rafmacalaba/datause-encoder-data huggingface dataset:
Table 1: Dependent Variable: Arms imports

|(1)<br>SIPRI|(2)<br>SIPRI|(3)<br>(4)<br>COMTRADE<br>COMTRAD|(5)<br>E<br>Import share|(6)<br>Import share|(7)<br>GDP share|(8)<br>GDP share|
|---|---|---|---|---|---|---|
|Discovery in past 5 years<br>0.284<sup>_∗∗_</sup>|0.260<sup>_∗_</sup>|0.398<sup>_∗∗_</sup><br>0.411<sup>_∗∗_</sup>|0.234|0.252|0.297<sup>_∗_</sup>|0.322<sup>_∗_</sup>|
|(0.135)|(0.138)|(0.176)<br>(0.177)|(0.150)|(0.151)|(0.165)|(0.165)|
|Nb of previous discoveries|0.104|-0.049||-0.067||-0.100|
||(0.145)|(0.110)||(0.087)||(0.100)|
|N<br>3408|3408|3392<br>3392|3392|3392|3436|3436|
|R-sq<br>0.71|0.71|0.72<br>0.72|0.42|0.42|0.37|0.37|
|The dependent variable i|s in inv|erse hyperbolic sine.|The regressi|ons include|country|and year|
|fxed efects. Robust sta|ndard|errors in parenthesis,|and * stan|ds for stati|stical sig|nifcance|
|at the 10% level, ** at t|he 5%|level and *** at the|1% percent|level.|||

Table 2: Dependent Variable: Arms imports. Sample: Only discovery countries.

||(1)<br>SIPRI|(2)<br>SIPRI|(3)<br>COMTRADE|(4)<br>COMTRADE|(5)<br>Import share|(6)<br>Import share|(7)<br>GDP share|(8)<br>GDP share|
|---|---|---|---|---|---|---|---|---|
|Discovery in past 5 years|0.327<sup>_∗∗_</sup>|0.344<sup>_∗∗_</sup>|0.397<sup>_∗∗_</sup>|0.425<sup>_∗∗_</sup>|0.233|0.261|0.291<sup>_∗_</sup>|0.324<sup>_∗_</sup>|
||(0.125)|(0.136)|(0.175)|(0.183)|(0.150)|(0.156)|(0.166)|(0.171)|
|Nb of previous discoveries||-0.060||-0.091||-0.095||-0.112|
|||(0.136)||(0.121)||(0.098)||(0.109)|
|N|1338|1338|1280|1280|1280|1280|1289|1289|
|R-sq|0.69|0.69|0.65|0.65|0.30|0.30|0.31|0.32|

The dependent variable is in inverse hyperbolic sine. The regressions include country and year fixed effects. Robust standard errors in parenthesis, and * stands for statistical significance at the 10% level, ** at the 5% level and *** at the 1% percent level.

14
surpluses.<sup>8</sup> By 2007/8, Germany exported almost half of its output, highest among large industrial countries.

# **C.2. Developing Countries**

**Table 3: Global Manufacturing Imports of Developing Countries from 1991/92 to 2001/02 and 2001/02 to 2007/08**

||Share|of World Im|ports|Annual<br>Growth<br>Wo|Import<br>from the<br>rld|A|nnual Import|Growth due to||
|---|---|---|---|---|---|---|---|---|---|
||in Dom|estic Deman|d (%)|(%|)|1991/92 -|2001/02|2001/02 -|2007/08|
|Country|1991/92|2001/02|2007/08|91/92 -<br>01/02|01/02 -<br>07/08|Demand<br>Change|Market<br>Share<br>Change|Demand<br>Change|Market Share<br>Change|
|Brazil|5.7|14.3|13.7|11.5|17.0|1.6|9.9|17.8|-0.8|
|China|15.8|20.5|13.4|12.7|22.1|9.8|2.9|30.9|-8.9|
|India|7.6|12.1|17.0|10.4|30.2|5.4|5.0|23.0|7.2|
|Korea, Rep.|18.8|20.1|23.5|5.5|15.9|4.8|0.7|13.0|3.0|
|Malaysia|53.1|58.1|45.8|7.3|9.2|6.3|1.0|13.6|-4.4|
|Mexico|42.3|78.4|69.6|15.2|7.7|8.3|6.9|9.8|-2.2|
|South Africa|16.1|20.1|27.0|3.1|19.9|0.8|2.3|14.2|5.7|
|Turkey|17.2|30.0|31.0|7.9|24.2|2.1|5.8|23.6|0.6|
|Total: above<br>developingco.|17.2|25.4|18.7|10.4|17.6|6.2|4.2|23.7|-6.1|

Note: Data is based on two-year averages of 1991-92, 2001-02, and 2007-08.

