rafmacalaba/datause-encoder-data huggingface dataset:
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18

# **6. DATA SOURCES**

The system model is econometrically consistent and intuitively appealing, but it has a major empirical challenge. This is because consistent and comparable data on both the development, institutional, and impact variables are very difficult to obtain. It is certainly possible to acquire observed data on some of the impact variables (e.g., productivity, employment, income, and wage rates) through, for example, published records or primary surveys. But, such data can represent only the past impact of an already implemented development program and can not capture the synergy from the expected impacts of ongoing and planned program. Still more serious are the difficulties in getting the data on institutional variables, especially on their diverse but specific roles in the generation and transmission of development impacts.

The absence or lack of data on most variables does not, however, mean a complete absence of information. Highly relevant information is constantly processed, coded, and stored in the minds of people involved in the development process either as planners and implementers or as beneficiaries. Such real but latent information, embodied in individuals, can be tapped through a carefully designed and conducted stakeholder surveys. This form of survey data, though based on stakeholders’ perception, have many desirable properties often missed by the so called objective or observed data. For example, unlike observed data characterizing a past and static situation, the perception-based data can capture and synthesize objective, subjective, and aspiration-related information. Similarly, they can also capture the _ex-ante_ and dynamic elements. It is also theoretically legitimate in view of the subjective nature of institutions (Commons, 1934; V. Ostrom, 1980; Douglas, 1986; E. Ostrom, 1990) and the roles that the ‘subjective model’ of the ‘agents of institutional change’ play in institutional change and performance (North, 1990). As a result, there is a long tradition of using such data for institutional analysis (e.g., Knack and Keefer, 1986; Gray and Kaufmann, 1998; Barret and Graddy, 2000; Kaufmann, Kraay, and Mastruzzi, 2006). As noted already, qualitative data are also used in the few impact assessment methods discussed earlier (Neubert, 2000; Coudouel, Dani, and Paternostro, 2006).
# **Acknowledgments**

This paper draws on a review of the first 17 Systematic Country Diagnostics (SCDs) conducted by the World Bank Group (WBG), in consultation with national partners. In addition to an analysis of the content, the paper reflects conversations with several World Bank staff members involved in the preparation of SCDs, both from country offices and from Washington D.C. They provided valuable inputs on the degree to which the SCDs were able to address social inclusion issues and the substantive and process related challenges involved.

The paper is written by Maitreyi Bordia Das, drawing upon results of a review conducted by (alphabetically) Ezgi Canpolat, Sabina Espinoza, Soumya Kapoor Mehta and Lisa Schmidt (independent consultants) who among them, read the SCDs, ensuring that they were evaluated on meaning and content rather than mere use of words such as “inclusive” or “inclusion”. Soumya Kapoor Mehta led the team of reviewers and her additional insights in synthesizing the results of the review as well as her editorial support are gratefully acknowledged. Sabina Espinoza helped in refining some of the ideas and background inputs. The paper also draws on the discussions held at a workshop on social inclusion in the SCDs in May 2015, where participants reflected on their experiences and had the opportunity to comment on the early findings from this review.

Thanks are also due to Gaia Hatzfelt (independent researcher), Valerie Morrica (Social Development Specialist, GSURR, World Bank), Ambar Narayan (Lead Economist, Poverty and Equity GP, World Bank), Maria Beatriz Orlando (Lead Social Development Specialist, GSURR, World Bank), Dianna Pizarro (Senior Social Development Specialist, GSURR, World Bank), Emcet Tas (Social Development Specialist, GSURR, World Bank) Jan Weetjens (Practice Manager, Social Development, West and Central Africa, World Bank) and Ieva Zumbyte (independent consultant), who read earlier drafts of the paper and gave useful comments.

4
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Profit‐sharing between the innovators, those who commercialize the innovation (who may or may not be the initial innovators), and others has been at the forefront of modern history and policy. For example, countries have varied in terms of who owned the initial innovations (e.g. Thomas Edison and other researchers for the light bulb), or who was able to capture associated profits by commercializing upon the invention (e.g., the light bulb producers, including Edison and Swan, consumers, other producers), who built upon it (e.g. Nikola Tesla, who developed AC transmission and generation technology, consumers, factory owners, retailers etc.), how fast and widely it all spread (depended on demand, infrastructure, etc.), and what type of market structure prevailed afterwards (e.g. free entry, low entry costs, in the light‐bulb producing and retail industries, or in the general electricity sector; Edison filed for a patent). Social unrest is more likely when technological change fails to improve the lot of visible chunks of the population, takes away their jobs, or raises inequality for long periods.

While cognizant of the role of employment‐displacing innovation in raising welfare, concerns with digitization<sup>7</sup> today are being fueled by: (a) a pace of change in markets that is perceived to be much faster than it was for previous episodes of innovation (Figures 3‐5 show the diffusion rate of some recent inventions); (b) fast movement of digital innovations across borders, faster global impact, competition and feedback channels. For example, a Citi GPS study (2017) cites that it took on average 119 years for the spindle to diffuse outside Europe, while the internet spread across the globe in only 7 years (and now cross‐border hacking is common); and (c) the spread of AI is affecting a (hitherto unexpectedly) wide range of human tasks across the population. With digital technology spreading so fast, there are bound to be large intertemporal and spatial disruptions in employment. For example, manufacturing using sophisticated technology and 3D printing may bring more U.S. companies home

> 7 Digital technology has several facets ranging from information provision on the internet to replacing a variety of services by AI (Citi, 2016, Autor 2017). The term digitization will be used to encompass all these effects.

