Making Do with What You Have:Conflict, Input Misallocation, and Firm Performance∗

Francesco Amodio† Michele Di Maio‡

January 7, 2017

Abstract

This paper investigates whether conflict induces distortions in the functioning and accessi-bility of markets for production inputs and in their allocation among firms. We study firmoperations and outcomes in the context of Palestine during the Second Intifada. We analyseinput usage over time across districts experiencing differential changes in conflict intensity.Conflict induces firms to substitute domestically produced materials for imported ones. Coun-terfactual analyses show that this mechanism can account for more than 70% of the fall in theoutput value of firms in high conflict districts.

Keywords: conflict, firm, misallocation, Second IntifadaJEL Codes: D22, D24, N45, O12.

1 Introduction

Well-functioning markets are conducive to an efficient allocation of resources. In the case ofproduction inputs, the presence of market imperfections may prevent resources from accruingto those sectors and firms which value them the most at the margin. This negatively affectsaggregate total factor productivity (Hsieh and Klenow 2009). There is still limited researchexamining the specific sources of market distortions and their relative importance, especially indeveloping countries. Filling this gap is essential in order to design policies that might removethese constraints to the functioning of markets and increase aggregate productivity and income.

∗We would like to thank the Editor Joachim Voth and three anonymous referees for their helpful commentsand suggestions. We also thank Laura Alfaro, Nava Ashraf, Maria Carreri, Matteo Cervellati, Giacomo De Giorgi,Oeindrila Dube, Joan Esteban, Nathan Fiala, Christian Fons-Rosen, Albrecht Glitz, Rema Hanna, Rocco Macchi-avello, Monica Martinez-Bravo, Hannes Mueller, Markus Poschke, Imran Rasul, Debraj Ray, Mark Rosenzweig,Shanker Satyanath, Alessandro Tarozzi, Giulio Zanella, and all participants to the “Understanding the Roots ofProductivity Dynamics” Bank of Italy Workshop, HiCN, ABCDE, NEUDC, Barcelona GSE Summer Forum andJamboree, and seminars participants at University of Bologna, LMU-University of Munich, Bank of Italy andSapienza University of Rome for helpful comments and discussion. We are grateful to Saleh Al-Kafri and thePalestinian Central Bureau of Statistics (PCBS) for providing us with the Industry Survey datasets. Errors remainour own.†francesco.amodio@mcgill.ca. Department of Economics and Institute for the Study of International Devel-

opment, McGill University, Leacock Building, 855 Sherbrooke St. West, Montreal, QC, H3A 2T7.‡michele.dimaio@uniparthenope.it. Department of Business and Economic Studies, University of Naples

Parthenope, Via G. Parisi 13, 80133, Naples, Italy.

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Violent conflict is one possible source of market distortions. For instance, the increasein violence and insecurity can lead to an increase in workers’ absence, limiting firms’ accessto labor (Ksoll, Macchiavello and Morjaria 2015). At the same time, the increase in uncer-tainty related to conflict is likely to discourage the establishment or continuation of lendingrelationships and limit firms’ access to capital. Finally, conflict can affect the sustainability andscope of international trading relationships, thus constraining firms’ access to foreign inputs.These distortions are likely among the microeconomic mechanisms behind the negative rela-tionship between conflict and aggregate economic activity (Alesina et al. 1996; Collier et al.2003; Blattman and Miguel 2010). Understanding the relative importance of distortions inthe accessibility of each production input is therefore of primary importance in the design ofconflict recovery policies.

In this paper, we quantify the extent of conflict-induced distortions in the context of theOccupied Palestinian Territories (OPT) during the Second Intifada.1 The unique features ofthe Israeli-Palestinian conflict make it particularly suitable for the analysis of the effects of aviolent conflict on firms’ access to markets. First, yearly establishment-level data for a repre-sentative sample of firms in the OPT are available for the entire conflict period and before, withdetailed information on input usage. Second, the conflict has been characterised by meaningfulvariation in violence across time and space, about which detailed information is available forthe entire period. Third, the economy never collapsed in either the West Bank or the Gaza Stripduring the Second Intifada, even if its functions were severely affected. This setting is there-fore representative of those situations where economic activity and continuous low intensityviolence take place side by side. This is an increasingly common scenario with high policyrelevance (World Bank 2011).

We think about conflict as affecting the functioning and accessibility of markets wherefirms buy their production inputs and/or sell their final goods. We develop this intuition withinthe conceptual framework proposed by Hsieh and Klenow (2009). In their formalization of theeconomy, firms in the same sector are endowed with the same production technology. In theabsence of distortions, all firms use inputs in the same proportions, while differences in totalfactor productivity determine the size of the firm. Firm-level distortions in the accessibilityof markets change the relative demand for inputs and their marginal product. Those firmswhich find it harder to access the market for one specific input use that input less intensivelyin production. It follows that heterogeneity arises within sectors in the proportions in whichfirms combine their inputs. Therefore, within-sector differences in the production choices offirms which are differentially exposed to conflict can be informative of the relative extent ofconflict-induced distortions in the accessibility of markets.

1The Occupied Palestinian Territories (OPT) are the West Bank (including East Jerusalem) and the Gaza Strip.The Second Intifada is a period of intensified violence which took place between 2000 and 2006. Section 2provides extensive background information on the Israeli-Palestinian conflict and the Second Intifada in particular.

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We take these arguments to the data by combining detailed establishment-level informationfrom the OPT during the Second Intifada (2000-2006) with information on conflict intensity, asproxied by conflict-related Palestinian fatalities. In the first part of the paper, we compare theproduction choices of firms in the same sector across districts experiencing differential changesin conflict intensity. This allows us to net out both overall time trends and unobserved time-invariant sources of heterogeneity in firms’ operations at the district level, possibly correlatedwith conflict incidence. Therefore, we identify the impact of conflict intensity out of yearlyshocks that are differential at the district level. We find that, within the same sector, firmsmore exposed to conflict substitute domestically produced materials for imported ones. Ourestimates indicate that conflict induces distortions in the accessibility of markets for importedmaterial inputs which are more than three times bigger than the distortions for markets fordomestically produced materials. They are also significantly higher than those for labor andcapital markets. We show that results are not confounded by changes in relative prices, in thepopulation of active or surveyed firms, omitted variables such as firm localization, and reversecausality, namely the possibility that workers are themselves involved in conflict.

Our results are robust to a number of checks. First, our findings do not change if we useof different proxies of conflict intensity, including per-capita measures and conflict data fromalternative sources. Second, violations of one assumption of the proposed conceptual frame-work, namely the homotheticity of production functions, do not confound our interpretation ofresults. We use data from the period before the Second Intifada to identify those sectors forwhich this assumption does not hold and we show that results are unchanged when we excludethem from our analysis. Finally, we check whether internal and external mobility restrictionsimposed by the Israeli Defense Forces (IDF) explain away our results. Evidence shows that thenumber of days of border closure correlates significantly with input usage, but the estimatedcoefficient associated with our proxy for conflict intensity is not affected. This indicates thatthe relationship we find between conflict intensity and input usage holds independently fromborder closures.

In the second part of the paper, we investigate the mechanisms behind the observed patternof input substitution. We explore in detail the specific nature of conflict-induced distortions.The relevant sources of market distortions include trade regulations, transportation obstacles,and transaction costs. In particular, evidence shows that importing firms in high conflict lo-calities pay a higher percentage of their inputs before delivery. This is consistent with thehypothesis that the increase in uncertainty due to conflict decreases the bargaining power offirms in their contractual relationship with foreign suppliers, possibly explaining the change ininput usage. Several pieces of qualitative evidence further support this hypothesis.

Finally, in the third part of the paper, we explore the extent to which the conflict-inducedsubstitution of foreign with domestically produced materials accounts for the fall in outputvalue of firms in high conflict districts. Building upon our conceptual framework, we use

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data from the no-conflict period and structurally estimate the factor-share parameters of theproduction function. Combining the latter with our previous estimates of the magnitude ofconflict-induced distortions, we perform a counterfactual policy analysis to derive the value offirm-level output that we would have observed in the absence of conflict. Our estimates suggestthat the value of output would have been 6.4% higher for the average firm in the period, withconflict-induced distortions explaining more than 70% of the drop in the output value of firmsin high conflict districts.

Our paper builds upon and contributes to several strands of the literature. The first refersto those studies which investigate the effects of violent conflict on economic performance.Evidence robustly shows that violent conflict is associated with output fall (Cerra and Sax-ena 2008; Chen, Loayza and Reynal-Querol 2008), lower investment (Eckstein and Tsiddon2004) and lower growth (Alesina et al. 1996). A few studies investigate the effect of a violentconflict at the micro level, looking at outcomes such as firm stocks (Abadie and Gardeazabal2003; Guidolin and La Ferrara 2007), investment (Singh 2013), and firm exit (Camacho andRodriguez 2013). Collier and Duponchel (2013) use firm-level survey data from Sierra Leoneand show that conflict reduces the number of employees and their income. Ksoll, Macchi-avello and Morjaria (2015) use detailed firm-level export data from Kenya and show that theethnic violence of 2007 negatively affected export volumes and revenues primarily through anincrease in workers’ absence. Studying the representative case of a flower-packaging plant,Hjort (2014) shows that this same increase in interethnic violence led to higher discriminationamong coworkers and lower allocative efficiency within the plant. Finally, Klapper, Richmondand Tran (2013) use census data from Cote d’Ivoire and show that firm productivity decreasedwith conflict following the coup d’etat in 1999. They provide suggestive evidence at the sectorlevel that the increase in firm operating costs, including cost of imported inputs, may drive theresults.

Our paper improves on the existing literature on the microeconomics of conflict in threeways. First, while the majority of previous studies have considered only one sector or somespecific group of firms, we use data on a representative sample of the whole population of es-tablishments in the manufacturing sector. Second, our detailed establishment-level data allowus to look at a wide range of firm-level figures, including foreign and domestic input usage.Third, we investigate the salience of conflict-induced distortions in the functioning and acces-sibility of markets for inputs. We are able to provide direct evidence of the nature of thesedistortions and estimate their impact on output value.

We also contribute to the empirical literature on factor misallocation. Several papers haveinvestigated how market frictions and distortions can affect aggregate output and productivity(Wasmer and Weil 2004; Restuccia and Rogerson 2008; Hsieh and Klenow 2009). A numberof studies focus on capital market distortions (Midrigan and Xu 2014), while others address thespecific impact of labor and size-dependent policies (Guner, Ventura and Xu 2008; Garicano,

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Lelarge and Van Reenen 2016). We contribute to this literature by identifying conflict as anadditional determinant of factor misallocation, with a specific focus on developing countries.Moreover, the suggestive evidence we present on the relationship between conflict, uncertainty,bargaining power with foreign suppliers and use of imported inputs is also in line with recentcontributions in the literature which specifically investigate the impact of uncertainty on re-source misallocation in the economy (Bloom 2009; Asker, Collard-Wexler and Loecker 2014;David, Hopenhayn and Venkateswaran 2016).

Given the salience of our results on imported inputs, our paper also relates to the literaturewhich links international trade and firms’ performance. Several theoretical papers have em-phasised the importance of intermediate inputs in generating productivity gains (Ethier 1982;Kugler and Verhoogen 2012). These predictions are confirmed by robust empirical evidence(Schor 2004; Amiti and Konings 2007; Kasahara and Rodrigue 2008; Topalova and Khan-delwal 2011). In particular, Boehm, Flaaen and Pandalai-Nayar (2015) use the 2011 Tohokuearthquake in Japan as a shock to the availability of imports for Japanese affiliates in the UnitedStates. They find that output falls roughly one-for-one with declines in imports. Although weexamine a very different context, our results are consistent with theirs, as we find that conflict-induced distortions in the accessibility of foreign inputs account for more than 70% of the fallin output value.

Finally, our paper contributes to the literature on the effect of the Israeli-Palestinian conflicton the Palestinian economy. Previous contributions analysed the impact of the conflict on anumber of different outcomes: labor market (Miaari and Sauer 2011; Cali and Miaari 2013;Abrahams 2015), child labor and health (Di Maio and Nandi 2013; Mansour and Rees 2012),education (Bruck, Di Maio and Miaari 2014), and welfare (Etkes and Zimring 2015). Whileseveral reports have discussed the aggregate economic impact of the Second Intifada on thePalestinian economy and firms in particular (see for instance World Bank 2004), there are noempirical estimates of this effect at the micro level, which we examine in relation to both theWest Bank and the Gaza Strip.

2 Background: The Israeli-Palestinian Conflict and the Sec-ond Intifada

The Israeli-Palestinian conflict dates back to 1948, making it one of the longest and most vi-olent conflicts in the world. In 1967, the Six-Day War ended with the Israeli occupation ofthe West Bank and the Gaza Strip, previously parts of Jordan and Egypt respectively. In theensuing years, the conflict went through different phases, each characterised by different lev-els of violence. Between 1967 and 1993, Israel held the West Bank and the Gaza Strip undermilitary rule. The Israeli occupation led in 1987 to an unarmed but violent and widespread

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Palestinian uprising. The First Intifada came to an end in 1993, when the Oslo Accord createdthe Palestinian National Authority (PNA), and gave it limited control over some civilian mat-ters (e.g. education, health and taxation) in both the West Bank and the Gaza Strip. The Israeliauthorities maintained control over some strategic issues such as security, border controls andforeign trade between the OPT and Israel, Jordan, and Egypt.

In the years immediately after the Oslo Accord, violence decreased. This relatively peace-ful period ended in September 2000 with the beginning of the so-called Second Intifada.2 Alsocalled the Al-Aqsa Intifada, this period heralded intensified violence between the occupyingIDF and the Palestinians.3 Violence raged on both sides, including Palestinian attacks in Is-rael, assassination of Palestinians leaders in Palestine and demolitions of Palestinian housesby the IDF. Clashes in the OPT between Palestinians and the IDF have often culminated withthe death of both civilians and soldiers. The causes of these clashes have varied; they includecommunication misunderstandings between Palestinian civilians and IDF at the checkpoints,skirmishes between young Palestinians throwing stones and the IDF, and actual armed fightingbetween Palestinian militants and the IDF (Sletten and Pedersen 2003). Given that the SecondIntifada has been essentially a period of violent resistance of different sectors of the Palestinianpopulation against the Israeli occupying force, it is not surprising that violence has been highlyasymmetrical. Between 2000 and 2006, Palestinians killed 234 Israeli civilians and 226 IDFpersonnel in the OPT while the IDF caused more than 4,000 Palestinian fatalities, the majorityof them non-combatants (B’TSELEM 2007). The conflict persisted during the whole period,but the intensity of violence varied substantially over time and space in both the West Bankand the Gaza Strip. There have been periods of relative calm in different areas. The Pales-tinian economy never completely collapsed, as opposed to what often happens to countriesexperiencing genocide episodes or interstate wars.

In order to enhance security and control in the OPT during the Second Intifada, the IDFseverely scaled up the restrictions on the mobility of goods and people within the OPT as wellas across the borders with Israel, Jordan, and Egypt.4 Internal and external movement and ac-cess restrictions have been identified as key constraints of Palestinian economic development(World Bank 2004, 2007). Movement restrictions imposed by Israeli authorities stifle economicactivity by increasing uncertainty, raising transaction costs, inflating the cost of imported in-puts, and reducing output. Since there are no ports or airports in the OPT, import and exportgoods need to travel through Israel, Jordan, or Egypt. Israel currently still controls all tradeaccess routes; thus Palestinian foreign trade flows heavily depend on the state of the conflictwith Israel. Foreign trade constitutes about 80% of the Palestinian economys gross domestic

2For a thoughtful discussion about the causes of the Second Intifada see Pressman (2003).3For a detailed description of the different periods of violence during the Second Intifada see Jaeger and

Paserman (2008).4According to the Israeli Army, this system has been devised as a security measure to protect its citizens (both

in Israel and inside Israeli settlements in the West Bank) from surges, or expected surges (Miaari and Sauer 2011;IDF Military Advocate General 2012.)

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product, of which 80% is trade with Israel (UNCTAD 2006). The negative impact of traderestrictions is thus likely to be very sizable. Operating costs of firms in the OPT also increased;24% of firms in the West Bank and Gaza report political instability as the biggest obstacle totheir operations, right after macroeconomic instability at 30%, and before transportation at 9%(World Bank 2013).

Arguably, the Second Intifada never ended. However, violence decreased substantiallyafter 2006. The 2006 elections caused a de facto division of OPT into a Fatah-controlled WestBank and a Hamas-controlled Gaza Strip. Israel imposed a complete blockade on the GazaStrip in 2007 as a sanction against Hamas. The West Bank and the Gaza Strip - which untilthen had similar institutions and very similar trends in prices and consumption - started todiverge in economic and political terms (Etkes and Zimring 2015).

