rafmacalaba/data-use-mentions-extended huggingface dataset:
2000s. In the case of Peru, a rapidly rising share of Peru‘s exports goes to China (Figures 11(b)); the share has more than doubled since the 1990s.

**Figures 11(a) and (b):  Chile and Peru in the ―Emerging Pacific Rim‖**

<!-- Start of picture text --> (b) Destination of Peru's Merchandise<br>(a) Destination of Chile's Merchandise<br>Exports (% shares)<br>Exports (% share) 18%<br>25%<br>16%<br>To China<br>To China<br>20% 14%<br>To Japan<br>To Japan 12%<br>15%<br>10%<br>8%<br>10%<br>6%<br>5% 4%<br>2%<br>0%<br>0%<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007<br><!-- End of picture text --> Source: Author‘s calculations based on WITS database Another dimension of the increasing degree of trade integration is increasing specialization across tasks.<sup>12</sup> Understanding trade patterns based on the origin of final products can be misleading, given that the final product generally incorporates value-added that has been imported from a variety of destinations. Measuring the value added or domestic content of exports is fraught with difficulties; however, a variety of new techniques have been developed and new efforts have been made to improve data collection that could deepen our understanding of patterns of trade specialization based on tasks.<sup>13</sup> Much of this literature has focused on China and a few particular high income or emerging market
We do not observe any significant program effects on the availability of periodic markets and irrigation systems in the villages (columns 2 and 4). However, we find positive program effects on the availability of electricity grids (column 3). With respect to schools, the program does not yield statistically significant effects on kindergartens and primary schools (columns 5 and 6), possibly because most communes already have these facilities in place, but it has positive and statistically significant effects on the availability of secondary schools in communes (column 7). Regarding health facilities, we find positive program effects on availability of commune health care centers (column 8). This finding could explain why the program has positive program effects on health care utilization, although its effects on health insurance are not significant.

We also examine the program impact on migration inflows and outflows, as measured by the percentages of in-migrants and out-migrants over the commune population over the past 12 months. However, the results show that the program has no significant effects on the migration flows (columns 9 and 10).

In Table 8, we investigate whether the 30A Program effectively encourages the establishment of firms. To examine this, we use data from the annual Vietnam Enterprise Censuses from 2000 to 2017 and compute the density of firms of different sizes (micro, small, medium, and large) at the district level. Table 8, however, shows that the program does not yield any significant effects on firm density (columns 1 to 5). The concentration of firms is primarily influenced by other factors, such as local infrastructure and the human capital of the population. The lack of a significant effect on firm density partially explains why the program does not have notable impact on local job opportunities and wages.

Finally, we evaluate the program impact on nighttime light intensity, which serves as a proxy for local economic development, but we find no statistically significant program 25
Table 1: Summary of RAIS matching (2019).

|Matched frm<br>|s<br>||
|---|---|---|
|Firms with a valid tax id|1,302|100%|
|Matched|1,067|82%|
|Not matched|235|18%|
|Matched frms by establi|shment|type|
|Public|129|12%|
|Mixed|238|22%|
|Participated|377|35%|
|Zero employment|323|30%|
|Total matched|1,067|100%|

_Note:_ This table summarizes the number of BOS identified through Orbis that we were able to locate in RAIS, using firm identifiers. In the lower panel, the table illustrates the legal nature information in RAIS of firms successfully located in RAIS, as well as the number of firms which are registered as having zero employment during the whole year of 2019. Data from RAIS and Orbis, 2019.

Table 2: Number of firms by year.

|Year|Firms|POEs|BOS|Public|Mixed|Participated|
|---|---|---|---|---|---|---|
|2016|2,531,848|2,529,989|1,859|910|583|366|
|2017|2,518,208|2,516,404|1,804|877|559|368|
|2018|2,501,936|2,500,773|1,163|389|408|366|
|2019|2,494,135|2,493,215|920|243|291|386|
|2020|2,464,407|2,463,514|893|233|290|370|

_Note:_ This table illustrates the total number
than individual predictions. For example, Dellavigna and Pope (2018) find that taking the average of even five experts leads to a large improvement in accuracy over individual forecasts. Otis (2022) uses data from seven randomized experiments and finds that the average of groups of expert predictions does better than individuals at predicting which of two treatments will have larger effects, with only 10 experts needed to produce a 18 percentage point improvement compared to individual-level predictions. Aggregating across respondents then makes the results less sensitive to the idiosyncratic beliefs of any one individual, and can also provide a smoother distribution that is easier to fit a parametric model to (see below). Second, we are primarily interested in obtaining priors for types of users (policymakers, academics, program respondents), since there are typically multiple decision-makers involved in using knowledge.

