sup> _i_<sup>.Draws are independent across</sup> goods, sectors and regions _A_<sup>_j_</sup> _i_<sup>canbeinterpretedasthefundamentalproductivityoffirms</sup> in town _j_ in industry _j_ , or the average productivity of a town _i_ with industry _j_ relative to other towns.<sup>24</sup> This captures factors equally affecting the productivity of all producers in a given location and a given industry.

> 23We denote _X_ ( _zi_<sup>_j_)avariableforafirminlocation</sup><sup>_i_,sector</sup><sup>_j_,andwithproductivitydraw</sup><sup>_z_</sup> _i_<sup>_j_.</sup>

> 24Formally, _E_ [ _zi_<sup>_j_] =</sup><sup>_ajAj_</sup> _i_<sup>forsomeconstant</sup><sup>_aj_.</sup>

16
The parameter _θ_<sup>_j_</sup> measures the intra-location heterogeneity in productivity. This is a key parameter in the model, as it is linked to the elasticity of trade flows with regard to changes in wages and land prices.

The price of a good _j_ sold in location _n_ , _Pj_<sup>_n_(</sup><sup>_zj_),dependson</sup><sup>_zj_,whichisthevector</sup> collecting the productivity realizations for all locations where goods _j_ are produced. In equilibrium, The production function is exogenous. Changes in travel times affect the combination of inputs via changes in the relative prices, rather than by shifting the production technology. The unit cost of an intermediate good with idiosyncratic shock _zi_<sup>_j_producedinlocation</sup> _i_ is: We assume that firms face an iceberg trading cost. For each unit of good _j_ shipped from town _i_ to town _n_ , only 1 _/τni_<sup>_j≤_1arrive,with</sup><sup>_τ j_</sup> _nn_<sup>= 1,</sup><sup>_τ j_</sup> _ni_<sup>_≥_1and</sup><sup>_τ j_</sup> _ni_<sup>_≤τ j_</sup> _ni_<sup>_′τ j_</sup> _i_<sup>_′_</sup> _i_<sup>_∀i′_.Thelast</sup> assumption can be interpreted as more economical to ship a good straight from _i_ to _n_ than it is shipping it from _i_ to _i_<sup>_′_</sup> and then from _i_<sup>_′_</sup> to _n_ . Given the iceberg structure of the trade costs, the marginal cost at destination has also to take into account the loss in output during the transport of the good. As a result the _x_<sup>_j_</sup> _<u>i</u>_ marginal cost at destination is _τni_<sup>_j_</sup> _zi_<sup>_j_.</sup>

# **4.4 Equilibrium Prices and Trade Shares**

There is perfect competition among firms producing the same variety _z_ in sector _j_ . Therefore, in any location, _n_ , the price for variety _z_ is the minimum among all firms producing that variety: Thanks the properties of the EV distributions, we can solve for the distribution of prices. It can be shown that the equilibrium price is described by the following relation: where _k_ 1 _j_ is a constant.<sup>25</sup> 17
ys 1 ~~(-)~~

> >

1» 1»

> »>/ > > 5° ~~—~~
## **4.7 Equilibrium**

**Definition.** Given land _{Hn}n_ and _{Ln}n_ , a competitive equilibrium for this economy is a utility level _U_ , a set of factor prices _{rn, wn}n_ , a set of allocations for labor and structure, final good consumption, and final goods prices, _{L_<sup>_j_</sup> _n_<sup>_, H_</sup> _n_<sup>_j, X_</sup> _n_<sup>_j, cj_</sup> _n_<sup>_, P_</sup> _n_<sup>_j}j,n_,andbilateraltradeflows</sup> for each sector _{πni_<sup>_j}n,i,j_suchthatthefollowingconditionshold:optimizationconditions</sup> for workers hold; optimization conditions for intermediate good producers; optimization conditions for final good producers; all markets clear; trade is balanced; utility is equalized across all locations.

# **5 of the mechanisms**

We want to know what would happen to the industrial composition of the economy in the hypothetical case in which mobility restrictions were removed and the road network was brought back to the condition of 1995. The model allows us to translate the anecdotal evidence into mechanisms through which the mobility barriers impact the differential industrial growth. We first map the channels through which changes in the road network impact industries. In the following sections, we describe estimation strategy.

Industries differ in their elasticity to travel times. Given the small geographic unit we consider here, all industries are assumed to be tradable, while allowing for varying levels of elasticity to travel times to match the trade flows observed in the survey between the firms interviewed and their trading partners. This is the direct mechanism of the differential impact of changes in travel times across industries.

