metadata: authors: 0: author: Floreani,Vincent Arthur
1: author: Lopez-Acevedo,Gladys C.
2: author: Rama,Martin G.
count: Afghanistan
docna: 0: docna: Conflict and Poverty in Afghanistan's Transition
docty: Policy Research Working Paper
subtopic: Conflict and Fragile States,Economic Theory & Research,Industrial Economics,International Terrorism & Counterterrorism,Economic Growth,Inequality,Armed Conflict
teratopic: Conflict and Development,Industry,Macroeconomics and Economic Growth,Poverty Reduction
abstracts: cdata!: Despite record economic growth for more
            than a decade, poverty has remained stubbornly high in
            Afghanistan, especially in the regions that suffered less
            from conflict. This paper aims to explain this paradox by
            combining a model of conflict intensity at the province
            level over period 2007-14 with a model of consumption at the
            household level in 2011. Provincial data show that higher
            levels of conflict were positively correlated with both a
            larger presence of troops (international and Afghan) and
            larger aid flows. Household data show that the negative
            impact of conflict on consumption was more than offset by
            the positive impact of aid and troops. According to the
            estimates, Afghan troops contributed more to poverty
            reduction than international troops, possibly because they
            spent more locally. The paper uses the estimated models to
            conduct an out-of-sample validation exercise, focusing on
            the transition initiated in 2014. The results should be
            interpreted with caution, as the quantitative models cannot
            account for strategic shifts in the insurgency and watershed
            political developments. But they suggest that the reduction
            in the number of international troops and declining foreign
            aid flows led to an increase in conflict intensity and a
            decline in consumption per capita, matching current trends.
geo_reg_key: 517198,517191,517212
repnme: repnme: Conflict and Poverty in Afghanistan's Transition
display_title: Conflict and Poverty in
            Afghanistan's Transition
listing_relative_url: /research/2016/10/26868027/conflict-poverty-afghanistans-transition
topicv3: Economic Growth m1326241 471,Insurgency m1326378 2464,Poverty m1326687 695,Terrorism m1327775 2467,Military m1505651 3036
publishtoextweb_dt: 2016-10-17T00:00:00Z
historic_topic: Conflict and Development,Industry,Macroeconomics and Economic Growth,Poverty Reduction
no_of_pages: 37
author_keys: 0: author_key: 1336823
1: author_key: 428192
2: author_key: 95769
model_extractions: WPS7864 Policy Research Working Paper 7864 Conflict and Poverty in Afghanistan’s Transition _Vincent A. Floreani Gladys López-Acevedo Martín Rama_ South Asia Region Office of the Chief Economist & International Finance Corporation Office of the Chief Economist October 2016
Policy Research Working Paper 7864

# **Abstract**

Despite record economic growth for more than a decade, poverty has remained stubbornly high in Afghanistan, especially in the regions that suffered less from conflict. This paper aims to explain this paradox by combining a model of conflict intensity at the province level over period 2007–14 with a model of consumption at the household level in 2011. Provincial data show that higher levels of conflict were positively correlated with both a larger presence of troops (international and Afghan) and larger aid flows. Household data show that the negative impact of conflict on consumption was more than offset by the positive impact of aid and troops. According to the estimates, Afghan troops contributed more to poverty reduction than international troops, possibly because they spent more locally. The paper uses the estimated models to conduct an out-of-sample validation exercise, focusing on the transition initiated in 2014. The results should be interpreted with caution, as the quantitative models cannot account for strategic shifts in the insurgency and watershed political developments. But they suggest that the reduction in the number of international troops and declining foreign aid flows led to an increase in conflict intensity and a decline in consumption per capita, matching current trends.

This paper is a product of the Office of the Chief Economist, South Asia Region and the Office of the Chief Economist, International Finance Corporation. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at gacevedo@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Conflict and Poverty in Afghanistan’s Transition

Vincent A. Floreani, Gladys López-Acevedo, and Martín Rama<sup>*</sup>

**Keywords:** Afghanistan, Conflict, Foreign Aid, Troops, Poverty and Living Standards. **JEL:** D74, E21, F35, I32, O17

> * Gladys López-Acevedo and Martin Rama are with the Chief Economist office for South Asia at the World Bank; Vincent A. Floreani is with the Chief Economist office at the International Finance Corporation. The corresponding author is Gladys López-Acevedo (gacevedo@worldbank.org). We gratefully acknowledge insightful comments and suggestions by Rapti Goonesekere, Omar Joya, Faruk Kahn, Claudia Nassif, Pedro Olinto, Silvia Redaelli and Robert J. Saum. Russell Porter and his team at USAID, as well as Renard Sexton, kindly shared their data and provided guidance on it. Very useful additional inputs were received from Tilman Bruck, Sasa Hezir, Ethan Kapstein and Jake Shapiro. The findings, interpretations, and conclusions in the paper are entirely those of the authors. They do not necessarily represent the views of the World Bank or its affiliated organizations.

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<!-- Start of picture text --> Annualized growth rate pc consumption, by quintiles<br>2.5<br>2.0<br>1:9<br>1.0<br><!-- End of picture text --> <!-- Start of picture text --> 0.5<br>0.0<br>-0.5 2nD MIDDLE 2nb RICHEST TOTAL<br>Poorest RICHEST<br>4.0 TMBottom 40%<br><!-- End of picture text -->
<!-- Start of picture text --> Change in Poverty Headcount - 2007:2011<br>4<br>a 4 C<br>i ee oa4%<br>a ae<br>ha cease et ee<br>; F a<br>\ae 7 a.<br>a Ly<br>;fo a m (9,38)<br>m (-2,9]<br>m (-13,-2]<br>(-40,-13]<br>ONo data<br>——<br><!-- End of picture text --> <!-- Start of picture text --> Low conflict intensity<br>Central<br>0.4<br>Southwest<br>/ 1.6 Poverty HCR (%) Casualties. per<br>L High conflict intensity Bis | 4900 inhabitant<br>(40,80) in 2012<br>Source: NRVA 2011-12<br><!-- End of picture text -->
to decline after 2011, and more recently a slowdown of the withdrawal process was agreed, with approximately 15,900 US troops remaining on the ground in 2015 and 2016. But this is a far cry from the more than 130,000 international troops present in Afghanistan at the height of the “surge”, in 2011. The other key component of the transition is the expected decline in foreign aid, especially from USAID.

