rafmacalaba/datause-encoder-data huggingface dataset:
Table 3. First Stage Regression Results, Dependent Variable: D(sz)

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|**RGr(MOD)**|1.005<sup>**</sup>|−25.42<sup>**</sup>|||||
||(0.180)<br>|(4.957)|||||
|**RGr(STEEP)**|−0.948<sup>**</sup>|1.831|||||
||(0.227)|(7.221)<br>|||||
|**RGr(VSTEEP)**|0.224|−11.56<sup>*</sup>|||||
||(0.187)|(5.270)|||||
|**P**|−0.000343|0.00270<sup>*</sup>|0.0816<sup>**</sup>|2.604<sup>**</sup>|||
||(0.000247)|(0.00116)|(0.0113)|(0.371)|||
|**RGr(MOD)×P**|||||−0.342<sup>**</sup>|6.391<sup>*</sup>|
||||||(0.0799)|(2.823)|
|**RGr(STEEP)×P**|||||−0.482<sup>**</sup>|−38.55<sup>**</sup>|
||||||(0.0898)<br>|(7.205)<br>|
|**RGr(VSTEEP)×P**|||||−0.676<sup>**</sup>|−7.181<sup>*</sup>|
||||||(0.199)|(3.320)<br>|
|**ERLENGTH×P**||||||−0.00222<sup>**</sup><br>(0.000447)|
|**Dams Size**|LARGE|SMALL|LARGE|SMALL|LARGE|SMALL|
|**Observations**|4,460|4,460|4,460|4,460|4,460|4,460|
|**Basins**|223|223|223|223|223|223|
|**F-Test for RGrit**|59.82<sup>**</sup>|44.26<sup>**</sup>|---|---|13.45<sup>**</sup>|10.89<sup>**</sup>|

_Note:_ Regressions include homeland, river basin elevation controls (three dummies indicating fraction of area with 250–500 m, 500–1,000, and above 1,000 m elevation), river basin overall gradient controls (three variables indicating the fraction of area with 1.5–3 percent, 3–6 percent, and over 6 percent gradient), area, total perennial and ephemeral river lengths, and time dummies. Standard errors given in parentheses are robust.<sup>**</sup> and<sup>*</sup> are 1 percent and 5 percent significance levels, respectively; RGr(MOD) is the fraction of perennial river gradient at 1.5–3 percent, RGr(STEEP) is the fraction of perennial river gradient at 3–6 percent, RGr(VSTEEP) is the fraction of perennial river gradient at above 6 percent, and ERLENGTH is the length of ephemeral rivers in kilometers. The F-test for RGrit refers to a test on the coefficients of the river gradient variables.

Examining small dams, we find that the presence of irrigation dams is less likely in very steep and moderate river gradient basins, as might be expected a priori. Perhaps somewhat peculiarly, we also find that small dams are more likely to be constructed in basins with larger shares of steeply sloped rivers. Importantly, we find that small dam location is more likely in basins where there are more ephemeral rivers.

28
Figures 2-7 <!-- Start of picture text --> 1990:RATE OF CHANGE EMPLOYMENT & OUTPUT<br>ffii .miEw _ fll 11 N.<br>0~~~~~~~~~~~~~~~~~~~~~<br>a o a: -<br>a a<br>0<br><!-- End of picture text --> <!-- Start of picture text --> fI9: RATE OF CHANGE EMPLDYMENT & OUTPUT<br>_EM* N flA W_ INW<br>S .<br>n<br>14~~~~~~~~<br>.t S , - .<br><!-- End of picture text --> <!-- Start of picture text --> Is~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~I<br>1992:RATE OF CHANGE EMPLOYMENT 8 OUTPUT<br>we . . .'.~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~..<br><!-- End of picture text --> <!-- Start of picture text --> 1992:RATE OF CHARGE EMPLOYMENT & OUTPUT<br><!-- End of picture text -->
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36
- 13 -

where η<sup>_Y_</sup> _i_ is the cost elasticity with respect to output _i_ . The degree of economies of customer density is also calculated by:

> where η _Z m_ is the cost elasticity with respect to the number of connections _Zm_ .

# **_Data_**

All the data are employed from the Private Participation in Infrastructure Services (PPIS) Database, which provides the detailed operational information on most private and public water utilities since 1993. The current paper uses only observations under some types of PPP (affermage, management contract, lease, concession and divestiture). The cross-sectional time-series data are pooled; in most cases in our sample, each utility has 3–5 annual observations. Note that in the PPIS Database, the information on employment and wage expenses is not always provided separately for each of the water and sewage operations. In practice, the decomposition is sometimes infeasible when staffs are working on both operations.