If we restrict the sample of ICT firms to those selling non-physical goods, which are thus unaffected by the changes in the cost of providing goods to their customers, we expect the changes in travel times to impact the ICT sector to a lesser extent compared to the rest of the economy. This is the story that has often been told: the ICT sector represents a promise for all those economies afflicted by high shipping costs, travel times, poor road infrastructure, and, in the case of the West Bank, road blocks and mobility barriers, to overcome such limitations and grow.

Another channel involves the industry-specific production technology: some industries rely more extensively than others on immobile factors of production. Through the lens of the model, this is linked to the assumption on the share of fixed land in the production function. Holding everything else fixed, firms relying more extensively on immobile factors of production are less able to relocate and would gain more from a removal of the mobility barriers. The ICT sector being more labor-intensive, it benefits more from the concentration of economic activity and resulting larger pool of labor that a better connected geography would allow.

The ICT sector tends to be more concentrated spatially than other industries (figures 3 and 4). Generally, these locations also coincide with the larger cities. In this model, we abstract from the causes of such phenomenon, which we take as given. We estimate average productivity levels for each location and industry. These quantities are chosen to match the 19
Table 5: Workers employed at ICT firms commute more than other workers

||ICT|ICT<sup>_∗_</sup>|non-ICT|Manufacturing|Retail|Other<br>Industries|
|---|---|---|---|---|---|---|
|Workers average|18.6|20.6|13.2|22.8|11.7|15|
|commute|||||||
|Observations|126|60|407|87|202|118|
|_H_0 : ICT = non-ICT|0.001|0.0005|||||

_Notes:_ ICT<sup>_<u>∗</u>_</sup> excludes retailers and repairers. Time expressed in minutes. P-values reported for Wald tests. _Sources:_ Authors’ survey.

observed data and are exogenous and constant throughout the exercise. We rationalize the observed concentration of the ICT sector in the model by setting the productivity for ICT in location in which there are no ICT firms to zero. This however implies that we are not able to study the formation of new ICT clusters. Nevertheless, we do not expect this to have a strong impact on the analysis of the paper.

Moreover, in the model there are not systematic differences between ICT workers and other workers. Yet, ICT workers may have preferences for larger cities, and thus driving the observed clustering of ICT firms. In our data, we observe that workers in ICT firms travel on average 18 minutes, compared to 13 minutes for workers in the other sectors (table 5).<sup>26</sup> In the paper, we abstract from the role of commuting. We refer to Fratto (2019) for a discussion of their role.<sup>27</sup> Spillovers via input-output linkages describe another channel of the impact of a change in the road infrastructure on the industrial composition. In the model, we assume that the cost of transporting goods in industry _j_ for consumption is the same as the cost of transporting those goods for use as material inputs.

We abstract from the role of externalities. If we decompose productivity into an exogenous and an endogenous component, ln _A_<sup>_j_</sup> _i_<sup>=ln</sup><sup>_aj_</sup> _i_<sup>+</sup><sup>_ψj_</sup> � _n_<sup>ln</sup><sup>_exp{κ_</sup> _ni_<sup>_jL_</sup> _n_<sup>_j}_,where</sup><sup>_κj_</sup> _ni_<sup>isa</sup> function of travel time between locations _n_ and _i_ , this is equivalent to assume that _ψj_ = 0. Based on this specification, the level of productivity in location _i_ industry _j_ depends on some underlying exogenous component and it is increasing in the number of workers employed in that industry weighted by some function of travel time. While we consider this latter channel valid, it would simply strengthen the quantitative results while not altering the implications.

Often the ICT sector is deemed promising because of its ability to tap into the international markets. Yet, we do not find clear evidence of its greater ability to access international markets (table 6): ICT firms are as likely as others to import inputs from and to sell output abroad, even when excluding retailers and repairers from the sample. The high percentage of inputs and output exchanged on the domestic market paints a picture of an ICT sector

> 26The figure includes only workers employed in Palestinian private firms. Most notably, it excludes workers employed in foreign firms either in Israeli settlements or in Israel.

> 27An additional element worth investigating is the role of the access to the Israeli labor market. See for instance Etkes (2012).