Until recently, there was optimism that this transition would not have major impacts on economic growth and Afghanistan would remain on an upward trajectory. Kapstein and Kathuria (2012) had concluded that foreign aid had set the country on a more inclusive growth trajectory, despite the political and social tensions. The landmark study _Afghanistan in Transition_ (World Bank, 2013), concurred that foreign aid had an important role in driving economic growth in the country. But it did not see the transition as a concern as long as other sources of economic growth—such as commercial agriculture and mining corridors—were developed to fill the void. Other, more recent economic reports maintained this upbeat assessment, forecasting growth rates in the vicinity of 8 percent over an extended period of time (IMF, 2011; World Bank, 2014).

Analyses of this sort were generally muted on conflict, and how it would evolve as a result of the withdrawal of international troops. Yet the security situation was already deteriorating in 2012, and conflict intensity has markedly increased since then (United Nations Assistance Mission to Afghanistan, 2015). In parallel, the revenue allocated to the security sector has been on an upward trend, accounting by now for more than a third of the country’s GDP. With fewer international troops on the ground, the country’s ability to counter a deteriorating security situation is challenged by its weak fiscal position (World Bank, 2016).

The goal of this paper is to gain a better understanding of the relationship between conflict, aid and living standards in Afghanistan. In doing so, the paper aims to explain the paradox of lower poverty in areas more severely affected by conflict. It also intends to address current concerns about the transition, shedding light on its possible impact on the dynamics of conflict and living standards in the medium term.

The analysis relies on provincial level data on conflict over period 2007-14, and on cross-sectional household data in 2011. The former is used to estimate a dynamic model of conflict intensity at the provincial level, depending on current and lagged presence of troops. The latter allows to identify the determinants of consumption per capita, and in particular to assess the role played by conflict, aid and the presence of troops. These two models are combined to predict consumption per capita from 2012 onwards, and conflict intensity at the provincial level from 2015 onwards. Comparing these out-of-sample simulations with actual figures on economic activity available until 2015 allows validating the proposed empirical approach.

Admittedly the quantitative model used to forecast the dynamics of conflict cannot incorporate strategic shifts in the insurgency and watershed political developments, such as a potential agreement between the government of Afghanistan and the Taliban, or possible tension between rival groups within the Taliban, or between the Taliban and the Islamic State. Therefore, the forecasts of conflict intensity should be interpreted with extreme caution. However, by suggesting 4
that conflict intensity could increase substantially the paper invites a recognition of the fragility risks Afghanistan faces going forward.

# **2. Previous research**

This paper is part of a growing literature on empirical studies of conflict. Departing from a longer tradition with roots in political science and strategic analysis, this literature builds on disaggregated data and statistical tools. Until not long ago, the lack of reliable data tended to dissuade this kind of approach. Conducting censuses and surveys is challenging in conflict affected areas, while administrative records tend to be incomplete in highly informal environments. But these obstacles are gradually being overcome thanks to the availability of non-traditional sources of data, from military statistics to satellite imagery to mobile phone traffic.

Several studies in this emerging literature explore the dynamics of conflict. Biddle, Friedman, and Shapiro (2012) and Hultman, Kathman, and Shannon (2013) use panel data to assess the relationship between troop deployment and violence-related casualties at the subnational or regional level. Data are from Iraq and across countries respectively. Both studies find that large deployments are effective at reducing violence, even in the most challenging environments. The impact of large deployments, however, depends on the number of troops and their equipment, as well as on having a clear mandate to use force to prevent conflict.

The key role played by international troops is confirmed by other studies. Also using data across countries, Hegre, Hultman, and Nygard (2010) conclude that the size of peacekeeping operations—as measured by troop strength or budget—is the main determinant of the ability to effectively curb a conflict. In the cases of Iraq and the Philippines, Berman et al. (2011), Crost et al. (2014a), Berman et al. (2013), and Sexton (2015) find that violence, as perceived by households, declines with the presence of international troops.

The impact of international troops on conflict intensity typically varies spatially. The abovementioned study by Hegre, Hultman, and Nygard (2010) suggests that it is important to take time and location into account, in addition to troop numbers. Using data from the Kunduz and Badakhshan Provinces in Afghanistan’s Northeast region, Derksen and Ruttig (2013) provide further evidence that the ability of troop deployments to contain conflict is not even.

Dynamics can be complex as well. Condra et al. (2010) document the persistence and pervasiveness of the conflict in Afghanistan, claiming that local exposure to civilian casualties caused by international forces has led to increased insurgent violence over the long run. Also using data from Afghanistan, Trebbi and Weese (2015) show that the insurgency is capable of acting strategically across districts and provinces.

Another relevant strand of this literature focuses on the relationship between conflict and household wellbeing. Brück et al. (2016) review the World Bank’s Living Standard Measurement Surveys to suggest concrete ways to capture in these surveys the causes, functioning, and 5
consequences of violent conflict at the local level. Dercon (2006) and Verwimp (2005) analyze the effects of local violence on household wellbeing in Ethiopia and Rwanda. Again, important spatial differences emerge. For instance, Yanagizawa-Drott (2012) finds evidence that in Rwanda radio stations encouraged people to participate in the genocide. But the reach of these stations varied depending on the terrain, resulting in considerable dispersion in violence across geographic areas.

Some of the studies in this strand of the literature refer specifically to Afghanistan. Using household data from 2007, D’Souza and Jolliffe (2013) find robust evidence that conflict and food security are negatively correlated. They attribute this relationship to disruptions of local markets leading to spikes in food prices. Callen et al. (2014) show the persistence and intensity of the conflict affect household expectations, with the poorest sections of the population having a more negative perception of their future wellbeing. Consistent with this perception, Ciarli et al. (2015) find that conflict reduces employment opportunities by making business investment less attractive, while boosting self-employment in activities that have low returns.

There are also empirical studies focusing on how foreign aid affects economic growth and household wellbeing in conflict-affected countries. During a conflict, aid can enhance state legitimacy and service delivery. After a conflict it could help implement peace agreements and support reconstruction and development. However, aid may also fuel violence because it might exacerbate tensions among local communities. Although the literature has yet to come to a consensus, findings so far suggest a positive impact. Girod (2012) finds that aid improves development outcomes after a civil war. Chandy (2011) reaches a similar conclusion for fragile countries, while pointing out that targeting, design and delivery are critically important.

