metadata: authors: 0: author: Aghabarari,Leila
1: author: Rostom,Ahmed Mohamed Tawfick
2: author: Sinha,Rishabh
count: Afghanistan
docna: 0: docna: Conflict and the nature of precautionary wealth
docty: Policy Research Working Paper
subtopic: Livestock and Animal Husbandry,Primary Metals,Mining & Extractive Industry (Non-Energy),Urban Housing and Land Settlements,Urban Housing,Urban Governance and Management,Municipal Management and Reform,ICT Economics
teratopic: Communities and Human Settlements,Industry,Agriculture,Information and Communication Technologies,Public Sector Development,Urban Development
abstracts: cdata!: The paper uses a detailed household
            survey to document precautionary wealth accumulation in
            Afghanistan, with wealth being significantly higher for
            households facing higher income uncertainty. Annual
            household expenditure on nondurable goods is also lower for
            these households. There is no significant difference in the
            wealth response to income uncertainty across high- and
            low-conflict provinces. However, the constituents of
            precautionary wealth vary drastically. While households in
            the low-conflict regions rely almost exclusively on
            livestock to iron out uncertainty, households in the
            high-conflict areas also build up a reserve of gold and
            silver. This shift in household portfolio suggests a more
            substantial decline in real returns of livestock relative to
            jewelry in high-conflict provinces.
geo_reg_key: 517198,517191,517212
repnme: repnme: Conflict and the nature of precautionary wealth
display_title: Conflict and the nature of
            precautionary wealth
listing_relative_url: /research/2018/05/29919824/conflict-nature-precautionary-wealth
topicv3: Mobile Money m1326234 827,Land and Housing m1327648 817,Gold m1503518 2936,Silver m1503519 2937,Livestock m1504490 4024
publishtoextweb_dt: 2018-05-23T00:00:00Z
historic_topic: Communities and Human Settlements,Industry,Agriculture,Information and Communication Technologies,Public Sector Development,Urban Development
no_of_pages: 44
author_keys: 0: author_key: 1461921
1: author_key: 1092295
2: author_key: 1411342
model_extractions: WPS8453 Policy Research Working Paper 8453 Conflict and the Nature of Precautionary Wealth _Leila Aghabarari Ahmed Rostom Rishabh Sinha_ Finance, Competitiveness and Innovation Global Practice & Development Research Group May 2018
Policy Research Working Paper 8453

# **Abstract**

The paper uses a detailed household survey to document precautionary wealth accumulation in Afghanistan, with wealth being significantly higher for households facing higher income uncertainty. Annual household expenditure on nondurable goods is also lower for these households. There is no significant difference in the wealth response to income uncertainty across high- and low-conflict provinces.

However, the constituents of precautionary wealth vary drastically. While households in the low-conflict regions rely almost exclusively on livestock to iron out uncertainty, households in the high-conflict areas also build up a reserve of gold and silver. This shift in household portfolio suggests a more substantial decline in real returns of livestock relative to jewelry in high-conflict provinces.

This paper is a joint product of the Finance, Competitiveness and Innovation Global Practice and the Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// www.worldbank.org/research. The authors may be contacted at arostom@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 the Nature of Precautionary Wealth<sup>∗</sup>

Leila Aghabarari Ahmed Rostom Rishabh Sinha May 2018

## JEL Classification: D14, D16, D31

## Keywords: Precautionary Wealth, Conflict, Livestock, Jewelry, Afghanistan

∗Authors in alphabetical order of last names; 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. This paper is part of a larger research effort under the Saving and Investment under Uncertainty (P159317) ESW that is delivered under the AFG: Navigating Risk and Uncertainty (P157288) PA. Leila Aghabarari is a Consultant, Ahmed Rostom is a Senior Financial Sector Specialist – (both Finance, Competitiveness and Innovation Global Practice (FCIGP); Middle East and North Africa-South Asia Region) and Rishabh Sinha is an Economist in the Development Economics Research Group (Macroeconomics and Growth). Authors would like to thank Shubham Chaudhuri (Country Director, SACKB), Niraj Verma (Practice Manager, FCIGP), Norman Loayza (Lead Economist, DECMG), Claudia Nassif (Lead Economist, MTI06), Christina Wieser (Economist, GPV06), Subika Farazi (Financial Sector Specialist, FCIGP), and Aminata Ndiaye (Financial Sector Specialist, FCIGP) for comments and suggestions. Authors also benefited from comments of participants in the World Bank Seminar Series – Navigating Risk and Uncertainty in Afghanistan: Promoting Savings and Investment under Uncertainty (Kabul). Corresponding author: Ahmed Rostom (arostom@worldbank.org).
# **1 Introduction**

Households often accumulate wealth as a self-insurance mechanism against future shocks.1 In the absence of complete markets, households build up a buffer of wealth to smooth consumption over the lifetime (Deaton, 1991). While a vast literature has analyzed the implications of the precautionary motive in different settings, including many that have focused on developing economies, not much is known about the precautionary behavior in economies trapped in fragility, conflict, and violence (FCV).2 This gap in research is unfortunate as the volatile situation in FCV economies arguably makes the precautionary motive much stronger. If true, welfare costs associated with precautionary behavior are likely to be much higher under such conditions.3 However, an analysis of precautionary behavior requires deep household-level data which are difficult to collect in such economies. In this paper, we use data from a detailed household survey from Afghanistan to shed light on the precautionary behavior of households in a country where a range of factors packed under the broad umbrella of FCV has created an environment of utmost uncertainty.

There are two principal components to our analysis. First, we want to ascertain whether the precautionary motive is a significant driver of wealth accumulation in Afghanistan and second, whether the presence of conflict affects the nature of wealth accumulation behavior of households. To make progress with these tasks, we analyze the relationship between household wealth and income uncertainty faced by households (Guiso _et al._ (1992), Carroll & Samwick (1997), Carroll & Samwick (1998) etc.). Critical to our analysis, the survey we use was designed with a special focus to include important sources of household wealth. It contains information on households<sup>′</sup> ownership of gold and silver together with livestock holding which we use to construct our measure of wealth. The inclusion of these asset-types is essential as it has been well-documented that jewelry and livestock form a vital share of household wealth in poor economies.4 These assets can be particularly handy in protecting against future shocks. On the one hand, the lack of financial access creates barriers to accumulating wealth using financial assets, but it is also likely that households consider jewelry and livestock to be a better store of value than cash and other financial

> 1 See Marshall (1920) and Keynes (1936) for the foundations of the theory.

> 2 In a large cross-country study consisting of both industrial and developing countries, Loayza _et al._ (2000) found widespread evidence of the precautionary savings motive.

> 3To be clear, self-insurance via precautionary wealth accumulation is still preferable to a no insurance scenario. In fact, they can be more effective than other insurance instruments in combating mild idiosyncratic shocks. However, there are both private and social losses when precautionary wealth is used to protect against large idiosyncratic risks or systemic risks. For a detailed discussion, see the 2014 World Development Report (World Bank, 2014).

> 4Studies going back to Jodha (1978), Watts (1983), Binswanger & McIntire (1987), Swinton (1988) etc. highlight the importance of livestock sales in smoothing shocks. Precious metals like gold and silver are used less frequently but is an important component of household wealth (Fafchamps _et al._ , 1998).

1
Cross-sectional analyses of precautionary motive have employed the occupational information to measure income uncertainty (Skinner (1988), Dardanoni (1991) etc.). We follow this literature and posit that different occupations are associated with different levels of income uncertainty. To this end, we classify households into different occupational groups. Following, we measure the variance in the residual of household income within each group after the effects of human capital variables and a host of fixed factors have been accounted for. This variance of the residual becomes our measure of income uncertainty which we use to analyze precautionary wealth accumulation in the country. Using two measures of wealth – one narrow and the other broad, we find a strong and highly significant positive relationship between income uncertainty and household wealth. The coefficients estimated using the narrower definition are five to six times as large as for the broader measure. On the other hand, the estimated coefficients for the broader wealth measure are similar in magnitude to what is obtained in some developed countries.5 A challenge associated with any wealth measure is that it might fail to account for some sources of wealth that are held for precautionary needs. This omission can occur due to gaps in data or due to the definition of wealth used. We address this concern by looking at annual household expenditures on non-durables. We find that households facing higher income uncertainty not only hold higher stock of wealth but also spend less compared to households with lower variance at similar levels of income. However, period adjustment in aggregate wealth via changes in expenditures is not a necessary condition for the existence of the precautionary motive under the buffer-stock model. In this light, we recognize this relationship as a secondary test of the precautionary channel. We also consider how the precautionary wealth accumulation varies across the income distribution. Poor households typically have lower access to formal and informal borrowing channels which will make the precautionary channel stronger. We compare wealth accumulation across income quantiles and find that the strength of the precautionary motive decreases systematically with rising income. The precautionary response is specifically higher for the households in the bottom 40 percent of the income distribution compared to the relatively richer households.

Having found a systematic association between income uncertainty and wealth accumulation, we next investigate how conflict interacts with the precautionary behavior of the households. What motivates this line of inquiry is the possibility that some instruments of wealth accumulation that are available in the relatively low-conflict regions may not be available in the high-conflict regions. If the sale of certain assets occurs at centralized markets, then it is a dangerous undertaking to sell them in a high-conflict region. Additionally, it is possible that certain assets like livestock are

> 5For example, Carroll & Samwick (1998) estimate these coefficients using US data and Fuchs-Schündeln & Schündeln (2005) study precautionary wealth accumulation in Germany. However, we note that both differences in data and definitions do not allow for a direct comparison of these coefficients across countries.

2
more prone to losing value even at home relative to jewelry in high-conflict regions. Livestock needs upkeep in terms of feed and care, both of which are relatively scarce in high-conflict regions. Finally, more direct effects of conflict, like animal raiding in case of livestock, might also tilt the scales against certain assets.6 We bring conflict under analysis by exploiting the variation in the intensity of conflict events across the provinces. We classify the provinces into two groups depending on whether they encounter more or fewer events compared to the median province. Consistent with the previous finding, wealth accumulation in low-conflict provinces increases with income uncertainty. The relationship remains highly significant and is robust to the choice of narrow and broad measure of wealth. The wealth accumulation response to rising income uncertainty in high-conflict provinces is marginally higher but the difference relative to low-conflict provinces is not statistically significant.

This invariable nature of precautionary behavior across the low and high conflict provinces is surprising. It is very likely that our measure of income uncertainty does not capture some uncertainty associated with conflict. To the extent that the unobserved conflict-specific uncertainty is positively correlated with income uncertainty, it is expected that households in high conflict provinces accumulate more wealth at the same level of uncertainty. There are three reasons why we do not observe higher precautionary wealth accumulation in high-conflict provinces. First, it is possible that households in high-conflict regions accumulate wealth in certain forms that are not included in the measure of wealth in our study. Hence, actual wealth in these provinces is potentially higher than what is observed. Second, the price of assets relative to consumption goods might be significantly lower in these provinces owing to a general scarcity. This relative scarcity worsens the terms of trade of wealth and reduces the real return which leads to lower precautionary wealth accumulation. Finally, this worsening of terms of trade might be asset-specific with real returns of certain assets being farther from real returns observed in low-conflict provinces. If the terms of trade deterioration is more acuter for assets that have higher real returns, then households substitute towards assets with lower returns further dampening the wealth accumulation. We explore this last possibility by focusing on jewelry and livestock holding patterns of households across provinces.

