rafmacalaba/datause-encoder-data huggingface dataset:
INTRODUCTION XXXiX made, which were specified above. The assumed trends in vital rates partly reflect the efferts that have been made throughout the developing world to restrain fertility, as well as to redulce mortality, in effect actually taicng into account some exceptional past experiences at reducing growth. Altering these trends and producing more rapid deceleration of population grouth is possible, bu!t would probably require at least as much, and probably much more, demographic intervention as in the recent past.

# REFERENCES

- Arnold, Fred. 1989. Revised estimates an.i projections of international migration. Policy, Planning, and Researchi Workipg Papers, No. 275. Washington, D.C.: World Bank.

- Bos, Eduard, and Rodolfo A. Bulatao. 1989. Proqjcting fertility for all countries. Policy, Planning, and Research Working Papers. Washington, D.C.: World Bank. Forthcoming.

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- Bulatao, Rodolfo A., and Eduard Bos. 1989. Projecting mortality for all countries. Policy, Planning, and Research Working Papers. Washington, D.C.: World Bank. Forthcoming.

- Bulatao, Rodolfo A., and Ann Elwan. 1985. Fertility and mortality transition: Patterns, projections, and interdependence. Wo:td Bank Staff Working Paper No. 681. Washington, D.C.

- Coale, Ansley J., and Paul Demeny (with Barbara Vaughn). 1983. _Regional Model Life Tables and Stabk Populadons._ 2nd ed. New York: Academic Press.

- Eurostat. 1987. _Demographic Statisdcs._ Luxembourg: Statistical Office of the European Communities.

- Hill, Kenneth. n.d. ProjPC-II: User's manual. Washington, D.C.: World Bank.

- Jamison, Ellen, Peter D. Johnson, and Richard A. Engels. 1987. World Population Profile: 1987. Washington, D.C.; Bureau of the Census, U.S. Department of zmmerce.

- Monnier, Alain. 1988. *La conjoncture dezmographique: L'Europe et les pays d6velopp6s d'outremner," _Population_ 43(4-5):893-904.

- United Nations. Department of International Economic and Social Affairs. 1984. _Population end Vital Stadstics Report: 1984 Special Supplement._ New York: United Nations.

- United Nations. Department of International Economic and Social Affairs. 1989. _World Population Prospects 1988._ New York: United Nations.

- United Nations. Department of International Economic and Social Affairs. Various years. _Demographic Yearbook_ New York: United Nations.

- United Nations. Department of International Economic and Social Affairs. Various years. _Population and Vital Statistis Report._ New York: United Nations.

- U.S. Bureau of the Census. 1983. _World Populadon 1983--Recent Demographic Estimates for the Countries and Regions of the World._ Washington, D.C.

- U.S. Bureau of the Census. 1985. _World Population: Recent Demographic Esdnmates for the Countries and Regions of the World._ Washington, D.C.

- World BankL 1989. _World Development Report 1989._ New York: Oxford University Press. Zachariah, K C., and My T. Vu. 1988. _World Population Projections: 1987.88 Edition._ Baltimore and Lendon: The Johns Hopkins University Press.
> **Aside** from **one** further variable collected from the head teachers, namely, how many pupils **had** to share each textbook in Math and in English, the remaining **clas** and school level information was collected from statistical returns to the Ministry of Education and Culture, on the ED.46 (Part II) form. This narrowed down the optimal list of variables to include the following, additional **class** level variablecs the qualifications and years of experience of the Form II Math and Englieh teachers and thG class size, and the following, additlonal school level var$ables: the percentage of teachers Ln the four different qualLficatlon bands, ranging from untrained to cortlficated graduate, the average years of teachlng experience at the school, the average years of teaching at the partlcular school, whether the school **was** day or boardlng, the size of the school, the overall teacher pupi ratio and the percentage of Africans at the school. A lLst of all the varlables Ln the study can be found in Appendix 1.

