rafmacalaba/datause-encoder-data huggingface dataset:
“The systematic errors in simulating current mean tropical climate and its variability and the large inter-model differences in future changes in El Niño amplitude preclude a conclusive assessment of the regional changes over large areas of Central and South America. ... The high and sharp Andes Mountains are unresolved in low resolution models, affecting the assessment over much of the continent.” (Christensen et al, 2007).

Despite this uncertainty, the IPCC report finds it very likely that temperatures will increase over all areas of South America over the rest of this century. If this turns out to be true, the recent cooling trend would be reverted, and the climate in Bolivia might return to “normal” (the 1961-90 average) within the next 2-3 decades.

However, “normal” climate in Bolivia includes tremendous climate variability with either El Niño or La Niña conditions almost every year. If each of these events causes losses of 3- 4% of GDP, they make all the difference between a country growing steadily towards prosperity and a country permanently stuck in poverty. Vulnerability has clearly been reduced since the devastating El Niño episode of 1982-83, but further steps to reduce vulnerability are still necessary.

# **References**

- Andersen, L. E. & D. Verner (2009) “Social Impacts of Climate Change in Chile: A municipal level analysis of the effects of recent and future climate change on human development and inequality, Draft, February.

- Andersen, L. E., A. Suxo & D. Verner (2009) “Social Impacts of Climate Change in Peru: A district level analysis of the effects of recent and future climate change on human development and inequality, Draft, February.

- Andersen, L. E., S. Román & D. Verner (2009) “Social Impacts of Climate Change in Brazil: A municipal level analysis of the effects of recent and future climate change on human development and inequality, Draft, February.

- Bolivia (2004) “La Gestión del Riesgo en Bolivia” Ministerio de Defensa Nacional de Bolivia.

- Brown, Lawrence A. & John Paul Jones III (1985) “Spatial Variation in Migration Processes and Development: A Costa Rican Example of Conventional Modeling Augmented by the Expansion Method.” _Demography_ , Vol. 22, No. 3, pp. 327-352.

- CAF (2000) “Las Lecciones de El Niño – Bolivia” Corporación Andina de Fomento, Caracas, Venezuela

- CEPAL (2007) “Informe sobre el impacto del fenómeno de El Niño en Bolivia.” Comisión Económica para América Latina y El Caribe, April.

- CEPAL (2008) “Informe sobre el impacto del fenómeno de La Niña en Bolivia.” Comisión Económica para América Latina y El Caribe, April.

- Christensen, J.H., B. Hewitson, A. Busuioc, A. Chen, X. Gao, I. Held, R. Jones, R.K. Kolli, W.-T. Kwon, R. Laprise, V. Magaña Rueda, L. Mearns, C.G. Menéndez, J. Räisänen, A. Rinke, A. Sarr and P. Whetton (2007) “Regional Climate Projections.” In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor

18
**The decline in vulnerability to poverty comes along with an increase in the role of risk factors; this could be due to parallel poverty decreases in the country.** As poverty also fell over time, the increased importance of risk factors in vulnerability comes as no surprise. The ratio of risk-induced to poverty-induced vulnerability increased from 1.1 to 1.7 between 2016 and 2019 (see Figure 7). Especially in rural areas, risks have started to play a larger role. While in 2016, vulnerability to poverty was to a larger extent driven by low access to assets, it is the risks that drive vulnerability in 2019. In urban areas, risk factors have started to play an increasing role between 2016 and 2019.

**Additionally, idiosyncratic factors increasingly became more critical than covariate shocks.** This applies to the population as a whole, for which the ratio of idiosyncratic to covariate shocks increased from 1.9 in 2016 to 3.4 in 2019, but also the urban and rural population (see Figure 8). The increasing importance of household-level shocks relative to community-level shocks was more significant in urban areas. This means that, especially in urban areas, households are more subject to individual shocks, such as crime, employment losses, or illnesses.

_Figure 6: The development of vulnerability over time_ <!-- Start of picture text --> 65.0%<br>58.9%<br>52.1%<br>39.4% 41.2%<br>34.6%<br>30.9%<br>24.6% 23.7%<br>19.2% 18.1%<br>13.5%<br>2016 2017 2018 2019<br>All Rural Urban<br><!-- End of picture text --> _Source: World Bank estimates based on EHPM (2016-2019)_ _Figure 7: The development of risk-induced versus poverty-induced vulnerability over time_ <!-- Start of picture text --> Risk-induced over poverty-induced ratio (2016-2019)<br>3.0 2.7<br>2.5 2.3<br>2.1<br>1.8<br>2.0 1.7<br>1.5 1.2 1.3 1.4<br>1.1<br>0.9 1.0<br>0.8<br>1.0<br>0.5<br>0.0<br>2016 2017 2018 2019<br>All Rural Urban<br><!-- End of picture text --> _Source: World Bank estimates based on EHPM (2016-2019)_ 15
and a higher rural wage due to agricultural productivity increase reduces employment in small towns. In contrast, when demand for goods and services produced in small towns comes mainly from the adjacent rural areas, the demand effect can more than offset the negative wage effect, and lead to higher labor allocation to the production of the the town good.

