rafmacalaba/datause-encoder-data huggingface dataset:
**Table C1. Food items and budget shares in GLSS7 (continued)**

|Consumption Survey Group|Budget Share|Price Survey Item|
|---|---|---|
|Dawadawa|0.007|(k)|
|Curry powder|0.000|(l)|
|Black/dried red pepper|0.007|Pepper (dried)|
|||Chili powder (black pepper)|
|Other spices|0.000|(m)|
|Other condiments|0.013|Royco|
|||Onga cube|
|||Benny|
|||Other (specify) _ _ _ _ _ _ _ _ _ _|
|Salt|0.010|Salt (iodized - local)|
|||Anapuna iodated salt|
|||U2 iodated salt|
|Coffee (Nescafe)|0.000|Nescafe - tin|
|||Pure cocoa powder (for example,|
|Pure cocoa powder|0.000|Brown Gold)|
|Tea|0.002|Tea bags (for example, Lipton)|
|Cocoa with milk powder|0.012|Cocoa with milk powder|
|||Bournvita|
|||Cow bell (coffee, choco, and so on)<br>This way chocolate drink|
|||Country milk - chocolate|
|Water|0.038|Sachet water|
|||Coca Cola/Fanta (bottle)/Sprite|
|Soft drinks|0.006|(bottle)|
|Malt|0.012|Malta Guinness|
|||Magic malt|
|||Other (specify) _ _ _ _ _ _ _ _ _ _|
|Fruit juice|0.005|Don Simon|
|||Frutelli|
|||Blue skies|
|||Pure Heaven|
|||Kalipo|
|||Healthy live|
|||Ceres|
|||Other (specify) _ _ _ _ _ _ _ _ _ _|

_Note:_ Average budget shares is each item’s household expenditure share on average. For items with multiple prices per consumption survey group, the price relativities are averaged before mapping to the budget shares. Items with () use prices of similar items as follows: (a) guinea fowl - live; (b) average of the meat group (excluding sausage and corned beef); (c) dried fish; (d) groundnuts (shelled) and groundnuts (roasted); (e) average of the fruit group; (f) average of the vegetable group; (g) half of the price of groundnuts (shelled); (h) yam - puna and water yam; (i) cassava (fresh); (j) average of the tuber group; (k) white beans (cowpea); (l) pepper (dried) and chili powder (black pepper); and (m) royco, onga cube, benny, and other condiments.

41
# **MAIN ABBREVIATIONS & ACRONYMS**

|**ANMEB**|**National Agreement for the Modernization of Basic Education**<br>**(Acuerdo Nacional para la Modernización de la Educación Básica)**|
|---|---|
|**EEEP**|**The Primary Education Assessment Survey, second round 1997**<br>**(Encuesta de Evaluación de Educación Primaria, segundo levantamiento 1997)**|
|**INEGI**|**National Institute of Statistics, Geography, and Information**<br>**(Instituto National de Estadística, Geografía e Informática)**|
|**SEP**|**Ministry of Education**<br>**(Secretaría de Educación Pública)**|
|**SNTE**|**National Union of Education Workers**<br>**(Sindicato Nacional de Trabajadores de la Educación)**|

2
contributors account for approximately 25 percent of the total labor force.<sup>14</sup> The participation rate has a negligible impact on the later calculations and is based on a historical mean value.

Although a broad approach may limit the precision of calculations, the regressions in sections 5 and 6 show that the variable has a high explanatory power on growth. Moreover, as can be seen from Figure 3, the estimated values give a more reasonable picture than the data from official sources. Notice that the _level_ of the economically active population, but not its growth rate, is basically in line with ILO or INE calculations. In 1980s, when the civil war had already taken genocide proportions, the labor force dropped dramatically by about 660,000.<sup>15</sup> For recent years, the estimate for the economically active population derived from IGSS statistics comes close to INE data.