20
Table 6: ICT firms do not import or export significantly more than other firms Average share of input and output exchanged domestically:

||||Inputs||||
|---|---|---|---|---|---|---|
||ICT|ICT<sup>_∗_</sup>|non-ICT|Manufacturing|Retail|Other<br>Industries|
|% inputs from West|66%|61%|65%|54%|64%|68%|
|Bank and Gaza|||||||
|Observations|126|60|407|87|202|118|
|_H_0 : ICT = non-ICT|(0.86)|(0.64)|||||
||||Output||||
||ICT|ICT<sup>_∗_</sup>|non-ICT|Manufacturing|Retail|Other<br>Industries|
|%<br>output<br>to<br>West|76%|70%|77%|59%|79%|80%|
|Bank and Gaza|||||||
|Observations|126|60|407|87|202|118|
|_H_0 : ICT = non-ICT|(0.75)|(0.29)|||||

_Notes:_ ICT<sup>_<u>∗</u>_</sup> excludes retailers and repairers. P-values for Wald tests reported in parentheses. _Sources:_ Authors’ survey.

as fundamentally oriented inwards. This is consistent with the conjecture that difficulties to import and export affect all firms, regardless of the industry. Given the lack of differentiation in the exposure to international markets between ICT and the rest of the economy, we abstract from these considerations in the model.<sup>28</sup>

# **6 Empirical Results**

In this section, we report empirical results that shed light on the individual mechanisms described above. We will complement the empirical strategy suggested by the model with other supporting evidence from the survey. Note that in this section we will not be able to estimate the relative importance of the different channels. This will be done in the next section, where we will compute counterfactuals, highlighting the effect of each mechanism separately.

The constraints to growth ICT firms face are the same as the constraints faced by other Palestinian firms. We asked business owners and managers their opinion on the major constraints to the growth of their businesses. Table 7 presents the results separately for ICT and for the other firms interviewed. The answers are remarkably similar whether the question was asked to an ICT firm or to other firms. A third replies that the current economic

> 28For a discussion of the role of internal transportation infrastructure on international trade, see Co ̧sar and Demir (2016).

21
condition of the country is a challenge for the growth of their business. Shortage of credit is also an important constraint, with ICT firms mentioning it twice as often as other firms, but it was one of the most chosen options for both industry groupings. The competition from local firms is also perceived as an important constraint, point again to the key role of the domestic market. Uncertainty places a key role, with uncertain government policies and political instability being the fourth most chosen option for both industry groupings. Results are in line with the recent takeaways from the OECD workshop on Strengthening the Palestinian business and climate investment (October 1, 2019) that mentions among the major constraints access to finance and better market access. However, the key market of reference is not the international market but rather the domestic market.

Table 7: What are the major constraints to the growth of your business? Pick up to three options. Fraction of owners that chose the following options:

|**ICT**<sup>_∗_</sup>||**non-ICT**||
|---|---|---|---|
|Current economic conditions of|34.2%|Current economic conditions of|30.7%|
|the country||the country||
|Shortage of credit|22.5%|Too much competition /unfair<br>competition<br>from<br>Palestinian<br>frms|12.1%|
|Too much competition /unfair<br>competition<br>from<br>Palestinian<br>frms|14.8%|Shortage of credit|10.4%|
|Uncertain<br>government<br>policies<br>and political instability|7.2%|Uncertain<br>government<br>policies<br>and political instability|10.0%|
|Size of the internal market|6.7%|Size of the internal market|9.4%|
|Difculty to import|2.6%|Difculty to access local markets<br>(within Palestine)|6.9%|
|Difculty to access local markets<br>(within Palestine)|2.2%|Lack of technical know-how|4.4%|
|Lack of skilled workers in the local<br>job market|1.7%|Shortage<br>of<br>modern<br>machin-<br>ery/equipment/physical capital|3.1%|
|Lack of raw material or lack of ac-<br>cess to raw material|_<_1.0%|Lack of skilled workers in the local<br>job market|2.1%|
|Difculty to export|_<_1.0%|Difculty to import|1.8%|
|Lack of marketing services or<br>transport facilities|_<_1.0%|Difculty to engage with foreign<br>frms|1.4%|
|Difculty to engage with foreign<br>frms|_<_1.0%|Too much interference by foreign<br>ofcials|1.2%|
|Too much interference by local of-<br>fcials|_<_1.0%|Too much competition /unfair<br>competition from foreign frms<br>(including Israeli frms)|1.1%|

