rafmacalaba/datause-encoder-data huggingface dataset:
coding the level of production and the proven reserves of oil, in per capita terms; Ross (2008) variable recording oil (and gas) rents per capita (i.e. accounting for levels of production, country specific costs and prices); Lujala et al. (2005) dichotomous indicator variable for diamond production, as well as for the type of diamond resource – primary (kimberlite) vs. secondary (alluvial); Lujala (2010) dichotomous indicator variables for the production of oil from both onshore and offshore deposits.

For the empirical estimations we use a logit model with robust standard errors and we control for the temporal relation among observations by including the duration of peace and cubic splines (Beck et al. 1998). The control variables are based on Fearon and Laitin (2003) and include: the lagged income per capita, the log of the country’s population, noncontiguous territory, the log of the estimated percentage of country’s mountainous terrain, lagged democracy score and religious fractionalization.<sup>10</sup> To test our conditional hypotheses, we use interaction terms between measures of resource endowment and measures of state weakness.

# **4. Results and discussion**

Tables 1 and 2 show the results for the context variables for which we expect natural resource endowments to exacerbate the chances of civil war onset: anocracy, political instability, riot 10 Income per capita comes from Penn World Tables and World Development Indicators, supplemented with per capita energy consumption. Population size is based on World Bank World Development Indicators. We do not control for recent independence because for some of our specifications data is only available post 1960, and this variable may not have any variance. Democracy is based on lagged values of Polity IV (-10 to 10). For the models using anocracy as an indicator for weak states only a democracy indicator is also included (Polity IV greater than five). Data on control variables is updated to 2003 using Lujala (2010).

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way democracy works in El Salvador?” Again the main results do not depend on dichotomization decisions.

Table 1 shows some basic summary statistics for the aforementioned dependent variables as well as for some set of individual-level characteristics. Panel A focuses on LAPOP data while Panel B on Latinobar ́ometro data. My samples of analysis come from pooling different waves available for the time period 2004-2018. Note that the total number of observations for each variable vary because some of the questions were not asked every wave and, in some cases, when asked some respondents choose to not answer. Roughly half of the respondents are men (48%) and the average age is 39 in both samples. In the LAPOP sample, 38% are individuals living in rural areas while 18% of the individuals in the Latinobar ́ometro sample lives in small urban areas. Average years of schooling is above 8 years while, depending on the sample, between 7% and 9% of the individuals are unemployed (i.e,individuals who are not working and look for a job).

The LAPOP sample reveals substantial heterogeneity in the level of trust depending on which institutions one looks at. According to the respondents, the Catholic Church is the most trusted institution (60% of Salvadoran strongly trust in the Church) followed by mass media and municipal government (both with 56%). On the other hand, the national legislature (with 39%) and the president (with 45%) are the less trusted over the time period of analysis. Finally, only 38% of Salvadorans think that the judicial system would punish the guilty if they were a victim of a robbery or assault.

The Latinobar ́ometro sample shows that 52% of respondents thinks that income distribution in El Salvador is “unfair” or “very unfair”. Few Salvadorans are optimistic about current economic conditions (only 16% answered that the economic conditions of the country were better than a year before). A large share of respondents (i.e., 70%) thinks that democracy is the preferable to other kinds of governments, but only half of them (i.e., 35%) were satisfied with the democracy. Finally, the Latinobar ́ometro reveals a very high level of mistrust among Salvadorans since only 22% of respondent said that one could trust most people (as opposed to the alternative “you can never be too careful when dealing with others”).

# **2.1.1 Recent Trends in Attitudes Towards Institutions**

Figure 1 depicts the temporal evolution of the aforementioned attitudinal measures over the 20042018 period. In Panel A I plot the evolution of trust measures from LAPOP. Two main patterns emerge from this figure. First, all measures but trust towards municipal government have experienced a steady reduction over the last years. Second, trust in the president seems to be the most volatile measure. It reached its highest value after the 2008 global financial crisis (almost 70% of respondents trusted the president in 2010) and then experienced a rapid drop reaching 27% in 2018 6
# **_1. Introduction_**

Tobacco use kills 5.4 million people a year.<sup>3</sup> This is likely to increase over the next decades as tobacco consumption grows in developing countries. In addition to health impacts, smoking diverts poor households’ resources away from other more productive use. For example, Banerjee and Duflo (2007) report that in Mexico, the extremely poor in rural areas spend about 8.1 percent of their budget on tobacco and alcohol.