3 Conceptual Framework

Conflict affects economic activity by inducing distortions in the functioning and accessibilityof markets. As a consequence, it changes the relative demand for inputs and their marginalproduct. On the one hand, the conflict may make it more difficult to access those markets wherefirms sell their final products and services. Such distortion acts like a tax on the value of output,thus reducing firm size: the demand for all inputs decreases accordingly, and their marginalproduct increases. On the other hand, conflict may generate (or exacerbate) distortions that areheterogeneous across inputs. Accessing markets for some production inputs may become moredifficult than others. In this case, differential distortions across inputs will have a differentialimpact on input usage: for each pair of inputs, a larger distortion for one input will lead to adecrease in demand and an increase in its marginal product relative to the other. The relativeamount of inputs used in production will change accordingly. The way we describe firm-leveldistortions is close to Hsieh and Klenow (2009). We build upon their formalization of theeconomy to provide the conceptual framework for our analysis.

Let the aggregate final output in the economy be produced by a single representative firmthat produces a single final good Y with price P . Good Y is produced using a Cobb-Douglasproduction technology by aggregating the output Ys from all S sectors in the economy, i.e.

Y = ΠSs=1Y

θss (1)

with∑S

s=1 θs = 1. Taking the price P of the final good as given, cost minimization impliesPsYs = θsPY for all s. This set of S first order conditions determines the allocation of demandacross sectors.

Production in each sector s is carried out by a single representative firm that aggregates ns

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differentiated input products by means of a CES (Constant Elasticity of Substitution) produc-tion function. Each input for sector s is supplied by a firm i producing output Ysi at price Psi,and operating under monopolistic competition. Production in each sector s is thus given by

Ys =

(ns∑i=1

Yσ−1σ

si

) σσ−1

(2)

with σ > 1. Cost minimization determines the allocation of sector-level demand Ys acrossfirms. The first order conditions imply

Ysi = Ys

(PsiPs

)−σ⇔ Psi = Ps

(YsYsi

) 1σ

(3)

for each firm i in sector s.

Each firm produces output by means of a Cobb-Douglas production function using asinputs capital K, labor L, and materials M . The production function of firm i is given by

Ysi = AsiKαssi L

βssiM

1−αs−βssi (4)

so that the output value of the firm is given by

PsiYsi = PsiAsiKαssi L

βssiM

1−αs−βssi (5)

Here we depart from the basic framework of Hsieh and Klenow (2009) in that we includematerials as input. The empirical analysis will also further differentiate between importedand domestically produced materials, keeping the Cobb-Douglas formulation of the productionfunction. This means we assume that the elasticity of substitution between foreign and do-mestically produced materials is equal to one. In Section A.2 of the Appendix, we structurallyestimate the elasticity of substitution in each sector and provide evidence that supports thishypothesis.

We capture distortions faced by firm i in the accessibility of markets for output and inputsusing τY i and τXi respectively, where X is one of the production inputs (capital, labor, ormaterials). Inputs are traded in a centralised market, with firms taking prices as given andequal to w for labor, R for capital, and z for materials. Profits of firm i are given by

(1− τY i)PsiYsi − w(1 + τLi)Lsi −R(1 + τKi)Ksi − z(1 + τMi)Msi (6)

Given product differentiation, in monopolistic competition each firm enjoys a certain de-gree of market power, so that Psi is endogenous to Ysi. Since Ps and Ys are exogenousto firm i and determined by the allocation of demand at the sector level, we can substitute

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Psi = Ps (Ys/Ysi)1σ in the firm’s profits expression in equation 6 and maximise with respect to

each input. From the corresponding first order conditions we get

Ksi =σ − 1

σαs

PsiYsiR(1 + τKi)

(1− τY i)

Lsi =σ − 1

σβs

PsiYsiw(1 + τLi)

(1− τY i)

Msi =σ − 1

σ(1− αs − βs)

PsiYsiz(1 + τMi)

(1− τY i)

(7)

Equation 7 shows that output and input distortions have a different impact on the demandfor each input and their marginal product. An increase in output distortion τY i, such as re-stricted access to the market for final goods, proportionally decreases the demand for all inputsand increases their marginal product. While the firm becomes smaller, the relative marginalproducts and demand for each input do not change. On the contrary, an increase in the dis-tortion faced by input X (τXi), such as restricted access to the input X market, reduces thedemand for that input only, and increases its marginal product.

Rearranging 7, we obtain the following expressions for the ratios of input values

RKsi

wLsi=αsβs

1 + τLi1 + τKi

RKsi

zMsi

=αs

1− αs − βs1 + τMi

1 + τKi

wLsizMsi

=βs

1− αs − βs1 + τMi

1 + τLi

(8)

These equations provide a number of useful results for our analysis. First, they show thatinput value ratios are invariant with respect to output distortion τY i, but not to input distortionsτXi. Therefore, the relative value of inputs used in production is informative of the relativesize of distortions in the functioning and accessibility of markets for inputs, independently ofdistortions related to markets for final goods and services. Second, in the absence of distortions,input value ratios are the same across firms within sectors, as they are uniquely determined bythe factor share parameters in the production function. Third, input value ratios are invariant tothe firm-level price Psi. This implies that they do not depend on the competition environmentfaced by the firm, and thus do not depend on the market structure of each sector s.

Equations 8 show that we can infer firm-level conflict-induced distortions in the function-

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ing and accessibility of markets for production inputs by comparing input value ratios acrossfirms operating in the same sector which are differentially exposed to conflict. Indeed, any sys-tematic relationship between conflict intensity and input value ratios across firms within sectorswould provide evidence of conflict-induced relative input distortions. For example, if the inputvalue ratio between capital and labor

(RKsiwLsi

)was systematically higher for firms operating in

conflict areas as compared to other firms in the same sector, this would indicate that conflictincreases relatively more firm-level distortions in the market for labor with respect to capital asmeasured by

(1+τLi1+τKi

).5

As a final step, we derive firm i’s output value. As in Hsieh and Klenow (2009), theoptimal firm-level output price under monopolistic competition is a constant mark-up over themarginal cost of production. The price is given by

Psi =σ

σ − 1

1

Asi(1− τY i)

[R(1 + τKi)

αs

]αs [w(1 + τLi)

βs

]βs [ z(1 + τMi)

1− αs − βs

]1−αs−βs(9)

An increase in any firm-level distortion increases the optimal firm-level price. Using thewithin-sector demand allocation condition in equation 3, we can rewrite input levels as a func-tion of Psi only and derive the firm-level demand of inputs given sector-level production andprices. Substituting into equation 5, the output value for firm i in sector s can be finally bewritten as

PsiYsi =σ

σ − 11

1− τY i

[1 + τKiαs

]αs [1 + τLiβs

]βs [ 1 + τMi

1− αs − βs

]1−αs−βs(RKsi)αs(wLsi)βs(zMsi)1−αs−βs

(10)

The framework we adopt is static in nature. Firms take distortions in the accessibility ofmarkets as given, and choose the amount of inputs to use in production accordingly. Outputand prices are also determined simultaneously. We conceptualize conflict as having an impacton firm activity in the current period and on the intensive margin only along the specific di-mension of input usage. We therefore abstract from dynamic considerations, and in particularfrom the possibility that conflict affects firm entry and exit. In our empirical analysis, we willexplicitly question the validity of this approach and provide evidence in favor of its underlyingassumptions.

5Within this same framework, the presence of distortions brings about dispersion in the marginal revenue prod-uct across firms. An alternative way of detecting conflict-induced distortions would therefore be to test whetherthe distribution of marginal revenue products within the OPT is more dispersed in high conflict years relatively tolow conflict years. This is indeed the approach followed by Hsieh and Klenow (2009) in studying misallocation ofinputs in India and China relative to the United States. Notice that our approach of detecting market distortions bycomparing input value ratios across firms does not involve any additional assumption. If anything, our reduced-form approach avoids relying on other countries’ data to benchmark the structural parameters of the model, suchas the elasticity of substitution σ, and the sector-specific factor shares of the production function.

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4 Data

For the purpose of our analysis, we combine two main data sources.6 The first source of in-formation is the Industry Survey, a yearly representative survey of Palestinian establishmentsin the manufacturing sector designed and administered by the Palestinian Central Bureau ofStatistics (PCBS 2007). In addition to the information contained in the publicly available ver-sion of the dataset, we received confidential information as to the district of location of eachestablishment. We are thus able to map each of the surveyed establishments in each of the 16Palestinian districts.7 Moreover, we have information on the ISIC 2-digit sector of each estab-lishment. We can therefore explore the relationship between our firm-level variables of interestwithin and between both sectors and districts over time for the years 1999 to 2006. Our finalsample comprises 14,287 establishment observations spanning 8 years.8 The main variableswe use in the empirical analysis are output value, the value of capital and labor, and the valueof imported and domestically produced materials used during the year.

We measure conflict intensity using the yearly number of Palestinians fatalities caused bythe IDF at the district level. Data on fatalities are collected and distributed by the Israeli NGOB’TSELEM (B’TSELEM 2007). Both the Israelis and the Palestinians consider these data,which are based on a number of sources and validated by several cross-checks, to be accurateand reliable. Other scholars studying the Israeli-Palestinian conflict have therefore used them(see for instance Jaeger and Paserman 2008 and Mansour and Rees 2012). The number ofconflict-related Palestinian fatalities provides the most accurate description of conflict intensityin the OPT during the Second Intifada. The B’TSELEM dataset provides a rich set of infor-mation, such as age, gender, and place of residence of the killed, as well as the date, place,and a description of the circumstances of the event. This allows us to determine the number offatalities in each of the 16 Palestinian districts for each year.

Table A.1 in the Appendix shows the summary statistics of the variables used in the em-pirical analysis. We observe meaningful variation across establishments in the variables ofinterest and, in particular, in output value and input value ratios. Figure A.1 in the Appendixprovides further information on the distribution of some of these variables across Palestinianfirms. More than 80% of establishments have fewer than six employees and an output valueof less than 400,000 NIS (approximately 50,000 USD). This indicates that small and mediumenterprises (SMEs) carry out the largest part of Palestinian manufacturing production. Estab-lishments appear to be evenly distributed across districts, although some of the smallest sectors

6For more details on the study sample, variables definition and additional data on the aggregate level pleaserefer to the Data Appendix B.

7These were established after the signing of the Oslo Accords, together with the division of the Israeli-occupiedterritories into the West Bank and the Gaza Strip. Districts in the West Bank are: Jenin, Tubas, Tulkarm, Nablus,Qalqilya, Salfit, Ramallah and Al-Bireh, Jericho, Jerusalem (including Israeli annexed East Jerusalem), Bethlehemand Hebron. Districts in the Gaza Strip are: North Gaza, Gaza, Deir al Balah, Khan Yunis and Rafah.

8Data issues and sample derivation are described in detail in the Data Appendix B.

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are clustered in a few districts.9 As for conflict intensity, the period 2000-2006 recorded an av-erage number of 35 Palestinians fatalities per district per year. The standard deviation is equalto 42, meaning that we have considerable variation across the 112 district-year observations.

5 Empirical Strategy

The conceptual framework in Section 3 illustrates how within-sector differences in input usageacross firms can be informative of conflict-induced distortions in the functioning and accessibil-ity of markets for inputs. Bringing this argument to the data poses several challenges. A simplecorrelation between conflict intensity and input value ratios cannot be interpreted as evidenceof a causal link between conflict and market distortions. Unobservable omitted variables maygenerate a spurious correlation between the two. Even within the same sector, firms’ access to agiven input may be systematically lower in some districts, and these same districts may also bemore prone to conflict. For example, conflict may be higher in those districts where the bankingsector is less developed. Firms in these districts would have a systematically lower access tocapital. We would therefore find the value of capital relative to other inputs to be negativelycorrelated with conflict intensity, even in the absence of a causal relationship between conflictand access to capital markets. Similarly, the tightening of both household and corporate creditin a given year would simultaneously affect both firms’ access to capital and the opportunitycost of fighting, increasing conflict intensity (Dube and Vargas 2013).

We address these issues by combining cross-district and time variation in conflict intensity.Including sector, district, and year-fixed effects in our regression specification, we can netout a large fraction of unobservable determinants of establishment-level outcomes, possiblycorrelated with conflict intensity. For identification, we therefore rely on yearly shocks toconflict intensity that are differential at the district level. This strategy requires that the numberof fatalities - our proxy for conflict intensity - exhibits meaningful variation both across andwithin districts over time. Figure 1 plots the average number of Palestinians fatalities over timeacross two subsamples of districts. The continuous line refers to those 25% of districts whichrecorded the highest number of fatalities in the peak fatalities year (2002), while the dashed lineshows the same variable for all other districts. Conflict intensity exhibits meaningful variationover time, with changes being heterogeneous across the two groups of districts. The maps inFigure A.2 in the Appendix also confirm cross- and within-district variation in conflict intensity.They show each district classified according to their quintile in the distribution of the yearlylevel of Palestinian fatalities and their two-years changes.

Starting from the conceptual framework, the identification concerns discussed above guide

9As for the sector of activity, 75% of the establishments in the sample operate in the following five sectors:Fabricated metal products, except machinery and equipment (22%); Furniture (15%); Food products and bever-ages (14%); Other non-metallic mineral products (14%); Wearing apparel and dressing, and dyeing of fur (12%).

12

us in the choice of the regression specification to implement. Taking logs of equations 8, weget

ln

(RKsi

zMsi

)= ln

(αs

1− αs − βs

)+ ln

(1 + τMi

1 + τKi

)

ln

(wLsizMsi

)= ln

(βs

1− αs − βs

)+ ln

(1 + τMi

1 + τLi

)

ln

(RKsi

wLsi

)= ln

(αsβs

)+ ln

(1 + τLi1 + τKi

)(11)

where RKsi, zMsi, and wLsi are the value of capital, materials and labor used by firm i insector s.

For every pair of inputs (X1si, X

2si) with corresponding prices (p1, p2), we implement

ln(p1X

1si

p2X2si

)gt

= δt + γg + ϕs + λ12 fatalitiesgt + Z′isgt ρ+ εisgt (12)

where p1X1si and p2X

2si are the value of input X1 and X2 respectively for firm i operating

in sector s surveyed in time t and located in district g, and fatalitiesgt is the number of Pales-tinians fatalities in year t in the same district, measured in standard deviation units from thedistrict-year distribution.10 This allows us to make coefficient estimates directly interpretableas the increase in the dependent variable associated with a one standard deviation increasein fatalitiesgt. The set of sector fixed effects ϕs captures average differences in input usageacross firms at the 2-digit sector-level. That is, we net out differences in production technolo-gies, matching the sector-specific factor shares in the conceptual framework. As anticipated,we also net out average time-invariant differences across districts and overall time trends byincluding the full set of district and year fixed effects, γg and δt respectively. In our mostdemanding specification we interact the two and include sector-year fixed effects, which netout differential time trends in technology (and input usage) across sectors.11 Zisgt is a vectorof establishment-specific controls which can proxy for unobserved differences in technologyacross firms. Finally, εisgt captures residual determinants of input usage. The coefficient ofinterest λ12 captures systematic differences in the corresponding input value ratio across firmswhich are differentially exposed to conflict.

10In practice, we divide the fatalities count variable by the standard deviation of its district-year distribution.11These fixed effects also average out systematic differences in factor prices across establishments in different

years, districts, and sectors. This implies that results are robust to deviations from our conceptual framework (inwhich prices are assumed to be the same for all firms). We discuss the role of prices in more detail in Section6.1.1.

13

6 Results

6.1 Conflict-Induced Distortions in Input Usage

Table 1 reports in each row the corresponding estimates of λ from specification 12 for eachof the input value ratios. Standard errors are clustered along both sector-year and district-year categories. This allows the residuals uisgt belonging to establishment observations locatedin the same district and year to be correlated, and the same for the residuals belonging toestablishments surveyed in the same year and operating in the same sector.12 Column 1 showsestimates from a specification where sector, district and year fixed effects are included, togetherwith our main variable of interest fatalitiesgt. Rows (a) to (c) consider the relative value ratiosof capital RK, labor wL and materials zM used in production. We find that the input valueratios between the three inputs do not differ across firms which are differentially exposed toconflict, with estimates of the λ coefficient being close to zero and insignificant. Nonetheless,conflict exposure is instead systematically correlated with the relative use of material inputsaccording to their foreign or domestic origin. In rows (d) to (h), we consider imported materialsM f and domestically produced materials Md separately. Results in row (d) show that a onestandard deviation increase in the number of fatalities is associated with a 1.2 increase in thevalue of domestically produced materials used in production relative to imported ones, with thisestimate being significant at the 1% level. Indeed, the value of capital and labor with respectto imported materials increases significantly with conflict intensity (rows (e) and (f)), while theratio of capital and labor value over the value of domestically produced materials decreasessignificantly (rows (g) and (h)). All these estimates are significant at the 1% level.