We test the predictions of the model using panel data compiled from the population and enterprise censuses from Bangladesh. Our data set covers the period between 2000 and 2010. Between 2000 and 2010, Bangladesh experienced substantial reduction in the incidence of poverty, from 48.9 percent to 31.5 percent. This decade also witnessed substantial expansion in non-farm employment as a result of which its share in total employment increased from 47 percent to 52 percent. The rice yield, which is taken as a measure of agricultural productivity, has grown at an annual rate of 3.6 percent.<sup>3</sup> To understand the implications of agricultural productivity, we exploit variations in rainfall across upazilas and over time, and implement an approach that focuses on the effects of rainfall shocks in reduced form regressions on the outcome variables (employment in different types of non-farm activities) and also on the measure of agricultural productivity (crop yield). Our approach relies on the fact that rainfall variations can be interpreted as shifts in the production function, because rainfall is a major determinant of crop yield in Bangladesh (Sarkar et. al. (2012)). We also provide an instrumental variables interpretation of our estimates, using rainfall deviations (from long term average) across upazilas and over time as an instrument for crop yield (rice yield). Empirical estimation issues and strategies to deal with them are discussed in detail in Section 3. It is worth noting here that while rainfall shocks have been used for identification in a variety of contexts, agricultural productivity is arguably one of the most natural contexts where rainfall can provide reasonable identifying variations ((Foster and Rosenzweig (2004), Adhvaryu, Chari and Sharma (2013), Bruckner and Ciccone (2011)).

The empirical results establish positive and statistically significant effects of rainfall on rice yield and agricultural wages. The positive effect of rainfall shock on agricultural productivity (as measured by rice

> 3Rice is the predominant crop in Bangladesh.

3
|age, 2005-2009<br>log sales_t_-log sales_t−_1<br>(4)<br>(5)<br>(6)|-0.071***<br>-0.072***<br>[0.001]<br>[0.001]<br>-0.013***<br>0.001<br>[0.001]<br>[0.001]<br>122,684<br>122,684<br>122,684|0.071<br>0.041<br>0.071|Y<br>Y<br>Y|Y<br>Y<br>Y|rms in the period 2005-2009. Robust standard|10% level.|
|---|---|---|---|---|---|---|
|h, size and <br>pl_t−_1<br>(3)|-0.037***<br>[0.001]<br>-0.015***<br>[0.001]<br>122,684|0.034|Y|Y|ufacturing f|**5% level, *|
|Firm growt<br>mpl_t_-log em<br>(2)|-0.026***<br>[0.001]<br>122,684|0.023|Y|Y|sed of all man|***1% level,|
|Table 5: <br>log e<br>(1)|-0.044***<br>[0.001]<br>122,684|0.031|Y|Y|ple is compo|by frm-year.|
|Dep variable:|log age_t−_1<br>log empl_t−_1<br>log sales_t−_1<br>N (obs.)|R-squared|industry-year efects|region efects|Notes: The estimation sam|errors in brackets, clustered|

30
# **References**

Alcãzar, L., Andrade, R. and Jaramillo. M. (2010): “Panel/tracer study on the impact of business facilitation processes on enterprises and identification of priorities for future business enabling environment projects in Lima, Peru – Report 5: impact evaluation after the third round.” Report to the International Finance Corporation. World Bank Group, Washington, DC.

Anderson, M. (2008): "Multiple Inference and Gender Differences in the Effects of Early Intervention: A Reevaluation of the Abecedarian, Perry Preschool, and Early Training Projects", _Journal of the American Statistical Association_ , 103(484), 1481-1495 Andrade, G. H., Bruhn, M., and McKenzie, D. (2013): “A helping hand or the long arm of the law” Experimental evidence on what governments can do to formalize firms”, _World Bank Economic Review_ , forthcoming.