The literature on the determinants of smoking behavior points to a clear link between price increases and a reduction in the number of cigarettes smoked. Increasing taxes on tobacco has therefore often been advocated as a means to reduce smoking. However, recent evidence casts some doubts on the health impacts of tobacco taxes. Indeed, Adda and Cornaglia (2006) show that price increases lead smokers to smoke more intensively ( _i.e._ , extract more nicotine per cigarette) which is detrimental to health.

Recently, some in the public health community have suggested that, in developed countries, mobile phone usage could play a role in reducing smoking, especially among cash constrained teenagers (Charlton and Bates, 2000). Analyses undertaken in European countries appear to contradict this claim, however (Steggles and Jarvis, 2003). No analysis has been carried out in a developing country context where mobile phones are spreading rapidly, even though the potential impact of such phones on tobacco consumption is the greatest because of stricter household budget constraints.

The paper uses household panel data to examine the impact of mobile phone ownership on tobacco consumption. Our results point to a large and robust negative impact of mobile phone ownership on tobacco consumption. Among households in which at least one member smoked in 2003, purchasing a mobile phone leads to a 32.6 percent decrease in tobacco consumption per adult over the age of 15. This is equivalent to one less pack of 20 cigarettes per month. In addition to simple OLS difference-in-differences estimates

> 3 <u>http://www.who.int/tobacco/mpower/tobacco_facts/en/index.html</u> Accessed 5/17/2008

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average ERR, we find countries in the 'high ERR' category had average rates of return twice as high as those countries in the "low ERR" category. Interestingly, these high ERR countries had many more riots, demonstrations and strikes per capita (adjusted for population effects) than countries with poor project performance<sup>2 8</sup> .

Table 5: Indicators of political and civil unrest by average ERR by country.

|ERR<br>categorya|Average<br>ERR|Number<br>of<br>countries|Regional<br>distribution|Number<br>of<br>projects|Indicat<br>unrest,<br>by cou<br>from p<br>means<br>Riots|ors of polit<br>averages p<br>ntry (devia<br>opulation-a<br>):<br>Demon-<br>strations|ical<br>er year<br>tions<br>djusted<br>Strikes|
|---|---|---|---|---|---|---|---|
|High|22.2|6|S. Asia: 3;<br>E Asia: 3|181|2.48|0.30|3.19|
|Medium|17|11|LAC: 5; SSA: 2;<br>EMENA: 3;<br>S. Asia: i|253|0.00|0.16|-0.02|
|Low|11.2|12|SSA: 9; LAC: 2;<br>S. Asia:1I___|209|-0.19|-0.04|-0.23|

Notes: a) ERR categories are determined by average rates of return classified by country for all countries with at least 10 projects over the period from 1974-1987.

Source: Autifors' calculations 2' The civil unrest variables (riots, protest demonstrations, and strikes,) came as number of incidents per country per year (Banks 1979, updates). This meant that countries with larger populations had a greater absolute number of incidents. However, it did not seem right to simnply normalize to per capita, as there is plausibly some increasing returns to scale in civil unrest. Consequently, for each of the three variables we regressed the absolute number of incidents on population*ln(population) (which is equivalen **t** o adjusting the per capita level for the total population in semi-log form) and report the residual of this regression as 'excess' civil unrest over the amount expected for a given level of population. The population adjustment was also very significant and the R-squared varied from .02 (strikes) to .18 (riots). The results reported below were unchanged by using other concave functional forms in place of this semi-log form.

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conducted.