The allocation of aid is also driven by political and security considerations. Douglas (2012) shows that in Afghanistan, foreign aid is positively correlated with conflict at the provincial level. This is so not only in the case of security assistance but also—albeit to a lower extent—in the case of development aid. According to Johnson, Ramachandran, and Walz (2012), in Afghanistan development aid has been fully anchored into the counterinsurgency strategy. Economic development in conflict areas is seen as essential to reduce local support for insurgents, in line with the “hearts and minds” doctrine (Chou, 2012; Douglas, 2012; Sexton, 2015).

Whether development aid has actually defused conflict in Afghanistan is less clear. There have been unambiguous successes in small-scale and community-driven development projects. Beath, Enikolopov, and Fotini (2014) conduct a thorough evaluation of the National Solidarity Program (NSP) relying on randomized control trials. Their findings show that this program had a positive effect on economic wellbeing and attitudes towards international and Afghan troops.

However, Sexton (2015) claims that successes have been confined to areas already controlled by pro-governmental forces. In disputed areas, aid—and in particular, large development projects— is said to fuel violence or be ineffective (Beath, Fotini, and Enikolopov, 2014; Chou, 2012). Findings have been similar in other fragile settings such as Iraq (Berman, Shapiro, and Felter, 2011) and the Philippines (Crost, Felter, and Johnson, 2014a).

6
Finally, several studies deal with the direct impact of the presence of troops on economic growth and household wellbeing. Using cross-country data, Jones and Kane (2011) conclude that the deployment of US troops has a positive impact on the economic growth of host countries. They claim that on average one extra year of presence by 100 U.S. soldiers adds 0.02 percentage points to the GDP annual growth rate. Such effects can last several decades; the relationship appears to be strongest in the poorest countries.

In Afghanistan’s case, Bove and Gavrilova (2014) show that the presence of troops has a positive impact on wages at the provincial level. Using non-experimental methods over the period from 2003 to 2009, they argue that this positive impact on wages more than offsets the negative impact of conflict. According to the estimates, the presence of troops increases provincial wages by 0.5 to 1.7 percent. A plausible explanation for this effect is that troops procure supplies locally. But data availability is an important limitation in this area of work, as empirical analyses tend to rely on either relatively small samples of households.

A few working hypotheses emerge from this literature review. First, the presence of international troops appears to be effective at reducing conflict intensity, albeit with a possible time lag and with varying effectiveness depending on the characteristics of the deployment and those of the communities affected. Second, conflict negatively affects household wellbeing by undermining food security and employment opportunities. Third, even in conflict-affected countries, foreign aid can have a positive impact on economic growth and household wellbeing. And fourth, military deployment also benefits households indirectly, presumably because the demand for goods and services to support the troops boosts local labor earnings.

# **3. Approach and data**

Expanding on previous research, this paper adopts an integrated approach that brings together conflict dynamics and household expenditures. The approach is designed so as to allow its empirical implementation in Afghanistan’s case, taking into account the nature of the available data. In practice, two simple econometric models are combined (figure 4).

Model 1 traces the evolution of conflict intensity over time as measured by the number of total casualties relative to the population, at the provincial level. Its explanatory variables are the presence of international and Afghan troops on the ground. Model 2 estimates expenditures per capita across households at a point in time. Expenditures per capita depend on household characteristics such as demographics and education, but they are also affected by conflict, by foreign aid, and by the presence of troops at the local level.

The specifications chosen are such that the outcome of Model 1 can be directly plugged into the estimated Model 2. Combined with assumptions about the pace at which the presence of foreign troops and the level of foreign aid will decline, this approach yields predictions on conflict intensity and household expenditures moving forward.

7
<!-- Start of picture text --> £ oo Household-level dataModel 2<br><!-- End of picture text --> <!-- Start of picture text --> @<br>° <.<br>3 e<br><!-- End of picture text --> <!-- Start of picture text --> 3<br>oe:<br>mw<br><!-- End of picture text -->
The NRVA surveys are nationally representative, with the 2011 edition providing detailed information on 18,399 households across all but two provinces. The data include household composition, household expenditures, educational attainment, and a range of other socioeconomic indicators. The NRVA surveys also contain information on variables related to fragility, including perceptions on security, revenues from opium, and shocks from violence. In addition, they indicate the household location and report information on the occurrence of local shocks such as severe weather.

The credibility of the proposed approach hinges on the consistency of the data used in the models. Since the databases used in each case are built on unrelated primary sources, disconnects cannot be ruled out. One key indicator that the two models share is the local intensity of conflict. A high correlation between the conflict-related indicators in the two databases should give some reassurance about the consistency of the two models. A further assessment of the reliability of the data used in the analysis comes from its consistency with data from extraneous sources.

The provincial database used to estimate Model 1 includes information on conflict intensity, the number of incidents, and the number of terrorism-related casualties. The NRVA survey used to estimate Model 2 includes data on theft and violence directly suffered by households and on household perceptions of insecurity. This data can be aggregated at the province level using survey weights. To ensure that correlations are meaningful, all seven indicators are scaled by population. Separately, the Asia Foundation Survey of the Afghan people (2015) includes questions regarding the household’s fear for safety and the household concern about bad security conditions. These survey data can also be aggregated at the province level using survey weights.

Some of the correlation coefficients between the various indicators of conflict are quite high (table 1). Not surprisingly, households that report having suffered from theft or violence also have higher perceptions of insecurity. Similarly, conflict intensity is higher in provinces experiencing a larger number of conflict-related incidents. While these two correlation coefficients involve indicators from the same database, there is also a relatively high and statistically significant correlation between conflict intensity reported by the provincial database and the perception of insecurity estimated based on the NRVA survey. This pattern is corroborated by households’ perception of security from the Survey of the Afghan People.

# **4. Modeling conflict intensity**

The econometric specification of Model 1 builds on the analyses by Biddle, Friedman, and Shapiro (2012) and Hultman, Kathman, and Shanon (2013). The model focuses on changes in conflict intensity at the local level and how they are affected by changes in the presence of troops on the ground. Conflict intensity is measured by the number of casualties. To allow for richer dynamics, the specification considers two periods: the current year and the previous year. It also differentiates between international troops and Afghan troops, as their effectiveness at reducing conflict may be different. Troops and casualties are deflated by provincial population.