# **4.3 Physical Capital Stock**

Internationally, the Perpetual Inventory Method (PIM) is a common way to estimate capital stock, but there are uncertainties associated with the calculation. In general, due to the lack of information about the initial capital stock, questionable validity of assumptions about the rate of depreciation, and lack of information about the utilization of capital, estimates should be taken with care. With these reservations in mind, the PIM was used to construct the physical capital stock for Guatemala. The following paragraphs present two distinct calculations, one with aggregated and another with disaggregated investment data.

# **_Estimate with Aggregated Investment Data_**

The physical capital stock that is used throughout the subsequent analyses is computed using the PIM with aggregated investment data. The procedure argues that the stock of capital is the accumulation of the stream of past investments: where _K_ is the capital stock, _I_ gross fixed capital formation, _δ K_ the annual depreciation rate of the capital stock, and _t_ an index for time. The initial value of the capital-output ratio for 1950 is taken from the Nehru and Dhareshwar (1993) data

> 14 UNDP (2003a) reports a participation rate of 24.5 percent (2002). Based on INE data, as reported by Global Info Group (1999), this compares to 27.6 percent (1995), 29.9 percent (1990) and 28.2 percent (1985).

> 15 It should be emphasized that the reliance on IGSS data may understate the drop of the economically active population during the 1980s. This is because the working population in the informal and rural sectors—typically not captured by the social security system—was particularly affected by violence and displacement policies.

-23-
Finally, most of the research on this subject examines the effects of group composition by income, academic or labor performance, and entrepreneurship propensity.<sup>3</sup> Existing evidence indicates that exposure to violent peers can also have negative impacts on human capital accumulation and criminal behavior later in life.<sup>4</sup> However, there has been little rigorous analysis of the impacts of group composition in terms of propensity for violence on behaviors and economic outcomes.

This paper conducts the first randomized controlled trial designed to address these three gaps. I experimentally manipulate whether students participated in an After-School Program (ASP) in homogeneous or heterogeneous groups according to their initial predicted propensity for violence. The empirical design overcomes some issues related to the identification of peer effects, such as the reflection problem and the strong assumption of the separability of peer composition and other confounding effects within groups (Manski, 1993; Angrist, 2014). In addition to estimating average treatment effects, my study design allows me to exploit a discontinuity in propensity for violence to estimate effects on marginal group members. The analysis focuses on the differential impacts of exposure to a particular composition of peers on academic, neurophysiological, and behavioral outcomes, or if it changes students’ effort at school. My results show that it is beneficial to mix students with different levels of violence rather than to segregate them into more and less violent groups. To my knowledge, this paper provides the first experimental evaluation of the effects of peer composition by propensity for violence.

The ASP I study in this paper consists of clubs implemented after school but within school facilities in El Salvador from April to mid-October during the 2016 academic year.<sup>5</sup> Students participated in two sessions per week that lasted 1.5 hours each. Every session combined: (i) a dis-

> 3For example, Duflo et al. (2011); Rao (2019); Lavy et al. (2012); Zimmerman (2003); Guryan et al. (2009); Bandiera et al. (2010); Lafortune et al. (2016)

> 4Billings et al. (2016); Di Tella and Schargrodsky (2013); Bayer et al. (2009); Damm and Dustmann (2014).

> 5El Salvador’s academic year begins in January and ends in November

3
11 TABLE 1: Sumary _Tabl_ for Couctri. R.vlwad (Unwe_ghted AvecWa **hr** 21 **Coied.)**