-

Additionally the model analyzes income distribution and poverty solely as a function of primary income - whereas much of the concern over the poverty impact of adjustment has fbcussed on secondary income, most notably declines in government social expenditure. If the model were to incorporate disaggregated government expenditure categories then models of categorical fungibility could fall neatly into place. Moreover, there is plenty of evidence linking different aspects of government spending and participation in social services to wider aspects of the welfare of the poor. Some of this literature is now reviewed to see what links may be made.

# _Soaal sector provision and welfare: some examples_

The studies in Psacharopoulos and Patrinos (1993) explore the determinants of poverty amongst the indigenous population i- Latin America. This group are dispoportionately poor - around 70 per cent of indigenous people are poor compared to about half for the population as a whole. One of the strongest determinants of poverty (based on logit regressions of the probability of being poor) is years of schooling, and educational attainment is much lower amongst indigenous peoples than non-indigenous. Moreover, educational attainment of parents affects the schooling and other human capital characteristics of their children. The authors therefore conclude that "equalizing schooling attaimnent would result in a considerable increase in relative earnings" (Psacharopoulos and Patrinos, 1993: 230). The implications for expenditure patterns are clear: the analysis needs to take account of the changes in provision of educational services to indigenous communities, being one of the major channels for improving (or worsening) the welfare of one of the poorest sections of the community in both the long and short run.

This implication is made explicit in the study of Cate d'Ivoire by Thomas _et al.,_ who analyze the determinants of three anthropometric measures of heath - child height, child weight and adult body mass index (weight over height squared).' They report that: The provision of basic services (such as immunizations) and ensuring facilities are equipped with simple materials (such as having basic drugs in stock) will probably yield _high-_ social returns in terms of improved child health.

(Thomas _et al.,_ 1992: 32) Hence they conclude that the cuts which have taken place in government expenditure, which fell particularly- upon the social sectors, are likely to have had a damaging impact on social welfare102
We now turn to an examination of the relationship between SMK attendance and levels of participation in tertiary education. There are two hypotheses on how SMK attendance can affect the odds of participation in tertiary education: one is that SMK attendance is stigmatized, as it is usually children from households of the lower socioeconomic strata that attend SMK. The other hypothesis is that SMK may not provide a conducive learning environment for students to pursue higher levels of education through lesser value-added in academic achievement, as shown in EBTANAS score at graduation.

To test these two hypotheses, the predicted value of SMK choice is used to estimate its effect on college entry. The result shows that attendance at SMK does not affect college entry when controlled for other variables, including test scores. However, if the score variable is omitted, attendance at SMK is demonstrated to have a significant and negative effect on levels of participation in tertiary education. It can be concluded that only by reducing levels of academic achievement as measured by test score that attendance at SMK reduces the chance of being able to participate in tertiary education; but that attendance at SMK per se does not create a disadvantage or stigma in terms of lowering the likelihood of participating in tertiary education.

# **VII. Conclusions**

This paper has compared vocational secondary schools with academic schools in terms of their effects on the ability and likelihood of attaining employment; labor market earnings; and participation in tertiary education in the Indonesian context. Using a panel of two waves of “Indonesia Family Life Survey” (IFLS II and IFLS III) in 1997 and 2000, a cohort of high school students in 1997 is tracked to determine their schooling and employment status in 2000. It is found that

- (1) Attendance at vocational secondary schools results in _neither market advantage nor disadvantage_ in terms of employment opportunities and/or earnings premium;

- (2) Attendance at vocational schools leads to _significantly lower academic achievement_ as measured by national test score;

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significant heterogeneity.<sup>20</sup> Similarly, results also show statistically significant estimates for females on “having an active project” both in 2011 and in 2014, but still without significant heterogeneity. The two sets of results are not entirely consistent and may be due to lower statistical power when performing heterogeneity analysis.

Lastly, there is no robust evidence with regard to differences in impacts by social capital (proxied by students who signed up individually or in pairs).