_Continued on next page_ 22
Table 7 – _Major constraints to the_ _<u>growth</u> - Continued_ _<u>from previous page</u>_

|**ICT**<sup>_∗_</sup>||**non-ICT**||
|---|---|---|---|
|Difculty<br>to<br>get<br>li-<br>|_<_1.0%|Lack of marketing services or|1.0%|
|cences/permissions<br>from<br>au-||transport facilities||
|thorities||||
|Inadequate premises/land|_<_1.0%|Difculty to export|_<_1.0%|
|Shortage<br>of<br>modern<br>machin-|_<_1.0%|Lack of raw material or lack of ac-|_<_1.0%|
|ery/equipment/physical capital||cess to raw material||
|Lack of transparency in ofcial or<br>government procedures|_<_1.0%|Workers absences|_<_1.0%|
|Too much competition /unfair|_<_1.0%|Too much interference by local of-|0.0%|
|competition from foreign frms||fcials||
|(including Israeli frms)||||
|Lack of technical know-how|_<_1.0%|Difculty<br>to<br>get<br>li-<br>cences/permissions<br>from<br>au-<br>thorities|0.0%|
|Workers absences|_<_1.0%|Inadequate premises/land|0.0%|
|Too much interference by foreign|_<_1.0%|Lack of transparency in ofcial or|0.0%|
|ofcials||government procedures||
|Lack of energy (electricity, fuel,|0.0%|Lack of energy (electricity, fuel,|0.0%|
|etc)||etc)||
|Difculty to engage with foreign|0.0%|Difculty to engage with foreign|0.0%|
|frms||frms||
|Use of intermediaries in order|0.0%|Use of intermediaries in order|0.0%|
|to get access to local markets<br>(within Palestine)||to get access to local markets<br>(within Palestine)||
|Other factors (specify)|2.0%|Other factors (specify)|3.3%|

_Notes:_ ICT<sup>_<u>∗</u>_</sup> excludes retailers and repairers. All options available are included in the table. _<_ 1.0% indicates a non-zero fraction. _Sources:_ Authors’ survey.

When asked directly whether they think that mobility restrictions affect the growth of their businesses, ICT answers are also similar to other firms (table 8). Differences in answers between industry groupings are not statistically significant with only a few exceptions. More than half of ICT firms answered that obstacles have an impact on the mobility of people, compared to virtually 40% of other firms, consistent with the fact that ICT workers are commuting for a longer distance, but the difference is not statistically significant. Firms were less likely to see obstacles to the mobility of goods as an obstacle, and, interestingly, ICT firms were significantly more likely to mention obstacles to the mobility of goods in and out of the West Bank as a factor affecting growth. The proximity to settlements and to areas of clashes was perceived as less important for growth.

Obstacles have also a similar effect on ICT and on other firms (table 9). As a direct 23
Table 8: How much are these factors affecting growth? Fraction of owners answering ”somewhat” or more.

||ICT<sup>_∗_</sup>|nonICT|Manufacturing|Retail|Other<br>industries|
|---|---|---|---|---|---|
|obstacles<br>to<br>mobility<br>of<br>people<br>within the West Bank|52%|40%|32%*|39%|48%|
|_Ho_: [_ICT _<sup>_∗_</sup>=_j_]||(0.20)|(0.10)|(0.21)|(0.72)|
|obstacles to mobility of people in|51%|42%|38%|44%|36%*|
|and out of the West||||||
|_Ho_: [_ICT _<sup>_∗_</sup>=_j_]||(0.29)|(0.35)|(0.47)|(0.10)|
|obstacles to mobility of goods within|38%|34%|29%|36%|30%|
|the West Bank||||||
|_Ho_: [_ICT _<sup>_∗_</sup>=_j_]||(0.67)|(0.45)|(0.85)|(0.32)|
|obstacles to mobility of goods in and<br>out of the West Bank|47%|31%<sup>_∗∗_</sup>|37%|36%|27%**|
|_Ho_: [_ICT _<sup>_∗_</sup>=_j_]||(0.02)|(0.50)|(0.28)|(0.03)|
|proximity to settlements|24%|26%|47%|25%|22%|
|_Ho_: [_ICT _<sup>_∗_</sup>=_j_]||(0.77)|(0.11)|(0.92)|(0.82)|
|proximity<br>to<br>areas<br>of<br>frequent<br>clashes|37%|35%|25%|37%|30%|
|_Ho_:[_ICT _<sup>_∗_</sup>= _j_]||(0.80)|(0.27)|(0.99)|(0.50)|

_Notes:_ ICT<sup>_<u>∗</u>_</sup> excludes retailers and repairers. Results are unchanged when including them in the sample. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. P-values for Wald tests reported in parentheses. _Sources:_ Authors’ survey.

effect of obstacles, firms in both industry groupings declare that demand from clients is reduced, they experience delays from suppliers, higher transportation costs. These findings are consistent with our own experience, during the COVID-19 pandemic, when reduced mobility decreased the demand for goods and services, and disrupted supply chains both locally and globally. The answers highlight the relevance of the interactions with clients and suppliers, and the tight economic linkages existing between firms.