||1986<br>al|1987<br>Pated_|Acta|198<br>Projected|ActI|1989<br>Proetd|Acta|1990<br>Projee|al|_1991_<br>Projted|Ad"|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Budse<br>_D._|-10.8|-9.9|-11.8|4.|413<br>_|**3.5**|**-11.5**|**43**|4.|-7.6|-7.9|
|Currem<br>Account<br>**Deficit**|-14.6|-14.6|-153|-11.6|-14.9|-15.2|-16.0|-16.1|-16.4|.15.6|-16.6|
|**(cmT**<br>**Adjuetd**<br>Acuala)III|**(-15.0)**||(-14.9)||(-15.2)||(-143)||(-14.1)||(-13.9)|
|invatoet|**_1S.5_**|16.6|17.5|18.10|17.9|16.9|17.3|17.4|17.6|17.7|16.4|
|S&VIOg|6.8|63|5.6|7.0|**_5.5_**|7.0|4.9|6.7|4.3|6.4|3.4|
|Export<br>Growth|2.7|6.9|4.7|43|1.5|7.8|2.6|6.7|7.3|93|-0.1|
|aDp|3.1|2.9|2.2|3.8|4.9|4.0|3.2|3.7|2.1|3.4|2.|

As for external balance, the acul value of the curmt account deficit as a rato of GDP has also exceeded the proected figures in all the years. There are two aspects of the expectd impact of adjustmnent programs on this ratio - the compression of demand allowing imports to go down, and improved pnce incentives for ceprtu to go up, reducing the current account balance. I shall come back to the growth of **exports** while discusng supply responses. The slow pace of externa sctor adjustment reflects a continued reiance on the flow of _extrnal_ resource. Even the proected values assumed that these countres would continue to have on average 14 to 15 prroent cunrent account gap. In fact, snce the projections are based on the latest PFP available, the projected values indicate the acceptance of the fact that the gap would continue to be large. Whereas the gap on average was projected to come down to lower than 12 percent
personal, cultural, and recreational services; and government services. To avoid giving undue importance to very small trade flows that might represent misreporting rather than genuine trade, we code our dependent variable to be unity whenever a trade flow is greater than one million dollars, and zero in all other cases.<sup>4</sup> Due to limitations on the availability of other data, we use trade data for 2009 only, which is in line with previous work on comparative advantage.<sup>5</sup> Data on trade in services suffer from a number of important issues that could cause difficulties for the results reported here. One is that our source only covers trade in Mode 1 and some Mode 2 trade (travel and related services). It does not cover Mode 3, which is an important means of entry in many sectors. Secondly, services trade is sometimes misreported by businesses, and ends up being recorded as a residual in the balance of payments statistics. There is no way to overcome this difficulty, but it is one from which all empirical work on services trade suffers. Thirdly, particular sectors, such as cultural and recreational services, sometimes suffer from additional problems of misreporting, due to the importance of royalties. Again, all empirical work using this kind of services trade data suffers from these potential drawbacks. Nonetheless, we are confident that our data represent the best source currently available on cross-border trade in services.

In including data on the sources of trade costs, we generally follow the specifications in Chor (2010) and van der Marel (2011), which draw on the standard gravity model literature. Although trade costs are more difficult to conceptualize in the services sector—because there is no equivalent of ad valorem tariffs, for example—they are nonetheless believed to be very large (Miroudot et al., Forthcoming). Costs include communication—which can be proxied by geographical and cultural distance—as well as trade policies that tend to open markets or reduce regulatory heterogeneity, such as RTAs.

Concretely, we specify:

> 4 Our results are qualitatively very similar if we use alternative thresholds, such as $0.5m or $0.18m (the tenth percentile of the trade data). These robustness checks are available on request.

> 5 Although a cross-sectional approach is appropriate in light of previous work, the use of data for 2009 is not unproblematic because it might be affected by the global financial crisis.

14
there are statistically significant differences between the treated and control parents at the 10 percent level for two variables: education and whether the household head works for a wage. While these differences are statistically significant, they are generally quite small (they are on the order of 15 percent). A joint test for significance of the full set of variables in predicting treatment fails to reject that they jointly equal zero. We nevertheless control for demographic characteristics in the analysis to account for any potential differences between the different groups.