9
# **Table 1. The correlation between conflict-related indicators from diverse sources is substantial**

||Casualties|Incidents|Terrorism Casualties|Theft/Violence|Insecurity/Violence|Fear for Safety|Bad Security Condition|
|---|---|---|---|---|---|---|---|
||(per 1,000<br>inhabitants)|(per 1,000<br>inhabitants)|(per 1,000 inhabitants)|(% Households victim at<br>the provincial level)|(% Households victim at<br>the provincial level)|(% Households<br>Responding)|(% Households<br>Responding)|
|Casualties||||||||
|(per 1,000 inhabitants)|1|||||||
|Incidents||||||||
|(per 1,000 inhabitants)|0.8377|1||||||
|Terrorism Casualties||||||||
|(per 1,000 inhabitants)|0.5255|0.581|1|||||
|Theft/Violence||||||||
|(Households victim, % of province)<br>Insecurity/Violence|0.1824|0.1474|0.1618|1||||
|(% Households victim at the provincial<br>||||||||
|level)|0.2867|0.1742|0.1627|0.2568|1|||
|Fear for Safety||||||||
|(% Households Responding)|0.4648|0.4733|0.1698|0.0563|0.2278|1||
|Bad Security Condition||||||||
|(% Households Responding)|0.6797|0.6877|0.3327|0.1346|0.3034|0.6027|1|

_Sources:_ The first three indicators are from the provincial database; the subsequent two indicators are from the NRVA survey; and the two last indicators are from the Asia Foundation Survey of the Afghan People. Correlation coefficients are computed for 2011.

10
A(Casualties), -=atBya, (ineernarvona')troops jt + By A (ieernarvona’troops jt-1 1 +71-( troopsAfghan ). + Y2.A (“arentroops Jes + pw. A(Casualties). ;¢-1 + &t J t E Bi Be Y1 Y2 LL bo +2 bytn Bo <!-- Start of picture text --> Y1 Y2<br><!-- End of picture text -->
# **Table 2. Estimation results for Model 1 (conflict dynamics)**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|(13)|(14)|(15)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||OLS|FE|OLS|FE|AB|OLS|FE|OLS|FE|AB|OLS|FE|OLS|FE|AB|
|VARIABLES||||||||||||||||
|International Troops (per 1, 000 inhabitant) t ‐ t‐1|0.0304**|0.0400**|0.0283*|0.0369**|0.0190|0.0252|0.0326*|0.0227|0.0290*|0.0226|0.0375**|0.0450***|0.0349**|0.0415**|0.0228|
||(0.0147)|(0.0162)|(0.0146)|(0.0161)|(0.0640)|(0.0164)|(0.0177)|(0.0162)|(0.0174)|(0.0807)|(0.0155)|(0.0168)|(0.0154)|(0.0167)|(0.0612)|
|International Troops (per 1, 000 inhabitant) t‐1 ‐ t‐2|‐0.135***|‐0.152***|‐0.130***|‐0.146***|‐0.150***|‐0.0966***|‐0.115***|‐0.0927***|‐0.109***|‐0.109***|‐0.131***|‐0.149***|‐0.127***|‐0.143***|‐0.144***|
||(0.0185)|(0.0206)|(0.0185)|(0.0205)|(0.0178)|(0.0204)|(0.0233)|(0.0202)|(0.0230)|(0.0175)|(0.0187)|(0.0207)|(0.0186)|(0.0206)|(0.0182)|
|Afghan Troops (per 1, 000 inhabitant) t ‐ t‐1|0.0232|0.0154|0.0122|0.00481|0.0610*|0.0105|0.00472|0.00189|‐0.00435|0.0389|0.0414|0.0315|0.0292|0.0196|0.0997***|
||(0.0367)|(0.0386)|(0.0366)|(0.0384)|(0.0349)|(0.0394)|(0.0417)|(0.0390)|(0.0410)|(0.0437)|(0.0387)|(0.0411)|(0.0387)|(0.0410)|(0.0258)|
|Afghan Troops (per 1, 000 inhabitant) t‐1 ‐ t‐2|‐0.00392|‐0.0327|0.00503|‐0.0200|0.000606|0.00594|‐0.0187|0.0118|‐0.0106|‐0.00490|‐0.0146|‐0.0358|‐0.00501|‐0.0231|‐0.00332|
||(0.0373)|(0.0518)|(0.0371)|(0.0515)|(0.0215)|(0.0388)|(0.0549)|(0.0383)|(0.0540)|(0.0386)|(0.0379)|(0.0519)|(0.0378)|(0.0516)|(0.0241)|
|Casualties (per 1,000 inhabitant) t‐1 ‐ t‐2|||‐0.148**|‐0.153**|‐0.162**|||‐0.176**|‐0.193***|‐0.191**|||‐0.143**|‐0.150**|‐0.148**|
||||(0.0649)|(0.0681)|(0.0709)|||(0.0689)|(0.0727)|(0.0787)|||(0.0649)|(0.0681)|(0.0619)|
|Year Trend|||||||||||0.0438|0.0361|0.0400|0.0328|0.0630**|
||||||||||||(0.0304)|(0.0320)|(0.0302)|(0.0316)|(0.0293)|
|Constant|0.148***|0.186***|0.154***|0.188***|0.135***|‐9.30e‐05|0.0651|0.0312|0.121|0.0846|0.0342|0.0867|0.0500|0.0983|‐0.0362|
||(0.0498)|(0.0606)|(0.0494)|(0.0598)|(0.0435)|(0.110)|(0.141)|(0.109)|(0.140)|(0.130)|(0.0931)|(0.106)|(0.0924)|(0.105)|(0.0865)|
|Year Dummies|No|No|No|No|No|Yes|Yes|Yes|Yes|Yes|No|No|No|No|No|
|Observations|192|192|192|192|160|192|192|192|192|160|192|192|192|192|160|
|Adjusted R‐squared|0.209|0.110|0.227|0.133||0.270|0.164|0.292|0.196||0.214|0.112|0.230|0.133||
|Number ofprovince|32|32|32|32|32|32|32|32|32|32|32|32|32|32|32|

_Note:_ Standard errors are reported below the estimated coefficients. Statistically significant coefficients at the 10, 5, and 1 percent level are indicated by one, two and three asterisks respectively. The preferred estimation is highlighted in yellow. AB = Arellano and Bond; FE = fixed effects; OLS = ordinary least squares.

