rafmacalaba/datause-encoder-data huggingface dataset:
difficult to assess how much of the increase in nominal income simply reflects the higher cost of living or changes in the composition of the local population. Second, these studies focus on developed economies such as Australia, Canada, and the United States. Thus, they are not very informative of the local economic effects of resource abundance in less developed economies.

As discussed in the analytical framework, the economic effects at the local level depend on several factors, such as the degree of economic linkages of extractive activities (which determine the size of the local demand shock), substitutability of labor between industries, and labor mobility. These factors are likely to be very context specific.

Recent work has started to fill this gap in the literature. Caselli and Michaels (2013) examine the local economic effect of oil-based fiscal windfall in Brazil. Using data at the municipality level, this paper shows that oil production is associated with an increase in oil royalties paid to local governments, and reported public spending. However, the impact on provision of local public services is minimal. There is no significant improvement in housing quality or quantity, supply of educational or health inputs, or welfare receipts.<sup>19</sup> There is also a negligible effect on household income and population size. The authors interpret these findings as evidence that oil production has not been particularly beneficial to the local population. Instead, circumstantial evidence suggests that the oil revenues were used to fund patronage, rent extraction, and embezzlement by officials. Brollo et al. (2013) provide further support to this interpretation. They also use municipality-level data from Brazil, but focus on all municipalities (not only the recipients of oil royalties). They find that fiscal windfalls are indeed associated with an increase in political corruption.

In the Brazilian case, the most important channel linking local economies to natural resources is through the fiscal windfall associated with oil royalties. This happens because in Brazil the extent of economic linkages between oil companies and local economies is very limited.

Arag ́on and Rud (2013) examine the importance of these economic linkages. They use the case of Yanacocha, a large gold mine in the Peruvian highlands, and exploit a significant increase in the demand for local inputs. Their empirical strategy uses microdata at the household level and a difference-in-differences approach. They find that this increase in backward linkages

> 19 Using the same case but a different methodology, Monteiro and Ferraz (2010) also document that the oil windfall is associated to reported increases in expenses and size of the public sector, but no improvement in public services to the local population.

27
panel B shows the effects of increased production in the survey year. There is no evidence of spillovers in the sense that there is a similar effect in neighboring districts. In fact, most coefficients point in the opposite direction for mining and neighboring districts indicating, if anything, a shift in employment from neighboring districts to the producing ones.

# **8 Conclusions**

Ghana has a long history of gold production and has recently been experiencing its third gold rush, during which annual gold production skyrocketed. It was the first gold rush the country has experienced as an independent nation, and it brings hope of improving the lives of its citizens. Natural resource extraction is often argued to have detrimental effects on countries, however, and the so-called natural resource curse may imply that resource wealth is harmful to social development and inclusive growth. We use rich geocoded data with information on households and mining production over time to evaluate the gold boom at the local and district levels in difference-in-differences analyses.

Men benefit from direct job creation within the mining sector, and women seem to benefit from indirectly generated jobs in the service sector (statistically significant within 10 km from a mine). Women are more likely to earn cash and less likely to work in agriculture after mine openings. We find similar results when we analyze the effects at the district level and when we use production levels instead of openings and closings of mines. We interpret this as there being additional effects of being very close to a mine (within 20 km), beyond the effects from being in a mining district. No spillovers into neighboring districts are detected.

The results are in accordance with the results in Kotsadam and Tolonen (2016), who find similar effects on occupation in mining communities across the whole of Sub-Saharan Africa, and with Aragón and Rud (2013), who find that agricultural productivity in Ghana is reduced by mining production nearby. We find no statistically significant results on wealth and inequality, although the results point toward increases in both. The effects on infrastructure are ambiguous; we cannot detect any better access to flush toilets and radios, and the effects on electricity access are negative. Further decomposing these effects, we learn that migrant households are less likely to have access to electricity (compared with the change among migrant households living further away), whereas nonmigrant households that never moved might gain better access to electricity (compared with the change among nonmigrant households living further away).

