rafmacalaba/datause-encoder-data huggingface dataset:
to live in conflict-affected countries by 2030, including Somalia.<sup>4</sup> Therefore, it is important to shed light and improve our understanding on the links between conflict and poverty.

This paper estimates the immediate (within a week) impact of terrorist attacks from Al-Shabaab against civilians in Somalia using micro-data from two waves of the Somali High Frequency Survey (SHFS), combined with geo-tagged information on attacks.<sup>5</sup> We exploit the spatial and time variation of interviews through a difference-in-difference identification strategy that compares outcomes of control and exposed households, before and after terrorist incidents. We also derive a shift-share instrument using changes in the number of US air/drone attacks against Al-Shabaab and employ an instrumental variables approach. We provide evidence to support the validity of our identification strategies and that our estimates are robust to different specifications, samples considered and several sensitivity checks.

Our results suggest that consumption of households exposed to terrorist incidents decreases by 33%, mainly driven by a decline in food consumption. The reduction in consumption increases poverty and the depth of poverty among the poor. The impact on consumption seems to be associated to a smaller share of household members (aged 15 to 50) working and earning income after an attack. In addition, we document that the negative impact on consumption is clustered within a 4 kilometer radius from the incident and has a heterogeneous impact, not affecting households in the top 20% of the consumption distribution. The perception of police competence also worsens as a result of a terrorist incident.

The literature models terrorists as rational actors, with terrorism having large consequences on economic outcomes, besides the loss of life, damage to persons and negative psychological effects.<sup>6</sup> Conflict can also lead to sharp increases in poverty and vulnerability and other adverse outcomes.<sup>7</sup> Our findings are in line with the disruption that could be expected from a terrorist attack. We contribute to the literature on the intersection between poverty and adverse shocks in developing countries, as well as to the policy debate by quantifying the impact of terrorist attacks on consumption and poverty,

> 4 World Bank (2020).

> 5 Somalia has a strong presence of Al-Shabaab, the largest militant organization seeking to control the territory with the goal of establishing an Islamic State based on its interpretation of Shariah Law. The United States Department of State declared Al-Shabaab a foreign terrorist organization in February 2008. The group has engaged in bombings, suicide attacks and armed assaults.

> 6 In Europe alone, the impact of terrorism has been estimated at around €180 billion between 2004 and 2016 (RAND 2018).

> 7 In South Sudan, conflict combined with a macroeconomic crisis brought into poverty an additional 16 percent of the population in a single year (Pape et al. 2018).

3
**-** **<u>Table A4: Effect of Reform on Gender of Holders of Registered Land: Female Co owner or Male Only</u>**

||<br>(1)|<br>(2)|<br>(3)|<br>(4)|<br>(5)|
|---|---|---|---|---|---|
|No. of sporadic parcels (NASpP)|0.221***|**–**0.340***|**–**0.340***|**–**0.316***|**–**0.339***|
||(0.080)|(0.042)|(0.042)|(0.052)|(0.051)|
|No. of systematic parcels<br>(NASystP)|0.732***|0.734***|0.734***|0.733***|0.734***|
||(0.009)|(0.009)|(0.009)|(0.009)|(0.009)|
|NASpP # SyD|0.168|0.072|0.072|0.121|0.072|
||(0.129)|(0.141)|(0.141)|(0.134)|(0.141)|
|NASpP # post-2010||0.657***|0.657***||0.656***|
|||(0.097)|(0.097)||(0.101)|
|NASpP # post-2006||||0.584***<br>(0.088)||
|NASpP # post-2006 & pre-2010|||||-0.001<br>(0.069)|
|No. of obs. (250*250 m blocks)|30,280|30,280|30,280|30,280|30,280|
|R-squared|0.894|0.899|0.899|0.896|0.899|

_Note:_ All regressions are for 250m x 250m blocks in 2000 **–** 19. The dependent variable is the difference between the number of parcels registered in year _t_ with a female co-owner and those with male owners only. NASpP and NASystP are the number of parcels initially registered, whether sporadically or systematically, in year _t_ . SyD is a block-level dummy for systematic initial registration conducted as in Tables 3 and 4. Population density, share of built-up area of the block, block and year fixed effects and a constant are included throughout; coefficients are not reported. The number of observations. is 30,280 (1,514 blocks annually for 20 years) throughout. Standard errors are in parentheses. * p<0.10, ** p<0.05, *** p<0.010.

34
Product markets have also become significantly more integrated, as regional trade increased and product prices became more similar across regions. In the rest of this section, we deal with the various aspects of domestic integration.