# **7. Impacts on broader employment outcomes**

Finally, we turn to analyzing impacts of the entrepreneurship track on broader employment outcomes beyond self-employment. Results show that there are no significant differences in overall employment status between individuals assigned to the entrepreneurship track and the control group (Table 10). While the estimates suggest a reduction in unemployment among men and women assigned to the entrepreneurship track, this reduction of unemployment is not counterbalanced by an increase in employment. Instead, it is in part explained by an increase in inactivity or studying (though it is not always statistically significant across different specifications), and in part by an increase in the probability of being a family helper. These results can be interpreted as consistent with the short-term increase in optimism and aspirations towards the future having receded by the time of the endline survey, and possibly with some graduates becoming discouraged.

Table 11 presents the impacts of the entrepreneurship track on employment characteristics such as hours worked, earnings, having a contract, being covered by social security, working in a large firm, and reservation wages. In line with short-term results one year after graduation (in 2011), the entrepreneurship track did not promote entry into higher quality jobs among participants. The only significant difference is the likelihood of employment at a large company. In MENA, smaller firms attach greater value to personal references and contacts and therefore “trust” when recruiting, while larger firms attach greater value to credential signaling. Smaller

> 20 The midline survey contains an indicator that is not presented in table 5 on whether respondents were self-employed 12 months and not 7 days prior to the survey. Estimates were statistically significant for women at the 5% level, with a coefficient of 0.03.

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Second, despite their potential significance, the impact of extreme events is not captured in the above model because of both data and methodological limitations. As shown in Figure 5, for example, the Alaknanda hydrological series appears to have become more volatile in recent years. However, the analysis based on monthly data cannot explain extreme events, such as flash floods and rain floods caused by extremely heavy precipitation in a few days.<sup>16</sup> Any econometric technique is more or less designed to measure an average effect, ignoring outliers like floods and droughts.

Notably, though, the above projections are still considered suggestive. For example, it can be shown that in the Vishnugad Pipalkoti case the skewness of a monthly water flow distribution is likely to increase from 0.74 to 1.02 by the 2020s. Generally, Figure 8 also indicates a considerable increase in water flows particularly during the rainy season in all cases. Especially for the Vishnugad Pipalkoti hydropower project, in comparison with the designed capacity the projected surge in river discharge may not be ignorable from the point of view of flood and sediment risk management.

Third, one might question whether the stochastic model is generally suitable for this type of study. It is open to discussion. The above analysis finds the hydrological series at the Thac Mo Reservoir may exhibit a unit root and thus not be applicable to the VAR technique.<sup>17</sup> When detrended data are used, the result has been found quite similar to the result presented above. Nonetheless, even if the model is appropriate, there is another level of problem. For example, Wong _et al_ . (2007) claims that the assumption of a river flow linearly depending on its lagged values is questionable. Our VAR model does _not_ rely on the linearity restriction, but it is simply a log-linear model, which still imposes certain restrictions on the system, e.g., constant elasticity.

> 16 Technically, it may be somewhat meaningful to predict the extreme river flows based on our estimated standard error. For instance, the predicted flows at the 90 percent dependable level—meaning an upper bound of the less significant interval—could be interpreted as a very unlikely flood event in a statistical sense.

> 17 If all time series in the system contain a unit root, the vector error-correction model is more appropriate.
# **Appendix A:  Poverty and Inequality by Region**

Table A1 Regional Distribution of Poverty in 2001

||Headcount|Poverty Gap|SquaredPoverty Gap|Watts|PopulationShare|
|---|---|---|---|---|---|
|Douala|10.89|2.07|0.72|2.61|9.70|
|Yaoundé|13.34|2.66|0.86|3.27|8.72|
|Adamaoua|48.38|15.39|6.38|20.31|4.47|
|Center|48.18|14.97|6.63|21.05|7.85|
|East|43.98|15.37|6.75|20.85|4.81|
|Far North|56.29|18.84|8.18|25.34|17.74|
|Coast|35.48|10.09|4.17|13.43|4.88|
|North|50.08|15.50|6.36|20.43|7.26|
|North West|52.48|20.90|10.70|30.83|11.52|
|West|40.33|11.10|4.19|14.19|12.06|
|South|31.55|7.35|2.43|9.04|3.45|
|SouthWest|33.83|10.50|4.51|14.13|7.53|
|Cameroon|40.18|12.79|5.55|17.38|100.00|