35
**Table 11b:** Regression of log per capita food expenditure by treatment – main respondent characteristics

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
|VARIABLES|Model 1|Model 2|Model 3|Model 4|Model 5|Model 6|Model 7|Model 8|Model 9|
|Recall short-list|0.110***|0.096**|0.058|0.071|0.097**|-0.054|0.083|0.030|-0.169|
||(0.038)|(0.039)|(0.087)|(0.068)|(0.049)|(0.057)|(0.056)|(0.064)|(0.127)|
|Recall long-list|0.214***|0.200***|0.283***|0.085|0.200***|0.134**|0.196***|0.145**|0.118|
||(0.041)|(0.041)|(0.074)|(0.077)|(0.047)|(0.056)|(0.057)|(0.063)|(0.118)|
|Recall short-list*female|||0.060||||||0.059|
||||(0.107)||||||(0.105)|
|Recall long-list*female|||-0.113||||||-0.115|
||||(0.088)||||||(0.084)|
|Recall short-||||||||||
|list*employed||||0.062|||||0.092|
|||||(0.078)|||||(0.086)|
|Recall long-||||||||||
|<br>list*employed||||0.182*|||||0.083|
|||||(0.096)|||||(0.093)|
|Recall short-list*non-||||||||||
|wage|||||0.017||||-0.071|
||||||(0.121)||||(0.123)|
|Recall long-list*non-||||||||||
|wage|||||0.004||||-0.067|
||||||(0.104)||||(0.103)|
|Recall short-list*floor||||||||||
|material||||||0.296***|||0.293***|
|||||||(0.074)|||(0.075)|
|Recall long-list*floor||||||||||
|material||||||0.144*|||0.150**|
|||||||(0.074)|||(0.072)|
|Recall short-list*water||||||||||
|(rainy)|||||||0.112||-0.084|
||||||||(0.082)||(0.117)|
|Recall long-list*water||||||||||
|(rainy)|||||||0.087||-0.009|
||||||||(0.087)||(0.103)|
|Recall short-list*water||||||||||
|(dry)||||||||0.175**|0.113|
|||||||||(0.085)|(0.114)|
|Recall long-list*water||||||||||
|<br>(dry)||||||||0.161**|0.096|
|||||||||(0.081)|(0.094)|
|Constant|10.791***|10.651***|10.933***|10.836***|10.758***|10.650***|10.671***|<br>10.672***|<br>10.762***|
||(0.034)|(0.057)|(0.070)|(0.055)|(0.038)|(0.039)|(0.044)|(0.053)|(0.087)|
|Observations|2,118|2,100|2,114|2,110|2,114|2,118|2,118|2,108|2,100|
|R-squared|0.017|0.178|0.038|0.020|0.032|0.135|0.072|0.074|0.189|

Note: Dependent variable is the log of per capita food consumption expenditure. Standard errors in parentheses. _Differences found to be statistically significant are indicated by level:_<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01. The reference category is the 14-day diary. Model 2 includes main respondent and household characteristics as controls, model 9 includes the interaction of all characteristics with treatment dummies.

42
Figure O3: Grocery store price indices by quantile of change in log synthetic benefits per population, 2006-2010 <!-- Start of picture text -->
1.15<br>1.1<br>1.05<br>1 FarmBill ARRA<br>2006m1 2007m7 2009m1 2010m7<br>Year-month<br>Above Median Below Median<br>Price Index<br><!-- End of picture text -->

Notes: This figure plots the revenue-weighted average grocery store price indices by the quantile of change in log synthetic benefits per population for the first and second quantile, respectively. To obtain these quantiles, the total change in the log synthetic benefits per population during the Farm Bill and the ARRA for each state, residualized by control variables, is used to separate the states into quantiles. The first quantile denotes the 24 states with the largest changes in residualized log benefits per population, and the second quantile denotes the 24 states with the smallest changes.