We find that households in low-conflict provinces increase their ownership of livestock with an increase in income uncertainty. This is in stark contrast to the ownership of jewelry, the stock of which reduces with a rise in uncertainty. This suggests that households employ livestock as an instrument of self-insurance in low-conflict settings but not jewelry. Rather, the ownership of jewelry in these provinces seems to be driven by non-precautionary needs.7 Looking at households

> 6See Bukurura (1995), Fleisher (1998), Gray _et al._ (2003), Bundervoet (2009) for a discussion on livestock loss in some East-African countries during conflict.

> 7See Chipchase _et al._ (2013) for a qualitative discussion.

3
in the high conflict provinces, we find a higher accumulation of livestock at the same level of uncertainty. Crucially though, we also find that the ownership of jewelry responds very strongly to any rise in uncertainty. This points to a marked difference in the available coping strategies across provinces. In the context of the previous discussion, this can be mapped to a larger decline in real returns of livestock which makes accumulating in the form of jewelry attractive in high-conflict provinces. The welfare cost of low real returns can in part be attributed to wealth being substituted away from productive wealth like livestock which contributes directly to income and consumption of households.

There exists a sizable literature that has looked at the importance of livestock as an instrument of self-insurance. Many studies have found the sale of livestock as a means to smoothing consumption (Corbett (1988), Kinsey _et al._ (1998), Rosenzweig & Wolpin (1993), McPeak (2004)). Nonetheless, there are many instances where livestock is either not employed or performs only a secondary role (Udry (1995), Fafchamps _et al._ (1998), Kazianga & Udry (2006)). The differences in the use of livestock as an instrument of self-insurance highlight the differences in settings in which these studies are conducted. The availability of other instruments is an important factor determining household decisions, and conflict reduces the set of available resources drastically. Verpoorten (2009) compares livestock sales during the period of peace with sales during the period of civil war in Rwanda and finds that it was during the conflict when livestock sales were used to obtain food. It is worth noting that the household response in the Rwandan context is tied to an unanticipated conflict situation. Households made adjustments after they had made decisions of wealth accumulation assuming a peacetime scenario. In contrast, we contribute by looking at long-term wealth accumulation behavior when conflict has become a part of everyday reality, and households understand the cushion provided by various assets under conflict. While we find an active role of livestock across high- and low-conflict provinces, we also provide evidence that households<sup>′</sup> precautionary wealth accumulation extends over to jewelry as the conflict situation worsens.

We conclude our analysis by performing a battery of robustness checks on our main findings. In the first set of tests, we consider different definitions of the low- and high-conflict provinces. In addition, our estimates remain robust to dropping provinces with very high conflict intensity and when we employ conflict as a continuous variable instead of an indicator variable. Second, we address the concern that our findings are driven by the inclusion of households in livestock occupations which require livestock ownership. The livestock ownership is prevalent independent of occupations practiced by members and we find our results to be robust when we consider only those households who report not being involved in livestock occupations.

The rest of the paper is organized as follows. We begin by giving a brief description of the 4
data and details on the construction of the main variables. Thereafter, we outline our econometric specifications, report our findings, and test for the robustness of our main results. We conclude the paper with a brief discussion on the policy implications of our study.

# **2 Data**

We use data from two primary sources to conduct our analysis. The essential data come from the Afghanistan Living Conditions Survey (ALCS) (Central Statistics Organization (2016)) household survey, which we use to construct measures of labor income uncertainty and household wealth. The ALCS is a large sample survey conducted by the Central Statistics Organization (CSO) and is representative at the province level. The detailed nature of the ALCS data allows us to control for many household-level characteristics which are relevant to the saving decisions. We use conflict data from the Global Terrorism Database (GTD) (START (2017)) to measure the conflict intensity at the province level. We discuss some details of these data in this section.

## **2.1 Wealth**

The ALCS has been conducted sporadically for more than a decade. Yet, the 2013-14 crosssection is of particular importance because the survey instrument for that year was designed to capture household wealth holistically. This survey round contains information on household ownership of gold and silver as well as livestock, which are essential components of household wealth as evidenced by the previous literature.

We begin by constructing a narrow measure of household wealth that corresponds to the total value of gold, silver, and livestock owned by the household. The survey contains detailed information in regards to the livestock ownership. Households are asked to report the ownership of livestock across nine categories ranging from cattle and camels to chicken and small birds. The price data for this diverse set of livestock category are not available for Afghanistan and we rely on price data from different sources to aggregate the total value of livestock.8 A first observation from the data is the widespread prevalence of livestock ownership across the Afghan households. Around two-thirds of the households in our sample report owning any livestock. Nonetheless, there exists a marked heterogeneity across the provinces. The share of households with any livestock ownership varies from a high of 90 percent in Paktika and Khost to 25 percent in the more urban Kabul province. The nature of ownership is diverse as well with households not only keeping animals

> 8See appendix section on livestock for more details on this aggregation.

5
necessary for farm work but also owning smaller animals and birds that are better associated with consumption. In fact, chicken and sheep are the most common types of livestock.

With regards to ownership of jewelry, the survey reports the gram weight of gold and silver held by the households. In contrast to livestock ownership, only 14 percent of households reported owning either gold or silver. The ownership of gold or silver is higher in Kunarha and Khost where around 40 percent of households report owning some amount of gold and/or silver. Interestingly, livestock ownership is also higher in these provinces. Yet, in many provinces, there is almost no ownership of gold and silver. Less than 1 percent of households in Baghlan, Ghor, and Zabul have some wealth in the form of gold and silver. No household in our sample from Nooristan and Daykundi had any ownership of gold and silver either. Figure 1 shows the average value of gold and silver and livestock across the provinces. Not only do more households own livestock, we find that livestock accounts for a large share of wealth in each province.

In addition to our narrow measure of wealth, we use information from the survey to construct another broader measure of wealth. Specifically, we augment the narrow measure to include any savings from the previous year less any debt that a household owes. The savings from the previous year is defined as annual income in excess of annual non-durable expenditure. Our motivation for including savings from the previous year is based on the notion that there may be a lag in converting such savings to assets which we observe in the narrow measure. Around half of the households in the sample reported having any debt. As is the case with asset ownership, there are huge variations in debt across provinces. Almost every household in Ghor in our sample reported having a debt obligation. The debt was also high in the provinces of Kunarha and Urozgan. In sharp contrast, households in Paktika and Faryab had minimal debt obligations. In figure 2, we show the average annual income and expenditure across the provinces. Average annual income ranges from more than AFN 200,000 in Kabul and Khost to just around AFN 50,000 in Ghor and Daykundi. In line with income, Kabul also has the highest level of annual expenditure. Average expenditure is also high in Helmand, almost as high as the average annual income for the province, resulting in a low savings rate. Daykundi and Ghor have the lowest levels of average expenditure in line with their lower average incomes.

We note that our measure of wealth does not include housing wealth. To be effective as an instrument of self-insurance, an asset needs to be liquid so that it can be readily exchanged when needed. Housing wealth, however, tends to be relatively much less liquid than other assets and we do not view this exclusion as a severe concern. Nonetheless, we attempt to account for housing wealth by adding controls for house ownership. Specifically, the ALCS data allow us to identify whether a particular household owns a house or not. Furthermore, we can also identify the broad characteristics of the house which we control for. Houses in Afghanistan range from temporary 6
shacks and tents to traditional, more durable buildings.9

# **2.2 Labor Income Uncertainty**

To measure uncertainty linked with the household income, we follow previous literature (Skinner (1988), Dardanoni (1991) etc.) by postulating that occupations are associated with different levels of income uncertainty. The ALCS contains information on households’ three principal sources of income. Most of these sources can be mapped to an occupation, like farming, teaching etc.10 Household income is derived from multiple sources not only because more than one member contributes to the aggregate but also because sometimes a single household member derives income from multiple sources. We use all three sources of income to classify households into various groups.

We put households that report the same first, second, and third sources in the same order into a single occupational group. We use a more restrictive classification to separate households that may have had the same sources of income but may have differed in terms of how important they are to the household income. To ensure we have enough households in a single group so that the corresponding variance is well measured, we drop those occupational groups that contain fewer than 40 observations. We end up with 83 such occupational groups. The quantitative analysis of precautionary behavior hinges on the extent of variation of income uncertainty that is observed in the data. Our classification of households into many occupational groups instead of a few is thus essential to tease out the relationship between income uncertainty and wealth accumulation. In regards to the sample selection, we exclude all households that do not report their income and those with heads aged less than 18 or over 64 years old. We also drop those households with retired or unemployed heads as well as households for which any of the three primary sources is a non-labor source such as pension earnings, rental income, and _Zakat_ (borrowing). This leaves us with 13,156 households which constitute our benchmark sample.

Not surprisingly, agriculture is the dominant activity from which household income is derived. The production and sale of field crops (non-opium) is the most common source of household income and is the primary source of income for 21 percent of the households in the sample. At the other extreme, road and building construction is reported to be the primary income source by only 0.01 percent of the households. The average annual income in our sample is AFN 124,000 while the median household earned income is AFN 93,000. There exists a considerable variation in aggregate household income across provinces. Households in Kabul and Khost have the highest

> 9We list the distribution of house types in the table A.3 of the appendix.

> 10Table A.2 in the appendix lists the 28 different sources of household income.

7
average levels of income (AFN 235,000 and AFN 211,000 respectively), while households in Daykundi and Ghor have the lowest average incomes of around AFN 50,000.

We now provide details on how we measure uncertainty associated with the household income. Our classification of households into occupational groups implies that we are able to measure the variability in household income that stems from the nature of occupations from which households derive their income. Nonetheless, we are aware that not all of the income variance within a group is driven by the fundamental nature of occupations. Worker-specific factors, including differences in the within-group human capital, might also affect the within-group variation in incomes. Thus, we first regress the log of household income using a bunch of individual-level controls such as age, education, and gender for each group g as specified in equation (1). We then use the log of the variance of the collected error terms for each occupational group as our measure of income uncertainty.

Figure 3 shows how the narrow measure of wealth at the provincial level varies with income uncertainty. The horizontal axis corresponds to the mean of the variance of the household income. The income variance of the households is normalized by household income to control for wealth accumulation driven by income changes. We find evidence of a significant positive association between the two measures. Wealth accumulation is lower in provinces where the income adjusted level of uncertainty is lower which suggests that the precautionary margin of wealth accumulation is active in the country. We also find some outliers at the either extreme. Households in Khost, for example, accumulate much higher wealth given the low variability of income in the province. On the other hand, households in Daykundi and Urozgan accumulate much lower wealth considering the high uncertainty associated with their incomes.

# **2.3**

The third important element of our analysis relates to the existence of conflict which makes FCV economies like Afghanistan different from the other developing economies. The country has been affected by conflict for many decades, though the conflict situation has ebbed and flowed over time. Crucial to our analysis though is the notion that the presence of conflict fundamentally affects the precautionary behavior of households. There are three important reasons why we think this might happen. First, there is a severe deterioration in the efficiency with which markets operate under a conflict situation. In extreme circumstances, markets might cease to exist. Yet, some markets are 8
affected more than others with large, central markets being more at risk. If certain assets can only be exchanged in such central markets, then they lose their purpose to serve as an efficient instrument of self-insurance. Second, certain assets are also prone to lose value at home compared to others. For example, livestock requires constant upkeep which not only includes fodder requirement which gets pressurized during a conflict but other specialized care like a veterinarian visit. In contrast, some assets like jewelry are more dormant and are more resistant to losing value at home. Finally, some assets might bear particular interest to conflict groups who might target these assets specifically. For instance, cattle raiding is a strategy often employed by many groups. All these reasons suggest that conflict drives a wedge in the decision-making behavior of households living in high-conflict provinces relative to their counterparts in low-conflict regions.