# ,Msthodgl=o

The choLce of a multilevel model was an obvious one, given the objectLve of the study. Whllst ordinary least squares regression could control for the intake ability of the students, it would not have been possible to infer at the class and school levels the implications of this control and other parameters. Similarly, if one focussed on the school level, it would not have been possLble to adequately account for the prior ability of the students. This would be rather like seeking explanations for the development of a hybrid species without knowing the constituent breeds. The arguments concerning the appropriateness of multilevel modelling for nested, hLerarchical data found in an education system in which pupils are nested in classes, which in turn are 12
implements emission standards. California is allowed to implement more stringent emission standards, which are set by the California Air Resources Board (CARB).

In 2000, both China and India introduced their first emission standards, based on European regulations of that time. Since then both countries have tightened their standards several times to address serious air pollution in urban areas. Based on the most more stringent European standards (Euro 6) already imposed in the EU, the new standards are slated to go into effect in 2020 in both China and India. The effectiveness and efficiency of these policies remain to be studied.

# **5 Conclusions and Future Research Areas**

Many developing economies, especially rapidly growing ones, are facing pressing environmental challenges due to the increased demand for fossil fuel-based urban transportation, and the lack of effective and stringent regulations on pollution control. As income rises in these countries, demand for environmental quality increases, putting pressure on governments to reevaluate their positions on managing urban mobility and environmental quality (Fouquet 2012).

The first best solution for managing the potential trade-off between mobility and air pollution comes down to managing urban space. Private autos are the most inefficient use of transportation space, while non-motorized mobility such as walking and cycling, and mass transit modes is the most efficient use of transportation space. Land-use planning affects individuals’ choice of travel modes. Simulation analysis shows that land use policies that favor a compact city in which land density and diversity were increased and distances to public transport were reduced can get more people out of private cars into non-motorized mobility and mass transit, representing the most promising way to reduce air pollution and urban congestion (Stankov et al. forthcoming).

Beyond land-use and transportation planning policies, an alternative suite of policies focuses on reducing vehicle miles traveled per capita and tailpipe emissions per mile of driving. These policies include driving restrictions, congestion pricing, shared mobility, fuel tax, subsidies for alternative fuel vehicles, fuel-economy standards and emissions regulation. Because most studies on land-use change and its impact on transportation are based on computer 29
when CO2 fertilization effects are removed. For a scenario of moderate temperature increases without fertilization effects (RCP2.6NF), all GCMs predict a moderate decline in potential yields (between 0.12 and 0.34 t / Ha). Three out of five GCMs predict a decline in potential yields of a comparable magnitude to scenario RCP8.5F. Finally, for a scenario of rapid temperature increases without fertilization effects (RCP8.5NF), all GCMs predict a larger decline in potential yields (between 0.72 and 1.32 t / Ha). These results are consistent with recent statistical evidence showing that “10 years of climate trend is equivalent to a setback of roughly 1 year of technology gains” (Lobell et al., 2011, p. 619).

Based on these results we construct 2 states for potential crop yields under climate scenarios. Under the optimistic state of the world, we observe moderate temperature increases with significant CO2 fertilization effects (RCP 2.6F climate scenario), resulting in small increases in crop yields, relative to baseline. This is the path on which we begin the simulation in 2004. In each period, there is a distinct probability of irreversibly deteriorating to the pessimistic state in which more rapid temperature increases and ineffective CO2 fertilization (RCP8.5NF climate scenario) result in a significant decline in crop yields by 2100.

## **3.2.3 Constructing Stochastic Crop Productivity Index**

Having characterized the realizations of crop productivity under alternative states of the agricultural technology and the climate change, we still need to assign transition probabilities for tipping from the baseline to possible low yields states to construct the stochastic crop productivity index. As the long term evolution of both the advances in crop productivity and climate change impacts are nearly impossible to forecast, we set the one-period probabilities of falling to pessimistic state _p_ 1 _,t_ = _p_ 2 _,t_ = 0 _._ 34% for all _t_ , so that the accumulated probability of each state _{hh, lh, hl, ll}_ is equal by the end of the model solution period.