# **3.4 Aggregation and fitting of elicited priors**

Since respondents all used the same grid of bins to place their stones, we can easily aggregate responses by determining the proportion of all stones that get allocated to different intervals. This gives us an empirical CDF prior for each of the three groups of respondents. However, because we use Markov-Chain Monte-Carlo (MCMC) methods to simulate draws from unknown distributions for our Bayesian analysis, we require PDFs rather than CDFs in order to use these priors in our impact evaluation. We fit prior distributions to the elicited priors using two distinct types of parametric models: skewnormal distributions and finite mixtures of Gaussians with up to 5 components. We then judge the fit of these models by checking the fitted quantiles are close to their empirical counterparts. Full details of this procedure and our registered priors can be found as part of the study’s registration in the AEA registry.

# **3.5 Literature-informed priors**

We complement and compare our elicited priors to priors that are informed by the literature. Since there were no previous studies in the literature that conduct management improvement experiments aimed at improving exports, we can not use a meta-analysis
MALAPIT, H., KOVARIK, C., SPROULE, K., MEINZEN-DICK, R. & QUISUMBING, A. 2015. Instructional guide on the abbreviated women’s empowerment in agriculture index (A-WEAI). _Washington, DC: International Food Policy Research Institute_ .

MALAPIT, H., QUISUMBING, A., MEINZEN-DICK, R., SEYMOUR, G., MARTINEZ, E. M., HECKERT, J., RUBIN, D., VAZ, A., YOUNT, K. M. & PHASE, G. A. A. P. 2019. Development of the project-level Women’s Empowerment in Agriculture Index (pro-WEAI). _World development,_ 122 **,** 675692.

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**Indonesia (SUSENAS, January Panel Module 2005)**

The data are taken from the consumption module of the National Socio-Economic Survey (SUSENAS) administered by Badan Pusat Statistik-BPS Statistics Indonesia. The fieldwork for the survey was carried out between January and March 2005.

The value of all non-purchased items (food only) was about 12 percent of total purchased expenditure. The ratio tended to decline at the upper quintile levels. The survey did not distinguish between purchased and non-purchased values for non-food items except for housing. The data were not deflated because adjustment factors were not available.
# 6. Conclusions

After more than two decades of civil war and conflict, Somalia remains a fragile state subject to conflict and violence. The Federal Government of Somalia aims to provide the political and security conditions for improving the development trajectory of the country and increasing the welfare conditions of its population. The challenge of improving security conditions will be larger in the coming years as countries participating in The African Union Mission in Somalia (AMISOM) are considering whether to withdraw from Somalia.<sup>32</sup> Terrorist groups and their attacks are one of the threats to the government and its stability, representing a risk for the well-being of the population and limiting the capacity of the government and international partners to design and implement effective development policies.

This paper documents the immediate (within a week) impact of terrorist attacks from Al-Shabaab against civilians in Somalia. We combine micro-data from two waves of the Somali High Frequency Survey and employ a difference-in-difference approach comparing outcomes of households exposed to terrorist attacks against households who were not exposed to the incidents, before and after the events. Our estimates are robust to the use of clustered and HAC standard errors, different samples and control groups considered, besides that a similar composition of repeated-cross sections and a falsification test – measuring the impact before the events occurred– support the validity of our empirical strategy. We further confirm the results through an instrumental variables approach, for which we obtain a valid shift-share type of instrument that exploits the spatial variation of incidents and changes in the number of US air/drone attacks against Al-Shabaab.

Our estimates indicate a sizable immediate effect on consumption for households exposed to attacks with a decrease of 33%, mainly driven by a decline in food consumption. For some households, the reduced consumption brings their expenditure level below the poverty line, increasing the share of poor population. Among the poor, the negative effect results in consumption levels further away from the poverty line. The impact on consumption seems to be explained by a smaller share of household members (aged
again, these estimates, labeled “No exclusion restrictions” in Table 10,<sup>38</sup> demonstrate the robustness of the PK results to the exclusion restrictions that RM believe are not credible.<sup>39</sup> To demonstrate that identification in the “no instruments” and “no exclusion restrictions” case does not depend on the Tobit nature of the first-stage equations, we re-estimate these models replacing the Tobit part of the pseudo-log-likelihood function in the first-stage with linear OLS. These estimates are presented in our Table 11. The first column of Table 11 just reproduces the “baseline” PK results of Table 10 and Pitt (2011a). Column (2) of the our Table 11 replaces Tobit with linear OLS with only a slight loss of precision as compared to the model

> 33 To remain consistent with the estimates of Pitt (2011a) and (2011b), we continue to use all three rounds of credit data in estimating the first-stage equation.

