rafmacalaba/datause-encoder-data huggingface dataset:
MALI’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 9. Evolution of operational indicators associated with EDM (water)**

|Year|Water<br>delivered<br>(millions<br>m<sup>3</sup>/year)|System losses<br>(%)|Collection ratio<br>(%)|Average cost<br>(US$/m<sup>3</sup>)|Average<br>effective tariff<br>(US$/m<sup>3</sup>)|Total hidden<br>costs<br>(US$ millions/year)|Total hidden<br>costs<br>(% revenues )|
|---|---|---|---|---|---|---|---|
|2002|34|36.7|96|1.14|0.34|36|184|
|2003|39|32.1|96|1.14|0.34|38|172|
|2004|43|29.8|96|1.14|0.34|41|156|
|2005|47|26.7|96|1.14|0.34|43|158|
|2006|50|25.4|96|0.90|0.34|32|107|
|2007|53|25.5|96|1.07|0.34|43|124|

Source: Derived from Briceño-Garmendia, Smits, and Foster 2009. Note: Due to lack of data average tariff assumed fixed at 0.34/ US$/m<sup>3</sup> as observed in 2006

# **Challenges**

Despite its operational achievements, EDM has not been able to neutralize the financial drain associated with a lack of fundamental mechanisms of tariff determination and indexation the operational inefficiencies in practice (figure 10). EDM’s hidden costs (box 1) escalated from $36 million annually in 2002 to around $43 million annually in 2007—close to 0.6 percent of Mali’s GDP, primarily driven by underpricing (table 8). As of 2006/7, EDM’s hidden costs were over 100 percent of revenues and were the worst among all other water utilities the region (figure 11). The concession contract provided for quinquennial tariff revisions, but systematic adjustments were never carried out. Mali’s residential water tariffs are substantially lower than those found in other African countries with scarce water resources (table 6) and certainly are not enough to allow for cost recovery. The hidden costs associated with other operational inefficiencies have fallen and remained at relatively low levels.

**Figure 10. Evolution of hidden costs in Mali’s water sector**

<!-- Start of picture text --> 200<br>180<br>160<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>2002 2003 2004 2005 2006 2007<br>underpricing losses collection inefficiencies<br><!-- End of picture text --> Source: Derived from Banerjee, Skilling, and others 2008.

Secondly, institutional arrangement for EDM continues to be a challenge. EDM manages both urban water and electricity services and despite enormous efforts attribution of costs has not been achieved. The 22
Table 8. Impacts of EGPP Participation on Monthly Expenditure