Figure 2 shows the expected path of the stochastic crop productivity index, and the range of all sample paths of this index over the course of next century. The accumulated probability of both pessimistic states happening is about 25% by 2200 (8.5% by 2100), and the accumulated probability of at least one of the pessimistic states happening is about 75% by 2200 (29% by 2100). We view this as a relatively conservative specification of tipping points.

# **3.3 Welfare**

The objective of the planner is to maximize the total expected welfare, which is the cumulative expected utility of the population’s consumption of final goods and services,<sup>_−→_</sup> _y_ , discounted at the constant rate _δ >_ 0 _,_ and the expected bequest value of unmanaged natural forests and managed forests.<sup>11</sup> The planner

> 11We do not consider the bequest value of protected forests, as they cannot be “scrapped” in our model.

19
# **Endnotes**

1 It should also be recognized that the rate of off-farm employment varies among provinces. A study based on six provinces, while informative, can not be used to represent the whole profile of a country the size of China.

> 2 The provinces are Hebei, Liaoning, Shaanxi, Zhejiang, Hubei, and Sichuan. The data collection effort involved students and staff from the Center for Chinese Agricultural Policy and a group of masters and Ph.D. students from a number of other agricultural universities. Households were paid 20 yuan and given a gift in compensation for the time that they spent with the survey team. In fact, the same villages and households were visited during 2000 during the first round of the CNRS. In 2000 there were 60 villages and 1200 households. Unfortunately two villages were in the Sichuan earthquake zone and were damaged so heavily that a year after the earthquake most of the households had not returned to their normal lives in the village.

3 It is possible that the respondents do not know accurate information about the family members that were working off the farm. In fact, we do not believe there is a systematic problem. In our survey there are two main types of households. The first type of household is a household with a son or daughter that is working off the farm. In a vast majority of the time these households keep in close contact with their children and know their employment status. If the family did not know the employment situation of the son or daughter (or other household member), the enumerators would ask the family members to call and ask them. In most of the cases, the call was made while the enumerator was in the house or the same evening that the surveyproper was administered.” Two is a household that at one time was living in the village (during our 2000 survey) and during 2008 was living in the city. In total, there were 89 households (out of our entire sample) that fit into this category. In this case, there was no one in the village that knew on a day to day basis what the individuals in the household were doing employment-wise. In the case of these households, we tracked them to the city. In 36
Table 4a. Key <u>Correlates of Positive Youth Behaviors, Mexico</u>