# **6.1 Computing Counterfactuals**

To solve the model, we follow the approach first proposed by Dekle et al. (2008), who compute the counterfactuals in changes. For each variable _x_ , they define the relative change of _x_ as 24
Table 9: How did obstacles affect your business? Choose 3 options Fraction of owners that chose the following options:

||ICT|non ICT|
|---|---|---|
|Reduced demand from costumers/clients|13%|12%|
|Delays from suppliers|12%|19%|
|Transportation cost|12%|9%|
|Access to costumers/clients|5%|5%|
|Workers delays to get to work/reach their homes|5%|6%|
|Delays to supply product to clients|3%|9%|
|Difculty to obtain supplies|1%|4%|
|None of the above|48%|31%|

_Notes:_ The options available were: access to clients; reduced demand from clients; difficulty to obtain supplies; delays from suppliers; delays to supply product to clients; workers delays to get to work/reach their homes; transportation cost; Other; None of the above. _Sources:_ Authors’ survey.

_x_ ˆ _≡ x_<sup>_′_</sup> _/x_ . The advantage of this approach is that it allows us to obtain estimates for the productivity levels for each industry and each location. In particular, the following holds:

**Proposition.** Given parameter values for the productivity dispersion _θ_<sup>_j_</sup> , the expenditure shares _α_<sup>_j_</sup> , and the input shares _β_<sup>_j_</sup> , and given values or estimates for the bilateral trade shares _π_<sup>_j_</sup> _ni_<sup>, employment by industry and location,</sup><sup>_Lj_</sup> _i_<sup>, and wage rates in each location</sup><sup>_wi_, and given</sup> exogenous changes in trade costs _τni_<sup>_j_,weareabletocomputecounterfactualsforwages,</sup><sup>_w_ˆ</sup><sup>_i_,</sup> employment _L_<sup>ˆ</sup><sup>_j_</sup> _i_<sup>,tradeshares</sup><sup>_π_ˆ</sup> _ni_<sup>_j_,expenditure</sup><sup>_X_ˆ</sup> _i_<sup>_j_andprices.</sup>

The parameters needed to solve the model are reported in table 10. The table also reports the data used to calibrate the parameters and compute the counterfactuals. In the next section we describe more in detail how we use the data to produce estimates for the model.

# **6.2 Estimation of Trade Costs and Trade Shares**

For the West Bank, estimates of the trade flows between towns and villages are not available. However, we rely on our novel data collected with the survey instrument to obtain estimates for these flows. In this section, we describe how we proceed.

In the survey, we ask firms detailed information on the location of the trading partners. We combine these data with data from the Establishment Census and data on wages from the Labor Force Survey to estimate the gravity equation stemming from the model. This approach requires to obtain an estimate of the elasticity of trade flows to distance and an estimate of location- and industry-specific productivities. This section proceeds as follows. First, for the estimation of the elasticity of trade flows to distance, we estimate a logit of the probability that a firm interviewed in the survey, with establishment in location _o_ has a 25
Table 10: Parameters and data used to solve the model

|Parameters||
|---|---|
|Productivity dispersion<br>_θ_<sup>_j_</sup> = 3_._29<br>|Caliendo, Parro (2015)|
|Trade costs by industry<br>_τ _<sup>_j_</sup><br>_ni_|Survey|
|Consumption expenditure shares<br>_α_<sup>_j_</sup>|PCBS (National Accounts)|
|Land input shares<br>_β_<sup>_j_</sup>|Survey|
|Share of value added in gross output<br>_γ_<sup>_j_</sup><br>_n_|EORA (Jordan 2010)|
|Input-output coefcients<br>_γjk_|EORA (Jordan 2010)|
|Data<br>||
|Total employment by industry and location<br>_L_<sup>_j_</sup><br>_i_|PCBS (Census 2007)|
|Wages<br>_wi_|PCBS (Labor Force Survey)|

trading partner in location _d_ . We regress this probability on origin fixed effects, destination fixed effects, and the (log of the) distance between origin and destination. Second, for the estimation of the productivities, we invert an equation linking the demand of traded goods with its supply. We argue that the solution is unique and that we describe an approach to find it numerically. Notice that we only need estimates for productivities for manufacturing in order to recover trade shares. The other sectors are considered non-tradable, and we will not have estimates for industry-specific productivities in that case.