Figure O4: Housing price and unemployment rate by quantile of change in log synthetic benefits per population, 2006-2010 <!-- Start of picture text --> (a) Housing price (b) Unemployment rate<br>1.05 2<br>1<br>1.5<br>.95<br>.9<br>1<br>.85<br>.8 .5<br>2006m1 2007m7 2009m1 2010m7 2006m1 2007m7 2009m1 2010m7<br>Year-month Year-month<br>Above Median Below Median Above Median Below Median<br>Housing price, normalized to 2006m1<br>Unemployment rate, normalized to 2006m1<br><!-- End of picture text --> Notes: This figure plots the housing price and unemployment rate, normalized to 2006m1, by the quantile of change in log synthetic benefits per population for the first and second quantile, respectively. To obtain these quantiles, the total change in the log synthetic per population during the Farm Bill and the ARRA for each state, residualized by control variables, is used to separate the states into quantiles. The 1st quantile denotes the 24 states with the largest changes in residualized log benefits per population, and the 2nd quantile denotes the 24 states with the smallest changes.

86
Additionally, at least two of the following three criteria must hold:

- a. The act must be aimed at attaining a political, economic, religious, or social goal;

- b. There must be evidence of an intention to coerce, intimidate, or convey some other message to a larger audience (or audiences) than the immediate victims;

- c. The action must be outside the context of legitimate warfare activities.

The data set contains dierent variables that characterize each attack by type: assassination, bombing, kidnapping, and others; and by weapons used, target, perpetrator, and consequences. Critically for this paper, the availability of the number of killed and people wounded as a result of each attack allows measuring the level of violence and the exact coordinates of the incidents to match them to household location (Districts, States or Zilas, depending on availability for each country).

Between 1972 and 2016, Bangladesh suered 1,605 terrorist attacks, India 10,978, and Sri Lanka 2,981 (GTD, 2016). Businesses have been the target in 10 percent of cases in India and 5 percent in both Sri Lanka and Bangladesh. Government institutions were targeted in 20 percent of cases in Bangladesh, 15 percent in India, and 12 percent in Sri Lanka. Citizens and their property - which includes public areas, markets, commercial streets, busy intersections and pedestrian malls - were targeted in 25 percent of cases in Bangladesh, 26 percent in India, and 19 percent in Sri Lanka. Transportation was targeted in approximately 8 percent of attacks in all three countries. The rest of the target types include tourists, utilities, and educational institutions, among others. Interestingly, religious sites are not particularly targeted, making up less than 4 percent of terrorist cases in all three countries. Lastly, military targets are important only in Sri Lanka (25 percent, versus 1 percent in Bangladesh and 7 percent in India).

As noted earlier, our identication strategy exploits the geographic variation in terrorist attacks. Figure 3 illustrates the argument using as example Bangladesh and Sri Lanka. In the former, the cumulative number of attacks since 1972 has been scattered throughout the dierent zilas while in the later, terrorist groups have targeted the Sinhalese-populated cities of Colombo and Batticaloa. Therefore, when estimating the eect of the incidence of terrorism on FLFP we compare the administrative units that are not attacked to those attacked after controlling for other relevant explanatory variables.

# 3.2 Labor Force Surveys

FLFP rates and distinct individuals' characteristics are measured using labor force surveys (LFS) that, due to our empirical strategy, we later collapse at dierent administrative divisions depending on the country. Importantly, labor force participation is not measured identically in the dierent country surveys and even within countries through years 7
poverty move away from the symptoms of exclusion to its underlying causes and explains _why_ some inequalities are “durable” or stubborn (World Bank 2013). Simultaneously, even the non-poor may face social exclusion due to a variety of reasons. For example, Dalit<sup>4</sup> entrepreneurs in India employ a range of tactics to hide their identity so that their caste does not prevent them from accessing credit or other markets (World Bank 2011; Jodhka 2010). These entrepreneurs are not necessarily poor, but they are excluded in a number of domains. In short, social identity needs to be salient in any discussion of social inclusion, which is not the same as ending poverty.

