rafmacalaba/datause-encoder-data huggingface dataset:
# **6. Conclusion**

This paper analyzed the well-being and characteristics of those subject to forced migration during the recent (ongoing) crisis in Northern Mali. We based our study on a unique micro-data set that used new technologies to collect information in highly risky environments and among a population with relatively high mobility. We provided descriptive statistics on respondents’ welfare over time, their employment status, their wealth, and access to basic services.

In addition to this, we compared refugees, returnees and internally displaced people. We looked at differences between those who were willing to go back to Northern Mali and those who did not want to return, and noted how the decision to return voluntarily reflect the conditions during displacement as well as perceptions about the situation at home. Better conditions during displacement (receipt of assistance; having a job) favor remaining displaced, while the prospect to be reunited with one’s family incentivizes return. Age or gender do not affect the decision to return.

We find that considerations of benefits and costs that affect the decision to return may help shape public policies, aimed at stimulating the voluntary return of displaced people. More support in host communities or refugee camps is likely to reduce the incentive to return; better conditions in the location of origin are likely to stimulate return.

It is important to note, however, that while the decision to return is subject to typical economic incentives of costs and benefits, it is remarkable that over the course of the study few additional households returned. Most displacement was of short duration and most returned prior to our survey. Even though the expressed willingness to return among those who were still displaced was high (more than 90% for refugees and over 80% for displaced people), actual return rates during the 12 months of the study were low for IDPs as well as refugees.

There is another, more sobering policy conclusion that follows from this analysis, namely that most people who remained forcibly displaced at the time of the study are likely to remain displaced in the foreseeable future. Already the rate of return is low and as the security situation in Northern Mali remains tense and as displaced people integrate more and more economically in their host communities, the likelihood that they will return is decreasing in time. Unless the security situation in Northern Mali improves – or the situation in refugee camps and host communities deteriorates.