> 34 “No Instruments” means that the first-stage equations ((3) and (4)) have no slope terms.

> 35 This looks very much like a Wald estimator as there is a single binary instrument corresponding to each endogenous variable.  Because there are two correlated endogenous variables, the credit effect parameter is not given by the ratio of the difference in the means of the dependent variable over the difference in the means of the endogenous variable for each sub-sample as it is in the single endogenous variable case.

> 36 The log-likelihood function in the “no instruments” case is bimodal.

> 37 There are three villages in a _thana_ (sub-district) in the sample frame.  Using all villages interacted with both female and male choice yields very similar estimates.

> 38 “No exclusion restrictions” means that the second-stage equation contains the full set of slope interactions.

> 39 RM have done something similar in their Table 1 of RM (2011).  They present two sets of estimates, labeled “ _De facto_ ”
In order to solve these inconsistencies, we went through a process of “consolidation” among HS1996, HS2002, and HS2007 classifications.<sup>21</sup> A similar process was used by Schott and Pierce (2012) to concord 10-digit United States Harmonized System codes between 1989 and 2007 and by Wagner and Zahler (2011) to homologate among 6-digit HS1992, HS1996, and HS2002 classifications. The basic principle of consolidation is to identify the HS codes related to each other (e.g., codes that were split or merged with the modifications introduced by the HS2002 or the HS2007) and to replace them with a single code for the entire period. In the example above, this process results in the replacement of codes 030267 and 030268 by the code 030269 from year 2007 onwards. In this way, the products that are represented by these three codes are all included in code 030269 during the entire period.

As a result of this consolidation process 1104 codes are replaced by 402 codes that already exist in the HS lists but whose contents are altered as discussed in Cebeci (2012). Consequently, the number of unique potential HS 6-digit codes in the 1996, 2002, and 2007 classifications of 6065 declines to a final number of 4961 unique potential HS 6- digit codes in the consolidated classification.<sup>22</sup> In the cross-country raw dataset 4767 of those 4961 codes are present.

**e) Exclusion of oil exports:** We eliminate from the cross-country raw dataset observations

in HS Chapter 27 (hydrocarbons such as oil, petroleum, natural gas, coal etc.) given that

> 21 See Cebeci (2012) for the methodology used in the consolidation. The paper along with a list of consolidated codes and concordances are available at http://econ.worldbank.org/exporter-dynamics-database. 22 The number of HS 6-digit codes not affected by the consolidation process is 4559, obtained as the total number of codes 6065 minus the 1104 codes that disappeared and
if recorded electricity consumption is closely tied to overall welfare. This is evidently not true in the case of Pakistan—either because electricity usage patterns are similar across households, or because the current system of metering does not accurately capture consumption. Seasonal fluctuations further distort the functioning of subsidies, exposing poor households to higher per‐unit costs for electricity in the summer, when their consumption is highest, and providing unwarranted assistance to rich households in the winter months. To address these drawbacks with the present system, in this section we look at how targeting can be improved, and how to make energy affordable while encouraging users to stay within the formal system.

# **_Improved targeting_**

It is evident from the electricity consumption analysis that price‐based subsidies are not an effective means of targeting assistance to the households that need it the most. Pakistan already has a demonstrably better targeting mechanism. The NSER contains ‘poverty scores’ for almost all households in the country, effectively ranking them from poorest to richest. The registry, indexed by CNIC number, is already used for targeting in over 30 programs in Pakistan. The NSER allows the government to select any subgroup of the population based on their welfare level; therefore assistance could be directed to any subgroup of the population.