|Log (monthly expenditure)|(1|)|(2|)|(3|)|(|4)|
|---|---|---|---|---|---|---|---|---|
||IV|Heckman|IV|Heckman|IV|Heckman|IV|Heckman|
|||A|. Household e<br>|xpenditure|||||
|Tl|-0.026|-0.046|0.065*|0.044|0.114**|0.077*|0.106***|0.077**|
|ota|(0.046)|(0.044)|(0.034)|(0.032)|(0.047)|(0.045)|(0.036)|(0.034)|
|Fd|-0.137***|-0.158***|-0.032|-0.052*|0.055|0.012|0.055|0.022|
|oo|(0.045)|(0.043)|(0.032)|(0.031)|(0.046)|(0.044)|(0.034)|(0.032)|
|Nfd thl it|-0.031|-0.048|0.019|0.004|0.069|0.040|0.054|0.032|
|on-oo (mony ems)|(0.045)|(0.043)|(0.037)|(0.035)|(0.047)|(0.045)|(0.039)|(0.037)|
|Nfd l i|0.184***|0.157**|0.265***|0.229***|0.240***|0.204***|0.214***|0.179***|
|on-oo (annua tems)|(0.071)|(0.068)|(0.062)|(0.059)|(0.075)|(0.071)|(0.066)|(0.062)|
|Covariates|N|o|Y|es|N|o|Y|es|
|Union-time fixed effects|N|o|N|o|Ye|s|Y|es|
|||B<br>|. Per capita e<br>|xpenditure<br>|||||
|Ttl|0.080**|0.062*|0.037|0.023|0.027|0.012|0.080**|0.058*|
|oa|(0.038)|(0.036)|(0.034)|(0.032)|(0.040)|(0.038)|(0.035)|(0.033)|
|Fd|-0.031|-0.050|-0.059*|-0.073**|-0.031|-0.053|0.028|0.003|
|oo|(0.035)|(0.033)|(0.032)|(0.031)|(0.037)|(0.035)|(0.033)|(0.032)|
|Nfd thl it|0.075*|0.060|-0.008|-0.017|-0.017|-0.025|0.028|0.014|
|on-oo (mony ems)|(0.042)|(0.040)|(0.037)|(0.035)|(0.045)|(0.042)|(0.039)|(0.037)|
||0.290***|0.266***|0.238***|0.208***|0.153**|0.139**|0.188***|0.160***|
|Non-food (annual items)|(0.066)|(0.063)|(0.061)|(0.058)|(0.069)|(0.066)|(0.066)|(0.062)|
|Covariates|N|o|Y|es|N|o|Y|es|
|Union-timefixed effects|N|o|N|o|Ye|s|Y|es|
|Num. of obs<br>Notes: Robust standard errors in p|6,7<br>arentheses. ***|18<br>p<0.01, ** p<0|6,7<br>.05, * p<0.1. T|18<br>he table present|6,7<br>s estimated coe|18<br>fficients of EG|6,<br>PP participatio|718<br>n on various|

Notes: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. The table presents estimated coefficients of EGPP participation on various outcomes using two different approaches and four different specifications.

25
Figure 4.1: INDIA, comparison of the ICA method and other imputation mechanisms for PF variables <!-- Start of picture text --> I. Kernel 1 estimates of output and input densities<br>A. Sales (log) B. Materials (log)<br>0 5 10 15 20 25 5 10 15 20 25<br>log-Sales log-Materials<br>Complete case ICA method Complete case ICA method<br>Random ICA EM alg. [1] Random ICA EM alg. [1]<br>EM alg. [2] EM alg [3] EM alg. [2] EM alg [3]<br>C. Capital stock (log) D. Employment (log)<br>0 5 10 15 20 6 8 10 12 14 16<br>log-Capital log-Employment<br>Complete case ICA method Complete case ICA method<br>Random ICA EM alg. [1] Random ICA EM alg. [1]<br>EM alg. [2] EM alg [3] EM alg. [2] EM alg [3]<br>.25<br>.25<br>.2 .2<br>.15 .15<br>.1 .1<br>Density of Sales<br>Density of Materials<br>.05 .05<br>0 0<br>.4 .5<br>.4<br>.3<br>.3<br>.2<br>.2<br>D e nsity of C ap ital<br>.1 D en sity of Em p loym e n t.1<br>0 0<br><!-- End of picture text --> Notes: 1 Epanechnikov kernel. Each point estimated within a range of 300 values. Source: Authors’ estimations with ICSs data.

Figure 4.2: TURKEY, comparison of the ICA method and other imputation mechanisms for PF variables I. Kernel<sup>1</sup> estimates of output and inputs densities A. Sales (log) B. Materials (log) 55
measurement error in these consumption data is that errors are negatively correlated with the true value of consumption.

Negatively correlated errors matter because they bias regression coefficients even if present in just the dependent variable. Surveyed consumption is often an outcome measure in impact evaluations, so negatively correlated errors may lead to understated impacts. Household consumption is also used as a key explanatory variable in many studies as a proxy for permanent income. Negatively correlated errors in an explanatory variable may bias regression coefficients either toward or away from zero. In contrast, random (classical) errors cause no bias when just in the dependent variable and always attenuate the coefficient on a single error-ridden explanatory variable. Moreover, economists’ main correction for measurement error bias – instrumental variables (IV) – is inconsistent when errors are correlated with true values (Black, Berger and Scott, 2000), while bounding estimates based on reverse regression are unlikely to be effective in practice (Gibson and Kim, 2010).