It is also worth mentioning that one variable stands out throughout this study: human capital. Better educated individuals are better positioned to deal with shocks and displacement. Human capital is 23
|LK Dam Safetyand Water Resources Planning (DSWRPP)|Shyam KC|
|---|---|
|Programmatic Broad-Based Growth Development Support Loan|Ana Lucia Armijos Hidalgo|
|Programmatic Broad-Based Growth Development Support Loan|Ana Lucia Armijos Hidalgo|
|Programmatic Broad-Based Growth Development Support Loan|Ana Lucia Armijos Hidalgo|
|Introduction of International Public Sector AccountingStandards|Jennifer K. Thomson|
|Introduction of International Public Sector AccountingStandards|Jennifer K. Thomson|
|Introduction of International Public Sector AccountingStandards|Jennifer K. Thomson|
|TH IDF Stengtheningof OAG|Nipa Siribuddhamas|
|Market led Smallholder Development in the Zambezi Valley|Pedro Arlindo|
|Market led Smallholder Development in the Zambezi Valley|Pedro Arlindo|
|Market led Smallholder Development in the Zambezi Valley|Pedro Arlindo|
|Market led Smallholder Development in the Zambezi Valley|Pedro Arlindo|
|Market led Smallholder Development in the Zambezi Valley|Pedro Arlindo|
|Market led Smallholder Development in the Zambezi Valley|Pedro Arlindo|
|Market led Smallholder Development in the Zambezi Valley|Pedro Arlindo|
|Market led Smallholder Development in the Zambezi Valley|Pedro Arlindo|
|Market led Smallholder Development in the Zambezi Valley|Pedro Arlindo|
|Market led Smallholder Development in the Zambezi Valley|Pedro Arlindo|
|Market led Smallholder Development in the Zambezi Valley|Pedro Arlindo|
|Market led Smallholder Development in the Zambezi Valley|Pedro Arlindo|
|Market led Smallholder Development in the Zambezi Valley|Pedro Arlindo|
|Market led Smallholder Development in the Zambezi Valley|Pedro Arlindo|
|Market led Smallholder Development in the Zambezi Valley|Pedro Arlindo|
|Market led Smallholder Development in the Zambezi Valley|Pedro Arlindo|
|Market led Smallholder Development in the Zambezi Valley|Pedro Arlindo|
|SENEGAL - Supreme Audit Institution|Saidou Diop|
|SENEGAL - Supreme Audit Institution|Saidou Diop|
|Nigeria National EnergyDevelopment Project - Carbon|Prasad V. S. N. Tallapragada|
|Nigeria National EnergyDevelopment Project - Carbon|Prasad V. S. N. Tallapragada|
|Nigeria National EnergyDevelopment Project - Carbon|Prasad V. S. N. Tallapragada|
|Promotion of a Wind Power Market|Ferhat Esen|
|Promotion of a Wind Power Market|Ferhat Esen|
|Promotion of a Wind Power Market|Ferhat Esen|
|Promotion of a Wind Power Market|Ferhat Esen|
|Promotion of a Wind Power Market|Ferhat Esen|
|Promotion of a Wind Power Market|Ferhat Esen|
|Promotion of a Wind Power Market|Ferhat Esen|
|Promotion of a Wind Power Market|Ferhat Esen|
|Promotion of a Wind Power Market|Ferhat Esen|
|Promotion of a Wind Power Market|Ferhat Esen|
|Promotion of a Wind Power Market|Ferhat Esen|
|Promotion of a Wind Power Market|Ferhat Esen|
|Promotion of a Wind Power Market|Ferhat Esen|
|Promotion of a Wind Power Market|Ferhat Esen|
|Promotion of a Wind Power Market|Ferhat Esen|
|SN-Elec Sec EfficiencyEnhance GU(FY05)|Stephan Claude Frederic Garnier|
|PRSO 2|RosalindaQuintanilla|
towards the energy-efficient light bulb than exposure to weak arguments in the moderate fear condition.” This positive role of the emotion of fear in information processing may be counter to ethnographic and interview work by Norgaard and work from focus groups by Immerwahr, although the latter focus on negative emotions as barriers to action, not barriers to information processing per se. The authors note that extreme fear can reduce information processing effectiveness, but do not characterize climate change in the realm of extreme fear as the impacts are in the future (a questionable assumption).

Sturgis, Patrick and Nick Allum 2004 Science in society: re-evaluating the deficit model of public attitudes <u>Public Understanding of Science.</u> 13 **:** 55–74.

Within literature on public perception of science, the “deficit model” holds that public understanding of science is a determinant of public attitudes towards science. The authors’ research does support the dominant model that knowledge is a determinant of attitudes toward science. However, the authors provide evidence that this relationship between knowledge and attitudes is more complex than has been presumed.

# **Policy**

Brewer, Thomas 2005 “U.S. Public Opinion on Climate Change Issues: Implications for Consensus building and Policymaking” Climate Policy, 4: 359-376.

Brewer compiles and analyzes over 40 US public opinion surveys from 1989 through 2004. He concludes that a substantial majority want the government to take stronger steps on climate change, welcome US participation in the Kyoto protocol, and do not support “key elements of the Bush administration policies.” Mandatory emissions reductions on industry and economic assistance to developing countries are both supported.

Bulkeley, Harriet. 2000. "Common Knowledge? Public Understanding of Climate Change in Newcastle, Australia." Public Understanding of Science 9:313-333.

Buckeley claims that public understanding of climate change not only involves knowledge of its physical processes, but also encompasses wider issues concerning the relation between society and nature. Based on her examination of the conclusions and assumptions of previous research on the public understanding of climate change, Buckeley argues that in each case, in accordance with the “information deficit model,” recorded levels of ignorance are seen as a barrier to effective public involvement in the policy process. However, she offers data from her work in Newcastle, Australia to show that whether and how people understand climate change is a function of scientific information, local knowledge, values, and moral responsibilities. Further, respondents connected the 69
9(29) Greer, J. and Thorbecke, E. (1986a) "A Methodology for Measurlng Food Poverty Applied to Kenya," Journal of Develogment Econom-es, No. 24, pp. 5974.