In the survey we ask the following information: From the largest to the smallest commercial partner (buyers or sellers), specify town, district, and average shipping time. This question allows us to compute the probability that a firm in location _i_ has a client or seller in location _n_ . We explicitly asked ICT firms if they were selling physical goods as output. 75% of them replied affirmatively.

We model trade costs as a function of distance. We assume that bilateral trade costs are a constant elasticity function of distance and a stochastic error, _τni_<sup>_j_=time</sup><sup>_t_</sup> _ni_<sup>_j_where</sup><sup>_tj_isa</sup> parameter to be estimated.

Equation 10 can be rewritten as The probability of a firm with productivity _z_ operating in sector _j_ , location _i_ , having trading partners in location _n_ is a function of origin-specific factors, _ζi_ , destination-specific factors, _ζn_ , and the distance between the origin and the destination. _ezjni_ is an error term.

To estimate this elasticity, we use the answers from the survey on the location of trading partners. Observations are the firms in the manufacturing sector with at least two trading partners outside of the town or village where their establishment is located.

Results are reported in table 11. The table reports results for a weighted and an unweighted regressions. Standard errors have been clustered at the firm level. Origin and destination fixed effects are included but not shown in the table. In order to assess the explanatory power of the travel time, we report pseudo- _R_<sup>2</sup> for two competing models, one 26
Table 11: Elasticities of trade flows with respect to distance

||_−t_<sup>_j_</sup>_θ_<sup>_j_</sup>|
|---|---|
|_j_ =_ICT_|-3.971**<br>(1.782)|
|_j_ = Manufacturing|-0.995***<br>(0.317)|
|_j_ = Retail|-1.354***<br>(0.354)|
|_j_ = Other activities|-1.189***<br>(0.178)|
|Observations|10,493|
|Firms|392|
|Survey weights|Yes|
|Origin FEs|Yes|
|Destination FEs|Yes|

Standard errors clustered at the firm level reported in parenthesis. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 _Sources:_ Authors’ survey, Fratto (2019) for travel time.

in which travel times are included and one in which they are excluded. The increase in the pseudo- _R_<sup>2</sup> when including travel times suggests the variable has explanatory power. The resulting elasticity of trade flows to distance for manufacturing is about 1, consistent with estimates from other studies (for instance Monte et al. (2015)).

We estimate a high elasticity of trade flows to distance for the ICT sector. As a matter of fact, the answers from the survey indicate a strong geographic concentration of suppliers and buyers of ICT firms.

We use data on wages from the Labor Force Survey for the years 2000-2007. We regress the observed hourly wages on year fixed effects and individual characteristics. Finally, we include a fixed effect for each workplace location.

where _whit_ are the wages of person _h_ in workplace location _i_ in year _t_ , _Xht_ are a set of individual characteristics, and _ζi_ is a fixed effect for workplace location. More details are reported in the appendix.

**Proposition.** Given the dispersion of productivity, _θ_<sup>_j_</sup> , estimates of wages, employment, marginal cost for intermediate goods _{wi, L_<sup>_j_</sup> _i_<sup>_, xj_</sup> _i_<sup>_}_,andaparameterizationoftradecosts</sup> ( _τni_<sup>_j_)</sup><sup>_θj_,thereexistuniquevaluesoftheunobservedproductivities</sup><sup>_Aj_</sup> _i_<sup>foreachlocation</sup><sup>_i_and</sup> each sector _j_ that are consistent with the data being an equilibrium of the model.

The proof is available in the appendix.

The resulting estimates of the internal trade shares are the solution to the following set 27
Table 12: Share of immobile inputs

||_FEj_ = ln <sup>1</sup><sup>_−βj_</sup><br>_β_<sup>_j_</sup>|_sd_(_FEj_)|[_Ho_:_FE_ICT =_FEj_]|_β_<sup>_j_</sup>|
|---|---|---|---|---|
|ICT|1.8|0.07||0.14|
|Manufacturing|1.5|0.17|0.09|0.19|
|Retail|1.4|0.03|0.00|0.20|
|Other Industries|1.5|0.17|0.13|0.18|

_Notes: FEj_ = ln<sup>1</sup><sup>_−_</sup> _β_<sup>_jβj_</sup> are fixed effects from a regression of the (log of the) share of land to labor on industry-specific fixed effects, _FEj_ , and location-specific fixed effects, _FEi_ . Given first-order conditions for the firm, industry-specific fixed effects map onto industry-specific input shares. OLS standard errors reported in the second column. P-values reported in the third column. _Sources:_ Authors’ survey.

of equations:

- if _j_ = _{_ ICT _,_ manufacturing _,_ retail and wholesale trade _,_ other industries _}_ . Total expenditure is set such that the trade balances in equation 13.