Our approach is to utilize the variation in conflict intensity across the Afghan provinces to consider whether the nature of precautionary wealth is affected by conflict. We measure conflict intensity using the data on the number of terrorist attacks which we source from the GTD. The database allows us to construct the total number of such events for each province from 1973 – 2014. Figure 4a shows the variation of conflict intensity across the provinces. The conflict situation is particularly grave in the southern provinces with more than 800 terrorist attacks being reported for Helmand and Kandahar over the entire period. The variation in intensity across provinces is stark, and many central and some northeastern provinces witness much less conflict activity. For instance, a total of three attacks have been reported in the northeastern province of Panjsher and a total of eight attacks in the central province of Daykundi over the four decades.

An alternative measure of conflict intensity will be to consider a period close to the survey year. We consider many smaller periods, not only limited to periods near the survey year, and find that the cross-sectional variation of conflict intensity remains stable across all these periods. For instance, in figure 4b we show the cross-sectional variation in attacks for the year 2012 which is the year preceding the survey period. We note that the variation of intensity remains similar to the figure on the left which is also confirmed with a high correlation coefficient of 0.92. In this light, we rely on the broad horizon and aggregate the total attacks over the four decades. This concludes our outline of the data, and we detail the elements of our formal analysis of these data in the next section.

# **3 Econometric Specification**

In the previous section, we discussed some details of the data that we use in our analysis. We also highlighted the heterogeneity in wealth accumulation, incomes, income uncertainty and 9
conflict intensity across the provinces which suggests the presence of a precautionary motive in the household wealth accumulation behavior. Taking our investigation further, we now formally investigate the presence and the strength of this precautionary motive in the country. We source our econometric specification from previous studies which we discuss in more detail below. We note again that in our investigation of the household wealth accumulation behavior, we are also concerned with whether conflict interacts with this behavior. For this purpose, our specification needs an extension of the standard specification followed in the past literature.

The data that we use in our analysis are of a cross-sectional nature. Hence, our strategy is to exploit the cross-sectional variation to test whether higher wealth accumulation is associated with higher income uncertainty. To this end, we use the following regression specification: where _Wi_ g _p_ denotes the wealth accumulated by a household _i_ belonging to an occupational group g living in a province _p_ . The precautionary motive dictates that this accumulated wealth rises with an increase in income uncertainty which is captured by _V ARLY_ g. The variable _V ARLY_ g is the variance of the log of income which varies with the occupational group of a household. Using a Deaton (1991) type buffer-stock model of wealth accumulation and data from the US, Carroll & Samwick (1998) show that this theoretical measure of uncertainty fits the data relatively well compared to a theoretical measure of relative uncertainty. We follow the same specification. The coefficient β1 is the above specification and hence becomes the first object of interest in our study.

To bring variation in conflict under analysis, we extend the standard specification by introducing a dummy for conflict intensity _CONFp_ . This dummy classifies the Afghan provinces into two groups – high- and low-conflict. Our objective is to check how the precautionary behavior changes with conflict and for this purpose we interact the conflict dummy with the measure of income uncertainty _V ARLY_ g. The parameter β3 captures the variation in wealth accumulation across high- and lowconflict provinces at the same level of income uncertainty. In our benchmark specification, we define a province to be a high-conflict province if the total number of terrorist attacks in the province exceeds that of the median province (189 attacks). We perform several alternative variations around this classification to ensure robustness. We note that it is straightforward to use conflict intensity as a continuous variable compared to constructing a dummy. We prefer the dummy classification because it allows for a simpler interpretation of the coefficient β3.

We complete our specification by controlling for a host of factors that are considered important 10
differences in household wealth accumulation behavior. Most importantly, we directly control for household income _INCi_ to account for any non-precautionary wealth accumulation associated with higher income. In addition, we add controls at the household-level which includes capturing characteristics of household-heads. Specifically, _Xi_ represents this vector of household-level controls which includes the size of the households, the number of male members in each household, the type of the dwelling, and the age, education, marital status, and gender of the heads. Finally, we control for non-conflict variation across provinces by adding province-level fixed effects θ _p_ . This concludes our econometric specification.

We make one final point before discussing our empirical findings. We consider two measures of wealth – one being broader than the other, which serves as the left-hand side variable in the econometric specification. The positive relationship between household wealth and income uncertainty like one being explored using these wealth measures follows from a buffer-stock model. Nonetheless, there are concerns with any wealth measure that it fails to account for some sources of wealth that households might use to smooth income shocks. To alleviate such concerns, we consider two supplementary variables – annual household expenditure on nondurable goods and excess of annual income over this non-durable expenditure. The idea behind using these measures is to whether households who face higher income uncertainty also spend less on non-durable goods, or add to their stock of buffer on a periodic basis. If there exist errors with measuring wealth that drive false evidence of precautionary accumulation, it might be indicated by no corresponding differences in the period expenditure and the period addition to the buffer stock across households with different levels of income uncertainty. However, a period adjustment is required only if the households have not yet reached the optimum level of the buffer stock. Hence, these variables require a somewhat stricter test of the precautionary motive.

# **4 Empirical Results**

We now discuss the results of our analysis which we present in the following three subsections. In the first subsection, we document the existence of precautionary wealth in Afghanistan and compare the responsiveness of the two wealth measures to changes in income uncertainty. In the second subsection, we highlight the differences in wealth accumulation behavior of households across the high- and low-conflict provinces. We conclude this section by performing robustness checks on our findings.

11
# **4.1 Income Uncertainty and Wealth Accumulation**

In the first set of regressions, we use a restricted version of the estimating equation (2) to focus on the precautionary wealth accumulation in the country independent of its interaction with conflict, that is, we set β2 = β3 = 0. Table 2 shows the results of this specification for the two wealth measures. First, consider the narrow measure of wealth which corresponds to the ownership of gold, silver, and livestock (GSL). The estimated β1 for the narrow measure are shown in columns (1)–(4). Column (1) corresponds to the case when no controls other than household income are added to the specification. We find that the narrow measure of wealth is positively correlated with the variance of income associated with a household’s occupational classification and the relationship between the two variables is highly significant. The estimates in the next columns correspond to specifications when we add additional controls beginning with householdlevel controls discussed earlier in column (2). In column (3), we further add province-fixed effects whereas in column (4) we add province-level control for the conflict intensity which we proxy by the aggregate number of conflict events that occurred in the province during 1973–2014. We find that the positive correlation between household wealth and variance of income is robust to the inclusion of household- and province-level controls. The relationship also remains highly significant in all cases. Our point estimates indicate that a 1 percent increase in the variance of income is associated with a 2.3 – 3.4 percent increase in the household wealth accumulated in the form of gold, silver, and livestock.

Next, we consider the relationship between income uncertainty and the broader measure of wealth – Non-Housing Wealth, which accounts for saving from the previous year. The specifications in columns (5)–(8) match the specifications in columns (1)–(4). The broader wealth measure endorses the positive and highly significant relationship found earlier. The point estimates when using the broader wealth measure are five to six times smaller compared to the estimates obtained when the narrower measure of wealth is used. A 1 percent increase in the variance of income is associated with an increase of approximately 0.5 percent in household wealth when accounting for savings from the previous year. While differences in data and definitions do not allow for a direct comparison of the coefficients, we note that our estimated coefficients for the broader wealth measure are close to some earlier studies in developed countries (Carroll & Samwick (1998), Fuchs-Schündeln & Schündeln (2005)).

Driven by the fact that we observe large adjustments in the narrow measure of wealth to changes in income uncertainty, we go a step deeper into the constituents of the narrow wealth measure. We compare the responsiveness of wealth accumulated in the form of gold and silver, and livestock separately to changes in income uncertainty. Both these assets can be effective in coping with shocks 12
but there exist fundamental differences between them which might be important when considering the precautionary behavior. For example, livestock not only can be used to smooth consumption in the event of experiencing a shock to income, it also contributes to income (or consumption) every period. Similarly, the demand for jewelry can also stem from non-precautionary motives. There is also a marked difference in how costly it is to preserve the two assets. On the one hand, there are costs associated with the general upkeep of livestock which are not incurred when wealth is accumulated in the form of gold and silver. But, gold and silver are more prone to losses due to theft.

Table 3 reports the results for the two assets separately. Columns (1) – (4) refer to the ownership of gold and silver and columns (5) – (8) show the results when we restrict our attention to wealth held as livestock. We find that the accumulation of both assets increases with an increase in the variance of income associated with a household’s occupational group. Though the relationship is not statistically significant when we only consider wealth accumulated in the form of gold and jewelry. The magnitude of variation in wealth accumulation due to variation in the variance of income is significantly higher for livestock. A 1 percent increase in the variance of income is correlated with 3 – 4 percent increase in the value of livestock owned. In sharp contrast, the ownership of gold and silver only increases by 0.4 – 0.6 percent which is close to the adjustments made for the broader wealth measure. This suggests a relatively higher importance of livestock as a precautionary asset compared to gold and silver.

# **4.1.1 Precautionary Wealth Accumulation across Income Quantiles**

Before moving to analyzing how conflict interacts with income uncertainty, we ask whether the strength of the precautionary motive changes across the income distribution. There are valid reasons why the precautionary motive might vary systematically across the income distribution. For one, the borrowing constraints are likely to be more severe for the low-income households who generally have poor access to both formal and informal credit. As such, it is expected that these households will accumulate larger buffer-stock when faced with similar levels of uncertainty. In order to test this hypothesis, we divide our sample into five income quantiles and estimate the coefficients for each income group for the two wealth measures. For brevity, we discuss only the results when all household-level controls are added jointly with province-level dummies.

Figure 5a shows the estimated coefficients for the five income quantiles when we use the narrow wealth measure as the dependent variable. Income quantile 1 represents the households in the bottom 20 percent of the sample income distribution and income quantile 5 represents the top 20 percent of the households. We find a systematic downward trend in the strength of precautionary 13
wealth accumulation as we move up the income distribution. The coefficients for the bottom two quantiles are not very different from each other and are significantly higher than coefficients obtained at other income quantiles. The point estimates for the upper-income quantiles are away from zero signifying prevalence of precautionary behavior but the estimates are much less precisely estimated. Figure 5b shows the results of the exercise when we consider the broader wealth measure. Like before, we find a similar downward trend in the estimated coefficients with rising income. The point estimates are again not distinct from each other for the bottom two quantiles and are roughly twice in magnitude relative to the aggregate. A point of departure in the case of the broader wealth measure is that the estimated coefficients for the top three income quantiles are close to zero implying no adjustment to household wealth with rising income uncertainty. The findings from this exercise hint that the burden of self-insurance is felt more by the low-income households perhaps because they have worse access to credit channels that they can tap into when exposed to shocks.

# **4.2**

We nowproceedwithoursecondlineofinquirybybringingconflictunderanalysis. Afghanistan is a region heavily influenced by conflict. In the previous section, we find evidence of the precautionary motive in households<sup>′</sup> wealth accumulation. However, the instruments of self-insurance might change with the incidence of war and conflict. To entertain this possibility, we analyze the variation of households’ precautionary wealth in provinces that are more prone to conflict compared to relatively safer areas. Table 4 shows the estimates of coefficients in equation (2) for the two measures of wealth together with constituents of the narrow wealth measure. For brevity, the table presents our results after including all controls at the household and province level. The first row of table 4 shows the result for the low-conflict provinces. The estimates in columns (1) and (4) are comparable to the estimates in columns (3) and (7) of table 2. A 1 percent increase in income variance is associated with 2.3 percent higher accumulation of the narrow wealth measure (GSL) and 0.4 percent higher accumulation of the broader wealth (Non-Housing Wealth). The second row of table 4 shows the marginal impact of conflict on precautionary wealth. The effect of conflict on precautionary wealth accumulation is marginally positive but is not significant. However, we take our analysis one step further. As conflict potentially distorts the availability and relative benefits of assets which are used for self-insurance, we take a deeper look at households’ choices. The central question is whether households in high-conflict provinces use the same instruments for their self-insurance that is used by households in low-conflict provinces. Hence, we decompose the narrow measure into its two constituents – jewelry and livestock.