# **II. Motivation: Effects of Random and Non-random Measurement Errors**

Consider some true (bivariate) model: for an outcome of interest _y_ , an independent variable _x_ , which may be a binary treatment variable, a response coefficient _β_ , and a pure random error, _u_ . Let measurement error cause the observed * value of the dependent variable, _y_ to be related to the true value by: The textbook case of classical measurement error places stringent restrictions on equation (2), specifically that θ = ,0 λ= 1 and _E_ ( _v_ ) = cov( _y_ , _v_ ) = cov( _x_ , _v_ ) = cov( _u_ , _v_ ) = ,0 so that just white noise is added to the true value. In contrast to this widely used assumption, validation poor. But Deaton and Paxson find the most negative effects of household size in their Engel curves for poor countries. Nothing in the current note resolves the puzzle, since there is a significant negative effect of household size with all survey designs. But the prior literature on the effect of measurement error on the food Engel curve (Gibson, 2002; Gibson and Kim, 2007; Ahmed e _t al._ 2010) provides diagnostics for how different types of errors affect the estimated coefficients.

4
<!-- Start of picture text --> 1<br>0.9<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>Median EU at risk of poverty line At risk of poverty line<br>Percentage of population living under<br><!-- End of picture text --> _Source: EUROSTAT, WB staff calculations._ _The role of agriculture in income convergence_ Although the speed of convergence remains low, member states are catching up with each other in terms of income, and at a faster rate for agricultural income. In recent years there has been a reduction in the dispersion of mean incomes between EU member states, or what is referred to as beta convergence in the literature. This means that member states have experienced a convergence in their income level, especially in agricultural income. In particular, agricultural income growth is converging faster than non‐agricultural income growth. This may also indicate a decrease in the agricultural income gap. However, results show that agricultural income is catching up faster with non‐agricultural income in old member states (OMS), compared to the pace for new member states (NMS). This result further highlights the importance of addressing how other policies can contribute towards more inclusive growth, in particular the CAP, which targets agriculture and may help in reducing the difference in convergence rates between member states. The potential of the CAP program in aiding inclusive growth depends on the state of structural transformation where each country finds itself.

**Table 1.** Speed of convergence for different incomes between OMS and NMS

||||OLS|||LSDV||
|---|---|---|---|---|---|---|---|
|||EU27|OMS|NMS|EU27|OMS|NMS|
|Mean total household income|Speed of Convergence,β|0.04%|0.00%|0.05%|0.21%|0.21%|0.24%|
||Half-life of convergence|1863|175913|1340|334|338|290|
|Mean agricultural income|Speed of Convergence,β|0.13%|0.13%|0.08%|0.72%|0.76%|0.65%|
||Half-life of convergence|550|525|903|97|91|107|

Notes: (1) Speed of Convergence is calculated using the coefficients of respective variables of interests; (2) Half-life of convergence is calculated as 0.6931/speed of convergence; (3) OLS columns report results using OLS regressions, while LSDV columns results using fixed effect models; (4) OMS are 14 old member states, while NMS are 13 new member states that joined the EU after 2004; (5) Data Source: EU-SILC, Eurostat (2005-2014) 5
# **4.7 Summary of Differences Between Sectors**

**Table 4.7** below summarizes the differences between the three innovation sectors along a number of key dimensions covered in this study. The asymptotic significance of the non-parametric KruskalWallis H statistic is reported for each variable to show if the observed differences are statistically significant. From the summary table, several interesting observations can be made.