The matching exercise conducted for this paper was in effect a preliminary test of the applicability of this targeting mechanism to electricity subsidies. Using the CNIC records in the DISCO billing databases, NADRA was able to match poverty scores to almost half of the 350,000 households. The match rate is impressive given that the NSER was constructed in 2010 and many households did not have a CNIC at the time. A higher match rate could be achieved if the NSER is updated, an exercise which the Government of Pakistan is currently undertaking. To be sure that records can be matched, households could be required to report in person to the DISCO office with their national ID card and a recent electricity bill. They would identify themselves using the biometrics stored in
# _Number of farms_

The 2015 Population and Housing Census indicate that some 687,805 households are engaged in rice farming (Table 3). Of this total, 62.3 percent are in engaged in the Upland ecological area and 31.6 percent are in the lowland area. Nearly two-thirds of agricultural households grew upland rice while just under a third cultivated lowland rice, which include inland valley swamp (IVS), boliland, mangrove swamp and riverine ecologies (Population and Housing Census, 2015).

**Table 3: Households Engaged in Rice Farming by Region (2015)**

|**Region**|**Upland**|**Rice**|**Lowlan**|**d Rice**|**Total**|
|---|---|---|---|---|---|
||**No.**|**%**|**No.**|**%**|**No.**|
|**_Sierra Leone_**|_456,470_|_62.3_|_231,335_|_31.6_|_687,805_|
|**Eastern**|158,341|21.6|66,904|9.1|225,149|
|**Northern**|187,997|25.7|128,995|17.6|316,992|
|**Southern**|107,796|14.7|33,842|4.6|141,638|
|**Western**|2,336|0.2|1,594|0.2|3,930|

Source: Sierra Leone Population and Housing Census, 2015.

# _Farm Size_

The 2009 National Sustainable Agriculture Development Plan 2010-2030 gives the average farm size as 1.63 hectares, based on the 1985
# **4.4 Post-Separation Transitions**

To further understand the potential drivers behind the higher separation rates of non-white workers following the loss of a non-white peer, we use the matched employer-employee data to track separated workers to their new jobs. For 57.4% of the incumbent workers (both non-white and white) who separate from their current job, we observe a new formal job in the same or the following calendar year. For these workers, we can compare the characteristics of the new and the old job. To investigate whether workers tend to switch to jobs with a higher non-white share, we re-estimate model (1) using the difference between the non-white share in the new job (at the time of joining) and the non-white share in the old job (at the time of separation) as dependent variable.<sup>22</sup> The results, presented in column (1) of Table 4, show that the loss of a non-white vs. white worker in the old job leads non-white workers to transition to a new job with a non-white share that is 3.4 percentage points higher than in the old job. In other words, they counteract the initial decline in the non-white coworker share by moving again to peer groups with a higher nonwhite share. These results align with the hypothesis that preferences for same-race coworkers shape the initial separations of non-white incumbents. Interestingly, we also find a significant positive effect on the change in the non-white share for white incumbents. Considering that these workers separated from their old jobs after experiencing the loss of a non-white vs. white worker, it is likely that we are capturing a subset of white workers who may have a preference for greater racial diversity and who thus also move to jobs with a higher non-white share.

In column (2) of Table 4, we also test whether transitioning workers are willing to accept wage penalties to work again in teams with a higher non-white share. The results show negative but small (and only for
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22
the density of rainfall stations can also help ameliorate basis risk. A second possible improvement to ameliorate liquidity constraints would be to sell policies at harvest time (Duflo et al., 2010) or to combine the product with a short-term loan, or equivalently, originate loans with interest rates that are explicitly state-contingent based on rainfall outcomes, to help alleviate credit constraints.<sup>23</sup> Second, the sensitivity of insurance demand to price underlines the benefits of developing ways to minimize transactions costs and improve product market competition amongst suppliers of rainfall insurance. It also suggests that government subsidies for rainfall insurance, like those now offered in several Indian states (Giné et al., 2010), would be effective in boosting participation, although it is not clear whether such subsidies improve overall welfare.

Third, the importance of trust and a history of positive past insurance payouts suggest that product diffusion through the population may be relatively slow, as the product develops a track record of paying positive returns. A potential design improvement to facilitate learning would be to amend the contract to pay a positive return with sufficient frequency. This needs to be weighed, however, against the fact that the value of the product is largest if payouts are concentrated during the most severe droughts, when marginal utility of consumption is highest.

Finally, findings that households have limited financial literacy and understanding of the product suggest that insurance policies could instead be targeted to groups, such as an entire village, a producer group or a cooperative, rather than to individuals. The insurance purchase decision would be taken by the group management, who are likely more educated and familiar with financial products, and may also be less financially constrained. The group could then decide or pre-arrange how best to allocate funds amongst its members in case of a payout.

Technological advances may improve the product offering, such as the use of satellite foliage coverage data to offer policies based on area crop yields. The degree of innovation already demonstrated by insurance providers, as well as