In our analysis of the conflict, we use a dummy for high- and low-conflict regions. This dummy 14
equals one when the aggregate number of terrorist attacks from the year 1973 to the year 2014 in a province exceeds the number of attacks experienced by the median province.11 Figure 1 shows the average level of livestock- and jewelry-ownership in all the provinces. In almost all high-conflict provinces, households own at least some amount of jewelry. In addition, the average value of jewelry in Kabul, Herat, Ghazni, Khost and, Paktika is higher than other provinces. To have a more formal estimate of the variation of livestock- and jewelry-ownership of households in provinces with different levels of conflict, we estimate regression (2) for livestock and jewelry separately. Columns (2) and (3) of table 4 correspond to the accumulation of jewelry and livestock respectively. A 1 percent increase in the variance of income is associated with a 2.8 percent higher accumulation of livestock in the low-conflict provinces. The accumulation of livestock in high-conflict provinces for a matched change in the variance of income is marginally higher but the difference is not statistically significant. However, there exists a stark difference in the nature of jewelry accumulation across the two province types. While the accumulation of jewelry is negatively correlated with the variance of income in the low-conflict provinces, a 1 percent increase in the variance of income is connected with a 1.5 percent increase in the value of jewelry accumulated in the high-conflict provinces. This outcome translates into the non-precautionary use of gold and silver in low-conflict provinces, as opposed to the high-conflict areas where jewelry is also used as an instrument of self-insurance in addition to livestock.

We speculate on three reasons why jewelry is used as an instrument of self-insurance in the high-conflict regions. A first possibility is that households may prefer keeping gold and silver to livestock due to the risks associated with selling livestock in the centralized markets when the market for jewelry exchange is relatively decentralized and safer. Second, to keep livestock one needs to feed them and provide some care. This livestock care might be relatively more challenging for households in high-conflict regions since both food and health services are scarce. Third, as human lives are more in danger in high-conflict regions, so are the lives of livestock. It is riskier to keep animals compared to jewelry with directed attacks on livestock as evidenced by animal raiding in many countries.

The outcomes after taking conflict into account are significant. In light of this, it is prudent to employ a different definition for the conflict dummy. We repeat the previous analysis using a different threshold for the conflict dummy. In table 5 we show the results when the conflict dummy takes the value of one for a province which experienced more attacks than the mean of attacks across provinces.12 The results of this exercise reinforce the results we report in table 4 where the

> 11Provinces that are considered high-conflict using the median definition are Farah (309), Faryab (220), Ghazni (474), Helmand (862), Herat (408), Kabul (536), Kandahar (537), Khost (380), Kunar (302), Kunduz (255), Nangarhar (537), Paktika (206), Urozgan (294) and, Zabul (230).

> 12Using this alternative measure for the conflict dummy, Farah, Ghazni, Helmand, Herat, Kabul, Kandahar, Khost,

15
median province provided the cut-off for the conflict dummy.

Another concern is that these results might be driven by the provinces with very high levels of conflict. Helmand and Kandahar are the two provinces with highest numbers of terrorist attacks. Therefore, in the next step, we show the results in Table 6 where we run the regressions for the sample after dropping observations from Helmand and Kandahar. The outcome of this table is very similar to the results in Table 4. After omitting the two highest conflict provinces from our samples, we can still confirm our previous results.

In our sample, we have households’ whole primary source of income is from selling livestock or livestock products. Including these households in our final sample, we may be misled by the income motive of these families, they might hold livestock as a source of income and not for their precautionary need. In our final primary analysis, we run the regressions for those households whose source of income is independent of the sale of livestock or its products. In Table 7 we show the results of this set of regressions. We still find that households in relatively low-conflict provinces rely on livestock for their precautionary needs but in the presence of high-conflict, they also build-up a stock of jewelry in addition to livestock as a precaution against income uncertainty.

# **4.3 Robustness Checks**

In this section, we perform some robustness checks on the findings of the previous section. The first concern that we address is related to a fundamental challenge associated with adopting any measure of wealth to study precautionary behavior. It is likely that some forms of wealth that should be included in the measure may be left unaccounted for because of a lack of information or because of the definition of wealth chosen. To address this, we look at the relationship between household non-durable expenditure and income uncertainty. The first four columns of table 8 show the nature of the association between annual non-durable expenditure and income uncertainty. We find that non-durable expenditure of households is negatively and significantly correlated with the variance of income. This essentially confirms that households with higher income uncertainty not only accumulate higher wealth but also spend less on non-durable goods and services. We find that, on average, non-durable households expenditure decreases by 10 basis points with a 1 percent increase in the variance of income. We also consider a measure of saving which we define as the excess of household income over the non-durable expenditures. Consistent with our previous findings, this measure of saving is positively and significantly correlated with the variance of income as seen in columns (5) – (8) of table 8. The point estimates suggest that a 1 percent increase in income Kunar, Nangarhar and, Urozgan are provinces with high-conflict.

16
variance is connected to 12 basis points increase in household saving.

Next, we go back to the concern of livestock ownership being associated with income needs and not necessarily with precautionary motive. Like before, we exclude all households that report some direct livestock activity as one of their three main sources of income and focus on the smaller sample of households. Table 9 shows the results of the exercise with the smaller sample and we find that our results are robust to the exclusion. In fact, the point estimates that we obtain for the smaller sample are higher compared to what we find for the benchmark sample. A 1 percent increase in the variance of income is associated with a 2.8 – 3.8 percent increase in the household wealth accumulated in the form of gold, silver, and livestock compared to a 2.3 – 3.4 percent increase seen in the baseline sample. We also detect an increase in the point estimates for the broader measure of wealth. Table 10 further shows that the previous results on the jewelry and the livestock accumulation separately are also preserved under this specification.

As the last robustness check for the results on precautionary wealth, we exclude households who reside in Helmand and Kandahar which are the two most conflict-prone provinces in the country. Our motivation behind this exercise is to check if the results are driven by the extreme nature of the conflict seen in the two provinces or if the behavior is prevalent in other provinces also. Table 11 reports the outcome after we drop Helmand and Kandahar. The wealth measures still respond to changes in income uncertainty. The point estimates also survive a major correction and remain close to the benchmark case. A 1 percent increase in the variance of income is associated with a 2.4–3.1 percent increase in the accumulation of the narrow measure and a 0.4 – 0.5 percent higher accumulation of the broader measure of wealth. We also find that further decomposing the narrow measure to its constituents yields similar results as those obtained earlier as seen in table 12.

# **5 Conclusion**

Using data from a comprehensive household level survey in Afghanistan, we examine the association between income uncertainty and wealth accumulation. Our analysis yields many expected results. First, we find that the stock of household wealth increases systematically with increasing income uncertainty after controlling for other factors including the level of household income. The elasticity of jewelry and livestock accumulation is particularly high. Second, we also find that households with higher uncertainty also spend less at similar levels of income. Third, the precautionary behavior is most active for the poorest households and decreases as income rises. This last finding suggests that poor households have much lower access to borrowing channels, either formal or informal, which makes self-insurance the dominant instrument of coping with 17
# risks.

Taking the analysis further, we ask whether the precautionary behavior of households varies with respect to how much conflict intensity they are exposed to. Conflict can alter household decisions because real returns on assets are expected to be lower in highly unsafe provinces. To the extent that this reduction in real returns is variant across different asset types, the conflict might influence the nature of wealth accumulation. We find evidence of this phenomenon in Afghanistan. Households in relatively low-conflict provinces rely heavily on livestock in order to iron out shocks and abstain from using jewelry. On the other hand, we find that while livestock remains an instrument of self-insurance in high-conflict regions, households also accumulate a large stock of jewelry as their uncertainty rises. Given a choice to alter their portfolio, households in high-conflict provinces allocate a higher weight to jewelry for which the real returns are preserved better compared to livestock.

The pertinent question though is what policy intervention can aid households to cope with risks in conflict settings. This is a difficult area to explore because of the systemic nature of conflictrelated shocks at the local level. Nonetheless, if conflict shocks are independent across localities in the high-conflict provinces, then it might be feasible to design insurance programs that can diversify conflict shocks over such localities. The use of technology can also help to reduce frictions in interand intra-household risk-sharing mechanisms. If conflict creates barriers to timely remittances from a member or relatives living far when the household experiences adverse shocks, then use of mobile banking can provide an alternative as such transactions are arguably less exposed to conflict shocks. Batista & Vicente (2013) argue that the introduction of mobile money in Mozambique led to a reduction in the vulnerability of households to weather and other shocks in light of increased remittances. Similarly, Blumenstock _et al._ (2016) document evidence of mobile money transfers amongst households separated by large distances for risk-sharing purposes in Rwanda. Of course, the context in Afghanistan in markedly different and extreme care needs to be exercised. For instance, a basic requirement for mobile money to succeed is that the infrastructure surrounding its operation remains robust in times of shocks.

The dynamic nature of the conflict also presents some interesting avenues. Though still nascent, a growing body of literature argues that in certain cases conflict wanes when interventions lead to economic development (Miguel _et al._ (2004), Beath _et al._ (2012), Dube & Vargas (2013)). In this regard, an insurance-based intervention that is infeasible in the short-run may become feasible when dynamic benefits of declining conflict intensity are taken into account. Finally, given the importance of livestock ownership in household precautionary behavior, an insurance program that protects against non-conflict shocks that are idiosyncratic like a predator attack or animal disease 18
will also aid households.13

# **References**

- Batista, Catia, & Vicente, Pedro. 2013. Introducing Mobile Money in Rural Mozambique: Initial Evidence from a Field Experiment. _Nova Africa Center for Business and Economic Development, Working Paper No. 1301_ .

- Beath, Andrew, Fotini, Christia, & Enikolopov, Ruben. 2012. Winning Hearts and Minds through Development? Evidence from a Field Experiment in Afghanistan. _Policy Research Working Paper 6129, World Bank: Washington, DC_ .

- Binswanger, Hans, & McIntire, John. 1987. Behavioral and Material Determinants of Production Relations in Land-Abundant Tropical Agriculture. _Economic Development and Cultural Change_ , **36** (1), 73–99.

- Blumenstock, Joshua E, Eagle, Nathan, & Fafchamps, Marcel. 2016. Airtime Transfers and Mobile Communications: Evidence in the Aftermath of Natural Disasters. _Journal of Development Economics_ , **120** , 157–181.

- Bukurura, Sufian Hemed. 1995. Combating Crime Among the Sukuma and Nyamwezi of WestCentral Tanzania. _Crime, Law and Social Change_ , **24** (3), 257–266.

- Bundervoet, Tom. 2009. Livestock, Land and Political Power: The 1993 Killings in Burundi. _Journal of Peace Research_ , **46** (3), 357–376.

- Carroll, Christopher, & Samwick, Andrew. 1997. The Nature of Precautionary Wealth. _Journal of Monetary Economics_ , **40** (1), 41–71.

- Carroll, Christopher, & Samwick, Andrew. 1998. How Important is Precautionary Saving? _Review of Economics and Statistics_ , **80** (3), 410–419.