**Table 4.7 Key differences between the three innovative sectors**

||**High-tech**|**KIBS**|**Media/**<br>**creative**|**Asymptotic Significance of**<br>**Kruskal-Wallis H test**<br>**statistic**|
|---|---|---|---|---|
|% of Firms that produce outputs covered by IP law|100.0|60.0|92.9|**.006****|
|% of Firms seeking innovation that can be protected<br>byIP law|87.5|53.3|71.4|.116|
|For firms seeking innovation, Average% of revenue<br>directed toward innovation<sup>4</sup>|12.2|21.0|41.9|**.048****|
|% of Firms that facedMajor Constraints<br> to Seeking<br>Innovations covered byIP Law|81.2|26.7|64.3|**.008****|
|% of Firms that that feel thatinternal<br>changes can<br>helpincrease efforts toproduce IPprotected outputs|62.5|40|57.1|.437|
|% of Firms that that feel that changesoutside the<br>firm<br> can help increase efforts to produce IP protected<br>outputs|81.2|33.3|64.3|**.025****|
|% of Firms that believe effective IP Law Regime is<br>Important in firm|87.5|46.7|71.4|**.051***|
|% of Firms that believe effective IP Law Regime is<br>Important in industry|100.0|66.7|85.7|**.040****|
|% of Firms that feel thatsupply of innovators<br> is a<br>real issue for them|90.6|78.6|92.9|.267|
|% of Firms that feel that there is particular skill or<br>ability whose absence has prevented firm from being<br>more innovative|81.3|69.2|50.0|.195|
|% of Firms that tried tosubstitute<br> for skills that are<br>scarce in Singapore by using people located<br>elsewhere|60.0|35.7|61.5|.318|
|% of Firms that think Singapore is anefficient city<br>from a businesspoint of view<sup>5</sup>|93.8|53.3|92.9|**0.008****|
|% of Firms that rate highly Singapore’s chances of<br>becominganinnovative city<br> within this decade<sup>6</sup>|56.3|33.3|50.0|.430|

* significant at 10%    ** significant at 5%

Firstly, in analyzing the extent to which firms engage in innovations that are protected by IP laws, the KIBS sector appears to be lagging behind the other two sectors, especially the high-tech manufacturing sector. This is seen in the significantly lower proportions of KIBS firms that are currently producing IP protected outputs and that perceive effective IP laws as important to either their industry or their own firms.

> 4 Significance of F statistic using ANOVA is .054, leading to a similar conclusion as when using the Kruskal Wallis test

> 5 Significance of F statistic using ANOVA is .002, leading to the same conclusion as when using the Kruskal Wallis test

> 6 Significance of F statistic using ANOVA is .370, leading to the same conclusion as when using the Kruskal Wallis test

