rafmacalaba/datause-encoder-data huggingface dataset:
# **5.2 Measuring Emotional Regulation**

To measure the effects of the ASP on ER and automaticity, we follow an alternative approach to Heller et al. (2017). In that paper, authors implemented a dictator game to measure if at-risk treated youth made more deliberate decisions compared to individuals in the control group. In our study, we estimate objective ER measures collected from participants’ brains to provide evidence of changes in their automatic responses and other neurophysiological outcomes, such as stress. Appendix 4 offers a more detailed explanation about previous evidence showing correlations of EEG-based, arousal and valence indexes with behaviors or outcomes.

As explained in Egana-delSol (2016a), self-reported measures are suboptimal for estimating the impact of interventions because they can be affected by other factors that may impact individuals’ emotional states and be confounding with the treatment itself. As such, ER can be proxied using the emotion-detection theory from affective neuroscience literature. In this study, we use the JamesLange theory of valence and arousal. In this model, arousal is a proxy of an individual’s stress, estimated directly from her brain activity. Valence can be interpreted as a positive or negative mood, as well as an attitude of either approach toward or withdrawal from a stimulus (Davidson et al., 1990; Harmon-Jones et al., 2010).

To generate these measures, we follow the methodology developed by Egana-delSol (2016b). We acquired portable EEG headsets to obtain a proxy measure of students’ emotional states and responsiveness to stimuli in the arousal-valence locus (Ramirez and Vamvakousis, 2012). These devices provide an average accuracy or correct detection that is similar to research grade instruments and an emotion-detection accuracy of about 79% (Egana-delSol, 2016b; Martinez-Leon et al., 2016).

Due to the relatively high costs necessary to collect neurophysiological data, we randomly selected a subsample of 598 individuals from the total 1,056 enrolled children, with a similar distribution of C and T groups (25% and 75%, respectively). Descriptive statistics of this subsample are presented in Table A7 in the Appendix. Then, following Egana-delSol (2016a) we established a lab-in-the-field setting to collect three streams of neurophysiological data for each student: (1) pretest resting emotional state from EEG recordings, (2) psychometric tests to measure noncognitive, creative, and cognitive skills, and (3) emotional responsiveness to both positive and negative stimuli. (See Figure A2 in the Appendix as a reference of the lab-in-the-field setting).

In the first stage, the pretest resting emotional state, we collected EEG recordings to estimate emotional arousal and valence indexes at resting state while students watched a black cross in the center of a gray screen for a period of 30 seconds.

In the second section of data collection, students responded to a battery of psychometric tests, which included the Rotter Locus of Control Scale (Rotter, 1966), Raven-like progressive matrices, 27
33

**Figure 1. Aid Profile of the Median Post-Conflict Country before and after Peace Start**

<!-- Start of picture text --> 2.9<br>Post-conflict 2<br><!-- End of picture text --> <!-- Start of picture text --> 4<br>3<br>Post-conflict 1<br>4<br><!-- End of picture text --> <!-- Start of picture text --> 5<br>2.8<br>Pre-peace<br><!-- End of picture text --> <!-- Start of picture text --> CPIA<br><!-- End of picture text --> Note: The figure is based on all the conflict countries that have RER misalignment data. See Appendix A for details
# **Transport Connectivity and Health Care Access:**

# **Evidence from Liberia**

Atsushi Iimi<sup>¶</sup> , Kulwinder Rao Transport & ICT Global Practice, The World Bank Group Key words: rural roads; health care access; transport connectivity; instrumental variable estimation.

JEL classification: C25, C26, I15, Q42, R40.

> ¶ Corresponding author.
<!-- Start of picture text --> a a<br>;<br>GUINEA ie c -<br>rPi — / [: wees<br>6B ‘an<br>/ aras ei .s y,<br>7 mEoetow je é<br>i=l!<br>a<br><!-- End of picture text --> <!-- Start of picture text --> Current Irrigation (% Area)<br><!-- End of picture text -->
CAMEROON’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

1.4 in 2007 to 2 in 2010. The ratio of staff costs to traffic revenues dropped from 27.6 percent in 2007 to 25.7 in 2010. In 2010, CAMRAIL’s sales increased by 5 percent (World Bank 2011c).