In columns 2 and 3, we start including establishment-specific controls as additional regres-sors. These are meant to proxy and control for unobserved differences in technology acrossfirms, possibly correlated with conflict intensity. One possibility is that those districts whichexperience a surge in conflict may also be those in which micro and small family-owned en-terprises are more prevalent. These are likely to employ a systematically different productiontechnology, more intense in domestically produced materials. In order to proxy and conditionon these differences, we include as controls the fraction of family workers and that of propri-etors over the total number of labor units. Finally, in column 4, we also include the full setof district-year fixed effects φst in order to control for sector-specific trends in input usage.Estimates for all input value ratios are stable across all specifications.

Our results show that the within-district and within-sector variation over time in the relativevalue of inputs used by Palestinian establishments is systematically correlated with conflict

12The number of clusters is above 50 in both dimensions, so that the cluster-robust estimates of the variance-covariance matrix of residuals are reliable. Table A.3 in the Appendix reports the results when standard errors areclustered at the district level, and calculating using wild bootstrapping after 100 repetitions (Cameron, Gelbachand Miller 2008).

14

intensity. We interpret this as evidence that conflict induces distortions which are differentialacross inputs: the relative value of imported materials is systematically lower for firms locatedin high conflict environments. Our estimates indicate that when conflict intensity increases byone standard deviation the average value share of imported materials used in production fallsfrom 8.5% of the total value of materials to 3%. Using our conceptual framework, we can alsoderive for each input the relative size of the distortions associated with conflict. Equation 11shows that, for every pair of inputs (X1, X2), the relative amount of distortions induced by aone-standard deviation increase in conflict intensity is given by

exp(λ12

)=

1 + τX2i

1 + τX1i

(13)

Our calculations reveal that one standard deviation increase in conflict intensity is asso-ciated with a significant 3.5 increase in the relative distortions faced by firms in accessing themarket for imported materials as compared to the one for domestically produced inputs.13 Bythe same token, conflict-induced distortions in imported materials are 1.7 and 1.6 significantlyhigher than those in capital and labor.

Our results shows that firms in high conflict districts use a relatively lower value of foreignproduced materials and a relatively higher value of domestically produced ones in production.Given that the relative total value of materials as compared to labor and capital does not change(see Table 1 rows (a), (b), and (c)), we infer that conflict distortions lead firms to substitute do-mestically produced materials for imported ones. The two inputs are likely not perfect substi-tutes.14 Evidence from the trade literature shows how access to imported inputs increases firmproductivity (Schor 2004; Amiti and Konings 2007; Kasahara and Rodrigue 2008; Topalovaand Khandelwal 2011). In this sense, our results provide evidence of a specific trade-relatedsupply-side mechanism through which conflict may negatively affect output. We will explorethis possibility in more detail in Section 8.

In accordance with the micro-level evidence above, aggregate trade data show that conflictintensity is associated with changes in Palestinian foreign trade, particularly imports.15 FigureA.3 in the Appendix shows that changes in the number of fatalities over the period are positively

13Table A.2 in the Appendix reports the corresponding estimates for all inputs, together with 95% confidenceintervals. Consistent estimates of standard errors are derived accordingly from the standard error coefficientestimates in Table 1 using the delta method.

14In Section A.2 of the Appendix, we structurally estimate the elasticity of substitution between foreign anddomestically produced materials in our data for each sector and find it to be remarkably close to one. See Section8.2 for a detailed discussion of this issue.

15The Palestinian economy is highly dependent upon imported goods and services. During the Second Intifada,the total value of Palestinian imports was 6 to 8 times the total value of its export, with the negative balance oftrade being equal to 40-50% of GDP at its current value. Israel was the main trade partner of the OPT during theperiod: Palestinian imports from Israel were around 70% of total value of imports while Palestinian exports toIsrael were 90% of total value of exports. For Israel, however, trade with the OPT represents only a small share offoreign trade. See the Data Appendix B for data sources and methodology.

15

correlated with changes in the net balance of trade. This indicates that the (negative) changein the value of imports was much more sizable than the change in the value of exports. Thecomposition of imports also changes differentially. Figures A.4 and A.5 in the Appendix showthe import and export composition respectively in 1999 - the year prior to the outbreak of theSecond Intifada - and 2002 - the year with the highest death toll - at the ISIC 1-digit sectorlevel. While export composition does not appear to be different across the two years, importcomposition shows meaningful changes.16 The categories that suffer the largest reduction inimport share are: Miscellaneous manufacturing articles; Manufactured goods classified by

material and Machinery and transportation equipments. These three categories account forabout 55% of the total value of imports in 1999, and only 37% in 2002. Figure A.6 also showsthe Israeli import value share for each category. Israel is the main supplier of imports in allcategories except for Miscellaneous manufactures articles. Comparing 1999 and 2002, we seethat the Israeli shares of import change proportionally to total import shares, suggesting nodifferential pattern according to country of origin.

Figure A.7 shows import value shares for 1999 and 2002 at a more disaggregated level.Those ISIC 2-digit sectors for which import shares decrease the most are: Textile yarn, fabrics,

and related products; Non-metallic mineral manufactures; Manufactures of metals; Machinery

specialized for particular industries; Industrial machinery and equipment, and parts; Telecom-

munications and sound-recording; Electrical machinery apparatus and parts thereof ; Road

vehicles (including air-cushion vehicles); Furniture and parts thereof ; Miscellaneous manu-

factured articles. Most of these sectors are producers of manufacturing inputs. Although wecannot directly disentangle inputs from finished products, we interpret this aggregate evidenceon trade flows as being consistent with our findings at the firm level.

6.1.1 Identification Threats and Sources of Variation

Our results show that the value of domestically produced materials relative to imported onesused in production increases with conflict. We interpret this as evidence that conflict inducesfirms to substitute domestically produced inputs for imported ones, with a change in relativequantities. There are five main threats to this interpretation of results. First, holding quantitiesconstant, the relative value of domestic vs. imported inputs used in production may increasebecause of changes in input prices, and, in particular, a decrease in the relative price of importedinputs. Although firm-level data on input prices do not exist, several studies indicate that thecosts of imports increased considerably during the Second Intifada (Akkaya et al. 2011; WorldBank 2006b). PCBS data show that wholesale price of imports increases during the period,

16The absence of meaningful changes in export composition also makes the possibility that changes in externaldemand drive the changes in firms input usage seem very unlikely, as the former does not correlate with conflictintensity. We discuss this issue further in Section 6.2.

16

and correlates negatively with conflict intensity.17 Figure A.8 in the Appendix shows that thereis no clear pattern of correlation between the ratio of import to domestic wholesale prices andthe number of Palestinian fatalities. If anything, the estimated correlation is positive. Theseresults leads us to exclude the possibility that a decrease in the relative price of imported inputsis responsible for our findings. On the contrary, they support the hypothesis that a change inrelative quantities is behind the conflict-induced increase in the relative value of domestic vs.imported inputs we document in Table 1.

A second threat to the proposed interpretation of our findings is that conflict may affectfirms’ activity on the extensive margin. That is, conflict can affect the size and characteristics ofthe population of active firms. We directly test for this hypothesis by implementing our baselineregression specification (equation 12) replacing as outcome the (weighted and unweighted)number of firms surveyed in each sector, district, and year. In addition, we test for a systematicrelationship between conflict intensity and all available firm-level outcomes measured at thebeginning of the year: initial value of capital, inventory of output, and inventory of materials,all in logs.18 Table 2 reports for each outcome the coefficient estimates from the two mostdemanding regression specifications. Results show that there is no evidence of a systematicrelationship between conflict intensity and the number of operating firms and their baselinecharacteristics. These findings support the hypothesis that conflict affects firm activity on theintensive margin, with no impact on the population of active firms and their characteristics.

A third threat is related to the identification strategy: firms located in districts where con-flict increases disproportionally more could have already been on a different trajectory in termsof production choices and input usage. However, Figure 1 and A.2 indicate that identification inthis context does not stem from divergent linear trends, but rather originates from yearly shocksin conflict intensity at the district level. The actual evolution of conflict intensity mitigates theconcern that pre-existing trends may be generating our results. Nonetheless, we address thisconcern by including in our main regression specification both the current and next year level ofconflict intensity, still measured by the number of fatalities. Table A.8 in the Appendix showsthe corresponding results. Input value ratios are significantly correlated with current but notwith next year conflict intensity. This allows to conclude that pre-existing movements in inputusage do not with conflict intensity, validating our approach to identification.

A fourth concern is that our results are only capturing systematic differences related tofirm localization. Firms located closer to the borders are likely to be more intensive in im-ported material inputs with respect to other establishments in the same sector. By the sametoken, districts near the border with Israel may experience higher variation in the number ofPalestinian fatalities over the period. We condition on the role of distance from the border by

17Data are available at: http://www.pcbs.gov.ps/Portals/ Rainbow/Documents/e-whpi-serise-2012.htm.18Unfortunately, the information on the value of inventory of materials at the beginning of the year is not

available separately for foreign and domestically produced materials.

17

saturating the input value ratio regression specifications (equation 12) with a full set of yearfixed effects interacted with a measure of the road distance of the district capital from the clos-est entry passage.19 Corresponding estimates are reported in Table A.9 in the Appendix. Pointestimates are very similar to those reported in Table 1. This suggests firms’ localization doesnot confound our results.

A final concern is that reverse causality could drive our empirical results. An independentshock to the availability of foreign inputs at the district level may lead to changes in input usage,possibly diminishing the marginal product of labor and thus wages. This would decrease theopportunity cost of fighting, thus increasing conflict intensity. However, our results show thatthe relative use of labor and number of fatalities are not correlated (see Table 1, rows b andc), in contrast with what one would expect if workers played an important role in the conflict.Still, holding constant the total amount of labor, it is possible that the loss of workers involvedin the conflict lead to changes in workforce composition. However, this change would generatethe result we find on input value ratios (substitution of foreign for local material inputs) onlyunder a very precise assumption: workers substituting those participating in the conflict aremore complementary to locally produced materials than to imported materials. No evidencesupports this assumption.

6.2 Robustness Checks

6.2.1 Alternative Measures of Conflict

We have obtained our results using one specific measure of conflict intensity: the number ofPalestinian fatalities. As explained in Section 5, in order to make coefficient estimates readilyinterpretable, we measure the number of fatalities in standard deviation units. In evaluating therobustness of results, we first implement our main regression specification (equation 12) usingas a measure of conflict intensity the actual number of Palestinian fatalities in district d andtime t. Table A.4 in the Appendix shows the corresponding results, which confirm our previousfindings.

As a second alternative proxy of conflict intensity, we use the per-capita number of Pales-tinian fatalities in district d and time t.20 Table A.5 in the Appendix reports the correspondingresults. As before, results indicate a strong systematic relationship between conflict exposureand the relative use of foreign vs. domestically produced material inputs.

To corroborate our estimates further, we derive a third proxy of conflict intensity from a

19Since geographical coordinates of the firm’s location are not available, we proxy the distance of the firm fromthe border gate using the district capital under the assumption that firms are more likely to be located close to thelargest urban center of the district.

20We compute the per-capita measure diving the district-level umber of Palestinian fatalities by the total popu-lation of the district in the year. PCBS provides population data for each district and year on its website.

18

separate source: the Integrated Crisis Early Warning System (ICEWS) dataset (Shilliday, A.and J. Lautenschlager 2012). This proxy counts the number of hostile political interactions oc-curred in each district in the OPT in each year during the Second Intifada. For consistency withour previous analysis, we restrict our attention to those hostile events triggered by the IDF.21

As before, we standardize the count variable and divide it by the standard deviation of thecorresponding district-year distribution. Table A.6 in the Appendix shows the correspondingestimates. Remarkably, using a completely separate source of data we unveil the same em-pirical regularities. Coefficient magnitudes indicate that conflict-induced distortions in relativeinput usage are highest for foreign vs. domestically produced materials. The correspondingcoefficient is lower than in our baseline results, but significant at the 10% level. We interpretthe smaller size of the effect as following from the additional noise that characterizes this mea-sure of conflict intensity, as hostile events do not always result in Palestinian fatalities. Thismay result in classical measurement error, biasing our estimates towards zero. Despite this, allcoefficient magnitudes are ordered as in Table 1, confirming our previous findings. The sameholds when we divide the number of ICEWS hostile events by population, as shown by TableA.7 in the Appendix.

6.2.2 Non-homothetic Production Functions and Demand-side Effects

The validity of our interpretation of empirical results rests on the assumptions of Hsieh andKlenow (2009). In particular, the model assumes homothetic production functions. While firmsin the same sector can be heterogeneous in terms of total factor productivity, this assumptionensures that - in the absence of distortions - they will all use inputs in the same proportion.It follows that within-sector differences in input value ratios which relate systematically toconflict exposure can be interpreted as evidence of the relative amount of distortions inducedby the conflict in the accessibility of markets for inputs. This is no longer the case if productionfunctions are non-homothetic. When differences in factor shares are correlated with firm’soutput, changes in input usage could be the result of conflict-induced changes in demand. Inparticular, if firms with lower output were to employ relatively more domestically producedmaterials than imported ones, output fall might drive the observed increase in the amount ofdomestically produced materials used in production, with possibly no role played by conflict-induced distortions in the accessibility of markets.

To rule out this possibility, we use data from year 1999 - the year before the start of theSecond Intifada - and directly test whether firm size correlates with input value ratios. Thisis based on our conceptualization of 1999 as a benchmark economy with no conflict-induceddistortions. Figure 2 panel (a) plots the relationship between the (log of) value ratio of domes-tically produced materials over imported ones and the (log of) output value in 1999, averaging

21For additional details on this dataset and our derived measure of proxy of conflict intensity, please refer to theData Appendix B.

19

out sector-level means. For any given level of output value, we observe substantial heterogene-ity across firms. The line fitting the scatterplot is downward sloping, with the correspondingcoefficient being significant at the 5% level. This means that, before the start of the conflict andwithin sectors, firms with higher output value employed relatively fewer domestically producedmaterials with respect to imported ones. While these results may suggest that demand-drivenmechanisms may be at work, further analysis of the data reveals that before the start of theconflict the relationship between input value ratio and output value is non-significant for 15out of the 25 sectors (to which 66% of surveyed establishments belong), as shown in Figure 2panel (b). We thus re-estimate the coefficient λ from our regression specification using only theobservations belonging to this restricted sample for which the homotheticity assumption findssupport in the data. Table A.10 of the Appendix reports the corresponding estimates. Resultsare almost exactly the same as the baseline ones in Table 1. Under the assumption that thewithin-sector relationship between factor shares and output value remained constant over time,this suggests that violations of the homotheticity assumption in the data do not confound ourinterpretation of the empirical findings: the observed changes in input usage are attributable toconflict-induced distortions in the supply side of the economy rather than to a fall in demand.

6.2.3 Internal and External Mobility Restrictions

As we discussed in Section 2, one of the distinctive features of the Israeli-Palestinian conflict isthe presence of restrictions imposed by the IDF on the mobility of goods and people within theOPT as well as across the border with Israel. In order to interpret our findings correctly, it is thusimportant to explore the extent to which changes in the number of Palestinian fatalities correlatewith changes in internal and external mobility restrictions. Internal mobility restrictions includecheckpoints, roadblocks and barriers between villages within the OPT put in place by the IDF.These measures may increase travel time and thus affect the accessibility of both foreign anddomestic inputs. However, the data needed to estimate their impact on firm input choice sufferfrom a serious limitations: data are missing for the most violent year (2002) and for all districtsin the Gaza Strip for all years.22 This implies that it is not possible to quantify the role ofinternal mobility restriction in our framework precisely using existing data.

External mobility restrictions take the form of closure of the border between Israel andthe OPT imposed by the IDF. During border closure days, movements of workers and importand export of goods are interrupted (World Bank 2008). This represents a negative shock tothe accessibility of foreign markets for Palestinian firms. If such shock was correlated with

22Available sources of data on internal mobility restrictions are the Applied Research Institute of Jerusalem(ARIJ), and, since 2003, the United Nations Office for the Coordination of Humanitarian Affairs (UN-OCHA).Using a combination of these data, Cali and Miaari (2013) find that internal checkpoints in the West Bank havea significant negative effect on employment, wages, and days worked per month. Abrahams (2015) finds insteadthat the effects of mobility restrictions within the West Bank are spatially differentiated: core locations benefitwhile peripheral locations suffer in terms of employment.

20

conflict intensity, we would be attributing to local conflict conditions (proxied by the numberof Palestinian fatalities) the negative effect of border closures on the availability of foreignproduced materials. Figure 3 shows the evolution of the total number of Palestinian fatalitiesover time in each quarter from 2000 to 2006, together with the quarterly number of days ofborder closure in the same period.23 A visual investigation reveals no systematic relationshipbetween the two. Indeed, the estimated correlation is equal to 0.12 and insignificant. Thismeans that the variation in the incidence of border closures does not overlap with the onecaptured by our proxy for conflict intensity, and that the changes in input usage we documentare independent from border closure. Moreover, notice that, as long as the impact of closures isuniform across Palestinian districts, the year fixed effects in our specification already take thatinto account.