Sources: Computations based on UN COMTRADE Statistics (trade data) and UNIDO database (production data).

Developing country import behavior in some ways is similar to that of the industrial countries. Import growth rates accelerate from already high rates of 10 percent p.a. during the first period to almost 18 percent p.a. during the second. The acceleration of demand growth is much more dramatic; from an average of 6.2 percent growth per annum during to first period to almost 24 percent p.a. during the second, much higher than that of industrial countries. During the first period, demand growth is high in Mexico and China and low in Brazil, Turkey and South Africa. During the second period, demand growth accelerates in almost every country, reaching 31 and 23 percent per annum respectively in China and India.

During the first period, import shares increase in all our sample countries. Some are very dramatic, such as Turkey and Mexico caused by joining NAFTA for Mexico and EU for Turkey. Other developing countries such as and Brazil also have high rates of import share increases.

> 8 Its trade surplus originates primarily with the EU-27 partners. They import 30 percent of Germany’s manufacturing output but export only 19 percent of Germany’s output equivalent.

9
of classes taken by the student. Recall that the latter is theoretically an upper bound for the former (see Section 5.2.) As such, our average predicted target is lower than the average observed number of classes.

**Figure 9: Goodness of Fit: College Outcomes.**

<!-- Start of picture text --> 100<br>On−time graduate Late graduate<br>90 Dropout 2nd year Dropout 1st year<br>Dropout after 2nd year<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1 2 3 4 5<br>Ability quintile<br>Percent<br><!-- End of picture text --> _Source_ : SPADIES for observed data; fitted values for predicted data. _Notes_ : Whole lines show observed values; dashed lines show values.

**Figure 10: Goodness of Fit: Tiers of Cumulative Classes Completed, by Year.**

<!-- Start of picture text --> 100 Tier 1 Tier 2&3 Tier 4<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1 2 3 4 5<br>Year<br>Percent<br><!-- End of picture text --> _Source_ : SPADIES for observed data; fitted values for predicted data.

_Notes_ : For each year, the figure shows the percent of students by tier of cumulative classes completed. Whole and dashed lines show observed and predicted percentages, respectively.

**Table 5: Goodness of Fit: Dropout Year.**

|Year|Observed|Predicted|
|---|---|---|
|1st|27.41|27.92|
|2nd|10.38|10.43|
|3rd|4.77|4.94|
|4th|2.7|2.83|
|5th|2.61|2.81|
|6th|2.74|2.92|
|7th|2.48|1.94|
|8th|1.28|1.19|
|Total|54.36|54.98|

_Source_ : SPADIES for observed data; fitted values for predicted data. _Notes_ : Table shows the observed and predicted percent of students who drop out each year.

25
**Product market policies, as measured by the overall summary product market indicator, are still relatively restrictive in Croatia compared to those of OECD countries.** Simple comparisons suggest Croatia still scores less well than the EU 15 average, although better than many of its peers did in 2003 (Figure 2).  Figure 3, which reports the summary PMR scores accounting for the uncertainty in the choice of weights used in the PMR system, confirms that Croatia falls into the group of relatively restrictive countries (which includes France, Greece, Italy and the Czech Republic as estimated with 2003 data).<sup>3</sup>