(30) Greer, J. and Thorbecke, E. (1986b) "Food Poverty Profile Applied to Kenyan Smallholders," Economic-DeveloRMent and Cultural hangg, pp. 115-41.

(31) Gregory, P. (1986) The Myth of at rtilure: Emolovent and the Labor Market in hexico, John Hopkins Univeroity Press: Baltimore.

(32) Haddad, L and Kanbur, R. (1989) "How Serlous Is the Neglect of Intrahousehold Inequality?" World Bank Working Papers, No. 296, November, Washington, C..

(33) Haddad, L. ani Kanbur, R. (1990) "Are Better-off Households More Unequal or Less Unequal," World Bank Working Paper Series, No. 373, March, Washington, D.C..

(34) Heath, J. (1990) "Enhancing the Contribution of the Land Reform Sector to Mexican Agricultural Development," World Bank Report No. 8310-ME, Washington, D.C..

(35) Hernandez Laos E. (1989a) "Medicion de la Incidencia de la Pobreza y de la Pobreza Extrema en Mexico," mimeo, Universidad Autonoma Metropolitana, Mexlco City.

(36) Hernandez Laos E. (1989b) "Tendencias Recier. s en la Distribucion del - Ingreso en Mexico: 1977 1984," mimeo, Universidad Autonoma Metropolitana, Mexico Cicy.

(37) Instituto Nacional de Estadistica, Geografia e Informatica (1984) InfgrMe Netodologico de la Encuesta NAcioLaal de Ingreos v GAston de los Haogares 1983J198, Mexico City.

(38) Jarque, C. (1987) "Patrones de Gasto en los Hogares de Ciudad de Mexico," Estudios Economicos de El Cole&io de Mexico, No. 1, Vol. 2, pp. 37-64.

(39) Lewis, A. (1978) The Evolution of tb International EconomLc Order, Princeton University Press: Princeton.

(40) Lipton, M. (1977) Why Poor People Stay Poor: A Study of Urban Bias in World DeveloMent, Australian National University Press: Canberra.

(41) Lipton, M. (1983a) Poverty. Undernutrition and Hunger, World Bank Staff Discussion Paper # 597, Washington, D.C..

(42) Lipton, M. (1983b) DemggaRky and Poverty, World Bank Staff Discussion Paper # 623, Washington, D.C..

(43) Lipton, M. (1988a) "Who are the poor? What do they do? What should we do?", mimeo, Michigan State Universlty.
# **Child Stature, Maternal Education, and Early Childhood Development**

In an ideal world, recommendations for social policies aimed at improving welfare and especially maternal and child outcomes would be based on well-established evidence on the causal relation between the variables of interest. In reality, however, this is not feasible. The increasing number of randomized controlled trials (RCTs) that are useful for establishing causal inferences, combined with the limited ability of RCTs to address every policy relevant question, has not been able to satisfy the increasing demand for evidence-based policy advice. At the same time, the higher standards for the internal and external validity of the empirical evidence generated have increased the urgency to extract credible causal relations, rather than just partial correlations, between variables of interest based on the already available and accessible cross-sectional surveys.

Yet, studies with credible causal inferences based on regression analysis using non-experimental household cross-sectional data are not common, or even purposefully avoided by young researchers in social sciences aspiring to establish their publication record in their respective fields in the context of a proliferation of RCTs as the standard for causal inferences. Instead, the common practice in fields such as maternal and child health is to present estimates from simple regression models with few chosen controls for confounding factors acknowledging the potential role of other confounding factors left out of the model and publish with the qualifying statement that such estimates establish significant partial correlations but not causation between the variables of interest (Miller et al, 2015). Frequently the acknowledged relative weaknesses in the internal validity of these studies is compensated by efforts to strengthen the external validity of the findings by carrying out the same regression (or partial correlation) analysis across different countries with the same type of survey (e.g., Li et al., 2020; Jeong et al., 2019; and Kang et al., 2018).