Benjamini, Y., Krieger, A. M., and Yekutieli, D. (2006): "Adaptive Linear Step-up Procedures that Control the False Discovery Rate", _Biometrika_ , 93(3), 491-507 Bruhn, M. (2013): “A Tale of Two Species: Revisiting the Effect of Registration Reform on Informal Business Owners in Mexico” _Journal of Development Economics_ 103: 275–83 Bruhn, M., McKenzie, D. (2009): “In Pursuit of Balance: Randomization in Practice in Development Field Experiments”, _American Economic Journal: Applied Economics_ , 1(4): 20032.

Bruhn, M., McKenzie, D. (2014): “Entry regulation and formalization of microenterprises in developing countries”, _World Bank Research Observer_ , 29(2): 186-201.

Campos, F., Goldstein, M., and McKenzie, D., (2015): "Short-term impacts of formalization assistance and a bank information session on business registration and access to finance in Malawi." _Policy Research Working Paper Series_ 7183, The World Bank.

De Giorgi, G., and R. Rahman. (2013): “SME’s Registration: Evidence from an RCT in Bangladesh.” Stanford University, Stanford, CA, and World Bank, Washington, DC.

De Giorgi, G., Ploenzke, M., and R. Rahman., (2015): "Small Firms’ Formalization: The Stick Treatment." _Policy Research Working Paper Series_ 7318, The World Bank.

De Mel, S., D. McKenzie, and C. Woodruff. (2013): “The demand for, and consequences of, formalization among informal firms in Sri Lanka.” _American Economic Journal: Applied Economics_ 5 (2): 122–50.

De Mel, S., D. McKenzie, and C. Woodruff. (2010): “Who are the Microenterprise Owners?: Evidence from Sri Lanka on Tokman v. de Soto” in _International Differences in Entrepreneurship_ , J. Lerner and A. Schoar (eds.), pp.63-87, 2010.

De Mel, S., D. McKenzie, and C. Woodruff. (2008) “Returns to Capital: Results from a Randomized Experiment”, _Quarterly Journal of Economics_ , 123(4): 1329-72 De Soto, H. (1989): _The Other Path_ , New York: Harper and Row Publishers.

28
# **The structural time series approach to the estimation of money demand**

In the structural time series modeling approach, the log of the demand for real money balances (im,) could be represented as, where _u,p_ represents the stochastic trend (unit root) component of the demand<sup>forreal</sup> money balances, **_vlm_** (i=1,2) represents a stochastic (trigonometric) cycle, _{tm_ is a seasonal component, and c," is an innovation.

Similarly, the variatle representing the level of real economic activity<sup>expressedin</sup> logs ( y, ), is assumed to have a representation where p" represents a stochastic trend component of real economic<sup>activity,</sup><sup>_iq y_(i=1,2)</sup> represents a stochastic cycle, **viy** is a seasonal component and<sup>6,Y</sup> is an<sup>innovation.</sup> Finally, we represerLt the opportunity cost or rate of return variable (which<sup>mayor</sup> may not be expressed in logs, depending on whether interest is in calculating<sup>elasticities</sup> or semi-elasticities of money demand, respectively) as where **lp,** represents the stochastic trend component of an opportunity<sup>costorrateof</sup> return variable, y,P (i=12) represents a stochastic cycle, /,P is a seasonal component, and **_ef_** is an innovation.<sup>5</sup>

> **5** For a discussion of structural (unobserved components) time series models in a multivariate context, please refer to Harvey (1989) cr Harvey (1993).

6 Money Demand in Venezuela, Mario A. Cuevas
**Table 1: Regression of GDP per capita growth 2000-2016 on various measures of education**