# **A. Regional Integration**

New analyses over the last 2 decades of research in economic geography have changed the way we think about location in development (World Bank 2008). Concentration looms large. Disproportionately attractive for firms with scale economies, large markets can effectively lower costs, enhance profits, encourage existing firms to expand, and attract new firms and workers, creating a circular and cumulative reinforcements. Market access and mobility do not lead to balanced growth of all places. As new producers locate close to the existing production, the gap of economic activity between leading and lagging regions widens. Development takes place in waves, where some areas are drawn out of poverty in sequence and are pulled rapidly through the development process. The PRC’s economic activities are no exception; they have increasingly been concentrated in large and middle-sized cities (Chan, Henderson, and Tsui 2008). Indeed, cities with higher administrative ranks are larger, richer, and more productive: GDP per capita is 50% higher in provincial capitals than in other prefecture-level cities. Investment per capita and foreign direct investment (FDI) inflows are four to six times higher for provincial capitals than for prefecture-level cities.

Growth and economic transformation have been accompanied by spatial transformation. Deng Xiaoping, the architect of reforms in the PRC, once said that if the PRC were to become rich, some people would get rich before others. To implement his idea, Deng Xiaoping initially concentrated resources on the country’s coastal areas as launching grounds for connecting the PRC to world markets. Partly as a result, its eastern region (e.g., Beijing, Fujian, Guangdong, Shanghai, and Tianjin) has been growing faster than the central and western regions.

The coastal agglomerations (Bohai and Southeast regions) indeed outperform the rest of the country on nearly every investment climate measure, according to a comprehensive review of the investment climate in 120 cities in the PRC (World Bank 2006). Differences in city characteristics, such as per capita GDP, economic growth, and transport costs alone explain about 33% of the variation in firm productivity. The PRC’s wealthiest regions tend to attract more capital and FDI 4
Policy Research Working Paper 8231

# **Abstract**

Accurately measuring oil production in low-governance contexts is an important task. Many terrorist organizations and insurgencies—including the Islamic State group, also known as ISIL/ISIS or Daesh—tap oil as a revenue source. Understanding spatial and temporal variation in production in their territory can help address such threats by providing near real-time monitoring of their revenue streams, helping to assess long-term economic potential, and informing reconstruction strategies. More broadly, remotely measuring extractive industry activity in conflict-affected areas and other regions without reliable administrative data can support a broad range of public policy decisions and academic research. This paper uses satellite multi-spectral imaging and ground-truth pre-war output data to effectively construct a real-time day-to-day census of oil production in areas controlled by the terrorist group. The estimates of production levels were approximately 56,000 barrels per day (bpd) from July–December 2014, drop to an average of 35,000 bpd throughout 2015, before dropping further to approximately 16,000 bpd in 2016.

This paper is a product of the Poverty and Inequality Team, 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://econ.worldbank.org. The authors may be contacted at abonfield@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
# **Table 9: Networks**

## **Tobit estimates, standard errors in parentheses, p-values in braces**

||**Friendship**<br>**(1)**|**Business**<br>**(2)**<br>**Age**|**Intimate Topic**<br>**(3)**<br>**12-17 at BL**|**Credit/Finance**<br>**(4)**|**Friendship**<br>**(5)**|**Business**<br>**(6)**<br>**Age**|**Intimate Topic**<br>**(7)**<br>**18-25 at BL**|**Credit/Finance**<br>**(8)**|
|---|---|---|---|---|---|---|---|---|
|**Disruption Effect**|-.210|-.093|.026|-.207|-.359|-.348*|-.261|-.683***|
||(.163)|(.195)|(.208)|(.306)|(.248)|(.207)|(.172)|(.264)|
|**Treatment Effect in High Disruption**|.337*|.459**|.199|.579*|.231|.386*|.370**|.939***|
||(.175)|(.189)|(.207)|(.312)|(.243)|(.201)|(.174)|(.262)|
|**Treatment Effect in Low Disruption**|.169*|.104|.046|.048|-.012|.059|-.003|.066|
||(.097)|(.129)|(.113)|(.142)|(.072)|(.104)|(.071)|(.128)|
|**Difference [β3, p-value]**|{.401}|{.131}|{.527}|{.122}|{.341}|{.155}|{.046}|{.003}|
|**Control Mean at BL**|2.17|.724|.803|.876|1.50|.969|.976|.930|
|**Observations**|1,599|1,599|1,599|1,599|1,628|1,628|1,628|1,628|