**Table 3: Summary statistics of background characteristics at end line, by treatment and control groups**

||||<br>**Tulungaggung**||<br>**Mal**|**ang**|<br>**Sumb**|**awa**|
|---|---|---|---|---|---|---|---|---|
||**Full Sample**|Treatment 1<br>(Letter)|Treatment 2<br>(Pocket book)|Control|Treatment<br>(SMS)|Control|Treatment<br>(meeting)|Control|
|Household in urban area|0.42|0.55**|0.31|0.30|0.96|0.96|0.19|0.10|
||(0.49)|(0.50)|(0.46)|(0.46)|(0.19)|(0.19)|(0.39)|(0.30)|
|HH head with junior|0.36|0.29|0.29|0.33|0.63*|0.54|0.23|0.20|
|secondary or more|(0.48)|(0.45)|(0.45)|(0.47)|(0.48)|(0.50)|(0.42)|(0.40)|
|HH head senior|0.26|0.19|0.19|0.22|0.51|0.46|0.15|0.13|
|secondary or more|(0.44)|(0.40)|(0.39)|(0.42)|(0.50)|(0.50)|(0.35)|(0.34)|
|HH is male|0.60|0.64|0.68|0.69|0.74|0.68|0.47|0.46|
||(0.49)|(0.48)|(0.47)|(0.47)|(0.44)|(0.47)|(0.50)|(0.50)|
|HH head is a parent|0.55|0.57**|0.61|0.64|0.70|0.64|0.46|0.44|
||(0.50)|(0.50)|(0.49)|(0.48)|(0.46)|(0.48)|(0.50)|(0.50)|
|HH is civil servant|0.05|0.04|0.03|0.03|0.11|0.11|0.03|0.03|
||(0.23)|(0.20)|(0.18)|(0.16)|(0.31)|(0.32)|(0.18)|(0.16)|
|HH is waged employee|0.25|0.25**|0.36|0.38|0.44*|0.38|0.19|0.16|
||(0.43)|(0.43)|(0.48)|(0.49)|(0.50)|(0.49)|(0.40)|(0.37)|
|HH monthly per capita|12.41|12.47|12.40|12.37|12.79|12.75|12.24|12.13|
|expenditure (log)|(0.63)|(0.45)|(0.46)|(0.45)|(0.55)|(0.58)|(0.54)|(0.57)|
|Number of observations|4482|270|270|270|256|264|252|240|
|Tests of joint significancein|predicting trea|tment|||||||
|Individual variables:|||||||||
|F-statistic||3.32|0.67||1.01||0.83||
|P-value||0.01**|0.72||0.45||0.58||

Note: Standard errors reported in parenthesis clustered at the school level. * (**) indicates statistical significance as compared to the control group at the 10 (5) percent level.

# **_3.2 Outcomes_**

This section organizes the results around the logic discussed above. First, we present the impact of the different approaches on whether information was assimilated and translated into increased knowledge about the program. Second, we test whether this has translated into more knowledge about the BOS program in the specific school, particularly knowledge about the formal mechanisms for participation in school and about the use of BOS funds. Last, we present the results on actual participation in the school, both through formal as well as informal channels.

16
# **Table 1. Summary of union formation and first parenting outcomes and explanatory variables**