Table 1 shows the summary statistics of costs, outputs, and factor prices. The sample observations are quite heterogeneous in any aspects, as indicated in Figure 3. For instance, annual operating expenses range from US$37,000 to more than US$ 100 million. The majority of observations are concession transactions.
# **1. Introduction**

Between the late 1990s and 2010, Latin America and the Caribbean (LAC) achieved substantial economic progress: GDP per capita grew by more than 50 percent in real terms (ECLAC), the proportion of poor individuals fell from 27.5 percent in 1992 to 12.6 percent in 2011 (SEDLAC, 2013), and inequality as measured by the Gini index fell by approximately 10 percent during the 2000s alone. Yet even as the region registered these gains, the attention of its public and policymakers has increasingly focused on an economic and social problem faced by countries throughout the region. This problem is the large number of youth who are neither in school nor at work—youth commonly labeled as _ninis,_<sup>_2_</sup> from the Spanish phrase “ _ni_ estudian _ni_ trabajan _”—_ and it has proved persistent and seemingly structural. Unless it is understood better and tackled through policy interventions, the problem of ninis could limit the region’s ability to reap the dividend from its demographic transition, as its large youth cohort moves from dependency into its prime working years.

To address the nini issue, policy makers in the region need to understand how broad the problem is, whether it is growing or subsiding, and who the ninis are. Yet until now, there has been little analysis of the nini phenomenon for the Latin America and Caribbean region as a whole.<sup>3</sup> To provide such a diagnosis of the 15 to 24 year-old ninis, this study draws on micro data from 238 household surveys for 15 Latin American countries spanning the period 1992–2010. For these country-year combinations, we use harmonized data from SEDLAC, the Socio-Economic Database for Latin America and the Caribbean maintained by the Centro de Estudios Distributivos Laborales y Sociales (CEDLAS) and the World Bank, supplemented by surveys assembled in Cárdenas, de Hoyos, and Székely (2015), which include comparable variables for school attendance, economic activity, and socio-demographic characteristics. The resulting quantitative description of ninis in Latin America is enriched by a comparison with the prevalence and characteristics of ninis in other parts of the world. This newly expanded dataset allows an unprecedentedly complete portrait of the problem.

Specifically, this study answers the following questions: What is the prevalence of ninis in LAC and how has it evolved over time? How do the share and characteristics of ninis in Latin America compare with those in other regions of the world? How does the likelihood of being a nini differ by age and gender? How many ninis are actively searching for work, versus choosing to be ninis or having disengaged entirely? And do these patterns vary with the decision to start a household?

> 2 In Brazil, they are known by the corresponding Portuguese term, _nem-nem._

> 3 One exception is the recent analysis by Cárdenas, de Hoyos, and Székely (2015), on which this study builds.

2
where _fN_<sup>_q_(</sup><sup>_w|m, V_) denotes the density function of a</sup><sup>_q_-dimensional Normal distribution on</sup><sup>_w_with</sup> mean _m_ and covariance matrix _V_ and they choose _g_ = 1 _/_ max _{n, k_<sup>2</sup> _}_ . Finally, the components of _β_ not appearing in _Mj_ are exactly zero, represented by a prior point mass at zero.

The prior model probabilities are specified by _P_ ( _Mj_ ) = _θ_<sup>_kj_</sup> (1 _− θ_ )<sup>_k−kj_</sup> , which implies that each regressor enters a model independently of the others with prior probability _θ_ . Thus, the prior expected model size is _θk_ . We follow Fern ́andez _et al._ (2001a) and FLS in choosing _θ_ = 0 _._ 5, which is a benchmark choice—implying that _P_ ( _Mj_ ) = 2<sup>_−k_</sup> and that expected model size is _k/_ 2. Throughout this paper, we shall use the same prior as in FLS. An explicit analysis of alternative priors in this context is the subject of current research.

FLS use a Markov chain Monte Carlo (MCMC) sampler to deal with the very large model space ( _k_ = 41 for their data set, leading to 2 _._ 2 _×_ 10<sup>12</sup> possible models to consider), which can easily be implemented through a Metropolis algorithm over model space alone—since the marginal likelihoods and thus the posterior model odds between any two models are analytically available; see FLS for details. Thus, the MCMC sampler is only implemented to deal with the practical impossibility of exhaustive analysis of the model space: the chain will visit only the most promising models, which are the ones with non-negligible posterior probability.

The results in FLS indicate that the posterior probability is spread widely among many models (Table 1), which implies the superiority of BMA over choosing a single model but, precisely because of its richness, also makes it harder to summarise posterior results.