This paper applies recently developed methods to address threats to internal validity using individual child data from two countries, the Republic of Congo and São Tomé and Príncipe, for the purpose of deriving “causal” inferences about the effect of child stature and mother’s level of education on early childhood development (ECD). Stunting is the impaired growth and development that children experience from poor nutrition, and repeated infection, and is based on measuring the height (length) of children and comparing it to the height of well-nourished children of the same age. The level of education of a mother affects early childhood development through a variety of channels: improved child health and nutrition, increased labor force participation, and income earning opportunities 2
# INTRODUCTION

Fuelwood is a major source of energy for households in many developing countries. It accounts for a significant portion of national energy consumption, and is most generally supplied in the form of firewood or charcoal. Ir low-income Africa, up to 90 percent of households use fuelwood. There is an active trade in wood products, and thousands of people are involved in processing, distribution and marketing. Yet few governments collect more than small fraction of the total traded value of wood products.

This paper discusses the economic justification for charging for wood resources and the effects of considering wood on the stump as a "free good". It reviews a number of methods that can be used for estimating stumpage value, 1/ and the advantages and disadvantages associated with each. It examines a model fuelwood pricing system and briefly explores other types of forest revenue systems. Finally, examples are provided of fuelwood stumpage practices in use in developing countries today.

The discussion of fuelwood pricing should be useful for both forestry economists, who must identify the best ways to manage their nation's wood resources, and to household energy planners, who use data on fuelwood prices when comparing the costs and benefits of use of fuelwood with use of alternative fuels, the relative costs and benefits of improved and unimproved charcoal stoves, etc.

Many of the examples used in this paper are taken from studies made by World Bank household energy economists in Africa. The examples are intended as illustrations only. The paper goes only briefly into the derivation of stumpage values using actual field data. This is done deliberately, as very little data exists that has been collected over a sufficient period of time using rigorous scientific methods. The Hlousehold Energy Unit is planning extensive research in the areas of fuelwood pricing, and will publish its findings in future papers.

# Definition of Terms

The terms "stumpage fee," "stumpage value," "royalty" and even "stumpage tax" are often used interchangeably in natural resource and forestry economics, both in the literature and in the field. The substitution of one term for the other often causes confusion. In the interest of clarity, the terms, as they are used in this paper, are defined below:

- Stumpage fee is the financial concept of the value of standing wood resources. It is the compensation the owner of the wood

1/ Stumpage is defined as "the value of a standing tree".
As equation (4) shows, the unobserved factor is not eliminated, and hence, a simple FE estimation of equation (3) will still be biased.

To address the potential endogeneity concern, we use two approaches: instrumental variable (IV) approach and propensity score-weighted fixed-effects model. An instrumental variable must satisfy two conditions: it is directly correlated with the treatment variable but not with the outcome variables; the outcome variables are only indirectly affected through its impact on the treatment variables. In this study, we explore the idea of using connection cost as possible instruments for electricity adoption. High initial charges households must pay to be connected to the grid are often identified as a significant obstacle to electricity adoption in developing countries (Winkler et al. 2011). In Bangladesh, like in many other countries, households must pay a fixed fee to be connected to the electric network, and the average connection cost per household was Taka 2800 (US$40) or about 5 percent of annual total household expenditure based on a 2006 study (Mainuddin 2006). The connection cost varies with a household’s proximity to the nearest electric pole. The closer the distance, the lower is the cost. If a household is located within 100 feet of the nearest pole, connection is given free of cost. Free or low connection offers a strong incentive for households to adopt grid electricity but it does not otherwise affect the outcomes of interest. Therefore, similar to Khandker, Barnes and Samad (2013), we use the following choice variable as an instrument to capture exogenous variation in electrification status: _Z_ = 1 if household is located within 100 feet of an electric pole.

= 0 otherwise.

We also interact _Z_ with household exogenous characteristics, including age, gender, and education of the households’ head to create additional instruments. Data on electricity availability are only reported for 2005, and so, IV method is combined with village-level fixed effects and implemented using the crosssectional data of 2005 to estimate the heterogeneous effects of electrification caused by variation in electricity reliability.