**Notes:** ***, ** and * denote significance at the 1%, 5%, and 10% levels. The outcome variable in each column is the respondent's network degree for each category. The main results are estimated with an ANCOVA specification. Control variables include: age, PPI score, household size, illiteracy, village size (nr of dwellings), village average PPI score, distances form key facilities (clinic, secondary school and market), a dummy equal to one if the village is a political stronghold (i.e. has a resident paramount and/or section chief), the number of NGOs active within the village before the Ebola outbreak (excluding BRAC), share of Christian households, distance from Freetown and distance from Kailahun. All specifications include dummies for the randomization strata (district) and errors are clustered at the unit of randomization (village).
and farms (Aragon 2021; Zhang, Malikov, and Miao 2024), coupled with limits on long-term adaptatability to heat. Together, these mechanisms would tend to make hotter location persistently poorer. Second, these results are consistent with residential sorting: that is, richer households are more likely to move away from extremely hot locations, leaving the poor behind. For example, Cattaneo and Peri (2016) find that heat is associated with increased migration in higher income countries but decreased migration in lower income countries, and suggest that this is because the poor face stronger financial constaints on migration. Third, poverty may deter investments that have a cooling effect, such as tree planting. For example, the relative absence of tree cover and green spaces is documented to be a major contributor to the greater heat intensity of poorer neighborhoods in U.S. cities (Chakraborty et al. 2019).

The result that households with lower relative wealth index in _urban_ areas are more exposed to flooding is consistent with earlier findings on the urban poor’s exposure to flooding globally and in specific countries like Vietnam (World Bank 2022). It may reflect the residential sorting of richer households into less flood-prone locations (Kim 2012), as well as the direct impacts of asset damage from flooding. The observed relationship between the number of floods and RWI provides evidence on these mechanisms. The result that households with higher relative wealth index in _rural_ areas are more exposed to flooding might reflect an important facet of South Asia’s geographic characteristics and its dependence on agriculture in rural areas: the region’s flood plains are fertile areas and hence productive for agriculture (Banerjee 2010; 2007). The long-term productivity benefits of living in such flood-prone but fertile areas may outweigh the risk of being flooded. The positive relationship between RWI and flooding in rural areas is consistent with the conjecture that higher agricultural fertility of flood plains drives the relationship between floods and RWI in rural South Asia.

There are multiple mechanisms which could explain a negative relationship between nonagricultural firm size and climate shocks such as floods and extreme heat. First, climate shocks reduce productivity. For example, worker productivity in Indian garment factories falls by almost 15 percent on hot days, reducing output (Somanathan et al. 2021). Recurrent exposure to negative productivity shocks from heat or floods may reduce long-term firm growth in the non-agricultral sector. This productivity channel would operate in both urban and rural locations: unlike agricultural-households that may benefit from higher fertility in flood plains, non-agricultural 12
**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.

27
WPS3795 secondary source of repayment, but the lending technology would still be relationship lending if the lending decisions are primarily based on soft information gathered over the course of a relationship.<sup>1</sup>

# **A. Financial Statement Lending**

Financial statement lending is a transactions technology based primarily on the strength of a borrower’s financial statements. There are two requirements for this technology that depend on hard information. First, the borrower must have informative financial statements, such as audited statements prepared by reputable accounting firms according to widely accepted accounting standards such as GAAP. Second, the borrower must have a strong financial condition as reflected in the financial ratios calculated from these statements. The loan contract that arises out of the analysis of these financial statements may reflect a variety of different contracting elements such as collateral and personal guarantees. However, under financial statement lending, the lender views the expected future cash flow of the SME as the primary source of repayment. Financial statement lending, unlike other lending technologies, is reserved for relatively informationally transparent firms.

# **B. Small Business Credit Scoring**

Small business credit scoring is a transactions technology based primarily on hard information about the SME’s owner as well as the firm. The owner information is primarily personal consumer data obtained from consumer credit bureaus. This is combined with data on the SME collected by the financial institution and often from commercial credit bureaus. The data are entered into a loan performance prediction model, which yields a score, or summary statistic for the loan. The models are usually designed for credits up to $250,000, but many institutions use them only for credits up to $100,000. The technology is relatively new – it was not widely used in the U.S. until the mid-1990s.