49
# **Table A-12: Results of behavioral model: odds ratios**

|||(1)<br>male|(2)<br>female|
|---|---|---|---|
|VARIABLES|LABELS|informal|informal|
|informal|LABOR MARKET: informal status status: 0 - formal, 1 - informal|.<br>|.<br>|
|||(.)|(.)|
|dag|DEMOGRAPHIC: age in years|<br>1.005|<br>1.034|
|||(0.037)|(0.041)|
|dag_2|DEMOGRAPHIC: age squared|1.000<br>|1.000<br>|
|||(0.000)|(0.000)|
|married|DEMOGRAPHIC: partnered|<br>1.179|<br>0.641*|
|||(0.248)|(0.148)|
|2.dms|DEMOGRAPHIC: Marital (civil) Status = 2, Registered marriage|0.600***<br>|0.922<br>|
|||(0.104)|(0.181)|
|3.dms|DEMOGRAPHIC: Marital (civil) Status = 3, Separated|<br>1.736|<br>0.943|
|||(0.968)|(0.638)|
|4.dms|DEMOGRAPHIC: Marital (civil) Status = 4, Divorced|<br>1.761*<br>|<br>0.800<br>|
|||(0.561)|(0.188)|
|5.dms|DEMOGRAPHIC: Marital (civil) Status = 5, Widowed|0.807<br>0534|0.694<br>0210|
|||(.)|(.)|
||DEMOGRAPHIC: Education: Highest Status = 2, Secondary general(=secondary|||
|2.deh|school diploma)|0.749<br>|0.666*<br>|
|||(0.133)|(0.160)|
||DEMOGRAPHIC: Education: Highest Status = 3, Secondary vocational(=ptu or|||
|3.deh|<br>technikum)|0.694*|0.541**|
|||(0.133)|(0.129)|
|5.deh|DEMOGRAPHIC: Education: Highest Status = 5, Tertuary|<br>0.417***|<br>0.335***|
|||(0.086)|(0.079)|
|2.sector|sector = 2|0.495**<br>|3.881*<br>|
|||(0.136)|(2.962)|
|3.sector|sector = 3|<br>2.009***|<br>9.371***|
|||(0.534)|(7.123)|
|4.sector|sector = 4|<br>0.549*|<br>1.411|
|||(0.170)|(1.068)|
|5.sector|sector = 5|<br>9.212**|<br>46.196***|
|||(8.893)|(67.498)|
|n_child|DEMOGRAPHIC: number of children in the household||<br>1.100|
||||(0.112)|
|n_pens|DEMOGRAPHIC: number of pensioners in the household||<br>0.760**<br>|
||||(0.087)|
|2.drgur02|DEMOGRAPHIC: Region: Type of settlement: 02 = 2, regional centers|1.675**<br>|<br>1.159<br>|
|||(0.382)|(0.240)|
|3.drgur02|DEMOGRAPHIC: Region: Type of settlement: 02 = 3, other urban|<br>1.237|<br>0.676*|
|||(0.286)|(0.144)|
|4.drgur02|DEMOGRAPHIC: Region: Type of settlement: 02 = 4, rural|<br>0.675|<br>0.478***|
|||(0.179)|(0.122)|
|hhsize|DEMOGRAPHIC: Household Size|<br>1.124***|<br>1.058|
|||(0.041)|(0.061)|
|lnwage|INCOME: logarithm of primary employment income|<br>0.779**|<br>0.906|
|_||(0.085)|(0.067)|
|Constant|Constant|<br>4.067|<br>0.163|
|||(5.411)|(0.209)|
|Observations||2,000|2,351|

Source: Author’s calculations based on RLMS- HSE-2016.

**Table A-13: Results of behavioral model: actual and predicted status.**

|||formal|predicted<br>informal|Total|
|---|---|---|---|---|
||formal|66.4%|7.7%|74.2%|
|actual|informal|13.4%|12.4%|25.8%|
||Total|79.8%|20.2%|100.0%|

Source: Author’s calculations based on RLMS- HSE-2016.

46
# **1. Introduction**

Growing evidence suggests that aspirations are a key factor shaping students’ schooling and later-life outcomes (Fruttero, Muller, and Calvo-González 2021). In theory, aspirations for the future act as reference points that guide decisions and inspire effort towards the aspired goals (Ray 2006; Dalton, Ghosal, and Mani 2016; Genicot and Ray 2020). Longitudinal analyses from Brazil, China, Ethiopia, France, and India show that adolescents with higher aspirations indeed also complete more schooling and get better academic outcomes (Glewwe, Huang, and Park 2017; Favara 2017; Ross 2019; Guyon and Huillery 2021; Gagete-Miranda 2022). Likewise, some experimental studies of interventions raising schooling outcomes suggest higher aspirations were the mechanism through which youth changed their behaviors and eventually got better outcomes (Beaman et al. 2012; Avitabile and Hoyos 2018; Ross et al. 2021; Bernard et al. 2022; Riley 2022). On the flip side, disadvantaged students, such as immigrants and those from poor families, are typically less aware of top educational pathways and underestimate their academic ability, leading them to develop low aspirations and eventually confirm these lower prospects (Guyon and Huillery 2021; Carlana, La Ferrara, and Pinotti 2022).

A remaining open question is how perceived constraints affect the motivation spurred by aspirations. Do high aspirations lead youth to better outcomes no matter the constraints they anticipate? Or do aspirations motivate youth only when the expected constraints on their educational path—such as the lack of money and family support, low perceived abilities, etc.—feel manageable? This is a key issue for policy design. If aspirations alone are sufficient to motivate students, interventions can focus on increasing awareness of opportunities and motivating students to dream big. On the other hand, if students’ expectations of their future are more salient, expected constraints may curb motivation, and interventions could focus on removing actual constraints and on fostering the perception that they are surmountable.