To examine how years of connection affects the accumulated benefits of electrification, we use propensity score-weighted fixed effects in a panel setting. Heckman (1981), Chamberlain (1984), and Arulampalam, Booth, and Taylor (2000) show that unobserved heterogeneity is often correlated with initial conditions. Based on the insights from the literature, we estimate a propensity-score-weighted fixed-effects model in two stages by controlling for initial household- and village-level characteristics. In the first stage, we estimate a household’s probability of being connected to the grid as a function of household and 10
housing quality, we use an indicator for whether the household has an improved roof as its own outcome (following the results in Haushofer and Shapiro 2016). For livestock, we measure the total value of all livestock (both including and excluding group owned livestock), and examine the numbers of dairy cows, dairy goats, pigs, and all poultry. Productive assets include two outcomes of interest: the total value of durable productive assets and the total value of businesses.

Our secondary outcomes include food security, health, education, income and revenue, and savings and borrowing. Food security is measured by looking at the average number of meals eaten in a day by an individual in the household. Health includes three measures: the proportion of household members who were sick or injured in the past week, whether the respondent’s selfreported health was good or average, and in index of disability for those over 15. Education looks at the proportion of school-aged children (aged X-Y) in school at the time of the survey and school expenditures. Income and revenue, all in the past year, includes agricultural revenue, livestock revenue, livestock expenditures, livestock profit and enterprise income. Finally, savings and borrowing looks at total savings and total borrowing.

# **3.5. Attrition and Balance**

We began the experimental component of the study with 1,017 eligible households having responded to the baseline survey. In Table A1, we examine whether subsequent attrition from the study was driven by any of the treatments. We see that attrition from the study was 5.5% at 12 months and 14.2% at 36 months from baseline. While it was substantially lower among group leaders than the rank and file, it was not correlated with being treated by TASAF, receiving training or the UCT, or getting both TASAF and the UCT (the UCT variable here, as in the impact regressions, is in units of hundreds of dollars received by a household).

16
Appendix A

# **A2: Spatial and temporal deflation of PNAD data sets**

Ferreira, Lanjouw and Neri (1999) specify a procedure to calculate spatial deflators for Brazil. Their regional price indices are based on the consumption patterns and implicit prices from the _Pesquisa sobre Padrões de Vida_ (PPV) 1996/7 surveys. Since the PPV covers only the Northeast and the Southeast, Ferreira, Lanjouw and Neri (1999) obtain spatial price deflators for the rest of Brazil by means of extrapolation. The spatial price deflators are as follows:

|**A Brazilian Spatial Price Index ( RM**<br>PNAD Region|**Sao Paulo= 1.0)**<br>Spatial PriceDeflator|
|---|---|
|RM Fortaleza|1.014087|
|RM Recife|1.072469|
|RMSalvador|1.179934|
|Northeast ( otherurbanareas)|1.032056|
|Northeast (rural)|0.953879|
|RM BeloHorizonte|0.958839|
|RM Rio de Janerio|1.002163|
|RMSãoPaulo|1.000000|
|Southeast (otherurbanareas)|0.904720|
|Southeast (rural)|0.889700|
|RM Porto alegre|0.987001|
|RMCuritiba|0.987001|
|South( otherurbanareas)|0.904720|
|South(rural)|0.889700|
|RM Belém|1.088830|
|North( other ruralareas)|1.037915|
|RM Brasília|1.037915|
|CenterWest ( otherurbanareas)|0.968388|

We assume that the structure of average regional cost-of-living described above remained constant over the period. This assumption is largely due to the lack of earlier comparable regional price information and in line with other research in this field (see Ferreira and Paes de Barros (1999)).

To deflated the different PNAD incomes over time, we use the monthly Brazilian consumer price indices, IPCA. In order to center the indices on the first day of the month, which is the reference date for PNAD incomes, we follow Ferreira and Paes de Barros 49