||Not in|active<sup>a</sup>|Sch<br>compl|ool<br>etion<sup>a</sup>|Older<br>first|age at<br>job<sup>b</sup>|Safe<br>beh|sexual<br>avior<sup>b</sup>|Older<br>fi<br>preg<br>/paren|age at<br>rst<br>nancy<br>thood<sup>b</sup>|Partic<br>in acti|ipation<br>vities<sup>b</sup>|He<br>atti<br>tow<br>alco|althy<br>tude<br>ards<br>hol<sup>b</sup>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Male|Female|Male|Female|Male|Female|Male|Female|Male|Female|Male|Female|Male|Female|
|**Macro**|||||||||||||||
|Rural<br>residence|-0.38<br>(0.09)|-0.90<br>(0.06)|-0.66<br>(0.06)|-0.84<br>(0.06)|-0.32<br>(0.05)|0.23<br>(0.05)|--|-0.17<br>(0.06)|--|-0.22<br>(0.06)|-0.38<br>(0.06)|--|0.56<br>(0.07)|0.80<br>(0.09)|
|**Micro**|||||||||||||||
|Low parental<br>education<br>level|-0.89<br>(0.19)|-1.21<br>(0.12)|-2.83<br>(0.14)|-2.41<br>(0.12)|-1.14<br>(0.08)|-0.81<br>(0.08)|--|-0.36<br>(0.11)|-0.75<br>(0.16)|-0.72<br>(0.12)|-0.69<br>(0.10)|-0.82<br>(0.09)|0.65<br>(0.10)|1.07<br>(0.12)|
|Live with both<br>parents|--|1.32<br>(0.05)|0.54<br>(0.07)|0.88<br>(0.05)|0.47<br>(0.05)|0.40<br>(0.04)|0.98<br>(0.06)|2.12<br>(0.05)|1.89<br>(0.08)|2.22<br>(0.06)|--|0.30<br>(0.06)|--|-0.39<br>(0.08)|
|Poor parental<br>response to<br>misbehavior|--|--|--|--|-0.13<br>(0.05)|-0.20<br>(0.04)|-0.13<br>(0.05)|-0.22<br>(0.05)|-0.21<br>(0.07)|-0.31<br>(0.05)|--|--|-0.20<br>(0.05)|-0.17<br>(0.07)|
|Poor parental<br>response to<br>good behavior|--|--|-0.61<br>(0.12)|-0. 73<br>(0.11)|-0.50<br>(0.09)|-0.45<br>(0.08)|--|--|--|--|-0.61<br>(0.11)|-0.55<br>(0.11)|--|--|
|Positive<br>relationship<br>with father|0.89<br>(0.23)|0.48<br>(0.16)|0.49<br>(0.18)|0.91<br>(0.16)|--|--|--|--|-0.70<br>(0.25)|--|0.76<br>(0.17)|0.87<br>(0.16)|--|--|
|Positive<br>relationship<br>with mother|--|1.35<br>(0.15)|1.56<br>(0.19)|1.43<br>(0.15)|--|-0.63<br>(0.11)|--|0.73<br>(0.14)|1.48<br>(0.26)|1.14<br>(0.15)|0.90<br>(0.18)|1.48<br>(0.16)|--|--|
|Social<br>exclusion|--|--|0.25<br>(0.05)|--|0.17<br>(0.04)|0.10<br>(0.04)|0.63<br>(0.05)|0.60<br>(0.05)|1.19<br>(0.11)|0.38<br>(0.06)|--|-0.13<br>(0.04)|0.33<br>(0.05)|0.27<br>(0.06)|
|Spiritual|--|0.30|--|--|-0.46|-0.32|0.21|0.32|--|0.49|0.95|0.93|--|--|

1
<!-- Start of picture text --> 3<br>2<br>_—_<br>LIC LMIC UMIC HIC<br>MO Percent of countries with UB scheme (%)<br>——— Minimum contribution period for UB (months)<br>——— Average maximum UB duration (weeks)<br><!-- End of picture text --> <!-- Start of picture text --> 2wo 50 53<br>Ssss 38 37<br>33 32<br>8 29<br>SsRg 18 18 s 17<br>oa 5<br>° LIC LMIC UMIC. HIC<br>MEE UB duration after 1 year of contribution (weeks)<br>MEE UB duration after 5 years of contribution (weeks)<br>MEE UB duration after 10 years of contribution (weeks)<br><!-- End of picture text --> <!-- Start of picture text --> S<br>3<br>3<br>i=}7<br>°<br>EAP ECA LAC MENA OECD-HIC SAR SSA<br>M0 Percent of countries with UB scheme (%)<br>———— Minimum contribution period for UB (months)<br>———— Average maximum UB duration (weeks)<br><!-- End of picture text --> <!-- Start of picture text --> 3 57 60<br>46<br>2 40 41 39<br>30 29 33 34 33 33 31 31<br>26 26<br>8 18 20 7 18<br>7<br>Oo EAP ECA LAC MENA OECD-HIC SAR SSA<br>ME UB duration after 1 year of contribution (weeks)<br>MEE-UB duration after 5 years of contribution (weeks)<br>MEE _UB duration after 10 years of contribution (weeks)<br><!-- End of picture text -->
2006; von Lersner et al., 2008). Lastly, in terms of arguments grounded in political participation as the affirmation of in-group identity, it is not clear what identity deportees could be affirming unless they left their home country because of persecution on the basis of identity. Deportees are a relatively small segment of any country’s population, do not typically know each other, and often arrive in small groups. In place of these possible mechanisms discussed by other scholars, we posit one novel mechanism tying victimization to depressed politi– – cal participation fear and one novel mechanism tying extortion specifically to increased – political participation economic shock.