- Central Statistics Organization. 2016. Afghanistan Living Conditions Survey 2013-14, National Risk and Vulnerability Assessment. _Kabul, Afghanistan_ .

- Chipchase, Jan, Rolston, Mark, Silver, Cara, & Blumenstock, Joshua. 2013. In the Hands of God: A Study of Risks and Savings in Afghanistan. _Institute for Money, Technology and Financial Inclusion_ .

> 13Wakhan Livestock Depredation Insurance Program is one such pilot intervention in the relatively low-conflict Badakhshan province.

19
- Corbett, Jane. 1988. Famine and Household Coping Strategies. _World Development_ , **16** (9), 1099– 1112.

- Dardanoni, Valentino. 1991. Precautionary Savings Under Income Uncertainty: A Cross-Sectional Analysis. _Applied Economics_ , **23** (1), 153–160.

- Deaton, Angus. 1991. Saving and Liquidity Constraints. _Ecomometrica_ , **59** (5), 221–248.

- Dube, Oeindrila, & Vargas, Juan F. 2013. Commodity Price Shocks and Civil Conflict: Evidence from Colombia. _Review of Economic Studies_ , **80** (4), 1384–1421.

- Fafchamps, Marcel, Udry, Christopher, & Czukas, Katherine. 1998. Drought and Saving in West Africa: Are Livestock a Buffer Stock? _Journal of Development Economics_ , **55** (2), 273–305.

- Fleisher, Michael L. 1998. Cattle Raiding and its Correlates: The Cultural-Ecological Consequences of Market-Oriented Cattle Raiding Among the Kuria of Tanzania. _Human Ecology_ , **26** (4), 547–572.

- Fuchs-Schündeln, Nicola, & Schündeln, Matthias. 2005. Precautionary Savings and Self-Selection: Evidence from the German Reunification ‘Experiment”. _Quarterly Journal of Economics_ , **120** (3), 1085–1120.

- Gray, Sandra, Sundal, Mary, Wiebusch, Brandi, Little, Michael, Leslie, Paul, & Pike, Ivy. 2003. Cattle Raiding, Cultural Survival, and Adaptability of East African Pastoralists. _Current Anthropology_ , **44** (S5), S3–S30.

- Guiso, Luigi, Jappelli, Tullio, & Terlizzese, Daniele. 1992. Earnings Uncertainty and Precautionary Saving. _Journal of Monetary Economics_ , **30** (2), 307–337.

- Jodha, Narpat Singh. 1978. Effectiveness of Farmers’ Adjustments to Risk. _Economic and Political Weekly_ , A38–A48.

- Kazianga, Harounan, & Udry, Christopher. 2006. Consumption Smoothing? Livestock, Insurance and Drought in Rural Burkina Faso. _Journal of Development Economics_ , **79** (2), 413–446.

- Keynes, John Maynard. 1936. General Theory of Employment, Interest and Money. _London: MacMillan_ .

- Kinsey, Bill, Burger, Kees, & Gunning, Jan Willem. 1998. Coping With Drought in Zimbabwe: Survey Evidence on Responses of Rural Households to Risk. _World Development_ , **26** (1), 89–110.

- Loayza, Norman, Schmidt-Hebbel, Klaus, & Servén, Luis. 2000. What Drives Private Saving Across the World? _Review of Economics and Statistics_ , **82** (2), 165–181.

20
- Marshall, Alfred. 1920. Principles of Economics. _London: MacMillan_ .

- McPeak, John. 2004. Contrasting Income Shocks With Asset Shocks: Livestock Sales in Northern Kenya. _Oxford Economic Papers_ , **56** (2), 263–284.

- Miguel, Edward, Satyanath, Shanker, & Sergenti, Ernest. 2004. Economic Shocks and Civil Conflict: An Instrumental Variables Approach. _Journal of Political Economy_ , **112** (4), 725–753.

- Rosenzweig, Mark, & Wolpin, Kenneth. 1993. Credit Market Constraints, Consumption Smoothing, and the Accumulation of Durable Production Assets in Low-Income Countries: Investments in Bullocks in India. _Journal of Political Economy_ , **101** (2), 223–244.

- Skinner, Jonathan. 1988. Risky Income, Life Cycle Consumption, and Precautionary Savings. _Journal of Monetary Economics_ , **22** (2), 237–255.

- START. 2017. Global Terrorism Database [Data File]. _National Consortium for the Study of Terrorism and Responses to Terrorism (START), Retrieved from https://www.start.umd.edu/gtd_ .

- Swinton, Scott M. 1988. Drought Survival Tactics of Subsistence Farmers in Niger. _Human Ecology_ , **16** (2), 123–144.

- Udry, Christopher. 1995. Risk and Saving in Northern Nigeria. _American Economic Review_ , **85** (5), 1287–1300.

- Verpoorten, Marijke. 2009. Household Coping in War-and Peacetime: Cattle Sales in Rwanda, 1991–2001. _Journal of Development Economics_ , **88** (1), 67–86.

- Watts, Michael J. 1983. _Silent Violence: Food, Famine, and Peasantry in Northern Nigeria_ . University of Georgia Press.

- World Bank. 2014. World Development Report 2014: Risk and Opportunity – Managing Risk for Development. _World Bank, Washington D.C._

21
||ck<br>of<br>ck<br>).<br>nal|er||_d)_|
|---|---|---|---|---|
|Defnition|Value of gold & silver & livesto<br>(in log).<br>Income+Expenditure+Value<br>Gold & Silver + Value of Livesto<br>- Debt (in log).<br>Annual expenditure (in log).<br>Annual household income (in log<br>Variance of income of occupatio|group (in log).<br>Total<br>number<br>of<br>incidents<br>p|province from 1973 to 2014|_(continue_|
|Source|ALCS<br>ALCS<br>ALCS<br>ALCS<br>ALCS|GTD|||
|Max<br>iables|14.57<br>15.74<br>14.01<br>15.76<br>2.50|862|||
|Min<br>old Var|0<br>0<br>0<br>7.6<br>0.70|3|||
|SD<br>n Househ|3.62<br>1.39<br>0.66<br>0.75<br>0.30|214.53|||
|Mean<br>Mai|1.35<br>11.26<br>11.36<br>11.43<br>0.81|226.12|||
|Observations|13156<br>8230<br>13156<br>13156<br>84|34|||
|Variable|GSL (log)<br>Non-Housing Wealth (log)<br>Expenditure (log)<br>Income (log)<br>Income Variance (log)|Confict|||

22
|Defnition|household head.<br> level of the household<br>r the gender of household<br>= 1).<br>atus of the household′s<br>elling of the household.<br>members in a household<br>f male members in a<br>(in log).|
|---|---|
||he<br>n <br>fo<br>ale<br> St<br>dw<br>of<br> o<br>ld|
|Level)|Age of t<br>Educatio<br>head.<br>Dummy<br>head (m<br>Marital <br>head.<br>Type of<br>Number<br>(in log).<br>Number <br>househo|
|Source<br>old-Head|ALCS<br>ALCS<br>ALCS<br>ALCS<br>ALCS<br>ALCS<br>ALCS|
|Max<br>Househ|64<br>7<br>1<br>5<br>6<br>3.61<br>3.04|
|Min<br>sehold,|18<br>0<br>0<br>1<br>1<br>0.69<br>0|
|SD<br>ls (Hou|11.02<br>1.37<br>0.07<br>0.55<br>0.85<br>0.38<br>0.40|
|Mean<br>y Contro|39.15<br>0.74<br>0.99<br>1.09<br>1.41<br>2.06<br>1.49|
|Observations<br>Supplementar|13156<br>13156<br>13156<br>13156<br>13156<br>13156<br>13156|
|ariable|tus<br>elling<br>Size (log)<br>Household (log)|
|V|on<br><br>Sta<br>Dw<br>old<br>er|
||ti<br>r<br>al<br>of<br>h<br>p|
||ca<br>de<br>rit<br>e<br>use<br>les|
||Age<br>Edu<br>Gen<br>Ma<br>Typ<br>Ho<br>Ma|

23
|(8)|(-0.53∗∗∗)<br>-(0.13)∗∗∗|(-0.62∗∗∗)|-(0.02)∗∗∗|(-Yes∗∗∗)|(-No∗∗∗)|(-Yes∗∗∗)|-13,156∗∗∗|-0.12∗∗∗<br>wo dependent variables<br>ousehold-head controls<br>s: ∗∗∗p<0.01, ∗∗p<0.05,|
|---|---|---|---|---|---|---|---|---|
|ng Wealth<br>(7)|(-0.40∗∗∗)<br>-(0.13)∗∗∗|(-0.92∗∗∗)|-(0.03)∗∗∗|(-Yes∗∗∗)|(-Yes∗∗∗)|(-No∗∗∗)|-13,156∗∗∗|-0.22∗∗∗<br>theses. The t<br> Household/H<br>fcance level|
|Non-Housi<br>(6)|(-0.55∗∗∗)<br>-(0.13)∗∗∗|(-0.62∗∗∗)|-(0.02)∗∗∗|(-Yes∗∗∗)|(-No∗∗∗)|(-No∗∗∗)|-13,156∗∗∗|-0.11∗∗∗<br>ors are in paren<br>livestock-debt). <br>dwelling. Signi|
|(5)|(-0.42∗∗∗)<br>-(0.13)∗∗∗|(-0.60∗∗∗)|-(0.02)∗∗∗|(-No∗∗∗)|(-No∗∗∗)|(-No∗∗∗)|-13,156∗∗∗|-0.00∗∗∗<br>t standard err<br>gold+silver+<br>d, and type of|
|(4)|(-3.29∗∗∗)<br>-(0.38)∗∗∗|(-0.49∗∗∗)|-(0.07)∗∗∗|(-Yes∗∗∗)|(-No∗∗∗)|(-Yes∗∗∗)|-8,230∗∗∗|-0.27∗∗∗<br>3 = 0. Robus<br>e expenditure+<br>per househol|
|GSL<br>(2)<br>(3)|(-3.35∗∗∗)<br>(-2.33∗∗∗)<br>-(0.38)∗∗∗<br>-(0.40)∗∗∗|(-0.48∗∗∗)<br>(-0.47∗∗∗)|-(0.07)∗∗∗<br>-(0.08)∗∗∗|(-Yes∗∗∗)<br>(-Yes∗∗∗)|(-No∗∗∗)<br>(-Yes∗∗∗)|(-No∗∗∗)<br>(-No∗∗∗)|-8,230∗∗∗<br>-8,230∗∗∗|-0.26∗∗∗<br>-0.34∗∗∗<br>ion (2) after setting β2 = β<br>Wealth (income-non-durabl<br>ehold size, number of males|
|(1)|(-3.16∗∗∗)<br>-(0.31)∗∗∗|(-0.32∗∗∗)|-(0.07)∗∗∗|(-No∗∗∗)|(-No∗∗∗)|(-No∗∗∗)|-8,230∗∗∗|-0.10∗∗∗<br>ents of equat<br>on-Housing<br>cation, hous|
|Variables|Variance of Income (log)|Income (log)||Household Controls|Province Fixed Efects|Confict|Observations|Adjusted R2<br>The table shows the estimated coefci<br>are GSL (gold+silver+livestock) and N<br>include gender, age, marital status, edu<br>∗p<0.1.|