**Figure 2: Product Market Regulation: Country Group Comparison**

<!-- Start of picture text --> 3.0 2.8 2.8<br>2.5<br>2.1<br>2.0<br>2.0<br>2.0 1.8<br>1.7<br>1.6<br>1.4 1.4 1.5<br>1.5<br>1.2<br>1.0<br>0.5<br>0.0<br>Albania Slovak EU15 OECD Romania Czech Bulgaria Hungary Croatia Other Poland Ukraine<br>2007 Republic 2003 2003 2006 Republic 2006 2003 2007 MICs 2003 2007<br>2003 2003 2003<br>Source: see Figure 3. Note: Other MICs are Brazil, Mexico, Turkey. Values refer to 2007 for Croatia, Albania and<br>Ukraine, 2006 for Bulgaria and Romania, 2004 for Brazil, and 2003 for all other countries. For full data set see<br>Annex I.<br>0 is least restrictive, 6 is most restrictive<br><!-- End of picture text -->

> 3 See Conway, Janod, Nicoletti (2005) for details.

5
## **Figure 9 Developing countries become a more important export destination for China**

<!-- Start of picture text --> Export destinations, percent Developing High income Import sources, percent Developing High income<br>100 100<br>90 90<br>80 80<br>70 70<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>2005 2030 2005 2030 2005 2030 2005 2030 2005 2030 2005 2030 2005 2030 2005 2030 2005 2030 2005 2030<br>Agriculture Processed Fossil fuels Manufacturing Services Agriculture Processed Fossil fuels Manufacturing Services<br>foods foods<br><!-- End of picture text -->

## **Figure 10 Developing countries become a more important export destination for India**

<!-- Start of picture text --> Export destinations, percent Developing High income Import sources, percent Developing High income<br>100 100<br>90 90<br>80 80<br>70 70<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>2005 2030 2005 2030 2005 2030 2005 2030 2005 2030 2005 2030 2005 2030 2005 2030 2005 2030 2005 2030<br>Agriculture Processed Fossil fuels Manufacturing Services Agriculture Processed Fossil fuels Manufacturing Services<br>foods foods<br><!-- End of picture text -->

# 3.4 Slower growth in China and India: consequences for the global economy

The previous sections have argued that the pace of growth in China and India over the next 25 years is likely to significantly outpace the growth in the rest of the developing world. How would our outlook on the global economy change if instead the two giants grew at a rate equal to the developing country average? In this section, we present an illustrative simulation where the 2015-2030 TFP growth rate in China and India is set to the low and middle income country average. In addition, foreign saving is kept at baseline levels to sterilize the effects of international capital mobility and focus the analysis on the spillover effects through international trade.

In this simulation, China’s 2005-2030 real GDP growth declines from 5.8 to 5.2 percent, while that of India is reduced from 4.9 to 4.4 percent. World GDP growth over the same period declines by 0.06 percent per year; this effect is mostly driven by a reduced contribution of the giants to global output (direct effects). The indirect effects—spillovers 16
Huber-Lee A, Swartz C, Sieber J, Goldstein J, Purkey D, Young C, Soderstrom E, Henderson J, Raucher R. 2006. _Decision Support System for Sustainable Water Supply Planning_ . Awwa Research Foundation: Denver, CO.

INEI. 2001. _Estimaciones y proyecciones de poblacion, 1950-2050, urbana-rural 19702025_ . .

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Joyce B, Purkey D, Yates D, Groves DG, Draper A. 2010. _Integrated Scenario Analysis for the 2009 California Water Plan Update, Volume 4_ . California Department of Water Resources: Sacramento, CA.

Kalra N, Hallegatte S, Lempert R, Brown C, Fozzard A, Gill S, Shah A. 2014. _Agreeing on robust decisions : new processes for decision making under deep uncertainty, Volume 1_ . Washington, D.C.

Lempert RJ, Groves DG. 2010. Identifying and evaluating robust adaptive policy responses to climate change for water management agencies in the American west. _Technological Forecasting and Social Change_ . Elsevier Inc. **77** (6): 960–974. DOI: 10.1016/j.techfore.2010.04.007.

Lempert RJ, Kalra N, Peyraud S, Mao Z, Tan SB, Cira D, Lotsch A. 2013a. Ensuring Robust Flood Risk Management in Ho Chi Minh City. The World Bank.

Lempert RJ, Popper SW, Bankes SC. 2003. _Shaping the Next One Hundred Years: New Methods for Quantitative, Long-Term Policy Analysis_ . RAND Corporation, MR-1626RPC: Santa Monica, Calif.