|||TIMSS a<br>|nd PISA<br>|||TIMS<br>|S only<br>|||PISA<br>|only<br>||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Av ears of|0.327||0.204||0.343||0.183||0.288||0.192||
|g. y<br>schooling (age 25-<br>29)|(5.28)**||(2.84)**||(4.05)**||(1.81)||(3.05)**||(1.77)||
|Learning Adjusted||||0.298||||0.319||||0.264|
|<br>Years of Schooling||||(6.27)**||||(5.08)**||||(3.58)**|
|Averae test score||0.973|0.606|||1.044|0.685|||0.914|0.581||
|g<br>(TIMSS or PISA)||(5.29)**|(3.01)**|||(4.40)**|(2.56)*|||(3.07)**|(1.70)||
|Initial GDP er|‐1.280|‐1.248|‐1.380|‐1.375|‐1.255|‐1.132|‐1.301|‐1.316|‐1.313|‐1.430|‐1.483|‐1.460|
|p<br>capita (log)|(11.63)**|(10.75)**|(12.54)**|(12.54)**|(7.92)**|(7.42)**|(8.69)**|(8.99)**|(8.53)**|(7.81)**|(8.23)**|(8.73)**|
|TIMSS dumm|‐0.079|‐0.331|‐0.203|‐0.003|||||||||
|y<br>(0/1)|(0.38)|(1.50)|(1.00)|(0.01)|||||||||
|Constant|10.783|9.564|10.308|12.539|10.306|7.854|9.231|11.815|11.544|11.632|11.581|13.708|
||(12.10)**|(10.26)**|(11.93)**|(14.32)**|(9.01)**|(6.36)**|(8.01)**|(10.73)**|(8.71)**|(9.23)**|(8.95)**|(10.51)**|
|_R_<sup>2</sup>|0.65|0.60|0.69|0.68|0.61|0.56|0.66|0.66|0.67|0.66|0.70|0.69|
||84|88|84|84|44|47|44|44|40|41|40|40|
|_N_<br>Data sources: averag<br>1999 is augment<br>Notes:*_p_<0.05;**|e years of sch<br>ed with results<br> _p_<0.01.T-sta|ooling for the<br>from 2003 f<br>tisticsinpare|cohort of 15<br>or additional c<br>ntheses.|years and ove<br>ountries. GD|r from Barro-<br>P per capita a|Lee. Test sco<br>nd growth fro|res from 199<br>m World Dev|9 TIMSS and<br>elopment Ind|2000 PISA m<br>icators.|athematics as|sessment. TI|MSS from|
Although the FMLN established itself as a political party in the presidential election of March 1994, it lost by a wide margin. The two major candidates were Ruben Zamora of Democratic Convergence (the FMLN) and Armando Calder ́on Sol of ARENA, a right-wing party. Voter fears that a left-wing victory might lead to a new wave of political violence were reinforced by ARENA’s campaign ads that featured images of wartime destruction (Wantchekon, 1999). These led to its victory and subsequent pessimism about the prospects for democratic consolidation in El Salvador. The FMLN finally secured a presidential victory with the election of Mauricio Funes in 2009. However, during Funes’s presidential term, several corruption scandals erupted and no substantial policy changes were implemented relative to ARENA.

# **III DATA**

This section describes the primary sources of data used in the study. Appendix A presents a detailed account of the database construction and Appendix B presents summary statistics of all variables employed in the analysis.

# **III.A Guerrilla-controlled territories**

To analyze the role of guerrilla territorial control in long-term development, we geocoded the map that depicts FMLN-controlled areas (see Figure 1). It shows areas the FMLN controlled during the conflict, areas the state controlled, and disputed ones. As Figure 2 illustrates, no boundaries of the guerrilla-controlled territories coincide with the administrative departments and municipal boundaries of El Salvador today. Therefore, our estimated treatment effects are unlikely to be contaminated by the influence of a compound treatment comprised of guerrilla control and changes in administrative boundaries. Similarly, Table 1 rules out that we are detecting the effect of the land reform and guerrilla control simultaneously. In sum, the evidence presented so far implies that our identification strategy credibly isolates the effects of guerrilla control from other potential confounders.

15
schools. This is the case in Rajasthan, India, where community schools employing paraprofessional teachers and part-time workers who escort girls from excluded groups to school have increased girls’ enrollment, attendance, and test scores relative to regular public schools (World Bank 1999).

Increasing the number of women teachers in schools is likely to have a strong positive impact on girls’ education. In some conservative communities, such as in some areas of Afghanistan, Pakistan, and Bangladesh, parents do not allow their daughters to be taught by male teachers. Therefore, the placement of women teachers can have an immediate impact on access. A study in Nepal indicates that mothers feel more comfortable talking about their children with a woman teacher, and in India (an environment in which local politics is often considered to focus on contacts, favors, and inside dealings), women teachers are considered “more sincere” because they are less likely to be involved in local politics (UNESCO PROAP 2000). The presence of women in schools can also positively affect girls’ retention in school and their achievement. Female role models can support and encourage girls to successfully complete their studies and even to continue their studies to become teachers themselves. At the school policy level, women teachers may act as advocates for girls, representing their perspectives and needs and promoting girl-friendly learning. For example, women teachers may be able to advocate for better toilet and washing facilities. Thus, women teachers play key roles in educating and socializing children beyond gender stereotypes and thus are crucial agents of change.