||**Males**|||**Females**||
|---|---|---|---|---|---|
|**Mea**||||||
|**n**|**SD**|**n**|**Mean**|**SD**|**n**|
|**Union Formation and First Parenting Outcomes (Y)**<br>_1) Ever in a union and whether young (under 18y) at initiation_|_of first u_|_nion and fi_|_rst parenting_|_:_||
|Ever in union<br>0.833|0.373|636|0.882|0.323|770|
|Whether young first union<br>0.065|0.246|636|0.284|0.451|770|
|Whetheryoungfirst parenting<br>0.036|0.185|647|0.217|0.412|779|
|_2) Age at first union and first parenting:_||||||
|Age at first union<br>22.1|3.8|530|19.8|4.0|679|
|Age at first parenting<br>23.2|3.9|523|20.3|3.8|692|
|<br>Gap between first union & parenting<br>1.3|2.4|469|0.6|2.2|641|
|_3) Age gap with partner and partner characteristics:_||||||
|Age gap (own age-partner's age)<br>2.8|4.6|495|-3.0|5.8|596|
|Partner's schooling attainment<br>4.5|3.2|608|4.9|3.6|444|
|Partner's assets at union<br>4342|17720|608|22111|44972|415|
|Height of partner<br>150.5|5.2|532|162.5|5.7|403|
|<br>BMI of partner<br>26.6|4.6|528|25.5|3.7|399|
|<br>_4) Partner’s parental family (“in-laws”) characteristics:_||||||
|Land of inlaws (manzanas)<br>5.0|70.3|487|2.8|9.5|404|
|<br>Schooling mother-in-law<br>1.6|2.2|573|1.7|2.2|541|
|Schoolingfather-in-law<br>2.1|2.4|519|2.2|2.8|516|
|**Explanatory Variables**||||||
|<br>_1) Early Adolescent Life-Cycle Stage Capital Stocks (Ka):_||||||
|Schooling attainment<br>5.3|3.3|638|4.5|3.1|725|
|Height<br>162.8|5.9|592|150.5|5.5|669|
|_2) Family Background in Early-Adolescent Lifecycle Stage (Fa_|_):_|110|||107|
|Mother's schooling<br>1.4|1.7|1|1.3|1.6|4|
|||104|||101|
|Father's schooling<br>1.8|2.2|3|1.7|2.1|9|
|||107|||101|
|Householdwealth index in 1975<br>-0.12|1.7|6|-0.13|1.8|1|

16
# **Models**

We use two separate sets of data and two models. The first set of data is a quarterly data set covering aggregate regional statistics derived from macro data and LFSs (1999-2012). We call the model applied to these data the _regional medium-term model_ . The second is a panel data set based on LFSs and covering individuals during the period 2007-2011. We call the model applied to these data the _individual short-term model_ . Note that these are both panel models, the first traces regions over quarters and the second traces individuals over years on a quarter to quarter basis.

<u>Regional medium-term model. The quarterly model is a longitudinal panel model with a</u> continuous dependent variable (quarterly FLP rate) where time ( _t_ ) is represented by year-quarter observations and the panel is represented by the regions ( _i_ ) of Morocco. We first estimate this model with an OLS Instrumental Variable (OLS-IV) estimator of the form: where _y_ is GDP per capita and _X_ is a vector of controls. We also use an Arellano-Bond General Methods of Moments estimator (AB-GMM) of the form: where μi are the panel effects and θit is the i.i.d error term. The outputs of the two models and the outputs of various types of reduced form of Equation 2 are then compared (see Table 1). This is the standard approach used in long-term models testing for the U-shape hypothesis (see for example Tam, 2011, Gaddis and Klasen, 2013 and Tsani et al., 2013). The main difference is that we use quarterly cross-region data in one country rather than annual cross-country data.

<u>Individual short-term model.</u> This model includes a binary dependent variable (FLP) and a one year lag quarter to quarter panel component and it is estimated on the pooled individual quarterly data for the period 2007-2011. The model is estimated with a logit model for panel data. The probability of participation _P_ is given by where ∆(.) is the cumulative logistic distribution, αi represents the individual specific effects and X<sup>′</sup> it is a core of independent variables. The individual effects could be treated as fixed effects or as random effects. The fixed effects model treats the individual specific effect αi as an unobserved random variable that may be correlated with the regressors X<sup>′</sup> it. The random effects model treats αi as following a standard i.i.d distribution. We use both fixed and random effects models and test for differences with a Hausman test. Tests show that the fixed effects model provides more consistent estimations but also loses up to 90% of observations due to the fact that the panel time lag is of only one year. We opt therefore to present results for both fixed effects and random effects (see Table 2). All regressions are carried out separately for urban and rural areas.