12
# **Figure 5. Robustness of the estimated impact of troops on casualties**

<!-- Start of picture text --> Change in international troops: effect within the year Change in Afghan troops: effect within the year<br>0.120 0.120<br>0.100 0.100<br>0.080 0.080<br>0.060 0.060<br>0.040 0.040<br>0.020 0.020<br>0.000 0.000<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15<br>‐0.020 ‐0.020<br>Change in international troops: effect one year later Change in Afghan troops: effect one year later<br>0.020<br>0.020<br>0.000<br>0.000<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 ‐0.020<br>‐0.020<br>‐0.040<br>‐0.040<br>‐0.060<br>‐0.060<br>‐0.080<br>‐0.080<br>‐0.100<br>‐0.100<br>‐0.120<br>‐0.120<br>‐0.140<br>‐0.140<br>‐0.160<br>‐0.160<br><!-- End of picture text --> _Note:_ The preferred estimation is highlighted in yellow.

13
On the surface the fit of the Model 1 is not particularly high, given that the adjusted R<sup>2</sup> coefficient is around 0.2 across specifications. However, it is important to keep in mind that the specification refers to the change in casualties, not to their absolute level. When the latter is considered instead, the correlation between predicted and actual intensity of conflict is 0.71, and the share of the variance in conflict intensity explained by the model is 0.85.

For half of the provinces, the share of the variance explained by the model exceeds 0.5. And the correlation coefficient between predicted and actual intensity of conflict exceeds 0.8 for more than a third of them. On the other hand, Model 1 does not predict conflict intensity well in nine provinces, where the correlation coefficient between predicted and actual intensity of conflict is below 0.3. Khost, Nooristan, and Helmand are among these nine provinces; they are also among the most violent ones.

A potentially important caveat to these results concerns the possible changes in the military capabilities of Afghan troops in recent years. Arrangements for the withdrawal of international troops included a sustained effort to upgrade the effectiveness of Afghan troops. At the same time, the complementarity between the two military forces suggests that the withdrawal of international troops could make Afghan troops less effective. Because the model assumes that the coefficients are constant over the entire period 2007–14, it may fail to capture these two effects.

To address this concern, the preferred estimation is re-run in two different ways (the results are available upon request). First, interaction terms for international troops and Afghan troops are introduced. The results confirm the hypothesis of a complementarity between the two military forces. An increase in international troops is more effective at reducing casualties after one year when it is accompanied by a parallel increase in Afghan troops, and vice-versa. From this perspective, the withdrawal of international troops should be expected to reduce the effectiveness of Afghan troops, which is consistent with recent reports (Giustozzi and Mohamad Ali, 2016).

The second variation of the preferred estimation is to allow for the coefficients associated with Afghan troops to be different before and after 2011. This is accomplished by introducing a dummy variable for the first sub-period and interacting it with the current and lagged change in the number of Afghan troops. The results suggest that during the first sub-period an increase in the number of Afghan troops was accompanied by a much higher conflict intensity within the same year. But the difference between the two sub-periods in conflict intensity the year after is not statistically significant.

Taken together, these two exercises suggest that the upgrade of Afghan troops in recent years might have resulted in a reduction in the conflict intensity in the short term, but not necessarily in the medium term. And given the complementarity between the two military forces, the improvement in the capabilities of Afghan troops may be offset, at least partially, by the decline in the number of international troops on the ground.

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|oe enbita | _<br>( Household }<br>Household<br>Location<br>( per capita<br>},,— Go + 61. demographics/;,<br>,+62.(caucationi+ 8s:(characteristics)<br>u<br>Foreign<br>.<br>ainda<br>(ore)<br>+gr-(<br>aid di+2.(Casualties)5.+o-(<br>troops<br>it+Pq. troops},+Vit<br>L<br>0<br>|
|---|
a crude approximation of the actual figure. An instrumental variables two-stage least squares approach (IV 2SLS) is used to address possible measurement error. The foreign aid variable is therefore instrumented based on more reliable information about USAID aid flows at the local level. The first-stage regression also includes provincial fixed effects.

The preferred estimation includes the maximum number of control variables and corrects for measurement error (table 3a). As an additional robustness check, the indicator on conflict intensity is replaced by with the proportion of people at the provincial level who raised concerns about bad security conditions in their village (table 3b). The signs and magnitudes of the estimated coefficients are similar, although the high correlation between conflict-related indicators makes them less precise than would be ideal. However, the conflict-related indicators are jointly significant at the 5 percent level when using the indicator on casualties, and at the 1 percent level when using the indicator on perceptions of bad security.

In line with previous research, expenditures per capita increase with foreign aid and with the presence of troops, and decrease with conflict intensity (figure 6). Impact estimates vary across specifications. For instance, when controlling for the 2007 poverty rate at the district level, the positive impact of foreign aid is amplified and the negative impact of conflict intensity is substantially dampened. However, the positive impact of troops is relatively stable across specifications. In the case of Afghan troops, it is significant at the 1 percent level in all cases. The impact is two to three times higher than that of international troops. This is possibly a result of Afghan troops procuring and spending more at the local level.

# **6. The overall impact of conflict**

The estimates for Model 2 above show that conflict-related variables have opposite effects on household expenditures. Foreign aid and the presence of troops have a positive effect, whereas conflict intensity has a negative effect. But these conflict-related variables are not independent from each other. Indeed, the estimates for Model 1 imply that there is a strong correlation between conflict intensity and the presence of troops. Previous research by Chou (2012), Douglas (2012), and Johnson, Ramachandran, and Walz (2012), also points out to a strong correlation between conflict intensity and the level of foreign aid. Because of these correlations, assessing the impact of conflict on household expenditures requires considering all conflict-related variables together, and not in isolation.