**Table 1.** FLS Data: distribution of posterior model probability and model size.

(MCMC sampler with 2 million recorded draws after a burn-in of 1 million draws.)

|Number|Posterior|Number|of Regressors|
|---|---|---|---|
|of Models|Prob.|Mean|St.Dev.|
|1|0.01|10|-|
|5|0.04|8.7|1.1|
|25|0.10|8.9|1.4|
|50|0.14|9.0|1.4|
|100|0.19|9.1|1.4|
|190|0.25|9.1|1.4|
|1,606|0.50|9.4|1.5|
|8,688|0.75|9.6|1.5|
|25,269|0.90|9.8|1.6|
|39,839|0.95|9.8|1.6|
|71,493|0.99|9.9|1.6|
|148,342|1.00|9.9|1.6|

In related work, Doppelhofer and Weeks (2005, DW henceforth) analyse the same linear regression model (using the BACE method of SDM) and introduce a measure of jointness “to address dependence among explanatory variables.” DW propose a jointness statistic defined as the log ratio of the joint posterior inclusion probability of a set of variables over the product of the individual posterior inclusion probabilities. Below we discuss this measure as well as another measure 2
|**Table** **3. OLS Regressions for “Per**|**ceived Freedom o **|**fChoice and Co**<br>**Perceived Fr**|**ntrol”**<br>**eedom of Choice**|**and Control**||
|---|---|---|---|---|---|
||[1]|[2]|[3]|[4]|[5]|
|Female [X001; 1=yes, 0=no]|-0.134***|-0.075***|-0.104***|-0.094***|-0.094***|
||[0.010]|[0.011]|[0.011]|[0.010]|[0.023]|
|Married [X007; 1=yes, 0=no]||-0.184***|-0.139***|0.055***|0.055***|
|||[0.011]|[0.011]|[0.011]|[0.021]|
|Education Level [X025R]||0.170***|0.155***|0.145***|0.145***|
|||[0.007]|[0.007]|[0.007]|[0.017]|
|Age [X003]||-0.009***|-0.012***|-0.013***|-0.013***|
|||[0.002]|[0.002]|[0.002]|[0.004]|
|Age Squared||0.000***|0.000***|0.000***|0.000***|
|||[0.000]|[0.000]|[0.000]|[0.000]|
|Income Scale [X047]||0.135***|0.130***|0.124***|0.124***|
|||[0.002]|[0.002]|[0.002]|[0.009]|
|Religiosity [F063]||0.059***|0.085***|0.049***|0.049***|
|||[0.002]|[0.002]|[0.002]|[0.010]|
|Part-time Employment [X028;||-0.009|0.009|-0.064***|-0.064***|
|_Idem below]_||[0.019]|[0.019]|[0.018]|[0.023]|
|Self-employed||0.078***|0.196***|0.074***|0.074**|
|||[0.017]|[0.017]|[0.017]|[0.029]|
|Retired||-0.312***|-0.268***|-0.133***|-0.133***|
|||[0.022]|[0.021]|[0.021]|[0.043]|
|Housewife||-0.235***|-0.198***|-0.213***|-0.213***|
|||[0.017]|[0.017]|[0.017]|[0.032]|
|Students||-0.165***|-0.106***|-0.084***|-0.084**|
|||[0.022]|[0.021]|[0.021]|[0.038]|
|Unemployed||-0.316***|-0.243***|-0.250***|-0.250***|
|||[0.018]|[0.018]|[0.017]|[0.029]|
|Low Income|||-0.963***|||
||||[0.028]|||
|Lower Middle Income|||-0.711***<br>[0.014]|||
|Upper Middle Income|||-0.368***<br>[0.012]|||
|Constant|6.990***|5.835***|6.111***|4.820***|4.820***|
||[0.007]|[0.046]|[0.046]|[0.124]|[0.268]|
|Observations|236,190|236,190|236,190|236,190|236,190|
|Adjusted R-squared|0.000791|0.0332|0.0453|0.123|0.123|
|Year fixed effects|No|No|No|Yes|Yes|
|Country fixed effects|No|No|No|Yes|Yes|
|Cluster-adjusted standard errors|No|No|No|No|Yes|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 23
# **Acknowledgements**

We are grateful to the Trust Fund for Environmentally and Socially Sustainable Development for providing financial assistance for this study. First, we would like to acknowledge Kirk Hamilton, Team Leader Policy and Economics Team for his encouragement and leadership. This work was undertaken in part to support on-going analytical work on irrigation management transfer lead by Mie Xie in the East Asia Rural Development Department of the World Bank. We are grateful to Syed Husain, Consultant and former Task Manager of two World Bank irrigation projects in the Philippines for his detailed feed-back. We would also like to acknowledge Project Manager Cristina Dela Paz, and survey team leaders Antonia Carlos, and Milagros Tetangco from Hassall/SDS and Associates and their team for doing an excellent job with the household and irrigation association surveys, and, all the respondents who patiently answered the survey questions. Any remaining errors are the authors’ responsibility. The opinions expressed in this paper are the authors’ own and do not necessarily represent those of the World Bank Group.