Yet, closures may affect firms located in different districts in different ways. Firms thatare located near the Israeli border may find it easier to cope with closures, as they can takeadvantage of non-closure days to acquire foreign inputs. Or, firms next to the border may bemore vulnerable to border closures, and adjust their input usage to a larger extent. In both cases,if the largest changes in conflict intensity were in districts next to the border, the differentialeffect of closures would be nested in our estimate of the impact of conflict on input usage.24

We provide direct evidence of the impact of border closures by including the interaction of theyearly number of closure days with the road distance of firm location from the closest entry gateas an additional regressor in our specification.25 We therefore allow the effect of border closureson input usage to be heterogeneous according to the distance from the border, capturing eitherscenario described above. Table 3 reports the estimated coefficients of the variables of interestfrom this augmented specification. The coefficient of the interaction variable is significant:the impact of border closures is systematically higher for firms located farther away from theborder. The higher the number of closure days and the higher the distance from the entrygates, the more firms substitute domestically produced inputs for imported ones. Perhaps moreimportantly, when compared to the baseline results in Table 1, the coefficients of the fatalities

variable are almost unaffected.

Evidence thus shows that the closure of borders plays a role in inducing changes in inputusage. The impact of closures has the same direction as the impact of local conflict intensity.26

23B’TSELEM also provides the data on closure days of the border between Israel and the OPT.24As discussed in Section 6.1 (see Table A.9 in the Appendix), the magnitude of the estimated distortions

induced by the conflict does not change if we control for the differential effects that any year-specific shock mayhave according to the distance from the border, as captured by the interactions of distance from the border withyear fixed effects.

25As in Section 6.1, we proxy the distance of the firm from the gate using the district capital under the assump-tion that firms are more likely to be located close to the largest urban center of the district.

26This result is consistent with the findings in Etkes and Zimring (2015), which provides evidence of the nega-tive impact of the Israeli-imposed 2007 Gaza blockade on the economy of the Gaza Strip. In particular, they findthat labour productivity decreased with the enforcement of the blockade, and that labor reallocated away fromtrade-oriented sectors.

21

Yet, the two are independent: the estimated coefficient of conflict intensity does not changesignificantly when we account for border closures, as the two variables are not correlated.This indicates that the relationship we find between conflict intensity and input usage holdsindependently from border closures and operates through other channels.

7 Unpacking Distortions

Results from the previous section show that firms located in districts that are differentially moreexposed to conflict substitute domestically produced materials for imported ones in production.We attribute these changes to conflict-induced distortions in the functioning and accessibilityof markets for imported materials, which lead firms to change their relative demand for inputs.In the following, we explore the nature of these distortions in detail.

We rely on additional evidence from the World Bank Enterprise Survey (World Bank2006a). This survey is particularly rich in information on firms’ operations and obstacles theyface. Data are available for 401 enterprises located in the West Bank and the Gaza Strip. Sur-veyed enterprises belong to the manufacturing, construction, transport, and service sectors withmore than five employees. For those located in the West Bank, information on the village of lo-cation is also available. We merge the World Bank Enterprise Survey data with the B’TSELEMdata on Palestinians fatalities, and derive the number of fatalities in 2006 in the village wherethe firm is located. Despite the fact that we are using data from a cross-section of enterprisesand only for the year 2006, we are confident that the extensive information in the survey cou-pled with the finer spatial variation in fatalities we can use in this analysis can provide insightson the sources of distortions affecting imported input usage during the Second Intifada.

Given our previous results on foreign input usage, here we ask whether conflict affectsfirm operations in a differential way according to their importing status. We thus implementthe following regression specification

yig = α+ β mig + γ fatalitiesg + δ fatalitiesg ×mig + X′ig θ + Z′g ψ + vig (14)

where yig measures the relevance of an obstacle for the operations of firm i located in villageg, on a scale from 1 (little) to 4 (very). mig is a dummy equal to one if the firm imports anygood or service, and fatalitiesg is the number of Palestinians fatalities in village g. Xig is avector of pre-determined firm-level variables, while Zg is a vector of village-level controls.27

The coefficient δ captures whether the relationship between conflict intensity and firm-level

27Pre-determined firm-level variables included in the dataset are: sales and employment in 2003, the year inwhich the firm began operating, whether the firm is female-owned and its legal status. As for village level controls,we include the population in 1997, which is the last year before the Second Intifada for which data on populationare available at the village level.

22

outcomes is systematically different according to the firm importing status.

As obstacles to firms operations we consider: a) custom and trade regulation, b) trans-portation, and c) percentage of inputs paid before delivery. Table 4 shows the correspondingcoefficient estimates from regression 14. In columns 1 to 5 we progressively add sector anddistrict fixed effects, together with a number of control variables. We therefore rely on varia-tion in conflict intensity across localities within sectors and districts, and look at its differentialrelationship with operation obstacles depending on the firm’s importing status. Panel A showshow the score attached to custom and trade regulation as an obstacle is differentially and sys-tematically higher for importing firms operating in high conflict localities.28 Panel B shows thatthe same pattern holds when we consider the score attached to transportation as an obstacle tofirm operations. Evidence in Panel C is particularly interesting. It shows that the percentage ofinputs paid before delivery is 4 to 6 percentage points higher for importing firms in high con-flict localities. This suggests that the uncertainty related to conflict is disproportionally moresalient for transactions on foreign markets with respect to local markets. This is consistent withthe hypothesis that exposure to conflict shapes the terms of the contract between the firm andforeign suppliers, decreasing the bargaining power of the former. This increases the operat-ing costs that the firm faces when accessing the market for foreign produced inputs, makingimported inputs relatively more costly.

Several pieces of qualitative evidence further support this hypothesis. Service Group(2002) reports that the outbreak of the conflict led many Israeli suppliers to demand advancepayment instead of payment-upon-receipt to protect themselves from collection difficulties.This has created serious problem for Palestinian firms, which face financing constraints. Ac-cording to UNCTAD (2004), the outbreak of hostilities has made banks adopt more conser-vative lending policies and restrict access to trade financing services. This has increased theliquidity problems of Palestinian companies, making it extremely difficult to pay for inputs inadvance. Indeed, in 2002 - when conflict intensity was the highest - more than 80% of thePalestinian SMEs relied on internal savings for covering operating costs.29 Moreover, usingdata from an original survey of traders, UNWFP (2009) shows how West Bank traders localisetheir activities within their districts when Israeli suppliers reduce their credit lines. Taken to-gether, this evidence suggests that a link between conflict-induced uncertainty and credit tight-ening from Israeli suppliers can possibly explain the substitution of foreign with domesticallyproduced materials that we documented in our analysis.

28The procedure for clearing Palestinian goods through Israeli ports and controlled border crossings is long andextremely complicated. Israel requires that Palestinian trucks use the “back-to-back” system according to whichall goods need to be unloaded from and re-loaded again onto trucks at checkpoints after the security check (WorldBank 2007).

29Data show that only 18% of small enterprises benefit from bank loans, and the percentage of Gaza-basedmedium enterprises accessing bank loans does not exceed 4%. By September 2002, private sector credit hadfallen by 24 per cent from its 2000 level. In particular, bank credit by sector shows that industry receiving smallershares of total credit in 2001-2002 than they had received before the beginning of the conflict (UNCTAD 2004).

23

The estimates in column 6 further validate this interpretation of results. We implement atriple difference specification where we look at whether the differential relationship betweenconflict and operation obstacles for importing firms further depends on the age of the firm.Older firms are likely to have engaged in more transactions with Israeli suppliers before theconflict. Through these past interactions, they have revealed information on their reliability.Therefore, if the uncertainty related to conflict affects the terms of the contract between Pales-tinian firms and Israeli suppliers, we expect these changes to affect older firms disproportion-ally less. Consistent with this hypothesis, the evidence in column 6 of Panel C shows thatthe negative relationship between conflict and the percentage of inputs paid before delivery byimporting firms is systematically lower for older firms.

8 Conflict, Misallocation, and Output Value

8.1 Reduced-form Evidence

Aggregate figures indicate the existence of a negative relationship between conflict intensityand output. The real GDP of the OPT falls by 20% between 2000 and 2002, mirroring the steepincrease in the number of Palestinian fatalities over the period. By the same token, a downwardtrend in the number of fatalities in the period thereafter is associated with an increase in GDP,with the latter reaching its 2000 values in 2004.30

Establishment-level data allow us to investigate this negative relationship between conflictintensity and output in a more systematic way. We implement the same specification as inequation 12, but replacing as outcome the (log) value of output produced by the firm. Table 5shows the corresponding coefficient estimates. Column 1 shows the estimate for the coefficientof the fatalities variable from a simple regression specification in which it is the only includedregressor. A one standard deviation increase in the number of fatalities in the district is asso-ciated with a 12.6% decrease in establishment’s output value, significant at the 5% level. Incolumns 2 to 4, we progressively include year and district fixed effects, sector fixed effects, andfirm-level controls. In column 5, we include the full set of sector-year fixed effects allowing forsector-specific trends. We find that one standard deviation increase in the number of fatalitiesin the district is associated with an 8.6% drop in output value, significant at the 1% level.

If the proposed mechanism of conflict-induced distortions in input usage is partly respon-sible for the fall in output value, the latter should be more pronounced among firms operating in

30Figures A.9 in the Appendix plots the value of Palestine GDP over time between 2000 and 2006, togetherwith the total number of Palestinians fatalities caused by IDF. The evolution of real GDP is inversely related toconflict intensity as measured by the number of Palestinian fatalities. Figure A.10 shows that similar inverselyrelated trends can be observed between conflict intensity real aggregate output value as computed using the datafrom the Industry Survey.

24

sectors that rely more on imported inputs. To explore this possibility, we calculate a sector-levelmeasure of intensity in imported inputs using firm-level data from 1999, prior to the beginningof the Second Intifada. Consistently with our framework, we calculate the ratio between the to-tal value of foreign vs. domestically produced materials used in production in each sector, andinclude it in our main regression specification, together with its interaction with the fatalities

variable. Columns 6 and 7 of Table 5 show the corresponding coefficient estimates. The rela-tionship between conflict and output value is systematically more negative for firms operatingin sectors with higher baseline intensity in imported inputs. We interpret this as reduced-formevidence that conflict-induced distortions in foreign vs. domestically produced input usage isa relevant mechanism for the negative relationship between conflict and output value that weobserve in the data.

These results show that conflict intensity is negatively associated with firm output value.While our data do not allow us to look at establishment-level output quantity and prices sep-arately, a number of considerations lead us to conclude that these results indicate a negativerelationship between conflict and output level. First, the conceptual framework suggests thatthis is the case. Equation 9 in Section 3 shows that, when firms enjoy a certain degree ofmarket power, any increase in output or input distortion will result in higher firm-level outputprices. It follows that, if conflict increases distortions, only a more than proportional decreasein output quantity would generate the negative result we find on output value. Perhaps moreimportantly, there is no empirical evidence that conflict reduces output prices. To test for this,we identify those sectors which are most geographically localised, and check for whether theProducer Price Index (PPI) of these sectors tracks the evolution of Palestinian fatalities in thesame district.31 Evidence does not suggest the existence of a negative relationship betweenprices and conflict intensity over time in any of the sectors we investigate.

8.2 Structural Estimates and Counterfactual Analysis

Our goal in this Section is to determine the extent to which increased distortions in the function-ing and accessibility of markets for production inputs account for the observed fall in outputvalue in high conflict districts. Once again, the proposed conceptual framework guides us in theempirical analysis. We start by recalling the equation 17 for the output value of firm i operatingin sector s. In the absence of any distortion, after taking logs, equation 17 reduces to

lnPsiYsi = lnσ

σ − 1+ln

[1αs

]αs [ 1βs

]βs [ 11− αs − βs

]1−αs−βs+αs lnRKsi+βs lnwLsi+(1−αs−βs) ln zMsi

(15)

31In our data, 70% of establishments in the Manufacture of tobacco products sector are located in the districtof Jenin; 70% of establishments in the Manufacture of leather products are located in Hebron, where 43% ofthe establishments in the Manufacture of basic metals are also located. Figure A.11 in the Appendix shows theevolution of PPI in these three sectors over time, together with the evolution of fatalities in the correspondingdistrict.

25

We thus restrict our sample to those firm-level observations belonging to the year 1999,prior to the beginning of the Second Intifada. Given the above equation, we structurally es-timate the factor share parameters of the production function by implementing the followingregression specification

lnPsiYsi = ϕs + αs lnRKsi + βs lnwLsi + (1− αs − βs) ln zMsi + uis (16)

where RKsi, wLsi and zMsi are the value used in production of capital, labor, and materialsrespectively, and ϕs accounts for the sector-specific intercept in the previous equation. Asbefore, we separate foreign from domestically produced materials. We also estimate factorshare parameters for each sector by interacting all regressors with 2-digit sector dummies, andunder the constraint that the sum of the corresponding coefficients is equal to one.32

We use these estimates to predict the value of output we would observe in the absenceof conflict-induced input substitution. To this end, we build upon the results in Table 1 fromour most demanding regression specification where the dependent variable is the ratio betweendomestically and foreign produced materials used in production (row (d), column 4). We set thelevel of conflict intensity to zero, and predict the counterfactual input value ratio in the absenceof conflict for each establishment in the period. Holding the total value of materials constant,we can then calculate the counterfactual value of domestically and foreign produced materialsrespectively.33 Using the estimated factor share parameters of the production function, we thencalculate the value of output that we would have observed in the absence of conflict.

Our calculations indicate that, in the absence of conflict, the value of output would havebeen 6.4% higher for the average firm in our sample. Such gains are lower than those suggestedby the reduced-form estimate in column 5 of Table 5. Taking the ratio of the two, we caninfer that conflict-induced distortions in the functioning and accessibility of markets for foreigninputs and the consequent changes in input usage can account for more than 70% of the dropin the output value of firms operating in high conflict areas. According to the weighted sumsof establishments’ actual and counterfactual output value figures, aggregate output value couldhave been as much as 14.5% higher if conflict-induced distortions in the accessibility of marketshad not materialised.34 In the period 2000-2006, the manufacturing sector accounts for 10-

32Table A.11 in the Appendix reports the corresponding factor share parameter estimates for each 2-digit sector.The lack of a panel dimension in our firm-level dataset prevents us from using more sophisticated techniques toestimate the factor shares of the production function such as Olley and Pakes (1996).

33Given the counterfactual input value ratio zdMdsi/z

fMfsi, and being total value of materials equal to zdMd

si +zfMf

si, we can derive the counterfactual value of foreign and domestically produced materials separately bysolving the corresponding system of two equations in two unknowns.

34The results from such counterfactual analysis are essentially unchanged if, instead of using the estimates fromthe specification in row (d) of column 4 of Table 1, we implement an alternative specification where we interactthe fatalities variable with sector fixed effects. Following the same procedure as above, we calculate that the valueof output would have been 6.8% higher for the average firm in our sample, and aggregate output value could have

26

13% of Palestinian GDP. We therefore infer that, without taking into account activity in otherindustries and inter-industry linkages, the Palestinian GDP could have been around 1.5% higherin the absence of conflict-induced distortions.

The proposed methodology builds upon the structural estimation of the factor share param-eters of the production function, and therefore relies on the validity of the estimation approach.This is determined by the extent to which the theoretical expression for output value in equation15 matches our data. First, we do not need to assume that no market distortions were presentin 1999, but rather we consider that as our benchmark. This means that we interpret the re-sults from our counterfactual exercise in relation to the level of distortions already present in1999, and focus on the distortions generated and/or exacerbated during conflict times. Second,the market structure assumption of monopolistic competition within sectors is relevant: if allfirms were price takers, the derived estimates of the factor share parameters using 1999 datawould be biased. This is because the choice of inputs would be endogenous to total factorproductivity, which would be captured by the residual in the regression specification. Still, ourexercise would remain valid as long as the extent of the bias is constant throughout the periodunder consideration. Third, our specification assumes a Cobb-Douglas production function.This restricts the magnitude of the elasticity of substitution between foreign and domesticallyproduced materials, possibly leading to an overestimation of the impact of material input sub-stitution on output value. In Section A.2 of the Appendix, we structurally estimate the elasticityof substitution between foreign and domestically produced materials in each sector, and find itremarkably close to one. Evidence is therefore supportive of our approach. Finally, our proce-dure implicitly assumes that a negative shock to demand would decrease the value of all inputsused in production, but not their relative proportions. The evidence presented when questioningthe homotheticity assumption in Section 6.2.2 is supportive of this hypothesis.

9 Conclusions

Understanding the microeconomic mechanisms behind the relationship between conflict andaggregate economic outcomes is crucial for the design and implementation of successful con-flict recovery policies. In this paper, we have provided direct evidence on one of such mech-anisms. Using firm-level data from the OPT during the Second Intifada, we have shown thatconflict disrupts the functioning and accessibility of markets for foreign inputs, leading firms tosubstitute locally produced materials for imported ones in production. Evidence suggests thatthis is linked to a worsening of the bargaining position of Palestinian firms in their relationshipwith foreign suppliers. Our counterfactual policy analysis shows that conflict-induced distor-tions in the accessibility of foreign market can account for more than 70% of the drop in output

been as much as 14.2% higher.