This technology may be applied to very opaque SMEs, given that much of the information is based on the personal history of the owner, rather than the SME. Consistent with this opacity, recent research finds that this technology is associated with credits under $100,000 that are rated as relatively risky, have high interest rates, and are often located outside of the banks’ local markets (Frame, Padhi, and Woosley 2004,

> 1 In some cases, the technologies may be most efficiently deployed in a particular organizational form or unit that is dedicated to that technology.  The unit may be an entire financial institution (e.g., a leasing company offering only business leases), or a separate department, division, or subsidiary (e.g., asset-based lending department of a commercial bank or finance company).

4
decentralization process. So, we expect a quadratic relationship between level of problems and level of success.

**SectrComposit** = **PC** (V58, V59, V60, V61, V62).  This PC variable measures the composition of the subsectors in the basin—irrigation, industry, domestic, hydropower, environment, which expressed as Y=1 if the sector exist, and N=0 if the sector doesn’t exist in the basin.  We expect that the more sub-sectors are involved in water utilization the more difficult is the decentralization process.

**SectrUseShars** = **PC** (V66, V67, V68, V69, V70).  This PC variable takes into account the distribution of water use shares of the five main water using sectors-irrigation, industry, domestic, hydropower and environment.  We expect that the more skewed the distribution the easier it will be to decentralize the RBO, because of power relations between the sectors.

**SuccObj1** = **PC** (V25, ..., V30).  This PC variables captures the integrated level of success of the three main objectives and the other 25 minor objectives, each measured on a scale of 1-5 with 5 indicating high level of success. _SuccObj1_ will serve as a dependent variable.

**SuccObj2** = **PC** (V25, ..., V27).  This PC variable, measuring the success of only the 3 main objectives, is constructed because we are afraid that there are not enough observations that distinguish among the minor objectives. It is measured on the same scale as _SuccObj1_ . _SuccObj2_ will serve as a dependent variable.

# **Indices created**

**%BgtSpnt** =V184+V185+V186+V187.  This variable measures the share of the budget that is spent in the basin and not returned to external governments.  It is expected that higher values of _BgtSpnt_ are likely to indicate a higher level of success.

**AdminBasnImprv** =V199-V99.  This variable measures the increase in share of administrative responsibilities at basin level.

**AdmnLocalImprv** =V118-V98.  This variable measures the increase in share of administrative responsibilities at local level.

**BgtPrCpta** =V179/V11.  This variable expresses budget per capita in the basin.  It is expected that higher values are associated with likely success of the decentralization process.

**InfrBasnImprv** =V123-V103.  This variable measures the increase in share of infrastructure financing at basin level.

72
In Table 10, we present the results of additional robustness checks using our preferred NB2 model.<sup>11</sup> Exclusion of Russia from the sample (column 2) makes no significant difference to our results. Bootstrapping (column 1) results in larger standard errors than using asymptotic results. Our variables _paved_ave_ and _docs_ are no longer statistically significant at the 10% level, but the other variables of interest remain significant. Specifying fixed effects in line with (4), i.e. by importer-sector, exporter-sector and sector, but imposing constant slope coefficients across sectors, results in a lower (and statistically insignificant) coefficient on _paved_ave_ , but does not result in large changes in the other parameters (column 4). Finally, adding country-pair random effects to (5) to account for possible omission of bilateral trade determinants (cf. Carrère 2006) leads to _paved_ave_ and _docs_ entering with unexpected signs, although both estimated coefficients are statistically insignificant (column 3).<sup>12</sup> _Paved_min_ remains significant at the 5% level, although its magnitude is reduced to 0.25. Overall, we conclude that our main findings are robust, in particular the importance of road infrastructure bottlenecks as captured by _paved_min_ .

Finally, we present results disaggregated by BEC sectors 1-6 (Table 11).<sup>13</sup> As expected, coefficient estimates vary considerably across sectors. This is due to two factors. Firstly, it follows from (3) that the reduced form parameters in (4), (5) and (6) will vary to the extent that the elasticity of substitution varies across sectors. Secondly, trade flows in different sectors may themselves be more or less sensitive to particular factors, due to certain product characteristics such as unit value, perishability and bulk.

> 11 An appendix (available on request) provides additional specification and robustness checks.

> 12 This model converged extremely slowly under BFGS optimization, and required the default tolerances to be relaxed slightly. Combined with the unexpected signs referred to in the text, this suggests that great caution should be exercised in interpreting these results.

> 13 Estimates obtained in this way are equivalent to pooled estimates of (4). We exclude BEC sector 7 (other goods), since the products it groups together are too heterogeneous to allow the drawing of meaningful conclusions.

- 17 -