We examine this issue for a large sample of students in junior secondary school in Indonesia. We use a cross-sectional survey of approximately 56,000 eighth-grade students (age 14 on average) from 2,095 public schools on the Indonesian islands of Java and Sumatera. The data allow to distinguish aspirations, measured from self-reported preferences of students’ future completed educational level in a hypothetical scenario in which they had no constraints, and expectations, which are their selfassessed most likely outcomes given their current situation. Having lower expectations than aspirations would mean that the student perceives binding constraints to achieve his or her educational aspirations.

2
The Impact of Violence on Individual Risk Preferences: Evidence from a Natural Experiment

# Pamela Jakiela and Owen Ozier<sup>_∗_</sup>

**JEL codes:** C91, C93, D01, D74, D81

**Keywords:** risk preferences, civil conflict, natural experiment

> _∗_ Jakiela: University of Maryland and IZA, pjakiela@umd.edu; Ozier: Development Research Group, The World Bank, oozier@worldbank.org. We are grateful to the staff at IPA-Kenya for their assistance and support, and to Michael Callen, Marcel Fafchamps, Jonas Hjort, Diego Garrido Martin, Ted Miguel, Anja Sautmann, and Waly Wane for helpful comments. All errors are our own.
# **4.2. Panel Estimates**

Panel estimates allow the introduction of trade costs that vary through time and that are lumped together in (5) under the term τ _ij_ assumed to capture the ad-valorem equivalent of trade costs such as tariffs, communication and other transaction costs. In the absence of time-series data on border measures and other transaction costs, we follow earlier contributions (e.g. Limao and Venables , 2001 and Brun et al., 2005) and construct a time-series index of the quality of infrastructure for each country which becomes the new proxy for the bilateral transport costs, _tijt_ . The augmented distance-dependent trade cost function becomes:<sup>30</sup> As constructed (see appendix A2), larger values for _Ki_ ( _j_ ) _t_ indicate a better infrastructure.<sup>31</sup> Again, the choice of functional form matters. If the cost function was additive with the infrastructure component independent of distance, the elasticity of transport costs to distance could increase if the fixed cost component were falling sufficiently faster than the variable component.<sup>32</sup> This trade cost function is introduced in (3) and (4) with country-pair fixed effects (FE) to capture the time-invariant characteristics of bilateral trade. Since these effects were not captured by the FE in the cross-section estimates, the panel estimates offer another robustness check on the earlier results although this is at the cost of imposing a trend specification for the evolution of distance. In particular, the country-pair fixed effects control for the North-South differences that Melitz (2007) found to reduce the estimate of θ by half over the period 1970-1995.<sup>33</sup> Furthermore, the 30 Since bilateral specific effects capture the time-invariant characteristics of bilateral trade, the trade cost function no longer includes the colonial, language and border dummy variables. We maintain Dij, our variable of interest, as the use of random effects (see below) allow us to estimate the corresponding coefficient (but not the fixed effects specification). Nor does trade costs function include fuel costs due to the year dummies. 31 To compute the infrastructure index, we use data from the telecommunication sector (number of main telephone lines per 1000 workers), and the transportation sector (the length of the road and railway network —in km. per sq. km. of land area) and an index of the quality in the service of transport (the share of paved roads in total roads) from Canning (1998) and World Development Indicators Database (see details in appendix A2).

32 See Brun et al. (2005, appendix D).

33 Melitz (2007) regresses in OLS an equation very close to our OLS on ln(M) on 158 countries over 1970-1995 (five-years sub-periods). He obtained an increase in distance coefficient of around +18.8% while we find, over the same period +18.3% (see OLS with country fixed effects on ln(M) in figure 6). Once he controlled for North-South distance, 26