CAMRAIL is one of the more intensively used networks in Sub-Saharan Africa, at 1.1 million traffic units per route-km, compared to others in the region, which serve well under one million traffic units per year (figure 10). Nevertheless, CAMRAIL’s traffic density is lower than the average for West Africa, and only a fraction of that found in southern Africa and North Africa. By global standards, these kinds of traffic volumes are little more than what might be carried by a moderately busy branch line. Moreover, such low traffic volumes do not generate the revenue needed to finance track rehabilitation and upgrading.

## **Challenges**

**Figure 10. Traffic densities on African railways**

<!-- Start of picture text --> 5.0<br>4.5<br>4.0<br>3.5<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>Million TU per route km<br><!-- End of picture text --> _Source:_ AICD 2010b.

_Note:_ Density is normally expressed as traffic units per route-km. The traffic units carried by a railway are the sum of the passenger-km and the net tones-km. TU = traffic units.

In spite of relatively good performance, CAMRAIL activities are affected by obsolete rolling stock and deteriorated infrastructure. Because of these supply constraints, CAMRAIL cannot adequately respond to the growing demand for passenger and freight transport. But plans to acquire 38 new passenger cars and rehabilitate 175 km of the most-deteriorated track sections of the 1,104 km–long railway line between Douala and Ngaoundéré are being implemented (World Bank 2011c). These projects will allow CAMRAIL to expand and meet increasing demand.

More broadly, the Government is working on a long-term master plan for the development on the railway sector. The required investments are likely to be high, and until they can be fully funded a logical first step will be optimize the operation of the existing concession. If some of the large-scale mining projects under consideration were to materialize, further rail development may be warranted to connect mining sites with ports. However, this is contingent on a careful analysis of demand.

# Air transport

## **Achievements**

Cameroon is a natural air-traffic hub for Central Africa, as demonstrated by relatively high traffic levels. With close to a million total seats, Cameroon’s traffic was higher than that of the Central African Republic, Gabon, Chad, and Congo, but well below that of Nigeria. After Nigeria, Cameroon had the second-highest number of international and intercontinental seats (table 7).

20
Deininger, K. And P. Mpuga, 2004. Does greater accountability improve the quality of delivery of public services? Evidence from Uganda. World Bank Policy Research Working Paper. 3277.

Devarajan, S., S. Khemani, And M. Walton. (2011). Civil society, public action and accountability in Africa, The World Bank.

Djankov, S., La Porta, R., Lopez-De-Silanes, F. & Shleifer, A. 2010. Disclosure by Politicians. American Economic Journal: Applied Economics, 2, 179-209.

Ferraz, C. & Finan, F. 2008. Exposing Corrupt Politicians: The Effects of Brazil's Publicly Released Audits on Electoral Outcomes. The Quarterly Journal of Economics, 123, 703-745.

Fisman, R And Gatti, R. 2002. Decentralization and corruption: evidence across countries, Journal of Public Economics.

Fiszbein, A, D. Ringold And H. Rogers (2009). Making Services Work; Indicators, Assessments, and Benchmarking of the Quality and Governance of Public Service Delivery in the Human Development Sectors. Mimoe, The World Bank, Washington DC.

Hunt, J. 2007. How Corruption Hits People When They Are Down. Journal of Development Economics, 84, 574-589.

Hussam, R., Rigol, N., and Roth, B. 2017. Targeting high ability entrepreneurs using community information: Mechanism design in the field. Working paper.

Koziol, M. And C. Tolmie (2010). Using Public Expenditure Tracking Surveys to Monitor Projects and Small-Scale Programs. Washington DC, World Bank.

Litschig, S. And Y. Zamboni (2011). Audit risk and rent extraction: Evidence from a randomized evaluation in Brazil, Department of Economics and Business, Universitat Pompeu Fabra.

Molina, E., & Carella, L. & Pacheco, A & Cruces, G & Gasoarini, L. Community monitoring interventions to curb corruption and increase access and quality of service delivery in low- and middle-income countries: a systematic review. Campbell Collaboration, 2016.

Mookherjee, D. 2006. Decentralization, hierarchies, and incentives: A mechanism design perspective. Journal of Economic Literature, 44(2):367–390.

Nyquist, M. & De Waque, D. & Svensson, J. Experimental evidence on the long-run impact of community-based monitoring, American Economic Journal: Applied Economics 2017, 9(1): 3369.

Olken, B. & Pande, R. 2012. Corruption in Developing Countries. Annual Review of Economics, 4.