# **6.3 Mobile and Immobile Factors of Production**

Mobility restrictions might affect differently the industries in the economy if they use mobile and immobile factors of production in different proportions. Land is, by definition, immobile: changes in the geography of the economy will translate into changes in the rental rate of land, and therefore higher costs of production for industries that more heavily use this input.

To pinpoint this channel, we use the fact that firms located in the same town face the same prices for land and workers. Therefore, differences in shares of input used between firms in the same town but in different industries can be attributed to differences in the production function rather than to differences in factor prices.

where _FEj_ = ln<sup>1</sup><sup>_−_</sup> _β_<sup>_jβj_.29</sup> In our survey we ask the following questions, which inform this part of the analysis:

- How many square meters did the company own for its business operations?

- How many square meters did the company rent for its business operations?

> 29In practice, the fixed effects _FEj_ are defined up to a scaling factor, which is sensitive to the units of measure used for the factors of production. So, we rescale the industry fixed effects such that the average is 0.2, which is the consensus in the literature on the economy-wide value for this parameter.

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Table 13: share of material goods

||Manufacturing|ICT|Retail|Other industries|
|---|---|---|---|---|
|Manufacturing|0.73|0.18|0.09|0.31|
|ICT|0.07|0.04|0.02|0.09|
|Retail|0.01|0.13|0.56|0.07|
|Other Industries|0.18|0.66|0.34|0.53|

_Sources:_ UNCTAD-EORA

## _•_ What is the total number of employees?

These questions have been asked for the years 2018, and 2017.<sup>30</sup> We use the number of square meters owned and rented by a firm as a measure for _Hi_<sup>_j_(</sup><sup>_z_</sup> _i_<sup>_j_).</sup> Table 12 reports the results from this analysis. The third column tests the hypothesis that the input share for the ICT sector is the same as for the other industry groupings, and the last column reports the resulting coefficient. The ICT sector uses significantly less immobile factors than the other industries in the economy, with the exception of the last industry grouping, which is 0.14, compared with an average economy of 0.19. Unsurprisingly, the point estimate for the wholesale and retail trade sector is the largest, given its higher need for storage.

# **6.4 Input-Output Linkages**

We use data from UNCTAD-EORA on input-output tables for the Jordanian economy in 2015 to estimate the relevant parameters for our model. This database measures trade in value added following the procedure described by Koopman et al. (2010). From the same database, we also use data on the compensation of employees for each industry. Using these data we compute the share of value added in gross output, _{γi_<sup>_j}n,j_,andtheinput-output</sup> coefficients _{γjk}j,k_ .<sup>31</sup> Table 13 reports the corresponding parameters for _{γjk}j,k_ . 73% of the material inputs in the production of manufacturing goods come from manufacturing sector itself; 18% are produced by the service sectors; 7% by the ICT sector and only 1% by the retail and wholesale trade sector. The ICT sector buys 66% of its material inputs from the service sector, 18% from manufacturing, 13% from retail and wholesale trade, and 2% from ICT.

> 30The first two questions are also asked for year 2016.

> 31We restrict the analysis to the industries included in our survey. The four industry groupings in our survey are matched to the following industries from the EORA database. Coefficients for manufacturing are estimated using data from the following industries: Food & Beverages; Textiles and Wearing Apparel; Wood and Paper; Petroleum, Chemical and Non-Metallic Mineral Products; Metal Products; Electrical and Machinery; Transport Equipment; Other Manufacturing. Coefficients for the wholesale and retail trade are estimated using data from the following industries: Maintenance and Repair; Wholesale Trade; Retail Trade. Coefficients for IT are estimated using data from Post and Telecommunications. Coefficients for the other industries are estimated using data from: Construction; Hotels and Restaurants; Transport; Financial Intermediation and Business Activities; Education, Health and Other Services.