27

value of firms in the OPT during the Second Intifada.

While the relative importance of different types of input distortions and the magnitudeof the estimated impact on output value may be context-dependent, our methodology to inferthe relative extent of conflict-induced distortions has general validity. We have shown that theanalysis of input usage can provide direct micro-founded evidence of the negative impact ofconflict on aggregate economic outcomes. Our results indicate that conflict recovery policiesthat target the supply side of the economy and restore the functioning of markets for inputscan be particularly effective. As conflict introduces distortions in the accessibility of foreignmarkets for inputs, policies aimed to restore trade and its financing are the most suited tomitigate the negative impact of warfare. For example, if conflict-related uncertainty is thesource of the loss in bargaining power that firms experience in their contractual relationshipwith foreign suppliers, a policy that insures the latter against insolvency risk would target thisfriction directly. What other actual forms policy intervention should take and how to evaluateits impact in a rigorous way are questions that we leave for future research.

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32

Tables and Figures

TABLE 1: INPUT DISTORTIONS - REGRESSION COEFFICIENTS

Coefficient of fatalities variable(1) (2) (3) (4)

(a) lnRKsi/zMsi 0.005 0.003 0.008 0.006(0.043) (0.043) (0.044) (0.046)

(b) lnwLsi/zMsi 0.025 0.022 0.024 0.010(0.039) (0.037) (0.037) (0.040)

(c) lnRKsi/wLsi -0.018 -0.014 -0.015 -0.000(0.040) (0.039) (0.039) (0.041)

(d) ln zdMdsi/z

fMfsi 1.216*** 1.216*** 1.234*** 1.243***

(0.272) (0.271) (0.270) (0.270)

(e) lnRKsi/zfMf

si 0.523*** 0.521*** 0.538*** 0.551***(0.122) (0.121) (0.119) (0.127)

(f) lnwLsi/zfMf

si 0.471*** 0.467*** 0.466*** 0.484***(0.138) (0.140) (0.140) (0.150)

(g) lnRKsi/zdMd

si -0.690*** -0.692*** -0.692*** -0.690***(0.171) (0.171) (0.171) (0.164)

(h) lnwLsi/zdMd

si -0.668*** -0.671*** -0.668*** -0.662***(0.184) (0.182) (0.182) (0.182)

Family WorkersTotal N Y Y Y

ProprietorsTotal N N Y Y

Sector FE Y Y Y n.a.Year FE Y Y Y n.a.District FE Y Y Y YSector × Year FE N N N Y

Notes. (* p-value< 0.1; ** p-value<0.05; *** p-value<0.01) The table reports estimates of the coefficient of thefatalities variable. Standard Errors are clustered along both sector-year and district-year categories. Dependent vari-able is log of ratio of Input Values in Israeli New Sheqel (NIS). Main independent variable is number of Palestinianskilled by IDF in the year and district where surveyed establishment is located (measured in standard deviation units).RKsi is value of capital; zMsi is value of materials; wLsi is value of labor; zfMf

si is value of imported materials;zdMd

si is value of domestically produced materials. The number of observations is 12410. Observations are weightedusing the original weight in the Industry Survey dataset (Sources: Industry Survey, Palestinian Bureau of Statistics,B’TSELEM).

33

TAB

LE

2:C

ON

FLIC

TA

ND

FIR

MS

EL

EC

TIO

N

Num

bero

fFir

ms

Num

bero

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eria

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(0.2

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***

-2.4

42**

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***

-2.3

36**

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.830

***

-2.8

38**

*(0

.169

)(0

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)(0

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)(0

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)(0

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)

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1240

712

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34

TABLE 3: INPUT DISTORTIONS, FATALITIES AND BORDER CLOSURES

REGRESSION COEFFICIENTS

(1) (2) (3) (4)

PANEL A Dependent Variable: ln zdMdsi/z

fMfsi

fatalities 1.263*** 1.263*** 1.279*** 1.290***(0.247) (0.247) (0.247) (0.246)

closure days × dtpassage 0.010** 0.010** 0.010** 0.010**(0.004) (0.004) (0.004) (0.004)

PANEL B Dependent Variable: lnRKsi/zfMf

si

fatalities 0.547*** 0.545*** 0.562*** 0.575***(0.115) (0.114) (0.112) (0.120)

closure days × dtpassage 0.005** 0.005** 0.005** 0.005**(0.002) (0.002) (0.002) (0.002)

PANEL C Dependent Variable: lnwLsi/zfMf

si

fatalities 0.499*** 0.494*** 0.492*** 0.515***(0.116) (0.119) (0.119) (0.127)

closure days × dtpassage 0.006** 0.006** 0.006** 0.006***(0.002) (0.002) (0.002) (0.002)

PANEL D Dependent Variable: lnRKsi/zdMd

si

fatalities -0.713*** -0.715*** -0.715*** -0.713***(0.157) (0.157) (0.157) (0.151)

closure days × dtpassage -0.005* -0.005* -0.005* -0.005*(0.002) (0.002) (0.002) (0.002)

PANEL E Dependent Variable: lnwLsi/zdMd

si

fatalities -0.694*** -0.698*** -0.694*** -0.690***(0.169) (0.166) (0.166) (0.166)

closure days × dtpassage -0.006** -0.006** -0.006** -0.006**(0.002) (0.002) (0.002) (0.002)

Family WorkersTotal N Y Y Y

ProprietorsTotal N N Y Y

Sector FE Y Y Y n.a.Year FE Y Y Y n.a.District FE Y Y Y YSector × Year FE N N N Y

Notes. (* p-value< 0.1; ** p-value<0.05; *** p-value<0.01) The table reports estimates of the coefficient of thefatalities variable and the interaction of the yearly number of days of border closure with the road distance of the districtcapital from the closest entry passage as measured in 10km units. Standard Errors are clustered along both sector-year and district-year categories. Dependent variable is log of ratio of Input Values in Israeli New Sheqel (NIS). Mainindependent variable is number of Palestinians killed by IDF in the year and district where surveyed establishment islocated (measured in standard deviation units). RKsi is value of capital; zMsi is value of materials; wLsi is valueof labor; zfMf

si is value of imported materials; zdMdsi is value of domestically produced materials. The variable

closure days captures the yearly number of days of border closure, while dtpassage measures road distance of the districtcapital from the closest entry passage as measured in 10km units. The number of observations is 12410. Observationsare weighted using the original weight in the Industry Survey dataset (Sources: Industry Survey, Palestinian Bureau ofStatistics, B’TSELEM).

35

TABLE 4: CONFLICT AND OBSTACLES TO FIRMS’ OPERATIONS

(1) (2) (3) (4) (5) (6)PANEL A Dep. Variable: Customs and Trade Regulations as Main Obstacle

fatalities -0.227*** -0.247*** -0.102 -0.031 -0.057 -0.226**(0.05) (0.05) (0.10) (0.10) (0.10) (0.11)

Importer 0.287 0.355 0.362 0.398 0.312 0.089(0.34) (0.34) (0.31) (0.30) (0.30) (0.43)

fatalities × Importer 0.249*** 0.237*** 0.256*** 0.259*** 0.312*** 0.470***(0.06) (0.06) (0.05) (0.06) (0.06) (0.08)

fatalities × Age 0.013***(0.00)

fatalities × Age × Importer -0.013***(0.00)

PANEL B Dep. Variable: Trasportation as Main Obstacle

fatalities -0.254*** -0.257*** -0.136* -0.045 -0.063 -0.104(0.07) (0.07) (0.08) (0.07) (0.07) (0.07)

Importer 0.255 0.305 0.367 0.462 0.445 0.456(0.34) (0.34) (0.31) (0.30) (0.27) (0.38)

fatalities × Importer 0.296*** 0.288*** 0.288*** 0.243*** 0.287*** 0.339***(0.07) (0.07) (0.07) (0.07) (0.06) (0.07)

fatalities × Age 0.004(0.00)

fatalities × Age × Importer -0.004(0.00)

PANEL C Dep. Variable: Percentage of Inputs Paid Before Delivery

fatalities -0.013 -0.003 -0.008 -0.013 -0.018 -0.036(0.02) (0.01) (0.02) (0.03) (0.03) (0.03)

Importer 0.110 0.100 0.123* 0.095 0.089 -0.001(0.07) (0.07) (0.07) (0.07) (0.07) (0.12)

fatalities × Importer 0.039** 0.041*** 0.039*** 0.055*** 0.067*** 0.125***(0.02) (0.01) (0.01) (0.02) (0.02) (0.02)

fatalities × Age 0.001(0.00)

fatalities × Age × Importer -0.003**(0.00)

Population 1997 N Y Y Y Y YAge N N N Y Y YSales in 2003 N N N Y Y YEmployment in 2003 N N N Y Y YOther Controls N N N N Y YAge × Importer N N N N N Y

Sector FE N N Y Y Y YDistrict FE N N Y Y Y Y

Notes. (* p-value< 0.1; ** p-value<0.05; *** p-value<0.01) Standard Errors are clustered along both sector-year and district-year categories.Dependent variable in Panel A is whether customs and trade regulations are reported as obstacles to the operations of the firm on a 1 to 4 scale.Dependent variable in Panel B is whether transportations are reported as obstacles to the operations of the firm on a 1 to 4 scale. Dependentvariable in Panel C is the share of inputs and services that the firm reports to pay before delivery. Main regressors are: number of Palestinianskilled by IDF in the year and locality where surveyed establishment is located (measured in standard deviation units), dummy for whetherthe firm reports a positive share of imported inputs in production, and the interaction between the two. We also include as other controls adummy capturing whether the firm is female-owned, and its legal status. Depending on the dependent variable used and the implementedspecification, the number of observations is between 192 and 222 (Sources: World Bank Enterprise Survey 2006, B’TSELEM).

36

TABLE 5: CONFLICT AND OUTPUT VALUE

Log of Output Value, ln(PY )(1) (2) (3) (4) (5) (6) (7)

fatalities -0.126** -0.073*** -0.063* -0.089*** -0.086*** -0.094*** -0.091***(0.049) (0.024) (0.036) (0.033) (0.033) (0.035) (0.035)

fatalities× zf Mfs

zdMds

-0.178** -0.436***(0.072) (0.111)

Family WorkersTotal

-1.522*** -1.533*** -1.526*** -1.537***(0.100) (0.097) (0.099) (0.096)

ProprietorsTotal

-2.713*** -2.717*** -2.700*** -2.703***(0.112) (0.112) (0.115) (0.116)

District FE N Y Y Y Y Y YYear FE N Y Y Y n.a. Y n.a.Sector FE N N Y Y n.a. Y n.a.Sector × Year FE N N N N Y N Y

Observations 10042 10042 10042 10039 10039 10006 10006R2 0.007 0.035 0.156 0.434 0.443 0.329 0.340

Notes. (* p-value< 0.1; ** p-value<0.05; *** p-value<0.01) Standard Errors are clustered along both sector-year and district-year categories.Dependent variable is log of Output Value in Israeli New Sheqel (NIS). Main independent variables are the number of Palestinians killed by IDFin the year and district where surveyed establishment is located (measured in standard deviation units), the sector-level input value ratio betweenforeign and domestically produced materials as measured in 1999, zfMf

s /zdMd

s , and the interaction between the two. Observations are weightedusing the original weight in the Industry Survey dataset (Sources: Industry Survey, Palestinian Bureau of Statistics, B’TSELEM).

37

FIGURE 1: CROSS-DISTRICT AND TIME CONFLICT VARIABILITY

025

5075

100

125

150

Aver

age

No. o

f Pal

estin

ians

Fat

alitie

s

2000 2001 2002 2003 2004 2005 2006Year

25% of Districts with Most Fatalities in 2002All Other Districts

Notes. The figure plots the average number of Palestinians fatalities over time in districts as dividedaccording to the number of fatalities in 2002 (Sources: B’TSELEM).

38

FIGURE 2: WITHIN-SECTOR HETEROGENEITY IN TECHNOLOGY

AND OUTPUT VALUE

a) All Sectors b) Restricted Sample

-20

-10

010

20Re

sidua

l Log

of I

nput

Val

ue R

atio

-6 -4 -2 0 2 4 6Residual Log of Output Value

-20

-10

010

20Re

sidua

l Log

of I

nput

Val

ue R

atio

-4 -2 0 2 4 6Residual Log of Output Value

Notes. The left and right figures plot the within-sector residual log of the ratio between the value of domestically produced materials andimported materials used over the residual log of output value for firms in 1999. Circle size correspond to the observation’s weight in thesample. The top figure shows the relationship of interest using all available observations, while the bottom figure considers only thosesectors for which the relationship between the two variables is non-significant (Sources: Palestinian Central Bureau of Statistics).

39

FIGURE 3: CONFLICT AND BORDER CLOSURES

010

020

030

040

0Pa

lest

inia

ns K

illed

by th

e ID

F by

Qua

rter

020

4060

8010

0N

umbe

r of D

ays

of B

orde

r Clo

sure

0 5 10 15 20 25Quarter

Number of Days of Border ClosurePalestinians Killed by the IDF by Quarter

Notes. The figure plots the number of days of border closures in each quarter between the 3rdquarter of 2000 and the end of 2006, together with the total number of Palestinians fatalitiesover time (Sources: Palestinian Central Bureau of Statistics, B’TSELEM).

40

A Appendix for Online Publication

A.1 Additional Tables and Figures

TABLE A.1: SUMMARY STATISTICS

Obs. Mean St. Dev. Min Max

Log of Output Value 11397 11.741 1.511 0 19.656

Log of Value of Capital 14221 10.138 1.942 0.693 18.531Log of Value of Labor 10243 10.492 1.24 5.994 16.746Log of Value of Materials 14160 11.308 2.045 3.932 18.769Log of Value of Domestic Materials 14160 8.826 3.138 0 18.785Log of Value of Imported Materials 14160 6.456 4.801 0 18.688

Fraction of Family Workers 14284 0.167 0.247 0 1Fraction of Proprietors 14284 0.444 0.324 0 1

Log of Value of Capital/Materials 14100 -0.553 1.816 -13.169 6.828Log of Value of Labor/Materials 10183 -0.856 1.361 -8.593 4.185Log of Value of Capital/Labor 10197 0.223 1.67 -10.786 6.161Log of Value of Domestic/Imported Materials 14160 2.37 6.345 -18.405 18.112Log of Value of Capital/Imported Materials 14100 3.687 4.645 -12.855 17.751Log of Value of Capital/Domestic Materials 14100 1.322 3.198 -13.155 17.231Log of Value of Labor/Imported Materials 10183 3.117 4.69 -6.367 16.544Log of Value of Labor/Domestic Materials 10183 1.046 2.96 -8.699 15.451

Log of Value of Initial Capital 14222 10.606 2.601 0 18.550Log of Value of Initial Inventory of Output 11397 4.143 4.945 0 16.317Log of Value of Initial Inventory of Materials 14161 6.317 4.327 0 16.710

Palestinians Killed by IDF 112 35.044 42.010 0 210(District × Year)

Notes. The table shows summary statistics for the variables used in the empirical analysis. Establishment-level valuevariables are in Israeli New Sheqel (NIS) (Sources: Industry Survey, Palestinian Bureau of Statistics, B’TSELEM).

41

TABLE A.2: INPUT DISTORTIONS - IMPLIED RELATIVE VALUES

Implied Relative Distortion Values(1) (2) (3) (4)

(a) (1 + τM )/(1 + τK) 1.005 1.003 1.008 1.006[0.919;1.090] [0.918;1.088] [0.920;1.095] [0.916;1.096]

(b) (1 + τM )/(1 + τL) 1.025 1.022 1.024 1.010[0.948;1.103] [0.947;1.097] [0.950;1.098] [0.931;1.089]

(c) (1 + τL)/(1 + τK) 0.982 0.986 0.985 1.000[0.905;1.059] [0.911;1.061] [0.910;1.060] [0.919;1.080]

(d) (1 + τMf )/(1 + τMd) 3.375 3.373 3.434 3.465[1.578;5.172] [1.579;5.168] [1.616;5.252] [1.634;5.295]

(e) (1 + τMf )/(1 + τK) 1.687 1.684 1.713 1.736[1.283;2.090] [1.283;2.084] [1.314;2.112] [1.302;2.169]

(f) (1 + τMf )/(1 + τL) 1.602 1.596 1.593 1.623[1.168;2.036] [1.157;2.034] [1.156;2.030] [1.147;2.099]

(g) (1 + τMd)/(1 + τK) 0.501 0.500 0.501 0.502[0.334;0.669] [0.333;0.668] [0.333;0.668] [0.340;0.663]

(h) (1 + τMd)/(1 + τL) 0.513 0.511 0.513 0.516[0.328;0.698] [0.329;0.693] [0.330;0.696] [0.332;0.700]

Family WorkersTotal N Y Y Y

ProprietorsTotal N N Y Y

Sector FE Y Y Y n.a.Year FE Y Y Y n.a.District FE Y Y Y YSector × Year FE N N N Y

Notes. The table reports implied relative distortion values as derived using coefficient estimates from Table 4, together with95% Confidence Intervals. Standard Errors are clustered along both sector-year and district-year categories. τK is averagedistortion level for capital; τM is average distortion level for materials; τL is average distortion value for labor; τMf is averagedistortion value for imported materials; τMd is average distortion value for domestically produced materials. The number ofobservations is 12410. Observations are weighted using the original weight in the Industry Survey dataset (Sources: IndustrySurvey, Palestinian Bureau of Statistics, B’TSELEM).