On the demand side, proposed incentives for households to send girls to school include conditional cash transfers, scholarships, stipends, and school meal programs. Conditional cash transfers extend resources to households to defray some of the costs of sending their children to school, tying social assistance payments to desirable behaviors. Programs in Bangladesh, Ecuador, and Mexico, among others, have been successful, although their specific impact on excluded groups must be carefully assessed. The experience of Progressa in Mexico suggests that without careful targeting, resources spent on conditional cash transfers may not have the desired results (de Janvry and Sadoulet 2006). Specifically, the program benefited indigenous boys more than indigenous girls. A conditional cash transfer program in Ecuador boosted school enrollment overall by 3.7 percentage points but did not differentially benefit girls or minority students (Schady and Araujo 2006).

25
23

# **Appendix 2. Connection Charge as Percentage of Family** **<u>Income</u>**

|Country|National<br>electrification<br>rates (2009;%)|Connection<br>charge<br>(US$)|GDP per person<br>(US$ 2011)|GDP per family<br>(US$ 2011)|Connection<br>charge as % of<br>monthly income|
|---|---|---|---|---|---|
|Bangladesh|41|23|743|3,715|7.5|
|Burkina Faso|15|264|600|3,000|100.0|
|Benin|23|150|802|4,010|44.9|
|Cape Verde|96|2|3,798|18,990|1.3|
|Central African Republic|5|283|489|2,445|138.9|
|Côte d’Ívoire|47|127|1,195|5,975|25.5|
|Ethiopia|17|75|357|1,785|50.4|
|Ghana|49|32|1,570|7,850|4.9|
|India|75|27|1,489|7,445|4.4|
|Kenya|16|400|808|4,040|118.8|
|Mauritania|23|106|1,151|5,755|22.1|
|Madagascar|19|81|465|2,325|41.8|
|Philippines|90|7|2,370|11,850|0.7|
|Sri Lanka|77|34|2,835|14,175|2.9|
|Tanzania|16|297|532|2,660|134.0|
|Thailand|99|18|4,972|24,860|0.9|
|Tunisia|100|67|4,297|21,485|3.7|
|Uganda|12|125|487|2,435|61.6|
|Zambia|19|200|1,425|7,125|33.7|
|Rwanda|10|350|583|2,915|144.1|
|Sudan|36|38|1,435|7,175|6.3|
|Lao PDR|55|100|1,320|6,600|18.2|
|Vietnam|98|20|1,407|7,035|3.4|

_Source:_ GDP per capita data are from the World Bank Data Resources 2013; connection charge data have been collected from utilities and regulatory agencies, World Bank reports, and from regional World Bank staff (see appendix 1); national electrification rates are mainly from IEA (2011).
43

## Is Ist gov. party special interest? GOVSPEC

Are any coalition parties special interest? COALSPEC

## Electoral Rules

Legislative Index of Political Competitiveness LIEC Executive Index of Political Competitiveness EIEC mean district magnitude (House) MDMH mean district magnitude (Senate) MDMS Number of senate/ (number of house + number of senate) $/S+H Plurality? (1 if yes, O if no) PLURALTY Proportional Representation? (1 if yes, 0 if no) PR If Plurality and Proportional Representation which governs the majority/all of the House seats? (1 if Plurality, O if Proportional) HOUSESYS If Plurality and Proportional Representation which governs the majority/all of the Senate seats? (1 if Plurality, O if Proportional) SENSYS What is the vote threshold for representation? THRESH Is the D'Hondt system used? (1 if yes, 0 if no) DHONDT Are closed lists used? (1 if yes, O if no) CL Candidate selection: national(1), sub-national(2), primary (3) SELECT Were vote fraud or candidate intimidation serious enough to affect the outcome of elections?’ FRAUD

# Stability and Checks & Balances

Longest tenure of a veto player TENLONG Shortest tenure of a veto player TENSHORT Tenure of system of government if democratic; tenure of chief executive otherwise TENSYS Index of Political Cohesion (based on Roubini, Sachs 1989) IPCOH Number of veto players CHECKS1 Checks? Check1, + 1 for each veto player whose orientation is closer to opp than gov CHECKS2 Maximum difference of orientation among Govt. parties (0-2) POLARIZ % veto players dropping from Govt., assume Senate changes STABS % veto players dropping from Govt., assume Senate doesn't change STABNS

## Federalism

Are there autonomous regions? AUTON Are the municipal governments locally elected? MUNI Are the state/ province governments locally elected? STATE Do Sub-national govts. have extensive taxing, spending or regulatory authority? AUTHOR .

Are the constituencies of the senators the states/provinces? STCONST