# **3.2 Victimization and Fear in the Context of Migration**

There is an extensive literature which shows that migrants tend to vote less than their native counterparts (e.g. Shaw et al., 2000; Cassel, 2002; Garcia, 2011; OECD, 2018). Internal migrants also participate less in formal and informal networks, and turnout declines in areas with larger proportions of migrants (Akarca and Tansel, 2015; Gay, 2012; Gaikwad and Nellis, 2020; Villamizar Chaparro, 2021). However, there is little literature on 1) the political participation among migrants who are deported to their country of origin or on 2) how variation in violence experienced while migrating affects political participation. We theorize that victimization such as extortion is correlated with increased fear among deportees. Furthermore, this fear is politically demobilizing.

What are the psychological impacts of migration journey violence? Experiencing abuse while migrating is predictive of depression and alcohol dependency (Altman et al., 2018). Among those individuals who experienced victimization during the migration process, such as being robbed or attacked, 21 percent are at risk for PTSD (Perreira and Ornelas, 2013). This is true not only in Central American migrants, but in migration populations around the world. For example, one meta-analysis of 113 articles confirms that exposure to violence during migration affects mental health; the most frequent consequences include post-traumatic stress disorder (Kirmayer et al., 2011). PTSD is closely tied to anxiety (Torres, 2020). More broadly, a great deal of research suggests that fear follows experiencing crime in general (e.g. Skogan, 1987; Walklate and Mythen, 2007). Thus, one of the most important mental health consequences of experiencing violence is increased levels of anxiety.

We argue that these mental health consequences of victimization affect political engagement. Some emotions, such as anger, mobilize voters (Valentino et al., 2009, 2011). In contrast, both depression and anxiety are politically demobilizing (Weber, 2012; Young, 2019; Burden et al., 2017; Landwehr and Ojeda, 2021). For example, when triggered to consider stressful life experiences unrelated to politics, individuals without a history of participation 7
coefficient of the interaction term between the government effectiveness variable and GovTech is positive and statistically significant at the 1 percent threshold, and the coefficient of GovTech becomes smaller when the study includes this interaction term. This suggests that the effect of GovTech platforms on citizen engagement is sensitive to the quality of public services, civil service, as well as policy formulation and implementation, which strongly impact the success of GovTech initiatives in boosting citizen participation. These findings are similar to those of Gulati et al. (2014). The results also highlight the negative impact of the presence and intensity of conflict and fragility on the effectiveness of GovTech platforms to foster citizen engagement. Furthermore, the negative and statistically significant interaction term between GovTech platforms and the developing countries dummy indicates that GovTech reforms may face challenges in effectively promoting citizen engagement in developing countries. However, this finding highlights the need for tailored strategies and targeted interventions to address the unique contexts and challenges faced by developing countries. It underscores the importance of refining GovTech implementation to align with the specific needs and conditions of these countries, presenting an opportunity for more context-sensitive and impactful digital governance solutions in the future. Concerning the efficacy of the empirical strategy, none of the predicted coefficients for the propensity score are statistically significant, suggesting the absence of self-selection biases.