24
|(8)|(-3.95∗∗∗)|-(0.41)∗∗∗|(-1.15∗∗∗)<br>-(0.07)∗∗∗|(-Yes∗∗∗)|(-No∗∗∗)|(-Yes∗∗∗)|-13156∗∗∗|-0.20∗∗∗<br>sehold/Household-head<br>ance levels: ∗∗∗p<0.01,|
|---|---|---|---|---|---|---|---|---|
|Livestock<br>(6)<br>(7)|(-3.95∗∗∗)<br>(-2.91∗∗∗)|-(0.41)∗∗∗<br>-(0.43)∗∗∗|(-1.15∗∗∗)<br>(-0.16∗∗∗)<br>-(0.07)∗∗∗<br>-(0.08)∗∗∗|(-Yes∗∗∗)<br>(-Yes∗∗∗)|(-No∗∗∗)<br>(-Yes∗∗∗)|(-No∗∗∗)<br>(-No∗∗∗)|-13156∗∗∗<br>-13156∗∗∗|-0.20∗∗∗<br>-0.31∗∗∗<br>ors are in parentheses. Hou<br>d type of dwelling. Signifc|
|(5)|(-3.65∗∗∗)|-(0.32)∗∗∗|(-1.12∗∗∗)<br>-(0.07)∗∗∗|(-No∗∗∗)|(-No∗∗∗)|(-No∗∗∗)|-13156∗∗∗|-0.03∗∗∗<br> standard err<br>ousehold, an|
|(4)|(-0.50∗∗∗)|-(0.48)∗∗∗|(-0.79∗∗∗)<br>-(0.06)∗∗∗|(-Yes∗∗∗)|(-No∗∗∗)|(-Yes∗∗∗)|-13156∗∗∗|-0.10∗∗∗<br> = 0. Robust <br>of males per h|
|+ Silver<br>(3)|(-0.39∗∗∗)|-(0.48)∗∗∗|(-0.90∗∗∗)<br>-(0.07)∗∗∗|(-Yes∗∗∗)|(-Yes∗∗∗)|(-No∗∗∗)|-13156∗∗∗|-0.18∗∗∗<br>etting β2 = β3<br>size, number|
|Gold<br>(2)|(-0.51∗∗∗)|-(0.48)∗∗∗|(-0.79∗∗∗)<br>-(0.06)∗∗∗|(-Yes∗∗∗)|(-No∗∗∗)|(-No∗∗∗)|-13156∗∗∗|-0.10∗∗∗<br>tion (2) after s<br>on, household|
|(1)|(-0.65∗∗∗)|-(0.53)∗∗∗|(-1.11∗∗∗)<br>-(0.06)∗∗∗|(-No∗∗∗)|(-No∗∗∗)|(-No∗∗∗)|-13156∗∗∗|-0.05∗∗∗<br>ents of equa<br>tatus, educati|
|Variables|Variance of Income (log)||Income (log)|Household Controls|Province Fixed Efects|Confict|Observations|R-squared<br>The table shows the estimated coefci<br>controls include gender, age, marital s<br>∗∗p<0.05, ∗p<0.1.|

25
|Livestock<br>Non-Housing<br>Wealth|(3)<br>(4)|(-2.77∗∗∗)<br>(-0.37∗∗∗)|-(0.50)∗∗∗<br>-(0.17)∗∗∗|(-0.32∗∗∗)<br>(-0.07∗∗∗)|-(0.87)∗∗∗<br>-(0.26)∗∗∗<br><br>(-2.61∗∗∗)<br>(-0.70∗∗∗)|-(0.80)∗∗∗<br>-(0.21)∗∗∗<br><br>(-0.16∗∗∗)<br>(-0.92∗∗∗)|-(0.08)∗∗∗<br>-(0.03)∗∗∗<br><br>-13156∗∗∗<br>-8230∗∗∗<br>-0.31∗∗∗<br>-0.34∗∗∗<br>usehold/Household-head controls include gender, age,<br>ance levels: ∗∗∗p<0.01, ∗∗p<0.05, ∗p<0.1.|
|---|---|---|---|---|---|---|---|
|Gold &<br>Silver|(2)|)<br>(-0.45∗∗∗)|∗<br>-(0.22)∗∗∗|)<br>(-1.97∗∗∗)|∗<br>-(0.89)∗∗∗<br>)<br>(-0.04∗∗∗)|∗<br>-(0.67)∗∗∗<br>)<br>(-0.90∗∗∗)|∗<br>-(0.07)∗∗∗<br>∗<br>-13156∗∗∗<br>-0.18∗∗∗<br>in parentheses. Ho<br>f dwelling. Signifc|
|GSL|(1)|(-2.27∗∗∗|-(0.48)∗∗|(-0.09∗∗∗|-(0.83)∗∗<br>(-1.09∗∗∗|-(0.79)∗∗<br>(-0.47∗∗∗|-(0.08)∗∗<br>-13156∗∗<br>-0.22∗∗∗<br>dard errors are<br>old, and type o|
|Variables||Variance of Income (log)||Confict×Variance of Income (log)|Confict|Income (log)|Observations<br>R-squared<br>The table shows the estimated coefcients of equation (2). Robust stan<br>marital status, education, household size, number of males per househ|

26
|Gold &<br>Livestock<br>Non-Housing<br>Silver<br>Wealth|(2)<br>(3)<br>(4)|)<br>(-0.42∗∗∗)<br>(-2.73∗∗∗)<br>(-0.38∗∗∗)<br>∗<br>-(0.19)∗∗∗<br>-(0.46)∗∗∗<br>-(0.15)∗∗∗<br>)<br>(-3.16∗∗∗)<br>(-0.66∗∗∗)<br>(-0.09∗∗∗)|∗<br>-(0.97)∗∗∗<br>-(0.99)∗∗∗<br>-(0.29)∗∗∗<br>)<br>(-0.80∗∗∗)<br>(-2.85∗∗∗)<br>(-0.72∗∗∗)|∗<br>-(0.73)∗∗∗<br>-(0.86)∗∗∗<br>-(0.23)∗∗∗<br>)<br>(-0.89∗∗∗)<br>(-0.16∗∗∗)<br>(-0.92∗∗∗)|∗<br>-(0.07)∗∗∗<br>-(0.08)∗∗∗<br>-(0.03)∗∗∗|∗<br>-13156∗∗∗<br>-13156∗∗∗<br>-8230∗∗∗<br>-0.18∗∗∗<br>-0.31∗∗∗<br>-0.34∗∗∗<br> high-confict if it experienced more terrorist attacks from 1973 to 2014<br> Household/Household-head controls include gender, age, marital status,<br>nifcance levels: ∗∗∗p<0.01, ∗∗p<0.05, ∗p<0.1.|
|---|---|---|---|---|---|---|
|GSL|(1)|(-2.25∗∗∗<br>-(0.44)∗∗<br>(-0.22∗∗∗|-(0.96)∗∗<br>(-1.19∗∗∗|-(0.86)∗∗<br>(-0.47∗∗∗|-(0.08)∗∗|-13156∗∗<br>-0.22∗∗∗<br>ct is classifed <br> parentheses. <br>f dwelling. Sig|
|Variables||Variance of Income (log)<br>Confict×Variance of Income (log)|Confict|Income (log)||Observations<br>R-squared<br>The table shows the estimated coefcients of equation (2). A confi<br>compared to the mean of all provinces. Robust standard errors are in<br>education, household size, number of males per household, and type o|

27
|Gold &<br>Livestock<br>Non-Housing|Silver<br>Wealth<br>(2)<br>(3)<br>(4)|(-0.56∗∗∗)<br>(-2.89∗∗∗)<br>(-0.38∗∗∗)|-(0.20)∗∗∗<br>-(0.47)∗∗∗<br>-(0.16)∗∗∗<br>(-2.59∗∗∗)<br>(-0.41∗∗∗)<br>(-0.07∗∗∗)|-(0.95)∗∗∗<br>-(0.93)∗∗∗<br>-(0.27)∗∗∗<br>(-0.43∗∗∗)<br>(-2.65∗∗∗)<br>(-0.69∗∗∗)|-(0.71)∗∗∗<br>-(0.83)∗∗∗<br>-(0.22)∗∗∗<br>(-0.96∗∗∗)<br>(-0.21∗∗∗)<br>(-0.89∗∗∗)|-(0.08)∗∗∗<br>-(0.08)∗∗∗<br>-(0.03)∗∗∗|-12060∗∗∗<br>-12060∗∗∗<br>-7804∗∗∗|-0.18∗∗∗<br>-0.32∗∗∗<br>-0.34∗∗∗<br>igh-confict if it experienced more terrorist attacks from 1973 to 2014<br>elmand and Kandahar which are the two most confict-ridden provinces.<br>e gender, age, marital status, education, household size, number of males<br>1.|
|---|---|---|---|---|---|---|---|---|
|GSL|(1)|(-2.41∗∗∗)|-(0.46)∗∗∗<br>(-0.08∗∗∗)|-(0.88)∗∗∗<br>(-1.09∗∗∗)|-(0.81)∗∗∗<br>(-0.48∗∗∗)|-(0.09)∗∗∗|-12060∗∗∗|-0.21∗∗∗<br>ct is classifed h<br>ds residing in H<br>controls includ<br>∗p<0.05, ∗p<0.|
|Variables||Variance of Income (log)|Confict×Variance of Income (log)|Confict|Income (log)||Observations|R-squared<br>The table shows the estimated coefcients of equation (2). A confi<br>compared to the mean of all provinces. The sample excludes househol<br>Robust standard errors are in parentheses. Household/Household-head<br>per household, and type of dwelling. Signifcance levels: ∗∗∗p<0.01, ∗|

28
|Livestock<br>Non-Housing|Wealth<br>(3)<br>(4)|(-3.32∗∗∗)<br>(-0.61∗∗∗)|-(0.47)∗∗∗<br>-(0.15)∗∗∗<br><br>(-0.29∗∗∗)<br>(-0.04∗∗∗)|-(0.84)∗∗∗<br>-(0.26)∗∗∗<br><br>(-2.91∗∗∗)<br>(-0.75∗∗∗)|-(0.78)∗∗∗<br>-(0.21)∗∗∗<br><br>(-0.24∗∗∗)<br>(-0.96∗∗∗)|-(0.08)∗∗∗<br>-(0.03)∗∗∗|-12364∗∗∗<br>-7484∗∗∗|-0.28∗∗∗<br>-0.29∗∗∗<br>experienced more terrorist attacks from 1973 to 2014<br>h direct links to some livestock activity as one of their<br>controls include gender, age, marital status, education,<br>∗p<0.05, ∗p<0.1.|
|---|---|---|---|---|---|---|---|---|
|Gold &|Silver<br>(2)|)<br>(-0.59∗∗∗)|∗<br>-(0.20)∗∗∗<br>)<br>(-2.46∗∗∗)|∗<br>-(0.95)∗∗∗<br>)<br>(-0.23∗∗∗)|∗<br>-(0.72)∗∗∗<br>)<br>(-0.94∗∗∗)|∗<br>-(0.08)∗∗∗|∗<br>-12364∗∗∗|-0.17∗∗∗<br> high-confict if it <br>t an occupation wit<br>d/Household-head<br>evels: ∗∗∗p<0.01, ∗|
|GSL|(1)|(-2.82∗∗∗|-(0.46)∗∗<br>(-0.11∗∗∗|-(0.82)∗∗<br>(-1.21∗∗∗|-(0.78)∗∗<br>(-0.43∗∗∗|-(0.08)∗∗|-12364∗∗|-0.19∗∗∗<br> is classifed <br>s who repor<br>s. Househol<br>ignifcance l|
|||||||||A confict <br>s household<br>parenthese<br>dwelling. S|
|Variables||Variance of Income (log)|Confict×Variance of Income (log)|Confict|Income (log)||Observations|R-squared<br>The table shows the estimated coefcients of equation (2). <br>compared to the mean of all provinces. The sample exclude<br>three main sources of income. Robust standard errors are in<br>household size, number of males per household, and type of|