The provincial database constructed for this paper sheds additional light on the correlation patterns linking conflict-related variables (figure 7). Contemporary correlations at the province level between conflict intensity, foreign aid and the presence of troops, range from 0.2 to 0.4. But based on the time structure of the correlation coefficients, conflict intensity precedes aid, which is in turn followed by an increase in the number of troops, both international and Afghan. This dynamic suggests that mobilizing more financial resources is easier than handling the logistics of a larger military presence on the ground.

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**Table 3a. Estimation results for Model 2 (household expenditures) with casualties as conflict indicator**

|||||**Casu**|**alties**||||
|---|---|---|---|---|---|---|---|---|
|VARIABLES|(1)<br>HH Weighted ‐<br>OLS<br>Log per capita<br>Consumption|(2)<br>HH Weighted ‐<br>OLS<br>Log per capita<br>Consumption|(3)<br>HH Weighted ‐<br>IV 2sls<br>Log per capita<br>Consumption|(4)<br>HH Weighted ‐<br>IV 2sls<br>Log per capita<br>Consumption|(5)<br>HH Weighted ‐<br>OLS<br>Log per capita<br>Consumption|(6)<br>HH Weighted ‐<br>OLS<br>Log per capita<br>Consumption|(7)<br>HH Weighted ‐<br>IV 2sls<br>Log per capita<br>Consumption|(8)<br>HH Weighted ‐<br>IV 2sls<br>Log per capita<br>Consumption|
|Demographics|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Education|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Location|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Opium Income District|No|Yes|No|Yes|No|Yes|No|Yes|
|Poverty Headcount District (2007)|No|No|No|No|Yes|Yes|Yes|Yes|
|Season Dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Foreign Aid (USD per 1,000 inhabitant)|0.0143|0.0103|0.0120|0.00799|0.0493|0.0453|0.0466|0.0426|
||(0.0359)|(0.0355)|(0.0346)|(0.0342)|(0.0477)|(0.0491)|(0.0461)|(0.0475)|
|Casualties (per 1,000 inhabitant)|‐0.0193|‐0.0225|‐0.0197|‐0.0229|0.00172|‐0.00116|0.00112|‐0.00180|
||(0.0467)|(0.0463)|(0.0457)|(0.0453)|(0.0450)|(0.0439)|(0.0437)|(0.0426)|
|International Troops (per 1,000 inhabitant)|0.00301|0.00311|0.00303|0.00313|0.00167|0.00179|0.00169|0.00181|
||(0.00587)|(0.00585)|(0.00575)|(0.00574)|(0.00543)|(0.00542)|(0.00532)|(0.00530)|
|Afghan Troops (per 1,000 inhabitant)|0.00527***|0.00540***|0.00525***|0.00539***|0.00648***|0.00650***|0.00645***|0.00647***|
||(0.00185)|(0.00182)|(0.00180)|(0.00178)|(0.00212)|(0.00210)|(0.00207)|(0.00205)|
|Observations|18,221|18,221|18,221|18,221|18,211|18,211|18,211|18,211|
|Adjusted R‐squared|0.364|0.367|0.364|0.367|0.378|0.378|0.378|0.378|
|**Joint Significance (Prob > Test)**|**0.109**|**0.0862**|**0.0695**|**0.0505**|**0.0358**|**0.0357**|**0.0148**|**0.0147**|
|**Joint Significance(%)**|**89.1%**|**91.4%**|**93.1%**|**95.0%**|**96.4%**|**96.4%**|**98.5%**|**98.5%**|

Robust standard errors (Province) in parentheses *** p<0.01, ** p<0.05, * p<0.1 _Note:_ Standard errors are clustered at the province level are reported below the estimated coefficients. Statistically significant coefficients at the 10, 5, and 1 percent level are indicated by one, two, and three asterisks, respectively. Joint significance tests are performed for the four conflict indicators. That is an F test for the OLS specifications and chi2 for the IV 2LS. The preferred estimation is highlighted in yellow. HH stands for households, OLS for ordinary least squares, and IV 2SLS for instrumental variables two-stage least squares.

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# **Table 3b. Estimation results for Model 2 (household expenditures) with perceptions as conflict indicator**

||||**Bad Sec**|**urity Conditions**|**in Village/Neigh**|**borhood**|||
|---|---|---|---|---|---|---|---|---|
||(1)<br>HH Weighted ‐|(2)<br> <br>HH Weighted ‐|(3)<br> <br>HH Weighted ‐|(4)<br>HH Weighted ‐|(5)<br> <br>HH Weighted ‐|(6)<br> <br>HH Weighted ‐|(7)<br>HH Weighted ‐|(8)<br> <br>HH Weighted ‐|
||OLS|OLS|IV 2sls|IV 2sls|OLS|OLS|IV 2sls|IV 2sls|
|VARIABLES|Log per capita<br>Consumption|Log per capita<br>Consumption|Log per capita<br>Consumption|Log per capita<br>Consumption|Log per capita<br>Consumption|Log per capita<br>Consumption|Log per capita<br>Consumption|<br>Log per capita<br>Consumption|
|Demographics|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Education|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Location|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Opium Income District|No|Yes|No|Yes|No|Yes|No|Yes|
|Poverty Headcount District (2007)|No|No|No|No|Yes|Yes|Yes|Yes|
|Season Dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Foreign Aid (USD per 1,000 inhabitant)|‐0.00439|‐0.00870|‐0.00276|‐0.00707|0.0331|0.0285|0.0344|0.0298|
||(0.0375)|(0.0368)|(0.0388)|(0.0381)|(0.0459)|(0.0468)|(0.0467)|(0.0476)|
|Casualties (per 1,000 inhabitant)|‐0.165|‐0.170|‐0.162|‐0.167|‐0.114|‐0.120|‐0.112|‐0.118|
||(0.177)|(0.178)|(0.176)|(0.176)|(0.158)|(0.157)|(0.157)|(0.156)|
|International Troops (per 1,000 inhabitant)|0.00291|0.00277|0.00290|0.00276|0.00293|0.00284|0.00292|0.00283|
||(0.00473)|(0.00470)|(0.00464)|(0.00461)|(0.00438)|(0.00438)|(0.00430)|(0.00430)|
|Afghan Troops (per 1,000 inhabitant)|0.00533***|0.00543***|0.00534***|0.00544***|0.00667***|0.00666***|0.00668***|0.00668***|
||(0.00178)|(0.00175)|(0.00175)|(0.00173)|(0.00187)|(0.00186)|(0.00185)|(0.00183)|
|Observations|18,221|18,221|18,221|18,221|18,211|18,211|18,211|18,211|
|Adjusted R‐squared|0.366|0.369|0.366|0.369|0.379|0.380|0.379|0.380|
|**Joint Significance (Prob > Test)**|**0.0158**|**0.0113**|**0.00454**|**0.00270**|**0.0229**|**0.0207**|**0.00790**|**0.00676**|
|**Joint Significance(%)**|**98.4%**|**98.9%**|**99.5%**|**99.7%**|**97.7%**|**97.9%**|**99.2%**|**99.3%**|