31
_Table 4.2 Results_ _<u>of the Dynamic Decomposition, 1988, 1996-02</u>_

||_Variable_|_Allocation_|_Income_|_Gross_|_Marginal_|
|---|---|---|---|---|---|
|_1988 – 1996 _||||||
||Education|15.4|47.6|63.0|30.2|
||Age|4.6|15.9|20.5|4.2|
||Economic Sector|-4.9|14.8|9.9|-2.6|
||Status|3.5|11.9|15.4|-7.2|
|_1996-2000_||||||
||Education|-4.2|45.3|41.2|37.8|
||Age|-6.6|10.0|3.4|19.8|
||Economic Sector|5.3|10.0|15.3|24.6|
||Status|2.5|9.6|12.1|2.2|
|_1996-2002 _||||||
||Education|-1.7|30.5|28.7|24.5|
||Age|-3.5|8.5|5.0|10.1|
||Economic Sector|1.8|8.0|9.9|14.7|
||Status|1.1|15.4|16.4|9.5|
|_2000-2002_||||||
||Education|0.3|20.3|20.6|15.0|
||Age|-2.0|6.7|4.6|4.1|
||Economic Sector|-0.5|5.6|5.1|10.2|
||Status|0.6|17.8|18.4|14.4|

_Source:_ Calculations based on the ENEU (third quarter).

Third, the income effect is always prevalent. If one considers, for instance, the 1988–96 period, changes in the relative earnings among educational groups alone would have generated a larger deterioration in the earnings distribution than the one observed. The same holds true for the other periods. Even the decrease in inequality observed in recent periods is explained by the changes in relative earnings (the income profile related to education became less steep as shown in Table 2.1). Therefore, it seems reasonable to conclude that the income effect is the leading force behind the increase in inequality, and this, in turn, suggests that the workings of the labor market and its interaction with educational policies should be thoroughly examined.

Fourth, the significance of changes in the distribution of education remains high even when one controls for changes in other relevant variables. As a matter of fact, the marginal contribution of age, economic sector, and status in the labor market is negative in the 1988-1996 period. This means that changes in these variables reduced the effects induced by changes related to education, as most of the time they reduced inequality after 23
_Annex 3 Table 1: Program impact on the household size and composition of nonbeneficiary_ _<u>households with 6-60-month-old children</u>_

||Household<br>Size|Dependency Ratio<br>(Children 0-15/Adults<br>15-59)|Dependency Ratio-<br>Female (Children 0-<br>15/Females 15-59)|Dependency Ratio-<br>Male (Children 0-<br>15/Males 15-59)|
|---|---|---|---|---|
|_Panel A: Difference-in-differ_|_ences_||||
|Program village|-0.006|-0.026|-0.031|-0.013|
||(0.148)|(0.045)|(0.087)<br>|(0.09)|
|After|1.096***|-0.062***|-0.115***|-0.140***|
||(0.092)|(0.019)|(0.032)|(0.034)|
|Program village*After|0.116|0.01|0.062|0.022|
||(0.137)|(0.026)|(0.043)|(0.051)|
|_Panel B: Triple difference int_|_eractionwith sat_|_uration_|||
|Program village|0.942*|0.239|0.488|0.515|
||(0.499)|(0.158)|(0.304)|(0.321)|
|After|1.101***|-0.084|0.036|-0.093|
||(0.274)|(0.056)|(0.105)|(0.119)|
|Saturation|-1.499**|-0.154|-0.505|-0.538|
||(0.641)|(0.185)|(0.365)|(0.367)|
|Program village*after|-0.246|-0.083|-0.209|-0.22|
||(0.39)|(0.091)|(0.153)|(0.191)|
|*|<br>-1.558*|<br>-0.440*|<br>-0.862*|<br>-0.873*|
|Program villagesaturation|(0.803)|(0.262)|(0.488)|(0.51)|
|Aft*Stti|0.295|0.038|-0.261|-0.078|
|erauraon|(0.47)|(0.101)|(0.166)|(0.191)|
|Program|0.683|0.156|0.464**|0.404|
|village*after*saturation|(0.672)|(0.148)|(0.236)|(0.287)|
|_Panel C: Triple difference int_|_eraction with ab_|_ove median saturation_|||
|Program village|0.360*|0.033|0.088|0.128|
||(0.196)|(0.06)|(0.121)|(0.128)|
|After|1.077***|-0.081***|-0.106**|-0.122**|
||(0.124)|(0.025)|(0.048)|(0.048)|
||-0263|-0065|-0196|-0169|
|Above median saturation|.<br>(0.227)|.<br>(0.069)|.<br>(0.127)|.<br>(0.136)|
|Program village*After|-0.03|0.008|0.051|-0.04|
||(0.17)|(0.035)|(0.065)|(0.077)|
|Program village*Above<br>|-0.830***|-0.131|-0.264|-0.314*|
|median saturation|(0.297)|(0.092)|(0.178)|(0.185)|
|After*Above median|0.058|0.047|-0.014|-0.036|
|saturation|(0.182)|(0.042)|(0.061)|(0.068)|
|Program|0315|-0001|001|0121|
|village*After*Above|.|.|.|.|
|<br>median saturation|(0.282)|(0.051)|(0.083)|(0.096)|
|_Panel D: Triple difference int_|<br>_eractionwith to_|<br>_p quartile of saturation_|||
|Program village|0.142|0.007|0.051|0.097|
||(0.166)|-0.049)|-0.098)|(0.102)|
|After|1.325***|-0.070***|-0.119***|-0.131***|
||(0102)|(0022)|(0037)|(0038)|
||.<br>-0.473|.<br>-0.087|.<br>-0.183|.<br>-0.123|