29
|Durable Expenditures<br>(7)<br>(8)<br><br>(-0.10∗∗∗)<br>(-0.14∗∗∗)|-(0.08)∗∗∗<br>-(0.07)∗∗∗|(-1.64∗∗∗)<br>(-1.50∗∗∗)|-(0.02)∗∗∗<br>-(0.02)∗∗∗|-Yes∗∗∗<br>-Yes∗∗∗|-Yes∗∗∗<br>-No∗∗∗|-No∗∗∗<br>-Yes∗∗∗|-9123∗∗∗<br>-9123∗∗∗|t variables are annual expenditure and<br>education, household size, number of|
|---|---|---|---|---|---|---|---|---|
|_less_Non-<br>(6)<br>(-0.15∗∗∗)|-(0.07)∗∗∗|(-1.50∗∗∗)|-(0.02)∗∗∗|-Yes∗∗∗|-No∗∗∗|-No∗∗∗|-9123∗∗∗|o dependen<br>rital status,|
|Income <br>(5)<br>(-0.12∗∗∗)|-(0.07)∗∗∗|(-1.37∗∗∗)|-(0.01)∗∗∗|-No∗∗∗|-No∗∗∗|-No∗∗∗|-9123∗∗∗|theses. The tw<br>ender, age, ma<br>|
|(4)<br>(-0.14∗∗∗)|-(0.04)∗∗∗|(-0.40∗∗∗)|-(0.01)∗∗∗|-Yes∗∗∗|-No∗∗∗|-Yes∗∗∗|-13156∗∗∗|are in paren<br>ols include g<br>.05, ∗p<0.1.|
|res<br>)||)|||||∗|ors<br>ontr<br>p<0|
|Expenditu<br>(3)<br>(--0.04∗∗∗|-(0.04)∗∗∗|(-0.33∗∗∗|-(0.01)∗∗∗|-Yes∗∗∗|-Yes∗∗∗|-No∗∗∗|-13156∗∗|standard err<br>old-head c<br>p<0.01, ∗∗|
|Non-Durable<br>(2)<br><br>(-0.14∗∗∗)|-(0.04)∗∗∗|(-0.40∗∗∗)|-(0.01)∗∗∗|-Yes∗∗∗|-No∗∗∗|-No∗∗∗|-13156∗∗∗|ion (2). Robust<br>usehold/Househ<br>cance levels: ∗∗∗|
|(1)<br>(-0.13∗∗∗)|-(0.05)∗∗∗|(-0.51∗∗∗)|-(0.01)∗∗∗|-No∗∗∗|-No∗∗∗|-No∗∗∗|-13156∗∗∗|ents of equat<br>le goods. Ho<br>ling. Signif|
|Variables<br>Variance of Income (log)||Income (log)||Household Controls|Province FE|Confict|Observations|The table shows the estimated coefci<br>Income_less_expenditure on non-durab<br>males per household, and type of dwel|

30
|(8)|(-0.75∗∗∗)|-(0.13)∗∗∗|(-0.67∗∗∗)|-(0.03)∗∗∗|(-Yes∗∗∗)<br>(-No∗∗∗)|(-Yes∗∗∗)|-7484∗∗∗|-0.20∗∗∗|d+silver+livestock) and<br> occupation with direct<br>ousehold-head controls<br>s: ∗∗∗p<0.01, ∗∗p<0.05,|
|---|---|---|---|---|---|---|---|---|---|
|ng Wealth<br>(7)|(-0.62∗∗∗)|-(0.12)∗∗∗|(-0.96∗∗∗)|-(0.03)∗∗∗|(-Yes∗∗∗)<br>(-Yes∗∗∗)|(-No∗∗∗)|-7484∗∗∗|-0.29∗∗∗|are GSL (gol<br>who report an<br> Household/H<br>ifcance level|
|Non-Housi<br>(6)|)<br>(-0.75∗∗∗)|∗<br>-(0.13)∗∗∗|)<br>(-0.68∗∗∗)|∗<br>-(0.03)∗∗∗|(-Yes∗∗∗)<br><br>(-No∗∗∗)|(-No∗∗∗)|-7484∗∗∗|-0.19∗∗∗|dent variables <br>es households <br>in parentheses. <br>dwelling. Sign|
|(5)|(-0.77∗∗∗|-(0.12)∗∗|(-0.67∗∗∗|-(0.02)∗∗|(-No∗∗∗)<br>(-No∗∗∗)|(-No∗∗∗)|-7484∗∗∗|-0.14∗∗∗|two depen<br>mple exclud<br>errors are<br>and type of|
|(4)|)<br>(-3.76∗∗∗)|∗<br>-(0.37)∗∗∗|)<br>(-0.49∗∗∗)|∗<br>-(0.08)∗∗∗|(-Yes∗∗∗)<br><br>(-No∗∗∗)|(-Yes∗∗∗)|∗<br>-12364∗∗∗|-0.08∗∗∗|= β3 = 0. The<br>-debt). The sa<br> Robust standard<br>per household,|
|SL<br>(3)|(-2.78∗∗∗|-(0.38)∗∗|(-0.43∗∗∗|-(0.08)∗∗|(-Yes∗∗∗)<br>(-Yes∗∗∗)|(-No∗∗∗)|-12364∗∗|-0.19∗∗∗|etting β2 <br>er+livestock<br>of income. <br>er of males|
|G<br>(2)|(-3.76∗∗∗)|-(0.37)∗∗∗|(-0.49∗∗∗)|-(0.08)∗∗∗|(-Yes∗∗∗)<br>(-No∗∗∗)|(-No∗∗∗)|-12364∗∗∗|-0.08∗∗∗|ion (2) after s<br>ure+gold+silv<br>main sources<br>old size, numb|
|(1)|(-3.85∗∗∗)|-(0.34)∗∗∗|(-0.30∗∗∗)|-(0.07)∗∗∗|(-No∗∗∗)<br>(-No∗∗∗)|(-No∗∗∗)|-12364∗∗∗|-0.01∗∗∗|ients of equat<br>rable expendit<br>of their three<br>cation, househ|
|Variables|Variance of Income (log)||Income (log)||Household Controls<br>Province Fixed Efects|Confict|Observations|R-squared|The table shows the estimated coefc<br>Non-Housing Wealth (income-non-du<br>links to some livestock activity as one<br>include gender, age, marital status, edu<br>∗p<0.1.|

31
|stock<br>(7)<br>(8)|(-3.43∗∗∗)<br>(-4.41∗∗∗)|-(0.40)∗∗∗<br>-(0.39)∗∗∗|(-0.24∗∗∗)<br>(-1.20∗∗∗)<br>-(0.08)∗∗∗<br>-(0.07)∗∗∗<br>(-Yes∗∗∗)<br>(-Yes∗∗∗)|(-Yes∗∗∗)<br>(-No∗∗∗)|(-No∗∗∗)<br>(-Yes∗∗∗)|-12364∗∗∗<br>-12364∗∗∗<br>-0.28∗∗∗<br>-0.16∗∗∗<br>report an occupation with direct links<br>old/Household-head controls include<br>levels: ∗∗∗p<0.01, ∗∗p<0.05, ∗p<0.1.|
|---|---|---|---|---|---|---|
|Live<br>(5)<br>(6)|(-4.47∗∗∗)<br>(-4.40∗∗∗)|-(0.35)∗∗∗<br>-(0.39)∗∗∗|(-1.15∗∗∗)<br>(-1.20∗∗∗)<br>-(0.07)∗∗∗<br>-(0.07)∗∗∗<br>(-No∗∗∗)<br>(-Yes∗∗∗)|(-No∗∗∗)<br>(-No∗∗∗)|(-No∗∗∗)<br>(-No∗∗∗)|-12364∗∗∗<br>-12364∗∗∗<br>-0.03∗∗∗<br>-0.16∗∗∗<br>e excludes households who<br>are in parentheses. Househ<br>e of dwelling. Signifcance|
|(4)|(-0.42∗∗∗)|-(0.48)∗∗∗|(-0.83∗∗∗)<br>-(0.07)∗∗∗<br>(-Yes∗∗∗)|(-No∗∗∗)|(-Yes∗∗∗)|-12364∗∗∗<br>-0.10∗∗∗<br>0. The sampl<br>tandard errors<br>ehold, and typ|
|Gold & Silver<br>(2)<br>(3)|(-0.43∗∗∗)<br>(-0.34∗∗∗)|-(0.48)∗∗∗<br>-(0.48)∗∗∗|(-0.83∗∗∗)<br>(-0.95∗∗∗)<br>-(0.07)∗∗∗<br>-(0.08)∗∗∗<br>(-Yes∗∗∗)<br>(-Yes∗∗∗)|(-No∗∗∗)<br>(-Yes∗∗∗)|(-No∗∗∗)<br>(-No∗∗∗)|-12364∗∗∗<br>-12364∗∗∗<br>-0.10∗∗∗<br>-0.17∗∗∗<br>n (2) after settingβ2 = β3 =<br>ources of income. Robust s<br>, number of males per hous|
|(1)|(-0.48∗∗∗)|-(0.53)∗∗∗|(-1.15∗∗∗)<br>-(0.06)∗∗∗<br>(-No∗∗∗)|(-No∗∗∗)|(-No∗∗∗)|-12364∗∗∗<br>-0.05∗∗∗<br>nts of equatio<br>ir three main s<br>household size|
|Variables|Variance of Income (log)||Income (log)<br>Household Controls|Province Fixed Efects|Confict|Observations<br>R-squared<br>The table shows the estimated coefcie<br>to some livestock activity as one of the<br>gender, age, marital status, education,|

32
|<br>(8)|)<br>(-0.49∗∗∗)|-(0.13)∗∗∗|)<br>(-0.64∗∗∗)|-(0.03)∗∗∗<br><br>(-Yes∗∗∗)|(-No∗∗∗)<br>(-Yes∗∗∗)|-7804∗∗∗|-0.27∗∗∗<br>old+silver+livestock) and<br> Helmand and Kandahar<br>clude gender, age, marital<br>p<0.1.|
|---|---|---|---|---|---|---|---|
|ng Wealth<br>(7)|(-0.41∗∗∗|-(0.13)∗∗∗|(-0.89∗∗∗|-(0.03)∗∗∗<br>(-Yes∗∗∗)|(-Yes∗∗∗)<br>(-No∗∗∗)|-7804∗∗∗|-0.34∗∗∗<br>are GSL (g<br> residing in<br>controls in<br>∗p<0.05, ∗|
|Non-Housi<br>(6)|(-0.49∗∗∗)|-(0.13)∗∗∗|(-0.65∗∗∗)|-(0.02)∗∗∗<br>(-Yes∗∗∗)|(-No∗∗∗)<br>(-No∗∗∗)|-7804∗∗∗|-0.26∗∗∗<br>ent variables <br>es households <br>ousehold-head<br>: ∗∗∗p<0.01, ∗|
|(5)|(-0.37∗∗∗)|-(0.13)∗∗∗|(-0.61∗∗∗)|-(0.02)∗∗∗<br>(-No∗∗∗)|(-No∗∗∗)<br>(-No∗∗∗)|-7804∗∗∗|-0.10∗∗∗<br> two depend<br>mple exclud<br>Household/H<br>fcance levels|
|(4)|)<br>(-3.11∗∗∗)|∗<br>-(0.38)∗∗∗|)<br>(-0.37∗∗∗)|∗<br>-(0.08)∗∗∗<br><br>(-Yes∗∗∗)|(-No∗∗∗)<br>(-Yes∗∗∗)|∗<br>-12060∗∗∗|-0.13∗∗∗<br>= β3 = 0. The<br>k-debt). The sa<br>n parentheses. <br>dwelling. Signi|
|SL<br>(3)|(-2.44∗∗∗|-(0.40)∗∗|(-0.48∗∗∗|-(0.09)∗∗<br>(-Yes∗∗∗)|(-Yes∗∗∗)<br>(-No∗∗∗)|-12060∗∗|-0.21∗∗∗<br>etting β2 <br>er+livestoc<br>errors are i<br>and type of|
|G<br>(2)|)<br>(-3.10∗∗∗)|∗<br>-(0.38)∗∗∗|)<br>(-0.36∗∗∗)|∗<br>-(0.08)∗∗∗<br>(-Yes∗∗∗)|(-No∗∗∗)<br>(-No∗∗∗)|∗<br>-12060∗∗∗|-0.12∗∗∗<br>ation (2) after s<br>diture+gold+silv<br>Robust standard<br>per household,|
|(1)|(-3.00∗∗∗|-(0.30)∗∗|(-0.28∗∗∗|-(0.08)∗∗<br>(-No∗∗∗)|(-No∗∗∗)<br>(-No∗∗∗)|-12060∗∗|-0.00∗∗∗<br>ients of equ<br>rable expen<br>provinces. <br>ber of males|
|Variables|Variance of Income (log)||Income (log)|Household Controls|Province Fixed Efects<br>Confict|Observations|R-squared<br>The table shows the estimated coefc<br>Non-Housing Wealth (income-non-du<br>which are the two most confict-ridden<br>status, education, household size, num|