If outcomes are equalized, has social inclusion been achieved? Yes and no. While equal outcomes are important, social inclusion is more than that – it is about the type of society we seek to build. It is possible to equalize measurable outcomes – for example in jobs, housing, credit, access to education to name a few areas - but to leave old forms of exclusion intact or even to simultaneously create new forms of social exclusion. Take the example of a hypothetical city that has universal access to housing. Yet, it may be an intensely divided city, segregated along lines of race or ethnicity. In this case, the outcome for access to housing has been equalized, but social exclusion remains intact.

New forms of social exclusion may be created even as good outcomes obtain. For instance, in trying to correct historical differentials in outcomes across say, ethnic groups, countries may err on the other side and penalize groups that were originally at an advantage. Take the case of Tamils in Sri Lanka, who had an initial advantage in access to education and jobs, but over time, some policies, that were intended to improve outcomes for the Sinhalese ended up penalizing Tamils (such as making Sinhalese the official language). In this case, outcomes may have improved for a previously excluded group, but reverse exclusion may also have occurred. In sum, social inclusion is not only about achieving equality; it is also about the process through which equality is achieved.

10
countries provides identical coefficient estimates but with lower standard errors than those reported later in the paper.

In this case, the variable of interest is R&D/Sales measured at the level of the firm. Since for this exercise we are not interested in uncovering industry- and country-level characteristics that might affect a firm’s propensity to innovate, we can safely control for both by including industry/country dummy variables. Since the enterprise surveys for all countries were not implemented in the same year and global economic conditions might affect firm behavior, we also control for survey-year dummies.

To assess the influence of measurement errors, we follow Leamer (1978, chapter 8) by estimating the reverse regression model. In this approach the dependent variable becomes R&D/Sales and the dummy variable for product innovation becomes an explanatory variable. If the innovation variable is measured accurately, whereas the R&D/Sales is measured with error, then the inverse of the estimated coefficient from the reverse regression is the “true” partial correlation between product innovation and R&D/Sales.

The same strategy can be followed for assessing the partial correlation between product innovations and licensing payments. In this case, however, the licensing variable is also dichotomous, but that does not mean that all firms accurately report whether they made some licensing payments.

# 4.b. _Results_

The results from the estimation of equation (5) with the appropriate set of dummy variables are presented under the first column of Table 2. The table reports the marginal coefficients, or the elasticities calculated at the sample mean. The R&D/Sales variable is not statistically significant and the point estimate of the elasticity is negligible. Is this unsatisfactory result due to measurement errors?

The results from the reverse regression model are presented under the second column. In this case, the estimated Tobit coefficient is highly significant. Furthermore, its inverse implies a rather large partial correlation between R&D/Sales and product innovation. The elasticity of the 12
# B. Generation and transmission planning index for the Philippines and comparators, 2015

||**Colombia**|**Peru**|**Philippines**|**Vietnam **|<sup>**International**</sup><br>**benchamrk**|
|---|---|---|---|---|---|
|**Planning and Procurement**|95%|77%|59%|59%|70%|
|**Generation Planning**|86%|43%|71%|71%|56%|
|Countryhas ageneration masterplan|||||94%|
|Countryhas an overall energy plan|||||65%|
|Competent entity is responsible for producing the<br>plan|||||88%|
|Inter-governmental committee oversees the planning<br>unit|||||29%|
|Powergeneration systemplan is mandatory|||||19%|
|Plan leads to timelyinitiation ofprocurement|||||38%|
|Planning process is transparent andparticipatory|||||59%|
|**Transmission Planning**|100%|75%|50%|100%|72%|
|Competent entity is responsible for producing the<br>plan|||||100%|
|Explicitlylinked topowergenerationplans|||||88%|
|Plan is mandatory|||||29%|
|Planning process is transparent andparticipatory|||||71%|

