rafmacalaba/datause-encoder-data huggingface dataset:
# **1 Introduction**

In the last ten years, 528,763 violent events have taken place worldwide, resulting in the loss of 904,881 human lives.<sup>1</sup> This human toll, which does not account for indirect deaths (including from displacement, deprivation, and disease), surpasses the estimated number of battle-deaths from the Iran–Iraq War (1980–1988) or the Afghan Civil War (1978–2002) (Lacina and Gleditsch, 2005). In 35% of these battles, violence against civilians or riots were reported in low-income economies and only 3% in high-income countries. This gap widens further in terms of human toll, with only 1% of total casualties reported in high-income countries, compared to 58% in low-income economies. By 2030, up to two-thirds of the world’s extremely poor people are expected to live in countries affected by violence and conflict (World Bank, 2020). In other words, contexts combining extreme poverty and exposure to conflict may become more common in the upcoming years. While there is little doubt about the damaging consequences of conflict on development (see for example Abadie and Gardeazabal, 2003; Islam et al., 2016), the effect of local economic conditions (i.e. incomes and economic prospects) on the risk of conflict is subject to academic discussion about the explanatory mechanisms and the appropriate empirical strategies to model them (Bazzi and Blattman, 2014; Laville, 2019). Understanding how incomes and economic prospects locally affect the risk of conflict is therefore important in order to provide adequate policy recommendations to low to intermediate income countries concerned about “conflict traps”<sup>2</sup> and exposed to income shocks related to climate change and commodity price disruptions.

Several empirical studies find that low levels of national incomes are robustly correlated with higher risks of internal conflicts (Hegre and Sambanis, 2006; Blattman and Miguel, 2010). However, the direction of the causal relationship is difficult to establish, especially at the infra-national scale. First, there is an endogeneity issue; the long-term negative consequences of insecurity on the economies and institutions could largely influence the direction of the relationship (Corral et al., 2020). Second, aggregated measures of poverty are coarse proxies for the economic constraints faced by individuals, especially in conflict affected areas (Laville, 2019). Third (and corollary), there is a lack of micro data, since obtaining micro evidence through field surveys is limited by obvious security concerns (Axinn et al., 2012). Fourth, other hard to quantify individual considerations might

> 1Source: Authors’ compilation from ACLED for the period between November 18, 2012 and November 18, 2022.

> 2In essence, the idea that civil war begets more civil war (See Collier, 2003).

2
Figure 1: Age of cohort, eligibility for various early childhood education, and project implementation phase for each survey round

|**Year**|**2009**|**2010**|**2011**|**2012**|**2013**|**2014**|**2015**|**2016**|
|---|---|---|---|---|---|---|---|---|
|**Survey**|**Round 1**|**Round 2**|||**Round 3**|||**Round 4**|
|**Project phase**|**Start of**<br>**project**||||**End of**<br>**project**||||
|**1-year-old**<br>**cohort**|**1**|**2**|3|4|**5**|6|7|**8**|
|Type of service<br>child is eligible<br>to enroll in|Children are t<br>enroll in pl|oo young to<br>aygroups|Children ar<br>enroll in p|e eligible to<br>laygroups|Children ar<br>enroll in ki|e eligible to<br>ndergartens|Children<br>enroll in p|are eligible to<br>rimary school|
|**4-year-old**<br>**cohort**|**4**|**5**|6|7|**8**|9|10|11|
|Type of service<br>child is eligible<br>to enroll in|Children are<br>eligible to<br>enroll in<br>playgroups|Children are<br>enroll in kin|eligible to<br>dergartens|Children a<br>enroll in pr|re eligible to<br>imary school|_These childr_<br>_but would h_|_en were no lo_<br>_ave been in p_|_nger surveyed_<br>_rimary school_|

Note: Figure depicts ages of the two cohorts studied and what types of services they are eligible for at each age. Note: Bold numbers denote that the children in that cohort were surveyed at that age/year.

Source: Authors 5
find the p-value on the difference in differences is 0.035 for quality, is 0.096 for inventory and 0.010 for output.<sup>23</sup>

## **_V.E Are the improvements in performance due to Hawthorne effects?_**

Hawthorne effects are named after a series experiments carried out at the Hawthorne Works in the 1920s and 1930s. The results apparently showed that just running experiments and collecting data can improve performance, raising concerns that our results could be spurious.