42

TABLE A.3: INPUT DISTORTIONS - REGRESSION COEFFICIENTS

ROBUSTNESS: WILD BOOTSTRAP-CLUSTERED STANDARD ERRORS

Coefficient of fatalities variable(1) (2) (3) (4)

(a) lnRKsi/zMsi 0.005 0.003 0.008 0.006(0.90) (0.90) (0.90) (0.92)

(b) lnwLsi/zMsi 0.025 0.022 0.024 0.010(0.54) (0.62) (0.58) (0.80)

(c) lnRKsi/wLsi -0.018 -0.014 -0.015 -0.000(0.80) (0.88) (0.84) (1.00)

(d) ln zdMdsi/z

fMfsi 1.216*** 1.216*** 1.234*** 1.243***

(0.00) (0.00) (0.00) (0.00)

(e) lnRKsi/zfMf

si 0.523*** 0.521*** 0.538*** 0.551***(0.00) (0.00) (0.00) (0.00)

(f) lnwLsi/zfMf

si 0.471*** 0.467*** 0.466*** 0.484***(0.00) (0.00) (0.00) (0.00)

(g) lnRKsi/zdMd

si -0.690** -0.692** -0.692** -0.690**(0.02) (0.02) (0.02) (0.02)

(h) lnwLsi/zdMd

si -0.668** -0.671** -0.668** -0.662**(0.02) (0.02) (0.02) (0.02)

Family WorkersTotal N Y Y Y

ProprietorsTotal N N Y Y

Sector FE Y Y Y n.a.Year FE Y Y Y n.a.District FE Y Y Y YSector × Year FE N N N Y

Notes. Wild bootstrap p-values in parenthesis. The table reports estimates of the coefficient of the fatalitiesvariable. Standard Errors are clustered at the district level, and calculating using wild bootstrapping after 100repetitions (Cameron, Gelbach and Miller 2008). Dependent variable is log of ratio of Input Values in Israeli NewSheqel (NIS). Main independent variable is number of Palestinians killed by IDF in the year and district wheresurveyed establishment is located (measured in standard deviation units). RKsi is value of capital; zMsi is valueof materials; wLsi is value of labor; zfMf

si is value of imported materials; zdMdsi is value of domestically

produced materials. The number of observations is 12410. Observations are weighted using the original weight inthe Industry Survey dataset (Sources: Industry Survey, Palestinian Bureau of Statistics, B’TSELEM).

43

TABLE A.4: INPUT DISTORTIONS - REGRESSION COEFFICIENTS

ROBUSTNESS: ACTUAL NUMBER OF FATALITIES AS REGRESSOR

Coefficient of fatalities variable(1) (2) (3) (4)

(a) lnRKsi/zMsi 0.000 0.000 0.000 0.000(0.001) (0.001) (0.001) (0.001)

(b) lnwLsi/zMsi 0.001 0.001 0.001 0.000(0.001) (0.001) (0.001) (0.001)

(c) lnRKsi/wLsi -0.000 -0.000 -0.000 -0.000(0.001) (0.001) (0.001) (0.001)

(d) ln zdMdsi/z

fMfsi 0.029*** 0.029*** 0.029*** 0.030***

(0.006) (0.006) (0.006) (0.006)

(e) lnRKsi/zfMf

si 0.012*** 0.012*** 0.013*** 0.013***(0.003) (0.003) (0.003) (0.003)

(f) lnwLsi/zfMf

si 0.011*** 0.011*** 0.011*** 0.012***(0.003) (0.003) (0.003) (0.004)

(g) lnRKsi/zdMd

si -0.016*** -0.016*** -0.016*** -0.016***(0.004) (0.004) (0.004) (0.004)

(h) lnwLsi/zdMd

si -0.016*** -0.016*** -0.016*** -0.016***(0.004) (0.004) (0.004) (0.004)

Family WorkersTotal N Y Y Y

ProprietorsTotal N N Y Y

Sector FE Y Y Y n.a.Year FE Y Y Y n.a.District FE Y Y Y YSector × Year FE N N N Y

Notes. (* p-value< 0.1; ** p-value<0.05; *** p-value<0.01) The table reports estimates of the coefficient of thefatalities variable, where the latter capture the actual number of fatalities (without normalizing by its standard devia-tion). Standard Errors are clustered along both sector-year and district-year categories. Dependent variable is log ofratio of Input Values in Israeli New Sheqel (NIS). Main independent variable is number of Palestinians killed by IDF inthe year and district where surveyed establishment is located (measured in standard deviation units). RKsi is value ofcapital; zMsi is value of materials; wLsi is value of labor; zfMf

si is value of imported materials; zdMdsi is value of

domestically produced materials. The number of observations is 12410. Observations are weighted using the originalweight in the Industry Survey dataset (Sources: Industry Survey, Palestinian Bureau of Statistics, B’TSELEM).

44

TABLE A.5: INPUT DISTORTIONS - REGRESSION COEFFICIENTS

ROBUSTNESS: PER CAPITA MEASURE OF CONFLICT

Coefficient of fatalities/population variable(1) (2) (3) (4)

(a) lnRKsi/zMsi -45.525 -63.317 -35.049 -31.554(263.724) (264.143) (269.527) (267.587)

(b) lnwLsi/zMsi 88.810 49.512 43.214 -23.117(251.826) (251.367) (254.335) (262.242)

(c) lnRKsi/wLsi -104.539 -47.727 -38.363 47.251(266.669) (260.723) (262.884) (268.779)

(d) ln zdMdsi/z

fMfsi 5586.620*** 5582.498*** 5700.515*** 5653.444***

(1913.741) (1912.605) (1908.057) (1893.607)

(e) lnRKsi/zfMf

si 2476.361*** 2457.017*** 2574.175*** 2569.458***(912.206) (908.337) (902.523) (935.686)

(f) lnwLsi/zfMf

si 2117.569** 2065.229** 2070.722** 2073.873**(993.970) (1005.264) (1003.561) (1037.184)

(g) lnRKsi/zdMd

si -3089.911*** -3106.254*** -3106.256*** -3076.875***(1125.136) (1126.431) (1125.300) (1082.118)

(h) lnwLsi/zdMd

si -2959.658** -3000.269** -3010.472** -2912.969**(1218.936) (1210.869) (1216.564) (1204.352)

Family WorkersTotal N Y Y Y

ProprietorsTotal N N Y Y

Sector FE Y Y Y n.a.Year FE Y Y Y n.a.District FE Y Y Y YSector × Year FE N N N Y

Notes. (* p-value< 0.1; ** p-value<0.05; *** p-value<0.01) The table reports estimates of the coefficient of the fatalities/populationvariable, calculated by dividing the number of Palestinian fatalities by the population of each district in the corresponding year. StandardErrors are clustered along both sector-year and district-year categories. Dependent variable is log of ratio of Input Values in Israeli NewSheqel (NIS). Main independent variable is number of Palestinians killed by IDF in the year and district where surveyed establishmentis located (measured in standard deviation units). RKsi is value of capital; zMsi is value of materials; wLsi is value of labor; zfMf

si

is value of imported materials; zdMdsi is value of domestically produced materials. The number of observations is 12410. Observa-

tions are weighted using the original weight in the Industry Survey dataset (Sources: Industry Survey, Palestinian Bureau of Statistics,B’TSELEM).

45

TABLE A.6: INPUT DISTORTIONS - REGRESSION COEFFICIENTS

ROBUSTNESS: ICEWS CONFLICT DATA

Coefficient of hostile ICEWS events count(1) (2) (3) (4)

(a) lnRKsi/zMsi -0.008 -0.006 -0.004 -0.002(0.015) (0.015) (0.016) (0.018)

(b) lnwLsi/zMsi 0.029 0.026 0.027 0.014(0.025) (0.025) (0.024) (0.026)

(c) lnRKsi/wLsi -0.033 -0.029 -0.029 -0.010(0.022) (0.021) (0.020) (0.030)

(d) ln zdMdsi/z

fMfsi 0.533* 0.535* 0.545* 0.560*

(0.322) (0.321) (0.320) (0.319)

(e) lnRKsi/zfMf

si 0.224 0.227 0.237 0.262*(0.148) (0.146) (0.144) (0.152)

(f) lnwLsi/zfMf

si 0.265** 0.262** 0.261** 0.275**(0.129) (0.130) (0.130) (0.137)

(g) lnRKsi/zdMd

si -0.306* -0.305* -0.305* -0.296*(0.182) (0.183) (0.183) (0.179)

(h) lnwLsi/zdMd

si -0.256 -0.259 -0.258 -0.255(0.177) (0.175) (0.175) (0.173)

Family WorkersTotal N Y Y Y

ProprietorsTotal N N Y Y

Sector FE Y Y Y n.a.Year FE Y Y Y n.a.District FE Y Y Y YSector × Year FE N N N Y

Notes. (* p-value< 0.1; ** p-value<0.05; *** p-value<0.01) The table reports estimates of the coefficientof the number of hostile ICEWS events in the district, normalized by its sample standard deviation. StandardErrors are clustered along both sector-year and district-year categories. Dependent variable is log of ratioof Input Values in Israeli New Sheqel (NIS). Main independent variable is number of Palestinians killedby IDF in the year and district where surveyed establishment is located (measured in standard deviationunits). RKsi is value of capital; zMsi is value of materials; wLsi is value of labor; zfMf

si is valueof imported materials; zdMd

si is value of domestically produced materials. The number of observationsis 12410. Observations are weighted using the original weight in the Industry Survey dataset (Sources:Industry Survey, Palestinian Bureau of Statistics, ICEWS).

46

TABLE A.7: INPUT DISTORTIONS - REGRESSION COEFFICIENTS

ROBUSTNESS: ICEWS CONFLICT DATA - PER CAPITA MEASURE

Coefficient of hostile ICEWS events/population(1) (2) (3) (4)

(a) lnRKsi/zMsi -2.491 -0.445 1.816 0.581(19.792) (20.311) (20.481) (22.404)

(b) lnwLsi/zMsi 24.301 20.076 19.989 7.920(25.335) (25.018) (24.126) (26.054)

(c) lnRKsi/wLsi -30.349 -24.473 -24.968 -8.536(21.935) (21.061) (20.316) (28.817)

(d) ln zdMdsi/z

fMfsi 500.402* 502.441* 512.438* 530.730*

(292.202) (290.696) (291.054) (290.729)

(e) lnRKsi/zfMf

si 219.874 223.131* 232.890* 253.719*(134.968) (133.181) (132.817) (139.994)

(f) lnwLsi/zfMf

si 249.515** 244.651** 244.727** 262.666**(117.401) (118.857) (118.962) (126.438)

(g) lnRKsi/zdMd

si -279.165* -277.912* -277.927* -276.457*(165.706) (166.167) (166.227) (162.546)

(h) lnwLsi/zdMd

si -242.422 -246.943 -247.083 -247.963(158.871) (157.313) (157.112) (155.468)

Family WorkersTotal N Y Y Y

ProprietorsTotal N N Y Y

Sector FE Y Y Y n.a.Year FE Y Y Y n.a.District FE Y Y Y YSector × Year FE N N N Y

Notes. (* p-value< 0.1; ** p-value<0.05; *** p-value<0.01) The table reports estimates of the coefficient of the numberof hostile ICEWS events in the district divided by the population in the district. Standard Errors are clustered along bothsector-year and district-year categories. Dependent variable is log of ratio of Input Values in Israeli New Sheqel (NIS).Main independent variable is number of Palestinians killed by IDF in the year and district where surveyed establishmentis located (measured in standard deviation units). RKsi is value of capital; zMsi is value of materials; wLsi is valueof labor; zfMf

si is value of imported materials; zdMdsi is value of domestically produced materials. The number of

observations is 12410. Observations are weighted using the original weight in the Industry Survey dataset (Sources:Industry Survey, Palestinian Bureau of Statistics, ICEWS).

47

TABLE A.8: INPUT DISTORTIONS - REGRESSION COEFFICIENTS

ROBUSTNESS: LEADS OF fatalities VARIABLE

(1) (2) (3) (4)

PANEL A Dependent Variable: ln zdMdsi/z

fMfsi

fatalitiest 0.773*** 0.773*** 0.806*** 0.815***(0.297) (0.297) (0.298) (0.291)

fatalitiest+1 -0.331 -0.331 -0.336 -0.329(0.408) (0.408) (0.409) (0.404)

PANEL B Dependent Variable: lnRKsi/zfMf

si

fatalitiest 0.300** 0.300** 0.331*** 0.320***(0.121) (0.121) (0.122) (0.121)

fatalitiest+1 -0.139 -0.136 -0.141 -0.133(0.171) (0.172) (0.171) (0.169)

PANEL C Dependent Variable: lnwLsi/zfMf

si

fatalitiest 0.275 0.261 0.261 0.266(0.175) (0.176) (0.175) (0.184)

fatalitiest+1 -0.071 -0.079 -0.086 -0.121(0.193) (0.194) (0.191) (0.193)

PANEL D Dependent Variable: lnRKsi/zdMd

si

fatalitiest -0.469** -0.469** -0.471** -0.493**(0.203) (0.203) (0.203) (0.194)

fatalitiest+1 0.195 0.198 0.199 0.196(0.262) (0.261) (0.261) (0.257)

PANEL E Dependent Variable: lnwLsi/zdMd

si

fatalitiest -0.415** -0.423** -0.423** -0.411**(0.194) (0.194) (0.197) (0.195)

fatalitiest+1 0.251 0.246 0.256 0.292(0.254) (0.254) (0.254) (0.249)

Family WorkersTotal N Y Y Y

ProprietorsTotal N N Y Y

Sector FE Y Y Y n.a.Year FE Y Y Y n.a.District FE Y Y Y YSector × Year FE N N N Y

Notes. (* p-value< 0.1; ** p-value<0.05; *** p-value<0.01) The table reports estimates of the coefficientof the fatalities variable and its lead. Standard Errors are clustered along both sector-year and district-yearcategories. Dependent variable is log of ratio of Input Values in Israeli New Sheqel (NIS). Main independentvariable is number of Palestinians killed by IDF in the year and district where surveyed establishment islocated (measured in standard deviation units). RKsi is value of capital; zMsi is value of materials; wLsiis value of labor; zfMf

si is value of imported materials; zdMdsi is value of domestically produced materials.

The variable closure days captures the yearly number of days of border closure, while dtpassage measuresroad distance of the district capital from the closest entry passage as measured in 10km units. The numberof observations is 12410. Observations are weighted using the original weight in the Industry Survey dataset(Sources: Industry Survey, Palestinian Bureau of Statistics, B’TSELEM).

48

TABLE A.9: INPUT DISTORTIONS - REGRESSION COEFFICIENTS

ROBUSTNESS: ROAD DISTANCE OF DISTRICT CAPITAL FROM CLOSEST ENTRY PASSAGE

Coefficient of fatalities variable(1) (2) (3) (4)

(a) lnRKsi/zMsi -0.004 -0.006 -0.001 -0.001(0.039) (0.040) (0.041) (0.043)

(b) lnwLsi/zMsi 0.007 0.003 0.004 -0.009(0.045) (0.044) (0.043) (0.046)

(c) lnRKsi/wLsi 0.001 0.007 0.007 0.020(0.036) (0.035) (0.035) (0.037)

(d) ln zdMdsi/z

fMfsi 1.174*** 1.173*** 1.194*** 1.204***

(0.260) (0.260) (0.260) (0.259)

(e) lnRKsi/zfMf

si 0.504*** 0.502*** 0.522*** 0.536***(0.121) (0.121) (0.119) (0.126)

(f) lnwLsi/zfMf

si 0.452*** 0.446*** 0.445*** 0.473***(0.122) (0.125) (0.124) (0.133)

(g) lnRKsi/zdMd

si -0.667*** -0.668*** -0.668*** -0.668***(0.162) (0.162) (0.162) (0.156)

(h) lnwLsi/zdMd

si -0.664*** -0.669*** -0.667*** -0.668***(0.178) (0.175) (0.175) (0.177)

dtpassage × Year FE Y Y Y Y

Family WorkersTotal N Y Y Y

ProprietorsTotal N N Y Y

Sector FE Y Y Y n.a.Year FE Y Y Y n.a.District FE Y Y Y YSector × Year FE N N N Y

Notes. The table reports implied relative distortion values (together with 95% Confidence Intervals) asderived from estimating the input value ratio regression, including as regressors the full set of year dum-mies interacted with the road distance of the district capital from the closest entry passage as measuredin 10km units. Standard Errors are clustered along both sector-year and district-year categories. τK isaverage distortion level for capital; τM is average distortion level for materials; τL is average distortionvalue for labor; τMf is average distortion value for imported materials; τMd is average distortion valuefor domestically produced materials. The number of observations is 12410. Observations are weightedusing the original weight in the Industry Survey dataset (for all other cases) (Sources: Industry Survey,Palestinian Bureau of Statistics, B’TSELEM).