# C. Generation and transmission procurement index for the Philippines and comparators, 2015

||**Colombia**|**Peru**|**Philippines**|**Vietnam **|<sup>**International**</sup><br>**benchamrk**|
|---|---|---|---|---|---|
|**Procurement of Generation**|95%|90%|100%|50%|85%|
|There is a framework forprocurement|||||82%|
|Country allows International competetive bidding or<br>public auctions forprocurement|||||94%|
|Types ofprocurement methods allowed||||||
|_Unsolicited bids_|||||29%|
|_Direct negotiation_|||||47%|
|_International competitive tendering_|||||88%|
|_Public auctions_|||||41%|
|_Stand-alone capacity market_|||||0%|
|Auction design score|0.86|0.71|NAP|NAP|80%|
|_Country usespublic auctionsforprocurement_|||||41%|
|Clear and comprehensive established rules|||_NAP_|_NAP_|100%|
|Crediblepenalties for violatingthe rules|||_NAP_|_NAP_|86%|
|Guarantees andpenalties to ensure timelycompletion|||_NAP_|_NAP_|86%|
|Standard,non-negotiable contracts|||_NAP_|_NAP_|86%|
|Stapled financingterms or risk mitigation instruments|||_NAP_|_NAP_|86%|
|No concerns regarding the transparency and fairness of the<br>auction|||_NAP_|_NAP_|14%|
|Efforts to inform and attract bidders to the auction|||_NAP_|_NAP_|100%|
|**Transmission Procurement**|100%|100%|17%|17%|64%|
|There is a framework for procurement of new<br>transmission lines|||||59%|
|Methods used toprocure new transmission-|||||69%|
|_Competitive tender_|||||65%|
|_Direct negotiation_|||||24%|
|All projects are awarded to the incumbent|||||47%|
|transmission company||||||

Satisfactory result Unsatrifactory result NAP Not applicable NAV Not available 61
Here we use the experimental data to illustrate four things: (1) implementation of the randomized order within MPL and order within binary choice, (2) MSB and NSB in these data, (3) estimation with a scaled random-effects probit, and (4) alternative approaches in the literature to addressing inconsistent choices and interval identification.

**Implementation.** Three different incentivized MPLs were administered as part of a survey and each subject was randomly assigned to receive two out of the three MPLs (N=767).<sup>20</sup> Here, we focus on one of the MPLs, administered to 506 subjects, which elicited a relative preference for receiving a transfer in the form of an electricity voucher or cash. The other two MPL elicited preferences over one voucher vs. receiving two vouchers on two different days, and two vouchers vs. cash. We include results for estimating WTP in all three MPL in Appendix A (see also below). One binary choice within one of the MPLs was drawn for implementation and the payoffs were immediately realized. We discuss some of our implementation choices in more detail in section 4.

The MPL randomized order both within MPL and within choice. We developed a SurveyCTO template and accompanying randomization files that can be used to carry out an MPL with these features and are described in section 4 and in Appendix S2. The survey module uses a visual representation of the options and shows binary choices one at a time. Subjects select their choice by tapping the screen of the tablet (with the help of an enumerator, if needed).

– The structure of the MPL here with descending value difference ∆ _t_ and showing – options in each choice in the same order is shown in Figure 2. The MPL is designed so that the highest possible value that can be obtained in each choice was 100 Rand. By varying the value of the other option, the value difference increases strictly monotonically between binary choices.

**MSB and NSB in our data.** Figure 3 shows the number of “switches” observed in the MPL. Around 30% of subjects always choose electricity or always choose cash. Another 46% of subjects switch exactly once. The remainder – 24% – exhibit MSB.<sup>21</sup> It is apparent that there is a higher share of odd than even switches, suggesting that subjects sometimes switch by mistake and then correct themselves. For example, 8% percent of subjects (33%

> 20Subjects also went through one practice MPL before being presented with real choices.

> 21We code 12 subjects who switch from the option whose value is ascending to the option whose value is descending as switching twice (thus counting the first choice of the option with increasing value as a “switch”). This avoids lumping inconsistent and consistent choices.

23