However, we think this is unlikely, for a series of reasons. First, our control plants also had the consultants on site over a similar period of time as the treatment firms. Both sets of plants got the initial diagnostic period and the follow-up measurement period, with the only difference being the treatment plants also got an intensive consulting during the intermediate 4 month implementation stage while the control plants had briefer, but frequent, visits from the consultants collecting data. The control plants were not told they were in the control group. Hence, it cannot be simply the presence of the consultants or the measurement of performance that generated the improvement in performance. Second, the improvements in performance took time to arise and they arose in quality, inventory and efficiency, where the majority of the management changes took place. Third, these improvements persisted for many months after the implementation period, so are not some temporary phenomena due to increased attention. Finally, the firms themselves also believed these improvements arose from better management practices, which was the motivation for them extensively copying these practices out to their other non-experimental plants (see Figure 2).

# **VI. THE IMPACT OF MANAGEMENT PRACTICES ON ORGANIZATION AND COMPUTERIZATION**

## **_VI.A The impact of management practices on firm organization_**

Although our interventions were never intended to directly change the treatment firms’ organizational design, theory gave us some reason to believe that organizational changes might follow as a result of better management practices due to changes in the information available to decision makers. In recent years a large theoretical literature on the economics of organization has developed dealing with the locus of decision-making within firms. However, this literature

> 23 Formally, we test this by regressing the 20 plant level differences on a 1/0 dummy variable for being a treatment firm, and report the p-value on that dummy, clustering at the parent firm level.

20
SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 16. Hidden costs of selected water utilities, as percentage of revenue**

<!-- Start of picture text --> Khartoum Water Corporation<br>South Darfur Water Corporation<br>Kenya<br>Ethiopia<br>Sudan (Average 2005)<br>Tanzania<br>Uganda<br>0 20 40 60 80 100 120 140 160 180 200<br>Percentage of revenues<br>Losses Underpricing Collection Inefficiencies<br><!-- End of picture text --> _Source:_ Derived from Briceño-Garmendia, Smits, and Foster (2009).

The valuation of these hidden costs helps identify the individual challenges faced by Sudanese utilities. Collection inefficiencies are leading concerns for the South Darfur Water Corporation and the Khartoum Water Corporation. The underlying observable performance indicators are presented in table 11.

**Table 11. Operational indicators for water utilities in Sudan, 2005**

|Utility|Water<br>delivered<br>(million<br>m<sup>3</sup>/year)|System<br>losses (%)|Collection<br>ratio (%)|Average<br>total cost<br>($/m<sup>3</sup>)|Average<br>effective tariff<br>($/m<sup>3</sup>)|Total hidden<br>costs ($ million/year)|Total<br>hidden<br>costs (%<br>revenues )|
|---|---|---|---|---|---|---|---|
|Khartoum Water Corp|250|40|62|0.68|0.59|73|122|
|South Darfur Water Corp|7|49|49|0.89|1.13|3|87|

_Source:_ Derived from Briceño-Garmendia, Smits, and Foster (2009).

_Note:_ For Sudan water delivered (million m<sup>3</sup> /year) and total hidden costs ($/year) are reported as the sum of the utilities; the other indicators are calculated as weighted averages. Average total cost per cubic meter was calculated assuming a unit capital cost of 40 cents.

> <sup>26</sup>
Finally, we considered the combination of the “reform” instrument with it’s interaction with father’s education as a set of instruments<sup>3</sup> (the main set of instruments in some other studies, such as in Denny and Harmon 2000). The problem with this combination of instruments is that the estimates are compromised, as the instrument set is driven by father’s education (see also Card 2001). The estimate of the return to schooling is 9.5 percent, which is very close to the average obtained from hundreds of studies reviewed in Psacharopoulos and Patrinos (2004).

# **5. Conclusion**

We have used a supply-side intervention in the education market in Venezuela capable of generating significant changes in schooling. Using an instrument based on this intervention, we generated estimates of the return to schooling which apply to a subgroup of mainly liquidity constrained individuals. Such estimates may be more interesting (at least from a policy perspective) and easier to identify than the return to the “average” individual. Such LATE estimates are particularly useful if they can be attributed to the subgroup identified in the experiment.