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Table 14: Industry groupings as a share of GDP

||Panel a|. Data|
|---|---|---|
|Aggregate|Manufacturing<br>ICT|Retail<br>Other Industries|
|100%|27%<br>10%|27%<br>36%|
||Panel b. Cali|brated Model|
|Aggregate|Manufacturing<br>ICT|Retail<br>Other Industries|
|100%|25%<br>12%|11%<br>53%|
|Panel c. Co|unterfactual - Bringing|mobility restrictions back to 1995|
|Aggregate|Manufacturing<br>ICT|Retail<br>Other Industries|
|100%|36%<br>7%|21%<br>36%|

_Sources:_ PCBS, authors’ calculations.

location and industry grouping. The overall contribution to GDP by industry grouping then depends on the prices for inputs, from wages, and from the estimated production function, including the trade shares. Table 14 compares the contribution to GDP of each industry grouping in the data (panel a) and in the model (panel b). We compare the model with data from the PCBS on national accounts for the West Bank in 2010.<sup>32</sup> The data only includes the industries covered by our survey. The model is able to replicate the relative importance of the different industry groupings in terms of contribution to GDP. It should be noted that we did not target the contribution to GDP when calibrating our model. Besides the data from the survey, we only used employment by location and industry, and an estimate of average wage by location. In particular, wages in the model do not vary across industries. This fact makes us confident on the ability of the model to be informative of the issue at hand.

Indeed, removing mobility barriers would disproportionately and negatively affect ICT relative to other industries. Table 14 reports the results broken by industry grouping. Bringing mobility restrictions back to 1995 would imply a decrease of the ICT contribution to GDP from 12% to 7%, to the advantage of manufacturing and retail. Also the other industries would experience a reduction in the contribution to GDP.

With the focus on industrial composition, the paper does not attempt to provide an estimate of the overall effect of mobility restrictions on GDP growth. For this reason, results on GDP growth are not shown. However, it is worth mentioning that, while the ICT contribution to GDP would decrease in the absence of mobility restrictions, the ICT value added would still increase, although less than other industries.

Removing mobility restrictions allows people and production to concentrate towards the middle of the West Bank, while shipping goods to the outer areas (figure 7). As a result, the region around Ramallah and Nablus would experience an inflow in population, while the regions around Jenin, Bethlehem, and Hebron would experience an outflow. As a matter of

> 32The first year in which the data include a break down for information and communication.

32
fact, the central areas would be better positioned to ship goods, and minimize transportation costs.<sup>33</sup> GDP would increase almost everywhere, which is an expected result of the reduction of trade costs (figure 8). However, there would be a concentration of output towards the central area, mirroring the findings on workers’ spatial distribution. Overall, GDP per-capita increases everywhere (figure 9). Even in locations with an inflow of workers, GDP increases more than proportionally to population growth, with GDP per capita increases ranging from 0.5% to 3.7% depending on the location.

> 33In the model, the total population is fixed. Therefore, some locations are bound to lose population to the benefit of others. Instead, in a world in which migration is possible, the removal of mobility restrictions would result in migration flows towards the West Bank. In this case, the central region would experience a more than proportional increase in population, while the outer regions would experience less than proportional increases.

33
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# **9 Conclusions**

Transport frictions are generally considered a significant barrier to growth, as they impose a high cost on the trade of goods. In fragile and conflict affected states, these frictions can be particularly burdensome, as in the West Bank, where a system of militarized checkpoints and roadblocks imposes heavy limits on the mobility of goods and people.

Yet, interestingly, the Palestinian ICT sector has witnessed a significant expansion. One explanation is that as mobility barriers decrease the incentives to invest in the production of physical goods, the opportunity cost of investing in ICT also falls – and the relative incentive to invest it ICT rises. In fact, focusing on software development and website design, the IT sector is able to employ the high-skilled labor force in the area while overcoming mobility restrictions.

Exploring the constraints to the development of the ICT sector can have important policy implications for other countries seeking to support the fast-growing ICT sector, especially conflict-affected countries where the transportation of goods is often difficult.

This paper points out the extent to which the ICT sector is deeply affected by deficiencies in physical infrastructures, such as the road network. The Palestinian ICT sector, although benefiting from an increase in the incentives to invest due to the mobility barriers, is still severely damaged in its growth prospects by the same constraints to growth as the other firms: the need to have clients nearby and the strong input-output linkages limit its ability to grow in an economy in which other industries suffer. This confirms the need to design a harmonious development plan and that focused investments in a limited set of industries can be hampered by the lag of other parts of the economy and its infrastructure.

Surprisingly, the Palestinian ICT sector is strongly focused on the domestic market, rather than producing for exporting. Besides the difficulties to export, typical of the political and economic situation in the West Bank, it would be interesting to further explore to what extent the ICT sector exhibits home bias as other industries.

38