6
**Table 8. The role of quality and innovation for economic upgrading through GVCs**

|VARIABLES|(1)<br>_DVA_|(2)<br>_DVA_|(3)<br>_DVA_|(4)<br>_DVA_|
|---|---|---|---|---|
|**Backward links**|||||
|_FVAX_|0.0595***<br>(0.0123)|0.0603***<br>(0.0129)|-0.0684<br>(0.0759)|0.0029<br>(0.0400)|
|_FVADP_|0.252***|0.253***|0.252***|0.252***|
||(0.0269)|(0.0268)|(0.0268)|(0.0268)|
|_FVAX*Quality ISOs_|4.5e-06<br>(3.7e-06)||||
|_FVAX*R&D intensity_||0.0086<br>(0.0083)|||
|_FVAX*Technology adoption_|||0.0265*<br>(0.0145)||
|_FVAX*Innovation index_||||0.0176*<br>(0.0101)|
|_Constant_|5.843***|5.775***|5.612***|5.847***|
||(0.178)|(0.180)|(0.177)|(0.185)|
|Observations|7,276|7,276|7,276|7,276|
|R-squared|0.852|0.852|0.852|0.852|
|**Forward links**|||||
|_DVAR_|0.183***|0.191***|-0.0062|0.113**|
||(0.0177)|(0.0200)|(0.111)|(0.0571)|
|_FVADP_|0.232***|0.233***|0.233***|0.233***|
||(0.0258)|(0.0257)|(0.0257)|(0.0257)|
|_DVAR*Quality ISOs_|1.7e-05***<br>(5.3e-06)||||
|_DVAR*R&D intensity_||0.0268**<br>(0.0117)|||
|_DVAR*Technology adoption_|||0.0427**<br>(0.0210)||
|_DVAR*Innovation index_||||0.0280*<br>(0.0143)|
|_Constant_|4.904***|5.070***|4.923***|4.957***|
||(0.170)|(0.182)|(0.176)|(0.179)|
|Observations|7,284|7,284|7,284|7,284|
|R-squared|0.870|0.870|0.870|0.870|

Source: OECD ICIO database. *** p<0.01, ** p<0.05, * p<0.1. Notes: Robust standard errors in parentheses. _FVAX_ and _DVAR_ lagged. All level variables in natural logarithms.

In fact, the evidence suggests a contrary strategy since it shows that labor standards thus promote economic upgrading through GVC integration as a seller. All six indicators have the expected sign and four are statistically significant (Basic ILO conventions, vulnerable employment, child labor, and wage dispersion). Minimum wages in our sample show no mediating effect, thereby rejecting the positive impact hypothesized by Mayneris and others (2014). Similarly, female intensity in our sample has no moderating effect, opposing the negative relationship hypothesized by Tejani and Milberg (2010). Finally, we conclude that raising labor standards is a sound and recommendable strategy for countries that are engaged in GVCs. Furthermore, the income group specific results show that, not only high, but also upper middle-income countries benefit from such standards, indicating that their facilitating potential is not dependent on the stage of development.

28
WSP assessment identified that in high performing districts (>90% toilet use in a representative survey of GPs that won NGP award) the subsidy for toilet construction was given as an incentive whereas in low performing districts (<35% toilet use) subsidy was used to push toilet construction without without commensurate efforts at demand creation. This impact evaluation is not designed to answer how the subsidy affects sustainability of the use of toilets or to study different subsidy mechanisms. However, this study provides a basis for future research to answer these questions.

Taken together, the study suggests that the TSC program shows promise for the successful expansion of access to improved sanitation in rural India. However, the challenge faced by rural India is increasing the use of newly constructed toilets to achieve commensurate reductions in open defecation. Additional measures of toilet construction, use, and maintenance over longer time periods in this cohort would provide additional evidence to support or refute whether the improvements observed in the present study are sustained (or even expanded) over a longer period. To the extent that such improvements reduce pathogen transmission, we would expect larger health improvements than those observed in the present study.

22 / 41