This evidence, using instruments based on the education reform as well as on father’s education, is consistent with most recent (as well as earlier, see Griliches 1977) findings, suggesting that the causal effect of education is as big or bigger than what is suggested by OLS estimates.

> 3 Results can be made available by the authors upon request.

18
using ordinary least squares for the sample period 2001-2013.<sup>6</sup> To allow for correlation among nightlights across cells and over time, we cluster standard errors at the administrative level below the state, which is the municipalities for most countries.<sup>7</sup> Table 1 shows, consistent with previous literature, that hurricanes have considerable short-run negative effects on local economic activity. Column 1 presents the results from the specification in Equation 2. Our estimate of _β_ is negative and statistically significant at the 5 percent level. The size of the coefficient indicates, for example, that nightlights in cells that experience category three hurricane winds ( _f_ = 0 _._ 2, wind speed of 208 km/h) decrease by 0.81 units ( _≈−_ 4 _._ 055 _×_ 0 _._ 2).<sup>8</sup> Since the average nightlights was 5.1 in 2000, this effect roughly corresponds to a 16% decrease in our proxy of local economic activity. The most affected cells in our sample experience an _f_ of 0.23, the average _f_ among impacted cells is 0.004.

Next in column 2, we investigate whether hurricanes have lasting effects on economic activity by introducing a lag of the _fit_ damage index, but find no evidence of effects beyond the year the hurricane occurs. Specifically, the estimated _f_ coefficient is statistically significant and nearly identical to that of column 1, while the lagged coefficient is small and statistically indistinguishable from zero. This result is in line with previous literature which has highlighted that the negative effect of hurricanes on economic activity is relatively short-lived (e.g., Elliott et al., 2015; Del Valle et al., 2018).

Last in column 3, we estimate the impact of hurricanes in storm and non-storm surge prone areas. Specifically, we create dummy variables for each of these areas and we interact these variables with our damage index _f_ . We then include the variables obtained in the previous step in place of _f_ in Equation 2. The resulting coefficients can be interpreted as the effect of hurricanes on nightlights for each of these areas. The coefficient for non-storm surge prone areas is negative, but noisily estimated, we can neither rule out that it is statistically indistinguishable from zero or from the coefficient for storm surge prone areas. By comparison, the coefficient for storm surge prone areas is sharply estimated and clearly indicates that hurricanes have considerable negative effects in this area. Specifically, nightlights in cells in storm surge prone areas that experience category

> 6We exclude the year 2000 from our sample to interpret mangrove width in 2000 as a predetermined covariate in the next section.

> 7We also compute and find very similar results using standard errors clustered at the state level, and spatial heteroscedasticity and autocorrelation consistent errors as described in Conley (1999). The Conley errors are computed with 12 lags and a 500 km bandwidth.

> 8Hurricane wind speeds of category one (f=0.03, wind speed 153 km/h) or two (f=0.09, wind speed 177 km/h) would decrease nightlights by 0.12 and 0.37, respectively.

