Neighbours and Enemies: Inter-Ethnic Exposure and Contentious Activity in Mandate Palestine

EPSS 2026

Luqman Abu El Foul

London School of Economics and Political Science

Motivation


Demographic change is generating political contention across the world, from anti-immigration riots in Europe to communal violence in South Asia.


RQ: How does local exposure to an ethnic outgroup shape the form and intensity of contentious activity?

Theory

Intergroup Contact

Intergroup Conflict

Through what channel?

Theory

Contentious Politics

  • Different repertoires of contention may possess different logics (Kadivar and Ketchley 2018; Tilly 2003)
  • Differentiating by repertoire leads to different observable implications (Barrie et al. 2024)
  • I apply this insight to all forms of interethnic contention (violent and non-violent)



Hypotheses

  • H1 (Baseline): Non-violent and violent forms of contentious activity will be higher in places where interethnic exposure is greater
  • H2 (Rapacity): Exposure should increase events characterised by appropriative repertoires (looting, theft, occupation)
  • H3 (Resentment): Exposure should disproportionately increase events characterised by destructive/punitive repertoires (arson, assault, destruction of property, bombing, stoning)
  • H4 (Opportunity Cost): The effect of interethnic exposure on contention will be greater where the economic costs of mobilising are lower

Case Study: Mandate Palestine

The Arab Revolt (1936-1939)

  • Phase 1: The Period of General Strike (15 April 1936 - 11 October 1936)

  • Phase 2: Open Armed Revolt (22 June 1937 - 26 August 1939)

Case Study: Mandate Palestine

The Arab Revolt (1936-1939)

  • Phase 1: The Period of General Strike (15 April 1936 - 11 October 1936)

  • Phase 2: Open Armed Revolt (22 June 1937 - 26 August 1939)



Advantages

  • Direct focus on effects of Palestinian exposure to increased Jewish presence in Palestine

  • Avoid time-varying confounders between phases (e.g. British anti-insurgency campaign)

Data

Event Dataset

  • Hand-coded event dataset covering the six-month period of the Revolt from an Arabic-language national-level daily newspaper (Filastin)

  • Key variables recorded: date, location, size, actor, target, repertoires, whether an event was violent

Other Data Sources

  • Palestinian Village Statistics (Palestine Remembered Project)
  • Jewish Settlement Records (National Archives)
  • Statistical Handbook of Jewish Palestine
  • Jewish Settlement Composition (Panza and Zylberberg 2024)
  • British Census Data

Data

“Just before noon yesterday, a bus headed toward Bayit Vegan was travelling via al-Ajami street under the protection of some members of the police. Yet, despite this police protection, several [Palestinian] children threw stones and pieces of wood at the bus causing the Jewish riders on board to duck for cover.”

  • Date: 13/05/1936

  • Location: Jaffa

  • Actor: Palestinians

  • Target: Jews

  • Repertoire: Stoning

Data



Descriptive Statistics of Contentious Events, April-October 1936
Actor Total Violent Non-violent
All Events 7090 1961 5129
British 42 41 1
Jews 200 164 36
Palestinians 6607 1526 5081
Unknown 241 230 11

Data

Research Design

Dependent Variable:

  • A count of contentious events in an Arab town/village where the actor is Palestinian and the target is Jewish
    • Subsetting my dataset yields 4011 events (728 violent and 3283 non-violent)
    • Total number of Arab towns/villages is 1089



Independent Variable

  • Distance weighted exposure of an Arab town/village to nearby Jewish populations
  • But location of Jewish settlements is likely endogenous

Research Design: Shift-Share Instrument

  • Construct predicted Jewish population at each settlement using:

    • Shares: pre-1930 composition of each settlement by immigrant origin region

    • Shifts: 1930-1935 immigration flows by origin country

  • Predicted population varies across settlements only because of differences in pre-existing origin composition

Assumptions:

  • Jewish immigrants more likely to settle in settlements with a pre-existing population of their ethnic kin

  • Origin-country immigration shocks are uncorrelated with village-level determinants of Palestinian contention

  • Immigration flows driven by push factors in origin countries, not local Palestinian conditions (Buggle et al. 2023)

    • Rising antisemitism in Europe, particularly Nazi persecution post-1933
    • Polish economic restrictions on Jews

Research Design: Shift-Share Instrument


From Settlement Exposure to Village Exposure

  • For each Arab village, calculate distance-weighted exposure to predicted Jewish population across all nearby settlements

  • Exponential decay: closer settlements receive more weight (decay rate = 0.2/km)

  • “Half-life” of exposure is about 3.5km

  • Robustness checks with alternative distance-decay functions

Estimation Equation

  • I estimate the following reduced form regression:

\[Y_i = \alpha + \beta Z_i + \delta_d + \epsilon_i\]

  • \(Y_i\): contentious events in Arab village/town \(i\)
  • \(Z_i\): distance-weighted predicted Jewish exposure
  • \(\delta_d\): sub-district fixed effects
  • \(\epsilon_i\): error term, clustered by sub-district

Main Results

Interpretation: Moving from the 25th to the 75th percentile of predicted exposure leads to 1.77 additional contentious events per village, representing a 48% increase relative to the sample mean.

Heterogeneous Effects: Violent and Non-violent Events

Interpretation: Moving from the 25th to the 75th percentile of predicted exposure leads to 0.53 additional violent and 1.24 additional non-violent events, equivalent to a 79% and 41% increase relative to sample mean.

Mechanism: Rapacity

  • If material gain motivates participation, expect appropriative repertoires (looting, theft, occupation)
  • Only 8 events in the dataset involve appropriative repertoires

Result: Insufficient evidence to support rapacity as a mechanism

Mechanism: Resentment

  • Exposure should disproportionately increase destructive/punitive events
  • Subset: arson, assault, destruction of property, bombing, stoning (n=243)

Mechanism: Opportunity Cost

  • The effect of exposure on contention should be greater where the economic costs of mobilising are lower
  • Test: If exposure raises contention by lowering economic costs of mobilization, the effect should be stronger in villages dependent on subsistence cereal agriculture.

Conclusion

  • Palestinian exposure to Jewish settlements seems to increase the rate of contentious activity (both violent and non-violent)
  • No evidence that this effect is operating through mechanisms of rapacity and opportunity cost
  • Suggestive evidence that this effect is operating through mechanism of resentment

Next Steps:

  • Examine how accurate my predicted Jewish population measure is
  • Establish a pre-treatment baseline of contention
  • More robustness checks

Thank you!

  l.abu-el-foul@lse.ac.uk

  luqmanabuelfoul.com

Appendix

Alternative Distance-Decay Functions

Negative Binomial Count Model

Direct Events (NB)
* p < 0.1, ** p < 0.05, *** p < 0.01
Standard errors clustered at the district level.
District fixed effects included.
Predicted Exposure 0.00074***
(0.00016)
Num.Obs. 1089
Log.Lik. −667.611

Balance Tests

Balance Tests: Covariate Balance on Predicted Exposure
Variable Mean SD Coefficient SE P-value N Stars
Cultivable Land (dunams) 7263.435 9899.836 -1.86e-01 2.53e-01 0.463 794
Cereal Land (dunams) 5806.063 8751.286 -1.55e-01 1.05e-01 0.140 782
Citrus Land (dunams) 842.958 1335.939 4.45e-02 9.38e-02 0.636 166
Elevation (m) 331.526 264.438 -2.42e-02 1.28e-02 0.058 768 *
Distance to District Centre (km) 15.951 8.851 -5.89e-04 6.37e-04 0.355 839
Arab Population (1931) 1418.664 4355.221 5.60e-01 6.37e-01 0.380 238

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