2
pendix Table A3, I present these estimations using different standardizations of academic grades and behavior reports.

Finally, the IVV predicts both intensive and extensive margins of future misbehavior. Using data from students in the control group, I find that the correlation between IVV and bad behavior at the end of the academic year is positive and statistically significant at 5%. The estimation strategy and main results are in Appendix Table A4.

# **Treatments**

After estimating the IVV, enrolled children were randomly assigned to two groups—control (C, 25%) and treatment (T, 75%)—within each school-by-educational-level “block.”<sup>14</sup> Then, in a second randomization stage, treated children were randomly assigned to two treatment arms— heterogeneous (HT, 25%) and homogeneous (HM, 50%)—as shown in Figure 1.

Next, students in the HM set were ranked and assigned to subgroups according to their index: all students with an IVV above the median at the HM-stratum level were assigned to the High-IVV group (HM-High, 25% of the full sample) and the rest were assigned to the Low-IVV (HM-Low, 25%) group. The HM-Low and HM-High groups are defined using the median in each schoolby-educational-level block because I wanted variation in the cutoff. Moreover, an uniform cutoff across all randomization blocks could generate differences in group sizes that would probably confound the effects of tracking with group size. This design permits me to judge if targeting helps to improve the ASP’s effects and allows me to test the potential existence of peer effects and heterogeneity by initial propensity for violence in the program. In addition, this strategy utilizes a regression discontinuity (RD) design approach to measure the impact of tracking on the marginal student and contrast it to the average impact.

> 14Each education level consists of three years of schooling: the first is from first to third grades, the second from fourth to sixth grades, and the third from seventh to ninth grades.

13
2 in SSA, the paper addresses three empirical questions using pooled time-series and cross-section data for 22 SSA economies for the period 1971-91. These are the dynamic relationship between nominal and real devaluations, the impact of real devaluations on per capita real GOP growth, and the causal relationships and the directions of causality between devaluations, inflation, GDP growth and exports.

It should be highlighted here that the real exchange rate is an endogenous variable and that nominal devaluation is just one of the instruments that affect its level. Nominal devaluations do not always result -in a change in the real exchange rate. The magnitude of such as change depends on the type of fiscal, monetary and income policies that accompany the devaluation. The paper is organized as follows. Chapter 2 discusses the evolution of exchange rate arrangements undertaken in SSA, examines the successes and failures of exchange rate unificationin adjusting countries without currency convertibility, and discusses the experience of the CFA zone countries in the 1970s and 1980s. Chapter 3 reviews existing literature on the effectiveness of output supply response of a real devaluation. And chapter 4 tests empirically the impact of real exchange rate devaluations on economic growth and exports and analyses the relationship between nominal and real devaluations.
49

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making the entry of competitors possible, which, again, would increase both activity levels and demand for labor.<sup>5</sup> Product market regulation (PMR) is measurable through a methodology developed at the OECD relying on the OECD Regulatory indicators questionnaire. The methodology and key findings of the PMR for OECD countries are presented in Nicoletti et al. (1999), Conway et al. (2005) and Wölfl et al. (2009). The PMR indicators are designed to reflect regulations that have the potential to restrict competition in areas where competition is viable. By construction, they have a number of features which make them useful not only for analysis, but, more importantly, for policy advice, since they allow to pinpoint specific policies that hamper competition in product markets. _First_ , PMR indicators are focused on enacted policies and not on outcomes, implying that they are ‘objective’, in that they are not based on opinion surveys. _Second_ , since the summary PMR indicator is constructed as the average of well-defined components, PMR scores can be related to specific underlying policies, thus providing precise inputs in the phase of policy recommendation. _Third_ , PMR indicators focus on regulatory measures that affect the economy at large and can therefore be considered as comprehensive measures of regulatory restrictiveness. Their advantages notwithstanding, PMR indicators are not designed to capture informal regulatory practices nor the effective enforcement of regulations, since they are only concerned with formal compliance with a number of criteria.

> 5Nicoletti and Scarpetta (2005), Griffith and Harrison (2004), Griffith et al. (2007) provide evidence that easing anticompetitive product market regulation may have a positive effect on employment.

5
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