49

TABLE A.10: INPUT DISTORTIONS AND HOMOTHETIC PRODUCTION FUNCTIONS

REGRESSION COEFFICIENTS: RESTRICTED SAMPLE

Coefficient of fatalities variable(1) (2) (3) (4)

(a) lnRKsi/zMsi 0.027 0.023 0.029 0.022(0.048) (0.048) (0.049) (0.052)

(b) lnwLsi/zMsi 0.058 0.059 0.057 0.045(0.046) (0.044) (0.045) (0.049)

(c) lnRKsi/wLsi -0.012 -0.013 -0.010 -0.005(0.052) (0.050) (0.049) (0.050)

(d) ln zdMdsi/z

fMfsi 1.247*** 1.246*** 1.265*** 1.263***

(0.300) (0.299) (0.296) (0.294)

(e) lnRKsi/zfMf

si 0.542*** 0.539*** 0.559*** 0.556***(0.137) (0.136) (0.131) (0.140)

(f) lnwLsi/zfMf

si 0.498*** 0.498*** 0.500*** 0.506***(0.176) (0.178) (0.177) (0.184)

(g) lnRKsi/zdMd

si -0.707*** -0.709*** -0.708*** -0.709***(0.185) (0.185) (0.185) (0.178)

(h) lnwLsi/zdMd

si -0.679*** -0.679*** -0.681*** -0.668***(0.211) (0.207) (0.206) (0.205)

Family WorkersTotal N Y Y Y

ProprietorsTotal N N Y Y

Sector FE Y Y Y n.a.Year FE Y Y Y n.a.District FE Y Y Y YSector × Year FE N N N Y

Notes. The table reports implied relative distortion values (together with 95% Confidence Intervals)as derived from estimating the input value ratio regression over the restricted sample of observationsbelonging to sectors where no significant relationship between material value ratio and output value isfound in 1999. Standard Errors are clustered along both sector-year and district-year categories. τK isaverage distortion level for capital; τM is average distortion level for materials; τL is average distortionvalue for labor; τMf is average distortion value for imported materials; τMd is average distortion valuefor domestically produced materials. The number of observations is 7957. Observations are weightedusing the original weight in the Industry Survey dataset (Sources: Industry Survey, Palestinian Bureauof Statistics, B’TSELEM).

50

TABLE A.11: FACTOR SHARE PARAMETER ESTIMATES

Sector (ISIC) K L Mf Md

Other mining and quarrying 0.288 0.022 0.040 0.649***(0.204) (0.054) (0.035) (0.231)

Manufacture of food products and beverages 0.176*** 0.035** 0.075*** 0.715***(0.058) (0.017) (0.017) (0.063)

Manufacture of tobacco products 0.622*** 0.236*** 0.100 0.042(0.067) (0.063) (0.086) (0.034)

Manufacture of textiles 0.287** 0.054* 0.136*** 0.524***(0.136) (0.028) (0.034) (0.116)

Manufacture of wearing apparel 0.132 0.034 0.135** 0.699***(0.127) (0.049) (0.058) (0.118)

Tanning and dressing of leather 0.215*** 0.014 0.088*** 0.683***(0.059) (0.021) (0.020) (0.057)

Manufacture of wood and of wooden products 0.170*** 0.020 0.079*** 0.732***(0.057) (0.023) (0.027) (0.070)

Manufacture of paper and paper products 0.296*** -0.091 0.136*** 0.659***(0.083) (0.072) (0.030) (0.097)

Publishing, printing, etc. 0.098* 0.036* 0.115*** 0.751***(0.059) (0.021) (0.030) (0.080)

Manufacture of chemicals and chem. products 0.344*** -0.003 0.199*** 0.460***(0.111) (0.031) (0.026) (0.108)

Manufacture of rubber and plastics products 0.472*** 0.059 0.087*** 0.382***(0.066) (0.040) (0.025) (0.063)

Manufacture of other non-metallic mineral prod. 0.154*** 0.042** 0.036* 0.768***(0.058) (0.020) (0.020) (0.058)

Manufacture of basic metals 0.073 0.083 0.108*** 0.736***(0.140) (0.080) (0.024) (0.095)

Manufacture of fabricated metal products 0.083 0.022 0.047*** 0.848***(0.056) (0.016) (0.017) (0.051)

Manufacture of machinery and equipment n.e.c. 0.289*** 0.008 0.113*** 0.590***(0.071) (0.023) (0.025) (0.084)

Manufacture of medical, precision and optical instruments 0.661*** 0.051 0.099*** 0.189*(0.068) (0.053) (0.038) (0.108)

Manufacture of motor vehicles, trailers and semi-trailers 0.005 0.068** 0.114*** 0.814***(0.106) (0.033) (0.037) (0.099)

Manufacture of other transport equipment -0.018 -0.215*** 0.004 1.229***(0.090) (0.030) (0.029) (0.109)

Manufacture of furniture; manufacturing n.e.c. 0.219*** 0.032* 0.056** 0.694***(0.066) (0.019) (0.026) (0.072)

Electricity, gas, steam and hot water supply 0.295** 0.174* 0.075** 0.456***(0.144) (0.094) (0.037) (0.087)

Collection, purification and distribution of water 0.431*** 0.038 0.107*** 0.423***(0.135) (0.059) (0.032) (0.157)

Notes. (* p-value< 0.1; ** p-value<0.05; *** p-value<0.01) The tale reports OLS factor share parameter estimates for each of the inputs as derivedfollowing the procedure explained in Section 8. We estimate the parameters from equation 16 by restricting the sample to those observations belongingto the year 1999, prior to the beginning of the conflict. In order to identify the parameters of interest, we exclude those four sectors for which we have 5or less establishment-level observations. The number of observations is 1322. Observations are weighted using the original weight in the Industry Surveydataset (Sources: Industry Survey, Palestinian Bureau of Statistics).

51

FIGURE A.1: LABOR AND OUTPUT VALUE

a) Distribution of Employment b) Distribution of Output Value

0.2

.4.6

.81

Cum

ulat

ive

Den

sity

Fun

ctio

n

0 10 20 30 40 50Labor (Employees and Proprietors)

0.2

.4.6

.81

Cum

ulat

ive D

ensit

y Fu

nctio

n

0 200000 400000 600000 800000 1000000Output Value (NIS)

Notes. The left and right figures show distribution of number of workers and value of output for Palestinian firms (Sources:Palestinian Central Bureau of Statistics; B’TSELEM).

52

FIGURE A.2: CROSS-DISTRICT AND TIME CONFLICT VARIABILITY - MAPS

a) Cross-district Variability

(27,32](20,27](14,20](7,14][0,7]

Year 2000

Year 2000

(38,52](35,38](34,35](9,34][1,9]

Year 2001

Year 2001

(86,161](67,86](59,67](16,59][3,16]

Year 2002

Year 2002

(64,122](40,64](14,40](6,14][1,6]

Year 2003

Year 2003

(117,191](42,117](11,42](4,11][1,4]

Year 2004

Year 2004

(20,32](16,20](5,16](1,5][0,1]

Year 2005

Year 2005

(52,210](42,52](5,42](2,5][1,2]

Year 2006

b) Within-district Variability

(67,131](50,67](31,50](10,31][-6,10]

Years 2000-2002

(22,126](-1,22](-20,-1](-51,-20][-88,-51]

Years 2002-2004

(4,41](-1,4](-5,-1](-24,-5][-83,-24]

Years 2004-2006

Notes. The maps show the distribution of the number of Palestinians killed by IDF across districts in given years and its changes over giventime spans. In each map, districts are colored according to the quintiles they belong to in the distribution of levels and changes respectively.Looking at the top maps, we see that there is large cross-district variation in the number of fatalities. The three bottom maps show that thereis also meaningful variation in the number of fatalities within each district over time. In particular, differential changes in conflict intensityacross districts constitute a source of variability which does not seem to overlap with the cross-sectional one (Sources: B’TSELEM).

53

FIGURE A.3: CONFLICT AND VALUE OF FOREIGN TRADE

200

450

700

950

1200

Pale

stin

ians

Kille

d by

Isra

eli S

F

-160

0-1

400

-120

0-1

000

-800

Rea

l Net

Bal

ance

Tra

de w

ith Is

rael

(Milli

on U

SD) -

Bas

e Ye

ar: 2

004

2000 2001 2002 2003 2004 2005 2006Year

Real Net Balance Trade with Israel (Million USD) - Base Year: 2004Palestinians Killed by Israeli Defense Forces

Notes. The figures plot the evolution of the total real value of the net balance of trade overtime, together with the evolution of total number of Palestinians killed by IDF (Sources:Palestinian Central Bureau of Statistics; B’TSELEM).

54

FIGURE A.4: TRADE COMPOSITION: IMPORTS

0 .05 .1 .15 .2 .25

% Value of Imports

Miscellaneous manufactured articlesMineral fules. lubricants and related materials

Manufactured goods classified chiefly by materialMachinery and transport equipments

Food and live animalsCrude materials. inedibe except fuels

Commdities and transactions. n.e.s. in the SITC Rev3Chemicale and related products. n.e.s.

Beverages and tobaccoAnimal and vegetable oils. fats and waxes

Year 1999

0 .05 .1 .15 .2 .25% Value of Imports

Miscellaneous manufactured articlesMineral fules. lubricants and related materials

Manufactured goods classified chiefly by materialMachinery and transport equipments

Food and live animalsCrude materials. inedibe except fuels

Commdities and transactions. n.e.s. in the SITC Rev3Chemicale and related products. n.e.s.

Beverages and tobaccoAnimal and vegetable oils. fats and waxes

Year 2002

Notes. The figures plot import composition (sector share over total import) in 1999 and 2002 (Sources: Palestinian CentralBureau of Statistics).

55

FIGURE A.5: TRADE COMPOSITION: EXPORTS

0 .1 .2 .3 .4% Value of Exports

Miscellaneous manufactured articlesMineral fules. lubricants and related materials

Manufactured goods classified chiefly by materialMachinery and transport equipments

Food and live animalsCrude materials. inedibe except fuels

Commdities and transactions. n.e.s. in the SITC Rev3Chemicale and related products. n.e.s.

Beverages and tobaccoAnimal and vegetable oils. fats and waxes

Year 1999

0 .1 .2 .3 .4% Value of Exports

Miscellaneous manufactured articlesMineral fules. lubricants and related materials

Manufactured goods classified chiefly by materialMachinery and transport equipments

Food and live animalsCrude materials. inedibe except fuels

Commdities and transactions. n.e.s. in the SITC Rev3Chemicale and related products. n.e.s.

Beverages and tobaccoAnimal and vegetable oils. fats and waxes

Year 2002

Notes. The figures plot the export composition (sector share over total export) in 1999 and 2002 (Sources: Palestinian CentralBureau of Statistics).

56

FIGURE A.6: TRADE COMPOSITION: IMPORTS BY ORIGIN

0 .05 .1 .15 .2 .25

Miscellaneous manufactured articles

Mineral fules. lubricants and related materials

Manufactured goods classified chiefly by material

Machinery and transport equipments

Food and live animals

Crude materials. inedibe except fuels

Commdities and transactions. n.e.s. in the SITC Rev3

Chemicale and related products. n.e.s.

Beverages and tobacco

Animal and vegetable oils. fats and waxes

Year 1999

% Value of Imports from Israel % Value of Imports

0 .05 .1 .15 .2 .25

Miscellaneous manufactured articles

Mineral fules. lubricants and related materials

Manufactured goods classified chiefly by material

Machinery and transport equipments

Food and live animals

Crude materials. inedibe except fuels

Commdities and transactions. n.e.s. in the SITC Rev3

Chemicale and related products. n.e.s.

Beverages and tobacco

Animal and vegetable oils. fats and waxes

Year 2002

% Value of Import from Israel % Value of Import

Notes. The figures plot import composition (sector share over total import) in 1999 and 2002, together with the Israeli share ofeach import category (Sources: Palestinian Central Bureau of Statistics).

57

FIG

UR

EA

.7:D

ISA

GG

RE

GA

TE

DIM

PO

RT

CO

MP

OS

ITIO

N

00%

02%

04%

06%

08%

10%

12%

Live animals other than animals or fish Meat and meat preparations

Dairy products and birds eggs Fish and preparations thereof

Cereals and cereal preparations Vegetables and fruit

Sugar, sugar preparations, and honey Coffee, tea, cocoa, and manufactures thereof

Feeding stuff for animals Miscellaneous edible products and preparations

Beverages Tobacco and tobacco manufacture

Oil seeds and oleaginous fruits Cork and wood

Pulp and waste paper Textile fibres and their wastes

Crude fertilizers Metalliferous ores and metal scrap

Crude animal and vegetable materials Petroleum and petroleum products

Gas, natural and manufactured Electric current

Animal oils and fats Vegetable fats, and oils crude or refined

Animal or vegetable fats, and oils processed Organic chemicals

Inorganic chemicals Dyeing, tanning and colouring materials Medicinal and pharmaceutical products

Essential oils materials, and cleaning Fertilizers (other than those of row fertilizers)

Plastics in primary forms Plastics in non primary forms

Chemical material and products Leather and leather manufactures

Rubber manufacture Cork and wood manufactures (no furniture)

Paper paperboard and articles of paper Textile yarn, fabrics, and related products

Non-metallic mineral manufactures Iron and steel

Non-ferrous metals Manufactures of metals

Power-generating machinery and equipment Machinery specialized for particular industries

Metal working machinery Industrial machinery and equipment, and parts

Office and automatic data processing machines Telecommunications and sound-recording

Electrical machinery apparatus and parts thereof Road vehicles (including air-cushion vehicles)

Prefabricated plumbing, heating, and lighing Furniture and parts thereof

Travel goods, handbags and similar containers Articles of apparel and clothing accessories

Footwear Professional scientific and controlling instruments

Photographic and optical equipment, watches, Miscellaneous manufactured articles

Special transaction and commodities not Coin (other than gold coin)

1999 2002

Notes.T

hefigures

plotimportcom

position(sectorshare

overtotalimport)in

1999and

2002atthe

ISIC2-digitsectorlevel(Sources:Palestinian

CentralB

ureauofStatistics).

58

FIGURE A.8: CONFLICT AND IMPORT AND DOMESTIC WHOLESALE PRICES

100

300

500

700

900

1100

Pale

stin

ians

Kille

d by

Isra

eli S

F

.8.8

5.9

.95

11.

051.

1

Rat

io o

f Im

porte

d vs

Dom

estic

Who

lesa

le P

rice

2000 2002 2004 2006Year

Ratio of Imported vs Domestic Wholesale PricePalestinians Killed by Israeli SF

Notes. The figure shows the evolution of the ratio between imported vs. domestic wholesaleprices, together with the evolution of the total number of Palestinians killed by IDF (Sources:Palestinian Central Bureau of Statistics; B’TSELEM).

FIGURE A.9: CONFLICT AND PALESTINIAN GDP20

060

010

00Pa

lest

inia

ns K

illed

by Is

rael

i SF

3000

3500

4000

4500

5000

Pale

stin

e R

eal G

DP

(Milli

on U

SD)

2000 2001 2002 2003 2004 2005 2006Year

Palestine Real GDP (Million USD)Palestinians Killed by Israeli Defense Forces

Notes. The figure shows the evolution of real Palestine GDP (Million USD) over time, to-gether with the evolution of the total number of Palestinians killed by IDF (Sources: Pales-tinian Central Bureau of Statistics; B’TSELEM).

59

FIGURE A.10: CONFLICT AND AGGREGATE OUTPUT VALUE

200

450

700

950

1200

Pale

stin

ians

Kille

d by

Isra

eli S

F

3000

4000

5000

Aggr

egat

e Re

al O

utpu

t Val

ue (M

illion

NIS

)

2000 2001 2002 2003 2004 2005 2006Year

Aggregate Real Output Value - Industrial Survey (Million NIS)Base Year: 2006

Palestinians Killed by Israeli Defense Forces

Notes. The figure shows the evolution of the total real value of production in Israeli New Sheqel(NIS) over time, as derived from the Industry Survey. It plots the weighted sum of establishments’output value over time together with Palestinians fatalities in the same period. Establishment-leveloutput values are aggregated after adjusting its value using yearly 2-digit sector-level deflators. Thefigures also plot the evolution of the total number of Palestinians killed by IDF in the same years(Sources: Industry Survey, Palestinian Central Bureau of Statistics; B’TSELEM).