33
|tock<br>(7)<br>(8)|(-3.04∗∗∗)<br>(-3.80∗∗∗)|-(0.43)∗∗∗<br>-(0.41)∗∗∗|(-0.21∗∗∗)<br>(-1.11∗∗∗)<br>-(0.08)∗∗∗<br>-(0.07)∗∗∗<br>(-Yes∗∗∗)<br>(-Yes∗∗∗)|(-Yes∗∗∗)<br>(-No∗∗∗)|(-No∗∗∗)<br>(-Yes∗∗∗)|-12060∗∗∗<br>-12060∗∗∗<br>-0.32∗∗∗<br>-0.23∗∗∗<br>ing in Helmand and Kandahar which<br>ls include gender, age, marital status,<br>5, ∗p<0.1.|
|---|---|---|---|---|---|---|
|Lives<br>(6)|(-3.79∗∗∗)|-(0.41)∗∗∗|(-1.11∗∗∗)<br>-(0.07)∗∗∗<br>(-Yes∗∗∗)|(-No∗∗∗)|(-No∗∗∗)|-12060∗∗∗<br>-0.22∗∗∗<br>useholds resid<br>ld-head contro<br><0.01, ∗∗p<0.0|
|(5)|(-3.64∗∗∗)|-(0.32)∗∗∗|(-1.18∗∗∗)<br>-(0.07)∗∗∗<br>(-No∗∗∗)|(-No∗∗∗)|(-No∗∗∗)|-12060∗∗∗<br>-0.03∗∗∗<br>e excludes ho<br>old/Househo<br>levels: ∗∗∗p|
|(4)|∗)<br>(-0.37∗∗∗)|∗∗<br>-(0.47)∗∗∗|∗)<br>(-0.90∗∗∗)<br>∗∗<br>-(0.07)∗∗∗<br>)<br>(-Yes∗∗∗)|)<br>(-No∗∗∗)|)<br>(-Yes∗∗∗)|∗∗<br>-12060∗∗∗<br>∗<br>-0.10∗∗∗<br>3 =0. The sampl<br>entheses. Househ<br>ling. Signifcance|
|& Silver<br>(3)|)<br>(-0.41∗∗|∗<br>-(0.47)∗|)<br>(-0.97∗∗<br>∗<br>-(0.08)∗<br>)<br>(-Yes∗∗∗|(-Yes∗∗∗|(-No∗∗∗|∗<br>-12060∗<br><br>-0.17∗∗<br>etting β2 = β<br>ors are in par<br>type of dwel|
|Gold<br>(2)|(-0.37∗∗∗|-(0.47)∗∗|(-0.90∗∗∗<br><br>-(0.07)∗∗<br>(-Yes∗∗∗|(-No∗∗∗)|(-No∗∗∗)|-12060∗∗<br>-0.10∗∗∗<br>on (2) after s<br>standard err<br>usehold, and|
|(1)|(-0.45∗∗∗)|-(0.52)∗∗∗|(-1.25∗∗∗)<br>-(0.07)∗∗∗<br>(-No∗∗∗)|(-No∗∗∗)|(-No∗∗∗)|-12060∗∗∗<br>-0.06∗∗∗<br>ents of equati<br>nces. Robust<br>males per ho|
|Variables|Variance of Income (log)||Income (log)<br>Household Controls|Province Fixed Efects|Confict|Observations<br>R-squared<br>The table shows the estimated coefci<br>are the two most confict-ridden provi<br>education, household size, number of|

34
Figure 1: Livestock and Jewelry Ownership Across Provinces <!-- Start of picture text --> Livestock (Afghani) Gold and Silver (Afghani)<br>Annual Household Income and Expenditure Across Provinces<br>Annual Income (Afghani) Annual Expenditure (Afghani)<br>300000<br>200000<br>100000<br>0<br>Kandahar KapisaHelmand DaykundiUrozganPaktyaKabul PanjsherBaghlanParwan Takhar JawzjanKunar Herat Ghazni Balkh LaghmanLogarNimroz Kunduz Khost Wardak Farah SamanganZabul Paktika BamyanGhor Badakhshan Sar−e−Pul Nooristan BaghdisNangaharFaryab<br>400000<br>300000<br>200000<br>100000<br>0<br>DaykundiGhor Wardak Farah BaghlanZabul SamanganBaghdisUrozganTakhar KapisaSar−e−Pul Paktika LaghmanKunar BamyanParwan JawzjanNangaharPanjsherBalkh Nooristan Kunduz PaktyaNimroz Badakhshan LogarKandahar Herat FaryabGhazni Helmand Khost Kabul<br><!-- End of picture text --> Figure 2: Annual Household Income and Expenditure Across Provinces 35
<!-- Start of picture text --> .068<br>Daykundi<br>Wardak<br>Ghor .066<br>Sar−e−Pul Baghlan<br>Farah<br>Samangan Urozgan<br>Bamyan<br>Kunarha<br>Baghdis<br>Kapisa<br>.064<br>Takhar<br>Zabul Jawzjan<br>Faryab<br>Laghman<br>Nangarhar BalkhParwan<br>Paktika Herat .062<br>Nooristan<br>Panjsher Kunduz<br>Nimroz<br>Income Uncertainty and Narrow Wealth<br>Badakhshan Ghazni<br>Paktya Logar Variance of Log Income (log)/Income (log)<br>Kandahar<br>Helmand .06<br>Figure 3: Khost<br>Kabul<br>.058<br>12 10 8 6 4<br>Gold, Silver and Livestock (log)<br><!-- End of picture text --> 36
<!-- Start of picture text --> (67,227] (35,67] (23,35] (11,23] [0,11]<br>(b) 2012<br>Number of Terrorist Attacks across Provinces<br>Figure 4: (380,862] (206,380] (109,206] (67,109] [3,67]<br>(a) 1973 – 2014<br><!-- End of picture text --> 37
<!-- Start of picture text --> 5<br>4<br>3<br>Quantiles<br>(b) Non-Housing Wealth 2<br>1<br>5<br>4<br>Wealth Accumulation by Income Quantiles<br>Figure 5: 3<br>Quantiles<br>(a) GSL<br>2<br>1<br>2 1 0 −1 −2<br>Estimate<br>6 4 2 0 −2<br>Estimate<br><!-- End of picture text --> 38
# **A Appendices**

## **A.1 Estimation of Livestock Value**

The price data for livestock are neither available at the provincial nor available at the national level. While it is possible to get some estimates for a couple of larger livestock types, we are not able to obtain reasonable prices for most other types. As such, we collect prices of livestock from three different sources to aggregate the wealth accumulated in the form of livestock. Table A.1 lists these prices.

Table A.1: Prices of Livestock Used In Estimating Livestock Wealth (2014)

|Income Source|US Dollars<br>(1)|
|---|---|
|Cattle|140|
|Oxen|150|
|Horses|1,500|
|Donkeys|500|
|Camels|1,000|
|Goats|100|
|Sheep|150|
|Chicken|20|
|Other Birds|10|

Sources: Cattle.com, BusinessInsider.Com, Ino.Com.

We note two concerns with the price data. First, we find the price of donkeys to be particularly high – more than three times that of price of cattle and oxen. Second, we find the price of chicken and other birds to be unreasonably higher as well. To check if these prices quantitatively affect our findings, we consider several specifications in which we reduce the price of these three livestock types by a factor of 3 – 5. We observe no major changes in our findings.

## **A.2 Household Income and Occupations**

To estimate the income uncertainty of households, we categorize them according to the occupations from which household income is derived. Table A.2 shows the complete list of occupations in our sample from the 2013-2014 round of the ALCS. As many of the household controls that we use are at the household head level, We exclude those households with heads who were under 18 or over 64 years old, or the household head was retired or unemployed. We also drop those 39
households for which any of the three main sources of income corresponds to non-labor income earning such as pension earnings, rental income, _Zakat_ , or borrowing.

Table A.2: Sources of Income

|Income Source|Frequency<br>(1)|Percent<br>(2)|
|---|---|---|
|Production and sale of feld crops (non-opium)|2780|21.13|
|Production and sale of opium|119|0.90|
|Production and sale of orchard products|371|2.82|
|Agricultural wage labor (non opium)|319|2.42|
|Production and sale of livestock|792|6.02|
|Shepherding wage labor|157|1.19|
|Sewing, embroidery etc.|127|0.97|
|Handicraft work, not classifed elsewhere|51|0.39|
|Food production and processing|124|0.94|
|Mechanics|121|0.92|
|Road/building construction|17|0.13|
|Production work, not classifed elsewhere|48|0.36|
|Teachers|417|3.17|
|Medicine (doctors, nurses, and others)|108|0.82|
|Military|294|2.23|
|Police|501|3.81|
|Ofce work, government|365|2.77|
|Ofce work, non-government|222|1.69|
|NGO, UN and government work not classifed elsewhere|42|0.32|
|Taxi/transport|827|6.28|
|Security|42|0.32|
|Service work, not classifed elsewhere|268|2.04|
|Shop keeping and small business|1419|10.78|
|Street and market sales|144|1.09|
|Trade, not classifed elsewhere|180|1.37|
|Wage labor, not classifed elsewhere|1764|13.41|
|Day labor, not classifed elsewhere|1238|9.41|
|Remittances from migrants|302|2.30|

# **A.3 Dwelling Characteristics**

Though we do not directly control for household wealth in our analysis, we add many dummies for dwelling characteristics which are reported in the survey. Table A.3 reports these dwelling types together with the distribution of households across the dwelling types. Approximately 72 percent 40
of the households report living in a single family home. Yet, more than 5 percent of the households live in fluid temporary settings like tents and shacks.

Table A.3: Dwelling Characteristics and Distribution

||Frequency|Percent|
|---|---|---|
||(1)|(2)|
|Single Family Home|9,471|72.0|
|Part of a Shared House|2,974|22.6|
|Apartment|18|0.1|
|Tent|374|2.8|
|Temporary Shelter/Shack|300|2.3|
|Other|19|0.1|

41