79
# Figure 1 **Base Money Growth and Inflation**

<!-- Start of picture text --> 140<br>120<br>100<br>80<br>60<br>40<br>Inflation (y-o-y)<br>Base money growth (y-o-y)<br>20<br>91 92 93 94 95 96 97 98 99 00 01 02<br><!-- End of picture text --> In this respect, consistent with the experience of other countries with highinflation episodes—i.e., Mexico and Brazil—recent empirical evidence on Turkey suggests that exogenous movements to monetary base have not been a cause of inflationary pressures in Turkey.<sup>10</sup> The empirical findings show that inflationary pressures in Turkey have their origin in the following factors: (i) the presence of external shocks that engender sharp exchange rate depreciations; (ii) changes in public sector prices; and (iii) inflationary inertia.<sup>11</sup>

> 10 See the Monetary Policy Report published by the CBRT in April 2002.  Existing studies, see for instance Lim and Papi (1997), Sakall ı oğlu and Yeldan (1999), Özcan et. al. (2001), tend to agree upon the importance of the inertia and the exchange rate.  However, there seems to be a disagreement over the importance of monetary variables.

> 11 The above-mentioned indirect transmission channel through which shocks to exchange rates and public prices can influence inflation expectations in Turkey can be illustrated by the following example.  Let us assume that the central bank faces an exogenous shock in the form of unanticipated adjustments to administered prices that are not compatible with the central bank’s inflation target.  In turn, this will generate a higher CPI, thereby raising nominal demand for money.  If the central bank increases the supply of base money to match the increase in demand, the central bank would have accommodated the rise in money demand engendered by an exogenous shock to prices caused by the increase in public prices. Under normal circumstances, this would be described as once for all adjustment in the price level, which should not create further problems for the central bank.  However, given Turkey’s history of high inflation, the dynamics triggered by the increase in public prices are complicated as the public might revise their inflationary

11
Table D.2 – _Continued from previous page_

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Electric lighting|-0.085|-0.134|0.262***|0.261***|0.581***|0.574***|
||(0.119)|(0.140)|(0.064)|(0.064)|(0.172)|(0.185)|
|Observations|375|375|815|815|817|817|
|R2|0.11|0.12|0.38|0.38|0.19|0.20|

_Notes_ : The dependent variables are in log, measured in 2017 PPP US$by year. Robust standard errors are clustered at the enumeration area level. *** p _<_ 0.01, ** p _<_ 0.05, and * p _<_ 0.1.
Heller, P.S. and Diamond, J. (1990). 'International Comparisons of Government Expenditure Revisited: The Developing Countries, 1975-1986', IMF Occasional Paper.

Hombug, S. (1990). 'The Efficiency of Unfounded Pension Schemes', _Journal of Institutional and Theoretical Economics._ Musgrave, R.A. and Musgrave, P.B. (1984). _Public Finance in Theory and Practice_ (4th Edition), McGraw-Hill, NewYork.