**Table 8: Results of the Sensitivity/Heterogeneity Analysis**

||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|**(9)**|
|---|---|---|---|---|---|---|---|---|---|
|GovTech|0.178***|0.178***|0.209***|0.208***|0.184***|0.184***|0.111**|0.105**|0.272***|
||(0.0475)|(0.0476)|(0.0482)|(0.0482)|(0.0476)|(0.0476)|(0.0492)|(0.0525)|(0.0705)|
|Pscore||0.107|0.0241|0.0371|0.0720|0.0720|-0.131|-0.0291|-0.0291|
|||(0.123)|(0.124)|(0.122)|(0.122)|(0.122)|(0.145)|(0.152)|(0.152)|
|GovTech*Conflict<br>(intensity level)|||-0.204**<br>(0.0807)|||||||
|GovTech*Conflict||||-0.126**||||||
|(cumulative intensity)||||(0.0510)||||||
|GovTech* Conflict-affected|||||-|||||
|situations|||||0.249***<br>(0.0342)|||||
|GovTech* Fragile and||||||-||||
|Conflict-affected situations||||||0.166***<br>(0.0228)||||
|GovTech* Government<br>effectiveness|||||||0.149***<br>(0.0424)|||
|GovTech*High income<br>countries||||||||0.167**<br>(0.0784)||
|GovTech*Developing<br>countries|||||||||-0.167**<br>(0.0784)|
|Constant|0.597***<br>(0.0371)|0.526***<br>(0.0893)|0.581***<br>(0.0885)|0.572***<br>(0.0878)|0.549***<br>(0.0883)|0.549***<br>(0.0883)|0.684***<br>(0.0998)|0.616***<br>(0.106)|0.616***<br>(0.106)|
|Observations|160|160|160|160|160|160|160|160|160|
|R-squared|0.082|0.087|0.116|0.116|0.102|0.102|0.150|0.114|0.114|

**Robust standard errors in parentheses**

***** p<0.01, ** p<0.05, * p<0.1**

16
_____________________________________________________________________________________________________________

|Country|Year of|Labor|productivity g|rowth premia|of export-starte|rs (percent|) [p-valu|e]|No. of||No. of|
|---|---|---|---|---|---|---|---|---|---|---|---|
|_______________|start<br>_________________|Sales/w<br>_________|orker<br>___________|Value a<br>__________|dded/worker<br>_____________|TF<br>_________|P<br>_______|observ.<br>_______|<br>_______|starters<br>_____||
|Republic of<br>Ireland|All Years|0.7|[0.61]|-0.4|[0.83]||||1,604||131|
||1994|-4.6|[0.38]|-6.8|[0.34]||||184||17|
||1995|3.2|[0.46]|8.6|[0.23]||||197||18|
||1996|-6.7|[0.07]|-11.0|[0.32]||||206||16|
||1997|7.5|[0.07]|6.9|[0.15]||||195||18|
||1998|4.0|[0.54]|4.2|[0.47]||||193||12|
||1999|2.8|[0.56]|4.1|[0.42]||||212||15|
||2000|-5.5|[0.04]|-4.8|[0.13]||||221||18|
||2001|-7.2|[0.02]|-2.5|[0.62]||||196||17|
|Italy|All Years|4.7||1.0||1.3|||8,172||948|
||1992|4.1|[0.03]|1.2|[0.07]|2.1|[0.05]||1,276||144|
||1993|4.3|[0.05]|0.5|[0.09]|0.5|[0.23]||1,126||143|
||1994|6.1|[0.07]|1.9|[0.08]|1.5|[0.09]||826||79|
|UK|All Years|3.2|[0.51]|1.7|[0.75]|4.6|[0.39]||1,222||56|
||1998|2.5|[0.77]|1.2|[0.87]|13.7|[0.12]||264||16|
||1999|-30.1|[0.19]|-35.1|[0.15]|-37.4|[0.07]||311||7|
||2000|24.1|[0.01]|17.6|[0.13]|17.4|[0.13]||338||14|
||2001|9.2|[0.33]|9.5|[0.29]|4.8|[0.40]||309||19|

_______________________________________________________________________________________________________________________ Note: Results are for firms from ISIC industries 15 – 36 with at least 20 employees at the median over the years covered in the panel. The firms with the bottom / top one percent of productivity in a year are excluded from all computations. The productivity growth premia are estimated in an OLS-regression of the growth rate of productivity (computed as the difference of the log of productivity in t+3 and t+1) on a dummy variable for export starters controlling for the log of number of employees and its squared value, log wages and salaries per employee, and dummy variables for 4-digit-industries, all measured at the start year t. To facilitate interpretation the estimated coefficients for the exporter dummy variable has been transformed by 100(exp(ß)-1).

59