9
INTRODUCTION 1)xiii Table 20 (continued)

||Stati<br>popul|onary<br>ation||||Statio<br>popula|nary<br>tion|||
|---|---|---|---|---|---|---|---|---|---|
||||Popula-|||||Popula-||
|CC.mtry or economy|Hillions|Ratio<br>to 1985|tion<br>momen<br> <br>**tin**|Year<br> <br>when<br>NRR-1|Country or economy|Millions|Ratio<br>to 1985|tion<br> <br>momen<br> <br>tum|Year<br> <br>when<br>NRR=1|
|Yemen Arab Rep.|84|11.0|1.5|2055|Poland|47|1.3|1.2|1990|
|Yemen, People's Dem. Rep.|<br>10|4.9|1.6|2035|Portugal|9|0.9|1.2|2030|
||||||Romania|29|1.3|1.2|1985|
|Europe and U.S.S.R.|846|1.1|1.1|2030|Spain|-<br>37|1.0|1.2|2030|
||||||Sweden|8|0.9|1.0|2030|
|Albania|6|2.0|1.6|2005|Switzerland|5|0.8|1.0|2030|
|Austria|6|0.8|1.1|2030|United Kingdom|54|1.0|1.1|2030|
|8elgiun|8|0.8|1.1|2030|Yugoslavia|28|1.2|1.2|2030|
|Bulgaria|9|1.0|1.0|2030|Other Europe (a)|0.4|1.4|1.1|1990|
|Chaniel Islands|0.1|0.8|1.0|2030|U.S.S.R.|_372_|1.3|1.2|1995|
|Czechoslovakia|18|1.2|1.1|2030|Oceania|45|1.8|1.3|2040|
|Demnark|4|0.8|1.0|2030||||||
|Firland|4|0.9|1.1|2030|Australia|23|1.5|1.2|2030|
|France|58|1.0|1.2|2030|Fiji|1|1.7|1.6|2005|
|German Dem. Rep.|16|1.0|1.0|2030|French Polynesia|0.4|2.6|1.7|2010|
|Germany, Fed. Rep. of|43|0.7|1.0|2030|Guam|0.2|1.8|1.6|1995|
|Greece|9|0.9|1.1|2030|Kirabati|0.2|2.6|1.4|2020|
|Hungary|10|0.9|1.0|2030|New Caledonia|0.3|1.8|1.5|2000|
|Iceland|0.3|1.4|1.4|2030|New Zealand|4|1.2|1.3|2030|
|ireland|5|1.3|1.4|1990|Pacific Islands|0.5|3.1|1.8|2020|
|Italy|42|0.7|1.1|2030|Papua New Guinea|12|3.4|1.4|2025|
|Luxembourg|0.3|0.8|1.0|2030|Solomon Islands|2|6.3|1.7|2040|
|Malta|0.4|1.0|1.2|2030|Tonga|0.2|2.4|1.6|2015|
|Netherlands|13|0.9|1.1|2030|Vanuatu|1|4.0|1.6|2030|
|Norway|4|1.0|1.1|2030|Western Samoa|0.4|2.6|1.7|2020|
||||||Other h;eronesia (a)|0.03|2.1|1.7|2005|
||||||Other Polynesia (a)|0.2|2.4|1.7|2015|

Note: Poputations of more than 1 miltion have been rounded to the nearest million.

a. For listings of countries in the "other" categories, see Table 18.
_The success of REDD-plus implementation is likely to be built on stakeholder confidence in the ability of the framework to create sufficient and fair incentives for the strategy options to be implemented._ (R-PP Template and Guidelines, p.40).

The guidelines direct countries to discuss benefit-sharing mechanisms and the distribution of carbon payments if applicable. However, many of the country R-PPs focus more on ownership of carbon stocks and land tenure, and do not explicitly address how they will engage local people and communities in REDD contracts, or how contracts by national authorities with individual landowners, or those who otherwise manage the land, will be structured to ensure compliance and incentive compatibility.

For example, the RR-P for the Central African Republic lists reforestation as one of its REDD strategies and acknowledges the potential challenges this poses with regards to land tenure, and thus the need to compensate land owners (CAR R-PP, 2011). However, a framework for what an actual contract would look like to solidify this strategy is still missing from most of the discussions on implementing REDD at this level. Similarly, Colombia identifies agricultural expansion, infrastructure development, and mining as drivers of deforestation in its R-PP and plans to use PES schemes to establish incentives for local people to limit these activities, but it does not include details about how the contracts will be negotiated or what parties will be involved (Colombia R-PP, 2011).

The guidelines for the R-PP address the need to integrate national monitoring systems with sub-national systems, which may include some combination of remote sensing data and ground-based inventories. However, they do not consider the importance of linking accurate and reliable monitoring on the ground with proper incentives for the community members responsible for the monitoring. Personal and community incentive structures need to be taken 8
Study % ID <!-- Start of picture text --> Uganda YOP (4-year) ge<br>'<br>Uganda YOP (9-year) :<br>Ghana microentenprises (3-year) !<br>Sri Lanka SIYB (6-year) —>—<br>Sri Lanka SLMS (5-year) ,<br>Sri Lanka SLMS (10-year)<br>'<br>Nigeria YouWin! (3-year)<br>Nigeria YouWin! (5-year) —_+——_<br>Overall (l-squared = 41.4%, p = 0.103) <><br>'<br>NOTE: Weights are from random effects analysis<br>-.128 0<br><!-- End of picture text --> ES (95% Cl) Weight

-0.077 (-0.128, -0.026) 6.48 -0.038 (-0.105, 0.029) 4.10 -0.073 (-0.043, 0.018) 13.26 0.002 (-0.018, 0.022) 19.99 -0.024 (-0.054, 0.005) 13.57 -0.033 (-0.067, 0.000) 11.72 -0.021 (-0.046,0.003) 16.82 -0.036 (-0.065,-0.007) 14.05 -0.024 (-0.038, -0.009) 100.00

<!-- Start of picture text --> 128<br><!-- End of picture text -->
where et is an expectation error, uncorrelated with information at t −1. Taking logs, the expression for child i is: or, the growth path is determined by the path of user-costs, and a term capturing expectational surprises.