FIGURE A.11: PRODUCER PRICE INDEX AND CONFLICT

050

100

150

200

250

2000 2002 2004 2006Year

PPIPalestinians Killed by IDF

Manufacture of Tobacco Products - Jenin

050

100

150

200

250

2000 2002 2004 2006Year

PPIPalestinians Killed by IDF

Manufacture Leather Products - Hebron

050

100

150

200

250

2000 2002 2004 2006Year

PPIPalestinians Killed by IDF

Manufacture of Basic Metals - Hebron

Notes. The figures plot the evolution of Producer Price Indexes for selected 2-digit sectors clustered in one particular district over time,together with the total number of Palestinians killed by IDF in the same district (Sources: Palestinian Central Bureau of Statistics;B’TSELEM).

60

A.2 Elasticity of Substitution between Foreign and Domestically ProducedMaterials

The validity of our counterfactual analysis in Section 8.2 rests on the assumptions we make on the shape

of the production function. That is, we assume a Cobb-Douglas production function with sector-specific

factor shares and structurally estimate the corresponding parameters from the data. One possible concern

with such procedure is that the true production function may have a different shape. In particular, foreign

and domestically produced materials may be very close substitute, with the elasticity of substitution

between the two being far greater than one. If this was the case, substitution between the two would

have a small impact on output. The procedure we implement by assuming a Cobb-Douglas production

function would thus lead to an overestimation of the impact of material input substitution on output

value.

Notice first that the claim that foreign and domestically produced materials are very close substitute

finds little support in the existing literature. A number of empirical papers document productivity in-

creases in domestic firms through access to imported inputs (Schor 2004; Amiti and Konings 2007; Kasa-

hara and Rodrigue 2008; Topalova and Khandelwal 2011; Boehm, Flaaen and Pandalai-Nayar 2015).

Despite the limitations of our data, we take these concerns seriously and structurally estimate the

elasticity of substitution between foreign and domestically produced materials in the data. Consider the

expression of output value in equation 17 in the following modified version

PsiYsi =σ

σ − 11

1− τY i

[1 + τKiαs

]αs [1 + τLiβs

]βs [ 1 + τMi

1− αs − βs

]1−αs−βs(RKsi)αs(wLsi)βs(zMsi)1−αs−βs

(17)

where zMsi is the adjusted value of materials in production. This value is not observed by the

econometrician. It combines both foreign and domestically produced materials according to the follow-

ing expression

zMsi =[µis

(zdM

dis

) εs−1εs + (1− µis)

(zfM

fis

) εs−1εs

] εsεs−1

(18)

where µis is the relative efficiency of domestically produced materials for firm i operating in sector

s. By contrast, εs is the sector-specific constant elasticity of substitution between foreign and domesti-

cally produced materials in production. As εs approaches 1, the above expression reduces to a Cobb-

Douglas production function, which maps into the empirical analysis we implement in Section 8.2. If

instead εs is far greater than 1, foreign and domestically produced materials are very close substitute,

thus moving away from the Cobb-Douglas framework and questioning the validity of our main exercise.

It follows that evidence of εs close to 1 would be supportive of our approach and counterfactual analysis.

Our goal is therefore to provide estimates of εs.

61

We implement a two-stage estimation procedure. First, notice that

ln zMsi =εs

εs − 1ln[µis

(zdM

dis

) εs−1εs + (1− µis)

(zfM

fis

) εs−1εs

]

ln zMsi = ln zfMfis +

εsεs − 1

ln

µis(zdMdis

zfMfis

) εs−1εs

+ 1− µis

(19)

After taking logs of equation 17, and substitute the above expression we get

lnPsiYsi = lnσ

σ − 1+ ln

[1αs

]αs [ 1βs

]βs [ 11− αs − βs

]1−αs−βs+

+αs lnRKsi + βs lnwLsi + (1− αs − βs) ln zfMfis+

+(1− αs − βs)εs

εs − 1ln

µis(zdMdis

zfMfis

) εs−1εs

+ 1− µis

(20)

Using firm-level data from 1999, we can therefore run a first regression of lnPsiYsi on lnRKsi,

lnwLsi and ln zfMfis, with sector fixed effects, and sector-specific factor shares. The residuals from

such regression are equal to

uis = (1− αs − βs)εs

εs − 1ln

µis(zdMdis

zfMfis

) εs−1εs

+ 1− µis

(21)

so that we have

uis1− αs − βs

=εs

εs − 1ln

µis(zdMdis

zfMfis

) εs−1εs

+ 1− µis

(22)

With the estimated residuals and parameters from the first regression in hand, we can take logs and

run a second regression of the above adjusted residuals over sector fixed effects. We can thus estimate

εs from the estimated sector fixed effects ψs from this second specification using the delta method, i.e.

εs =exp(ψs)

exp(ψs)− 1(23)

Figure A.12 shows the corresponding point estimates of εs, together with their 95% confidence

intervals. Point estimates are remarkably close to 1, with the smallest value being equal to 1.01 and

the largest being equal to 1.37. Evidence is therefore supportive of the hypothesis that foreign and

domestically produced materials are not close substitutes, and in favour of the adoption of the Cobb-

Douglas specification of the production function. We interpret this as validating our approach and the

counterfactual analysis in Section 8.2.

62

FIGURE A.12: ESTIMATED ELASTICITIES OF SUBSTITUTION WITHIN MATERIALS

12

34

56

78

910

1112

1314

1516

1718

1920

21IS

IC 2

-dig

it Se

ctor

Cod

e

.8 1 1.2 1.4 1.6 1.8Estimated Elasticity of Substitution

Notes. The figures plot the estimated elasticity of substitution between foreign and domesticallyproduced materials in each ISIC 2-digit sector following the procedure described above. Estimatedelasticities are remarkably close to one for all sectors, thus supporting the counterfactual analysisin Section 8.2 (Sources: Palestinian Central Bureau of Statistics).

63

B Data Appendix for Online Publication

This appendix contains a detailed description of the study sample and the variables used in the empiricalsection.

B.1 Industry Survey 1999-2006

Establishment-level variables are derived using micro data from the Industry Survey (IS) for the years1999 to 2006, provided by the Palestinian Central Bureau of Statistics. Implemented since 1994, togetherwith other economic surveys, it aims at providing a detailed description of the Palestinian economy. Thesample of the IS in each year is a single-stage stratified random sample, meaning a systematic sample inwhich the establishment constitutes the primary sampling unit (PSU). Three levels of strata are used toarrive at an efficient representative sample (i.e. economic activity, size of employment and geographicallevels). Survey responses are typically higher than 90%. The sampling weight of the establishment isthe reciprocal of the sampling probability of that establishment. Weights are adjusted to compensate fornon-responses. In the released version of the IS dataset we employ, we also have information on thedistrict of location of each surveyed establishment.

We build our final dataset by combining IS data from each of the considered years. We end up witha final sample of 16418 observations, with sum of sampling weights equal to 113912. Inspecting thedistribution of each one of the variables of interest, we notice the variables referring to fixed assets tohave implausible peaks corresponding to values lower than 5. Fixed assets variables are: book value ofassets at the beginning of the year; value of imported, new and second-hand assets purchased during theyear; value of internally produced assets; value of capital additions and improvements; value of written-off and losses; value of assets sold during the year; capital depreciation during the year; book valueof assets at the end of the year. We thus group together those 1788 observations (10.89% of the finalsample) for which all of the capital information variables assume values lower than 5. Establishments inthis subsample do not appear to be systematically different from others in terms of year of the survey ordistrict of location. However, they are found to employ a significantly lower amount of labor and beingattributed lower sampling weights. We exclude these observations from our analysis, trading off thenational representativity of the employed restricted sample for the reliability of information on capital.Furthermore, surveyed establishments are given the option to choose the currency to use in reportingvalue information. While the vast majority of establishments (13903 of the remaining sample) chooseto report information in Israeli New Sheqel (NIS), 275 establishments report information in JordanianDinnar and 109 establishments in US Dollars. We do not have information on currency used for 343establishments. Using yearly information on exchange rates, we thus convert Jordanina Dinnars andUS Dollars values to NIS, while eliminating observations belonging to establishments with no currencyinformation. Again, these are not systematically differentially represented in given years or districts.Our final sample of analysis thus contains 14287 observations, divided by year as follows: 1778 (1999),1530 (2000), 1439 (2001), 1497 (2002), 1689 (2003), 2251 (2004), 2155 (2005), 1948 (2006).

Output Value. We consider the reported total value of output produced during the year. A totalnumber of 2890 establishments (20.23% of the study sample) do not provide this information. Theseare not systematically differentially represented in given years or districts. Nonetheless, when studyinginput values and distortions, we thus show the robustness of results when restricting the sample to thoseobservations for which we have information on the value of output. When taking logs, we take thenatural logarithm after augmenting all variable values by 1.

Aggregate Output Value. The current aggregate output value is computed by calculating theweighted sum of establishment-level output value separately in each year, using the provided samplingweights. The real value is computed by first deflating establishment-level output values using 2-digitsector Producer Price Index values for each year (base year 1996), and then calculating the weighted

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sum of deflated establishment-level output value.

Value of Capital. Similarly to Hsieh and Klenow (2009), we take the average of the book value offixed assets at the beginning and end of the year. When taking logs, we take the natural logarithm afteraugmenting all variable values by 1.

Value of Labor. We derive the total value of labor by adding up the total value of salaries foradministrative, operative, other, and home employees. We also add the value of other benefits andpayments in kind. When taking logs, we take the natural logarithm after augmenting all variable valuesby 1.

Value of Materials. When considering total value, we take the reported value of materials con-sumed during the year. We also consider separately the value of domestically and foreign producedmaterials consumed during the year, and, within those, the reported value of oil and fuel. When takinglogs, we take the natural logarithm after augmenting all variable values by 1.

Fraction of Family Workers and Proprietors. We divide the number of family workers and thenumber of proprietors by the total amount of labor as defined by the total number of employees plusproprietors.

Input Value Ratios. For each one of the ratio, we divide the total value of one input by the other,with both values augmented by 1. We then take the natural logarithm of the resulting value.

Initial Value of Capital. The book value of fixed assets at the beginning of the year. When takinglogs, we take the natural logarithm after augmenting all variable values by 1.

Initial Inventory of Output. The value of output inventory at the beginning of the year. Whentaking logs, we take the natural logarithm after augmenting all variable values by 1.

Initial Inventory of Materials. The value of materials inventory at the beginning of the year. Thedata report a single value, without coding separately the value of domestically and foreign producedmaterials. When taking logs, we take the natural logarithm after augmenting all variable values by 1.

B.2 Conflict Variables

Fatalities. Data on fatalities contains all Palestinian fatalities caused by the IDFs during the Second In-tifada. These data are collected by the Israeli NGO B’TSELEM and are considered accurate and reliableby both the Israelis and the Palestinians (Mansour and Rees 2012). Data on all (Israeli and Palestinian)fatalities related to the Second Intifada are available at http://www.btselem.org, accessed on March 1,2014. The B’TSELEM website lists the name of the fatality, the person’s age and gender, place of res-idence, the date and place of death, and a description of the circumstances of the event. The websitereports all the fatalities occurred in relation to the conflict, namely: 1) Palestinians killed by IDF; 2)Palestinians killed by Israeli civilians; 3) Israeli civilians killed by Palestinians; 4) Israeli security forcepersonnel killed by Palestinians; 5) Foreign citizens killed by Palestinians; 6) Foreign citizens killedby IDF and 7) Palestinians killed by Palestinian. Using the available information for each fatality, weconstruct our main conflict variable as the total number of all Palestinians killed by IDFs (IDF) in eachdistrict throughout each year. As it clearly emerges from the descriptions of the events, the situationsin which Palestinian fatalities happened are the most varied. For this reason, Palestinians killed by IDFare categorised in three groups, as follows: 1) took part in the hostilities - these are persons who wereparticipating directly in hostilities at the time they were killed (for example, a person on the way to firea rocket, to shoot soldiers, or detonate an explosive belt in the midst of civilians, during the action itself,and on returning from the action); 2) did not take part in the hostilities - these are persons who were notparticipating directly in hostilities at the time they were killed; 3) unknown if took part in the hostilities- in some cases, B’TSELEM was unable to collect sufficient information, or the existing informationwas insufficient to determine whether the person participated directly in the hostilities, and if so, what

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was the nature of the person’s involvement. Palestinian subject of targeted killing, i.e. persons whomthe Defense Force deliberately killed in the framework of a targeted-killing operation, were recordedin a separate list. The decision to kill them was based on confidential intelligence that B’TSELEM isunable to examine, making it impossible for the organization to determine with certainty whether theperson took part in the hostilities. The classification of the different Palestinian fatalities is based onthe principles of international humanitarian law, which distinguishes between combatants and civiliansand between an attack carried out by state agents and attacks carried out by independent organizationsor private individuals. As a rule, Palestinians in the West Bank and the Gaza Strip are classified ascivilians, in part because Palestinian combat there is not carried out by an organised army of a sovereignstate. However, the lists distinguish between civilians who took part in hostilities, and thus lost the pro-tection given to civilians not involved in the hostilities, and civilians who were completely uninvolvedin the hostilities. The information on Palestinian fatalities is based on B’TSELEM’s investigation intothe circumstances of the death in each case. As part of the investigation, B’TSELEM collects eyewit-ness testimony; gathers medical documents and photographs; and cross-checks its information with IDFSpokesperson announcements, information appearing on websites and blogs of armed Palestinian orga-nizations, information gathered by Palestinian and international human rights organizations, and mediareports. B’TSELEM emphasises that publication of the name of a person among the list of fatalities ormention that the person was a civilian or, alternatively, was killed while taking part in hostilities doesnot indicate that the agent causing the death violated the law and does not prove this person’s innocence(http://www.btselem.org/statistics/casualties clarifications). We create our fatalitiesgt variable by count-ing the total number of fatalities recorded in the B’TSELEM database as Palestinians killed by IDF inyear t and district t. In most specification, we rescale the variable and divide it by its standard deviationin the distribution of fatalities per district-year.

ICEWS. Integrated Crisis Early Warning System dataset (Shilliday, A. and J. Lautenschlager2012). Prepared by the Lockheed Martin Advanced Technology Laboratories, these data have beenrecently made available online. The dataset covers the period from 1995 to 2015. It records any inter-action between socio-political actors in the world. Each entry provides information on the source andtarget of each interaction. Events are assigned to specific categories using the Conflict and MediationEvent Observations (CAMEO) classification. Each of these categories is assigned an intensity variableusing a scale from -10 to 10, from most hostile to most cooperative. Events are automatically identi-fied and extracted from news articles, and geo-referenced and time-stamped accordingly. In order toderive a measure of conflict that is consistent with the fatalities variable discussed above, we keep allthose events geo-referenced in the Occupied Palestinian Territory that satisfy the following three re-quirements. First, the value attached to their intensity variable is equal to -10 (highly hostile events).Second, the country of the source is Israel. Third, the name of the sources is any of the following: AirForce (Israel), Attack Craft (Israel), Attack Helicopter (Israel), Commando (Israel), Combatant (Israel),Government (Israel), Israel, Israeli Defense Forces, Military Personnel - Special (Israel), Mossad, Naval(Israel), Police (Israel). We then count the number of such events in each district in each year from 2000to 2006.

Border Closures. Data are provided by B’TSELEM at http://www.btselem.org, accessed on March1, 2014. Figures were provided by the IDF Spokesperson’s Office on August 7, 2011 and by the IsraeliMinistry of Defense on December 6, 2009. We use these data to construct or variable closures as thenumber of closure days, i.e. the number of days during which the IDF imposed comprehensive closureof the borders between the OPT and Israel and between the West Bank and the Gaza Strip in each year.

B.3 Other Variables

Gross Domestic Product. Data on real and current value of Palestine GDP over the years 2000 to 2006are provided by the PCBS in the National Accounts subsection of the Statistics section of their website

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(http://www.pcbs.gov.ps/), accessed on March 1, 2014.

Producer Price Index. Yearly Producer Price Index numbers by classes in Palestine for years 1999to 2006 (base year 1996) are elaborated by the PCBS using Producer Price Index Survey, 1999 - 2006.

Aggregate Value of Trade. Data on total value of Palestian Imports and Exports over the years2000 to 2006 are provided by the PCBS in the Foreign Trade subsection of the Statistics section oftheir website (http://www.pcbs.gov.ps/), accessed on March 1, 2014. We derive real figures by usingprice deflators as derived by combining information on real and current GDP from the same source.Yearly information on the value of Palestinian Net Trade Balance are derived by subtracting the value ofImports from the value of Exports in each year.

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