Robust standard errors (Province) in parentheses *** p<0.01, ** p<0.05, * p<0.1 _Note:_ Compared to the previous specification, casualties are substituted by the share of households, at the province level, who raise concerns about bad security conditions in their village. Standard errors are clustered at the province level and are reported below the estimated coefficients. Statistically significant coefficients at the 10, 5, and 1 percent level are indicated by one, two, and three asterisks, respectively. Joint significance tests are performed for the four conflict indicators. That is an F test for the OLS specifications and chi2 for the IV 2LS. The preferred estimation is highlighted in yellow. HH stands for households, OLS for ordinary least squares, and IV 2SLS for instrumental variables two-stage least squares.

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# **Figure 6. Robustness of the estimated impact of conflict-related variables on household expenditures, with casualties as conflict indicator**

<!-- Start of picture text --> Foreign aid<br>0.06<br>0.05<br>0.04<br>0.03<br>0.02<br>0.01<br>0.00<br>1 2 3 4 5 6 7 8<br><!-- End of picture text --> <!-- Start of picture text --> Casualties<br>0.01<br>0.00<br>1 2 3 4 5 6 7 8<br>‐0.01<br>‐0.01<br>‐0.02<br>‐0.02<br>‐0.03<br><!-- End of picture text --> <!-- Start of picture text --> International troops<br><!-- End of picture text --> <!-- Start of picture text --> 0.007<br>0.006<br>0.005<br>0.004<br>0.003<br>0.002<br>0.001<br>0.000<br>1 2 3 4 5 6 7 8<br>‐0.001<br><!-- End of picture text -->

# Afghan troops

<!-- Start of picture text --> 0.007<br>0.006<br>0.005<br>0.004<br>0.003<br>0.002<br>0.001<br>0.000<br>1 2 3 4 5 6 7 8<br><!-- End of picture text --> _Note:_ The preferred estimation is highlighted in yellow.

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A simple counterfactual to estimate the overall impact of conflict on household expenditures is to consider a hypothetical situation in which the levels of foreign aid, casualties, and troops on the ground, would be the same as in the province with the lowest intensity of conflict. The overall impact can be computed as the joint effect of the increase in the level of these variables relative to the level of the province with the lowest intensity of conflict multiplied by the value of the corresponding � coefficients in the preferred specification. This computation is conducted at the level of each individual household in the NRVA sample, but it is straightforward to aggregate the results at the level of the province, the region, or the country.

**Figure 7. Conflict precedes aid and the build-up of troops**

<!-- Start of picture text --> Correlation coeffients with USAID flows in year T<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0.0<br>Casualties ISAF ANA<br>T‐1 T T+1<br><!-- End of picture text --> _Note:_ ISAF stands for international troops (ISAF) and ANA for Afghan troops. Observations are at the provincial level between 2009 and 2014.

The results show that the overall impact of conflict on household expenditures is positive at the national level, but also in each of the provinces (figure 8). The impact is particularly strong among the provinces in the East, South, and the Southwest. Household expenditures are 10 to 18 percent higher than in the counterfactual, despite the large number of casualties. Conversely, the impact is weakest in the North and the Northeast, where the average increase in household expenditure per capita is less than 8 percent. These findings help explain the paradox of limited poverty reduction in the Afghan provinces and regions less affected by conflict.

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<!-- Start of picture text --> Low conflict intensity ;<br>High conflict intensity ( 8,10)10,18)<br>C[0,8}<br><!-- End of picture text -->
<!-- Start of picture text -->
-_<br>—, a : “<br>a Cm cs<br>m (2.3,57]<br>m (1.523]<br>(0.7,1.5]<br>[0.2.0.7]<br><!-- End of picture text -->

<!-- Start of picture text --> al fo i JJ<br>ia Cm Ge<br>m (0.6,2.1]<br>m (0.2.06<br>(0.2.0.2]<br>[0.1.0.2]<br><!-- End of picture text -->
trend continues in 2016. The ranking across provinces remains unchanged, with the larger increase in conflict intensity taking place in the regions where conflict levels were already high in 2014 (Figure 9).

The same assumptions on foreign aid and the presence of troops are used to forecast household expenditures beyond 2014, with inputs on conflict intensity coming from the simulation above. The exercise shows that during the transition households are subject to a triple shock: foreign aid declines, casualties increase, and whatever local demand there was from international troops quickly disappears. Admittedly this local demand from international troops is not as strong as that generated by Afghan troops, but it is not negligible. Although international troops may procure most of their supplies abroad, their construction and civil works most likely involve local labor. Furthermore, international troops’ withdrawal has a negative effect on household welfare through the increased conflict intensity it may generate.

**Figure 10. Household expenditure per capita declines relative to its 2013 peak**

<!-- Start of picture text --> Consumption per capita<br>110<br>105<br>100<br>95<br>2011 2012 2013 2014 2015 2016<br>Central East North Northeast<br>South Southwest West Westcentral<br><!-- End of picture text --> As a result of this triple shock, household expenditure per capita is expected to be 1.2 percent lower in 2016 than it was in 2011. Although this may seem a modest figure, the decline is much more pronounced (7.3 percent) relative to the time when the presence of troops and foreign aid flows were at their peak, a couple of years earlier. The magnitude of this triple shock is in fact comparable to that of a major recession.

Given the spatial concentration of the conflict—and consequently of foreign aid and international troops—the impact on household expenditures differs across provinces. According to the 23
simulation, the Southwest region will be impacted most severely by the transition, followed by the South (Figure 10). In the South and Southwest regions, declines in household expenditure per capita relative to their 2013 peak will reach 10.8 and 12.4 percent, respectively.