The key issue is how changes in the two types of teacher inputs m<sup>q</sup> t<sup>andμ</sup> t<sup>impacttheoptimal</sup> path of cognitive achievement. To provide a direct measure of the impact of shocks in teacher inputs on the growth path of cognitive achievement, we add a direct measure of the shock in Equation (9). If there is an (unanticipated) shock μt to teacher inputs ex post, then the change in the growth path is given by ln(<sup>TSt</sup> TS<sup>−1+</sup> t−<sup><u>μ</u></sup> 1<sup><u>tFm</u></sup> ), which depends on the relative size of the terms in brackets. In light of the above, our empirical speci fi cation is given by: where TSijkt is the test score of child i with teacher j in school k at time t; μjkt is a measure of a shock to teacher inputs of teacher j in school k; ∆tjkt represents a vector of changes in observable teacher characteristics and ∆Xjkt represents a vector of changes in other variables thought to affect the relative user-cost of boosting cognitive achievement. The more negative is α1, the larger the impact of shocks on test scores. Finally, the error term consists of changes to unobserved teacher characteristics ∆m<sup>q</sup> t<sup>andachild-levelshock�ijkt.</sup>

# 4.1 Identi fi cation Among "Non-Movers"

The identi fi cation assumption implicit in Equation(10) is that cov(μjkt, {∆m<sup>q</sup> t<sup>+εijkt}) = 0, i.e, the</sup> error term in the equation is not correlated with teacher-level shocks. Consider the non-movers sample fi rst. For this sample, ∆m<sup>q</sup> t<sup>= 0sothat</sup> α<sup>�</sup> 1 is identi fi ed if cov(μjkt, �ijkt) = 0, i.e., teacherlevel shocks are orthogonal to student-level shocks. This assumption breaks down if, for instance, the shocks that teachers are susceptible to are covariate with shocks that students face–a drought in a village would likely affect both student and teachers equally, biasing our results away from zero.

There is also an additional speci fi cation error if households are unable to substitute for teaching inputs. In this case, unobserved teacher characteristics still have a persistent effect on student performance and m<sup>q</sup> t<sup>willenterindependentlyintheerrortermofEquation(10)above.While</sup> the Euler model considered here explicitly rules out such persistence (for instance, through lagged10
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contract, meaning that the contract amount for a small project will be relatively high and may be profitable for contractors. At the same time, for a relatively small contract, a number of firms are expected to apply. Therefore, it is reasonable that the competition effect would play a powerful role in curtailing procurement costs of small projects. Figure 17 illustrates two competition effects evaluated at different contract sizes.

Figure 17. Predicted Road Unit Bid by Size and Competition <!-- Start of picture text --> 4.0<br>Median Case where lot length = 49 km<br>3.5 (sample mean).<br>Case where lot length = 10 km.<br>3.0<br>2.5 Avg. number<br>of bidders per<br>2.0 auction = 6.1<br>1.5<br>1.0<br>0.5<br>0.0<br>2 3 4 5 6 7 8 9 10 11 12 13 14 15<br>Number of bidders<br>Source: Author’s calculation.<br>Predicted bid amount per km (million US$)<br><!-- End of picture text --> The estimated bid functions allow to calculate the average unit cost of each project element contained in a standard contract. Except the road case, the above “average package” concept may not be clear enough. By setting irrelevant attributes at zeros, for instance, the cost of installing a 100 MW generator can be estimated at about one million U.S. dollars per MW (Table 8). Obviously, the unit cost is much lower due to economies of scale if a larger turbine is purchased. An installation work of 10 km transmission lines will cost 89 million U.S. dollars. Again, if only 1 km of transmission line is put in place, it will be prohibitively expensive. The unit cost would likely decline dramatically when the bulk of transmission lines are contracted out all together.