Source: Authors’ Calculations Table A2 Regional Distribution of Poverty in 2007

||Headcount|Poverty Gap|SquaredPoverty Gap|Watts|PopulationShare|
|---|---|---|---|---|---|
|Douala|5.50|0.87|0.21|1.01|9.96|
|Yaoundé|5.94|0.97|0.24|1.13|9.60|
|Adamaoua|52.95|14.49|5.41|18.46|5.18|
|Center|41.19|9.48|3.10|11.68|7.63|
|East|50.40|15.69|6.22|20.25|4.66|
|Far North|65.87|24.58|11.21|33.35|18.11|
|Coast|31.08|7.65|2.71|9.60|3.50|
|North|63.66|20.99|8.58|27.43|9.85|
|North West|51.00|16.61|6.83|21.78|10.14|
|West|28.95|6.64|2.27|8.24|10.58|
|South|29.25|7.37|2.65|9.31|3.24|
|SouthWest|27.51|6.87|2.47|8.65|7.55|
|Cameroon|39.90|12.31|5.03|16.11|100.00|

Source: Authors’ Calculations 37
**Table 4: Outcome variables by treatment status**

||To<br>|tal<br>|Con<br>|trol<br>|Treat<br>|ment<br>||
|---|---|---|---|---|---|---|---|
||Mean|SD|Mean|SD|Mean|SD||
|Perceived risk of expropriation (1 if medium and above<br>risk out of a 5 scale measure)|0.846|0.361|0.859|0.348|0.827|0.378|***|
|Perceived risk of expropriation (5 scale measure:<br>5=Very high risk, 1=Very low risk)|4.061|1.146|4.090|1.068|4.021|1.248|***|
|**Change in proportion of parcels receiving soil conserv**|**ation meas**|**ures betwe**|**en 2007 an**|**d 2010**||||
|Construction of new conservation structures|0.066|0.430|0.042|0.424|0.098|0.435|***|
|Maintenance of existing structures|0.089|0.366|0.073|0.355|0.110|0.378|***|
|New/maintenance of existing structures|0.140|0.510|0.101|0.499|0.193|0.520|***|
|Changed seed type from local to improved variety since<br>2007|0.529|0.499|0.498|0.500|0.571|0.495|***|
|Change in new/maintenance of buildings|0.034|0.297|0.037|0.315|0.030|0.270||
|Female head/spouse jointly or alone owns parcel|0.869|0.338|0.879|0.326|0.855|0.353|***|
|Share of parcel owned by female (%)|42.486|27.400|42.028|27.120|43.112|27.770||
|Know who will inherit the parcel|0.638|0.481|0.596|0.491|0.695|0.461|***|
|Sons will inherit land|0.741|0.438|0.718|0.450|0.767|0.423|***|
|Daughters will inherit land|0.690|0.463|0.661|0.474|0.723|0.448|***|
|Spouse will inherit parcel|0.321|0.467|0.343|0.475|0.291|0.455|***|
|Children will inherit parcel|0.763|0.425|0.719|0.449|0.821|0.383|***|
|Change in land market (sales/purchases) participation<sup>a</sup>|0.011|0.498|0.022|0.511|-0.008|0.477||
|Change in the size of land traded in hectares<sup>a</sup>|0.005|0.421|0.021|0.380|-0.020|0.477|**|
|Number ofparcels|63|27|36|34|26|93||

**Source:** Own computation from 2010 World Bank Land Tenure Regularization Survey

**Note:** Significance levels are reported for t-tests of the equality of the means for each of the variables between control and treatment areas (* significant at 10%; ** significant at 5%; *** significant at 1%). aHousehold level outcome variables.

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