Lempert RJ, Popper SW, Groves DG, Kalra N, Fischbach JR, Bankes SC, Bryant BP, Collins MT, Keller K, Hackbarth A, Dixon L, LaTourrette T, Reville RT, Hall JW, Mijere C, McInerney DJ. 2013b. _Making Good Decisions Without Predictions_ . RAND Corporation.

McKay MD, Beckman RJ, Conover WJ. 1979. Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code. _Technometrics_ . Taylor & Francis **21** (2): 239–245. DOI: 10.1080/00401706.1979.10489755.

Mendler de Suarez J, Suarez P, Bachofen C, Fortugno N, Goentzel J, Gonçalves P, Grist N, Macklin C, Pfeifer K, Schweizer S, Van Aalst M, Virji H. 2012. _Games for a New Climate: Experiencing the Complexity of Future Risks_ . The Frederick S. Pardee Center for the Study of the Longer-Range Future, Boston University.

National Research Council. 2009. _Informing Decisions in a Changing Climate_ . Panel on Strategies and Methods for Climate-Related Decision Support, Committee on the Human Dimensions of Climate Change, Division of Behavioral and Social Sciences and Education: Washington, DC.

39
exposure (pre-survey) using the randomly-assigned survey waves. Both approaches yield similar results: we find that Kenya’s post-election crisis had a large and significant positive impact on individual risk aversion. Specifically, the crisis led to an 11 percentage point increase in the likelihood that a subject always chose the safest, lowest expected value alternative (i.e. lottery) available — this effect constitutes more than an 80 percent increase in the rate of extreme risk aversion. We also observe a 5.7 percentage point (or roughly 26 percent) decrease in the fraction of subjects who are classified as either risk neutral or risk loving. Such substantial impacts highlight an important channel through which civil conflict might affect growth and development: increased risk aversion might lead individuals in post-conflict settings to avoid high-risk, high-return activities (e.g. entrepreneurship) that contribute to economic growth.

The key strength of our study is that we are able to estimate the impact of civil conflict on the risk preferences of the general population, as opposed to the specific (marginal) effect of being victimized or more exposed to violence. Very few KLPS2 respondents were themselves victims of violence during the unrest: more than three quarters of respondents were temporarily deprived of basic necessities because it was not safe to visit markets or other public places, but less than 4 percent indicated that anyone in their household was physically assaulted during the crisis. Thus, KLPS2 respondents experienced the conflict, but were not, by and large, among the most impacted Kenyans; they therefore provide an important window into the impacts of civil conflicts on the preferences of the general population.

Our results differ from several recent studies. For example, Voors, Nillesen, Verwimp, Bulte, Lensink, and Van Soest (2012) find that greater exposure to violence leads to an increase in riskseeking behavior, while Callen, Isaqzadeh, Long, and Sprenger (2014) find that priming subjects with recollections of violent events increases their risk tolerance in situations involving only uncertainty (but also increases their certainty premia). However, these differing results can be reconciled by the difference in estimands, for example, if those who narrowly escape being exposed to violence or victimized become more risk averse. Our aim in this paper is to estimate the overall impact of violence on the population as a whole; doing so, we may not find the same effect. Understanding these overall impacts is of critical importance as we seek to characterize the ways that conflict may change a country’s overall growth trajectory.

4
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highest levels of congestion, such as the center of Kolkota and Bangalore, should be the focus of policy effort to alleviate congestion, and of future research to identify the most effective policies, as in Kreindler (2018).

Second, we compared travel patterns in India with those from more developed cities, and we uncovered important differences. In particular, Indian cities do not experience the familiar twin due to and commutes. There is almost no peak congestion patterns morning evening distinct morning peak, and instead a slow buildup of congestion that often persists until late into the rainfall to These evening. Light appears speed up traffic slightly. unique patterns are consistent with Indian roads being multi-purpose public goods serving a wide variety of uses other than motorized transport that slow down travel. If this conjecture is correct, then further research on technologies and policies for separating roadway uses appears especially promising, with appropriate consideration for the costs of restricting non-vehicle uses. More generally, our findings of unique Indian travel patterns imply that country-specific policies are necessary, and that using our data sources and methodology to study other countries individually may uncover distinctive patterns.