Although the simulation results reported so far refer to average household expenditures at the regional level, the chosen approach also make it possible to compute statistics for subsets of the population in each region. In particular, by simulating the impact of the triple shock on each individual household in the NRVA sample, it is possible to assess which share of the population in each region will have expenditures per capita below any given expenditure threshold. When the chosen threshold is the poverty line, this procedure yields the estimated poverty rates over the simulation period (Figure 11).

Using this approach, poverty incidence in Afghanistan can be expected to increase from 35.8 percent in 2011 to 36.7 percent in 2016. Although this change may seem minor, the increase is much more substantial when comparing the predicted poverty rate for 2016 to that observed around 2013, when foreign aid levels and the presence of troops on the ground were at their peak. The increase will be more marked in the East, South, and Southwest regions.

# **8. Out-of-sample validation**

The results in this paper help understand the paradox of lower poverty in regions more severely affected by conflict, but the relevance of the forecasts on the dynamics triggered by the transition could be questioned. The estimated models ensure a good fit with the data during the estimation period, but they could be less relevant beyond it, when political processes and exogenous shocks may put the country, and conflict intensity, on a different trajectory relative to the forecasts. This is a valid concern. One way to address it is to assess how well the forecasts in this paper predict actuals for the year 2015.

Data recently made available on conflict intensity in recent months reveals a strong similarity between the actual intensity of conflict in 2015 and the forecasts from Model 1. The predicted number fell short of the actual by only 0.2 percent, suggesting that the proposed approach is quite accurate at the national level. There is also a reasonable closeness between predictions and actual figures at the provincial level, with the correlation coefficient between the two series reaching 0.96. However, there are also a few important discrepancies, with the model not fully anticipating the increase in conflict intensity in the Northeast, and overestimating conflict intensity in the Central region, and even more so in the Westcentral region (figure 12).

An out-of-sample validation can also be conducted in relation to expenditures per capita, Because Model 2 was estimated using data from 2011, forecasts and actuals can be compared for all the years since 2012. The exercise also allows to assess the performance of the empirical approach used in this paper relative to other forecasts produced building on the 2011 NRVA survey and other data sources issued at about the same time. Such analyses include the already mentioned landmark 24
Changes in Poverty Headcount - 2011:2016 <!-- Start of picture text --> 3 aea ad<br>TM (1.6,5.9]<br>m (1.1,1.6]<br>(0.5,1.1]<br>(0.0,0.5]<br>CO No data<br>enel<br>Poverty Headcount - 2016 (%)<br>ban pote<br>ria<br>Fi<br>m (52.69]<br>m (40,52)<br>(29,40]<br>[4,29]<br>CONo data<br>enae<br><!-- End of picture text -->
since then. Given rapid population growth in Afghanistan, the official statistics imply that GDP per capita has been on a declining trend in recent years.

**Figure 12. Out-of-sample forecasts of conflict intensity are accurate at the aggregate level**

<!-- Start of picture text --> Total casualties (forecast/actual)<br>3.5<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br><!-- End of picture text --> The analysis in this paper refers to expenditures per capita, rather than to GDP per capita. But with savings rates being generally stable it is reasonable to assume that the two series are highly correlated. With this caveat in mind, the out-of-sample forecast predicts well the increase in GDP per capita associated with the surge in international troops, although it sees it happening in 2013 rather than in 2012. The forecasts also predict a decline in GDP per capita in subsequent years, in contrast with the relative stability implied by official statistics and the steady increase anticipated by other, contemporary analyses (figure 13).

There are additional indications that the approach used in this paper yields reliable results. In the already mentioned Survey of the Afghan People (2015), one of the questions asks households about the change in their financial situation, relative to the previous year. The difference between the percentage of households reporting that their situation had improved and those answering that it had worsened can be seen as an indicator of the change in living standards. The variation of this indicator matches well the change in expenditures per capita generated by the out-of-sample simulation.

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**Figure 13. Out-of-sample forecasts of consumption anticipated actual trends well**

<!-- Start of picture text --> 120<br>90<br>115<br>70<br>110<br>50<br>105<br>30<br>100 10<br>95 ‐10<br>2011 2012 2013 2014 2015 (est.)<br>HH Financial Situation Better ‐ Worse [RHS] Actual (IMF May 2016)<br>This paper IMF Article IV (2011)<br>WB Afghanistan in Transition (2013) WB Pathways to Inclusive Growth (2014)<br><!-- End of picture text --> A final validation of the out-of-sample forecasts in this paper comes from the preliminary results of the latest poverty assessment for Afghanistan according to which the share of the population living below the poverty line increased from 35.8 percent in 2011 to 39.1 percent in 2013 (Afghanistan Central Statistical Office, CSO-ALCS, 2015). This particular poverty assessment used survey-to-survey imputations, as opposed to actual expenditure data, so that the poverty headcount it provides, could be less precise than its predecessors. However, if the figures are taken at face value they imply an even more dramatic deterioration of the economic situation than anticipated by the out-of-sample forecasts in this paper.

# **9. Conclusion**

This paper proposes an empirical approach to understand the relationship between conflict and household wellbeing in Afghanistan, and uses the results to predict the consequences of Afghanistan’s ongoing transition. The approach combines a dynamic model of conflict intensity at the province level with a cross-sectional model disentangling the impact of conflict-related indicators on consumption expenditures at the household level. One of the contributions of the paper is to assemble the database on conflict at the province level. Although much of the data used to this effect is in the public domain, the added value is to bring the data together on a spatial platform and to check its consistency with the conflict-related indicators estimated based on household survey data and opinion polls.

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Several important insights emerge from the analyses in the paper. First and foremost, the paradox of higher poverty in safer provinces and regions can be explained. The estimates make it clear that higher conflict intensity is associated with lower household expenditure per capita. But Afghanistan is an unusual case in which foreign aid and the presence of troops on the ground are correlated with conflict, and the positive impact of these two added variables on household expenditures more than offsets the negative impact of casualties across all provinces. Related to this point, the estimates show that the impact of Afghan troops on expenditures is two to three times larger than that of international troops, most probably because local spending and procurement by Afghan troops is larger. All in all, the simulation exercise performed in this paper suggests, in line with the latest available evidence, that the transition in Afghanistan involved a severe deterioration in living standards.

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