Newbery, D. (1993). 'Tax and Expenditure Policy in Hungary', _Economics of Transition._ Newbery, D. ed. (1995). _Tax and Benefit Reform in Central and Eastern Europe,_ Center for Economic Policy Research, London.

Pleskovic, P. and Sivitanides, P. (1993). 'Priorities for Poor: A Conceptual Framework for Policy Analysis', _Journal of Development Areas._ Rodrik, D. (1995). 'Getting Interventions right: How Korea and Taiwan Grew Rich', _Economic Policy._ Sachs, J. (1995). 'Post-Communist Parties and the Politics of Entitlements', _Transition_ (World Bank).

Sachs, J. and Warner, A. (1996). 'Achieving Rapid Growth in Transition Economies of Central Europe', Harvard Institute for International Development, Development Discussion Paper, No.544, July 1996.

_Transition Report,_ EBRD, London, 1994.

Tulkens, H. (1993). 'On FDH Efficiency Analysis: Some Methodological Issues and Applications to Retail Banking, Courts and Urban Transit', _The Journal of Productivity Analysis._ Vanden Eeckaut, P., Tulkens, H. and Jamnar M.-A. (1993). 'Cost-Efficiency in Belgian Municipalities' in H.O. Fried, C.A. Knox-Lovell and S.S. Schmidt (eds.), _The Measurement of Productive Efficiency: Techniques and Applications,_ Oxford University Press, Oxford.

Zellner, A. (1962). 'An Efficient Method of Estimnating Seemingly Unrelated Regressions and Tests for Aggregation Bias', _Journal of the American Statistical Association._ _World Development Report: From Plan to Market,_ World Bank, Washington, 1996.

32
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Results show that WTP for stove characteristics for the control groups are stable over the study period. As noted in the introduction, this was the first test-retest choice experiment study conducted in a developing country setting. This result supports the use of values obtained from nonmarket studies over multiple years.

The results also show that the WTP for stove attributes for the treatment group increases over time and this is consistent for all stove attributes. We analyze the robustness of this result by focusing on respondents in the treatment group that are long-term stove users. It is important to note that about 1/3 of the treatment group has stopped using the stoves. We are unable to analyze this subgroup given the complexity of the estimation process and model convergence issues. Understanding the preferences of the households that have stopped using the stoves could be very important.

The results show that fuelwood reduction is the most highly valued attribute of the stoves and WTP for fuelwood reduction more than doubles between 2013 and 2016. Overall, the evidence from this study highlights that the Mirt stove, a technology developed by the Federal Government of Ethiopia in conjunction with donors, is well liked by long-term users and their overall valuation of the benefits of the improved stoves increases with long-term use of the Mirt stove.

# **References**

- Adamowicz, W., Boxall, P., Williams, M. and Louviere, J. (1998). Stated Preference Approaches for Measuring Passive Use Values: Choice Experiments and Contingent Valuation. _American Journal of Agricultural Economics_ 80, 64-75.

- Adamowicz, W., Swait, J., Boxall, P., Louviere, J. and Williams, M. (1997).Perceptions Versus Objective Measures of Environmental Quality in Combined Revealed and Stated Preference Models of Environmental Valuation. _Journal of Environmental Economics and Management_ 32, 65-84.

- Bliem, M., Getzner, M., Rodiga-LaBing, P. (2012) “Temporal Stability of Individual Preferences for River restoration in Austria Using a Choice Experiment. _Journal of Environmental Management_ 103, 65-73.

- Brouwer, R., Bateman, I. (2005). Temporal Stability and Transferability of Models of Willingness to Pay for Flood Control and Wetland Conservation. _Water Resources Research_ 41, W03017.

- Carlsson, F. and Martinsson, P. (2003) Design Techniques for Choice Experiments in Health Economics, _Health Economics_ 12, 281-94.

- Carlsson, F., Mørkbak, R. and Olsen, B. (2012). The First Time is the Hardest: A Test of Ordering Effects in Choice Experiments. _Journal of Choice Modelling_ 5(2), 19-37.

- Conley, T. and Udry C. (2010). “Learning about a New Technology: Pineapple in Ghana.” _American Economic Review_ 100, 35-69.

- Dissanayake, S. T. M, Beyene, A. D., Bluffstone, R, Kiggundu, G., Kooser, S, Gebreegziabher, Z, Mekonnen, A, Martinsson, P. and Toman, M. (2018). Improved Cook Stoves for Climate

15