Third, our most surprising and perhaps controversial finding is that in most Indian cities travel is slow at all times, not just peak times. As a result, standard policy recommendations like congestion pricing, hov lanes, or other types of travel restrictions may do little to improve mobility. Instead, potentially costly travel infrastructure may be the only way to improve uncongested mobility. Our paper provides a first set of results suggesting a modest positive role for the design of a regular network grid and the presence of more primary roads. We hope that future research and engineering studies can identify cost-effective ways to build faster urban networks. On an optimistic note we find that better uncongested mobility generally correlates with the process of economic development. Unfortunately, this relationship is neither perfect nor linear.

We believe a lot more can be learned from the data we use here. In an extension of this paper (Akbar _et al._ , 2018), we provide complementary measures of urban accessibility in Indian cities, decompose accessibility into proximity and mobility, and provide an analysis of the urban correlates of accessibility and proximity. This sort of data can thus be used to learn about the fundamentals of urban travel beyond mobility and congestion. It can also potentially play an important role in our understanding of patterns of land use and property prices in cities in relation to transportation. Relative to more traditional travel surveys, the information used here is less complete but can be gathered at a small fraction of the cost, 37
latter is then exported to RMSM-X.<sup>Under the "requirements" model</sup> closure, calculating the supply schedule<sup>means obtaining the values</sup> of i*ef F*eI and i n at each<sup>point in time.</sup> Under the "availabilities" model closure, we<sup>must calculatei</sup> and F t. In both cases, no iterations are necessary<sup>to match supply and demand</sup> for foreign credit.

The rest of this chapter provides<sup>a detailed description of</sup> the DM. We start by presenting<sup>the data requirements of the DM.</sup> Second, we look at how existing<sup>debt is obtained and at how</sup> alternative debt-restructuring techniques<sup>can be analyzed. Third,</sup> we indicate how the DM computes values<sup>for new debt. Fourth, we</sup> explain how the totals are assembled.<sup>Finally, we discuss some</sup> possible extensions of the current<sup>version of the DM.</sup>

**III.2** Initial Data and Assumptions

Before solving the DM it is necessary<sup>to enter some</sup> information concerning already contracted<sup>debt and to make</sup> assumptions about the terms and conditions<sup>at which new debt would</sup> be contracted. The user must enter<sup>the stock of debt (FJBY) in the</sup> base year,8 the projected path for<sup>gross disbursements (GDJp)</sup> 1 amortization payments (APJ p) and, interest payments (IPJ p~~~~~~~~~~ p) The subscript p is used to indicate<sup>pipeline variables.</sup> These variables, which come from the DRS system,<sup>must be entered for each</sup> 8 Throughout this section, j indicates<sup>the j-th creditor and</sup> m the total number of creditors. For simplicity we omit the asterisk superscript when we refer<sup>to a creditor variable.</sup> We still keep it for totals.

42
# **Impact Evaluation of Three Types of Early Childhood Development Interventions in Cambodia**

Adrien Bouguen*, Deon Filmer**, Karen Macours***, Sophie Naudeau**<sup>1</sup>

* J-Pal Europe and the Paris School of Economics; ** World Bank; *** Paris School of Economics and INRA.

JEL Codes: I21; I25; I28 Sector Board: EDU

> 1 We thank Luis Benveniste, Norbert Schady, Beng Simeth, Tsuyoshi Fukao, and members of Early Child Development Department of the Royal Government of Cambodia’s Ministry of Education for input and assistance in carrying out this work, and David Evans and Emanuela Galasso for useful comments on an earlier version of this paper. Ella Humphry and Alison Whyte provided valuable research assistance. The authors are responsible for any errors. This work benefited from funding from the World Bank, the EPDF Trust Fund (TF095369), and the Spanish Impact Evaluation Fund (TF097107). The findings, interpretations, and conclusions expressed in this paper are those of the authors and do not necessarily represent the views of the World Bank, its Executive Directors, or the governments they represent.