6
Growth and Risk: A View from International Trade<sup>_∗_</sup> Pravin Krishna<sup>_†_</sup> Andrei A. Levchenko<sup>_‡_</sup> William F. Maloney<sup>_§_</sup> JEL Codes: F14, O3, O4 Keywords: Product quality, financial development, risk

> _∗_ We are grateful to Amit Khandelwal for his help and advice in generating his quality measures and Peter Schott for advising on data. We thank Andr ́es Rodr ́ıguez-Clare for invaluable comments on a previous version.

> _†_ Johns Hopkins University and NBER, `Pravin` ~~`K`~~ `rishna@jhu.edu` .

> _‡_ University of Michigan, NBER, and CEPR, `alev@umich.edu` .

> _§_ World Bank, `Wmaloney@worldbank.org` .
Seyboth K, Adler A et al (eds) Climate change 2014: mitigation of climate change. Contribution of working group III to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, pp 1–33. Available at: <u>https://doi.org/10.1017/CBO9781107415324</u> **[3]** <mark>Jalan, J., & Ravallion, M. (2002). Geographic poverty traps? A micro model of consumption growth in rural China.</mark> _<mark>Journal of Applied Econometrics</mark>_ <mark>,</mark> _<mark>17</mark>_ <mark>(4), 329-346.</mark> **<mark>[</mark> 4]** <mark>Jessoe, K., Manning, D. T., & Taylor, J. E. (2018). Climate change and labour allocation in rural Mexico: Evidence from annual fluctuations in weather.</mark> _<mark>The Economic Journal</mark>_ <mark>,</mark> _<mark>128</mark>_ <mark>(608), 230-261.</mark> **<mark>[</mark> 1]** <mark>Kaczan, D. J., & Orgill-Meyer, J. (2020). The impact of climate change on migration: a synthesis of recent empirical insights.</mark> _<mark>Climatic Change</mark>_ <mark>,</mark> _<mark>158</mark>_ <mark>(3), 281-300.</mark> **<mark>[</mark> 1]** Karanja Ng’ang’a, S., & al., e. (2016). Migration and self-protection against climate change: a case study of Samburu County, Kenya. _World Development, 84_ , 55-68. **[1]** <mark>Kattumuri, R., Ravindranath, D., & Esteves, T. (2017). Local adaptation strategies in semi-arid regions: study of two villages in Karnataka, India.</mark> _<mark>Climate and Development</mark>_ <mark>,</mark> _<mark>9</mark>_ <mark>(1), 36-49.</mark> **<mark>[</mark> 1]** <mark>Kelley, C. P., Mohtadi, S., Cane, M. A., Seager, R., & Kushnir, Y. (2015). Climate change in the Fertile Crescent and implications of the recent Syrian drought.</mark> _<mark>Proceedings of the national Academy of Sciences</mark>_ <mark>,</mark> _<mark>112</mark>_ <mark>(11), 3241-3246.</mark> **<mark>[</mark> 1]** <mark>Kirchberger, M. (2021). Measuring internal migration.</mark> _<mark>Regional Science and Urban Economics</mark>_ <mark>,</mark> _<mark>91</mark>_ <mark>, 103714.</mark> **<mark>[</mark> 2]** <mark>Kolstad, C. D., & Moore, F. C. (2020). Estimating the economic impacts of climate change using weather observations.</mark> _<mark>Review of Environmental Economics and Policy</mark>_ <mark>,</mark> _<mark>14</mark>_ <mark>(1), 1-24.</mark> **[3]** <mark>Koubi, V., Schaffer, L., Spilker, G., & Böhmelt, T. (2022). Climate events and the role of adaptive capacity for (im-) mobility.</mark> _<mark>Population and Environment</mark>_ <mark>, 1-26.</mark> **<mark>[</mark> 1]** <mark>Koubi, V., Spilker, G., Schaffer, L., & Bernauer, T. (2016). Environmental stressors and migration: Evidence from Vietnam.</mark> _<mark>World Development</mark>_ <mark>,</mark> _<mark>79</mark>_ <mark>, 197-210.</mark> **[1]** <mark>Koubi, V., Stoll, S., & Spilker, G. (2016). Perceptions of environmental change and migration</mark> 44
households in 499 wards conducted in 2010. About two‐thirds of the households sampled are rural. The data show that in 2010‐11 only 3% of households in Nepal used biogas as their primary cooking fuel (see Table 1). However, cow dung is the principal feedstock for biogas plants and 50% of all Nepali households own at least two cows or buffaloes, so the potential reach of biogas is large.

Table 1: Primary Cooking Fuel in Nepal in 2010‐11

|Main cooking fuel|Percent of households|
|---|---|
|Firewood|62|
|Cylinder gas|23|
|Dung|8|
|Biogas|3|
|Leaves, straw, etc|3|
|Kerosene|1|
|Other|1|

Note: Percentages do not add to 100 due to rounding. Survey weights used to obtain population fractions.

We follow other studies (Baland et al. 2010)(Nepal, Nepal, and Grimsrud 2011) in using firewood collection, not consumption, as the dependent variable. This is because firewood consumption, although reported by some households, is mostly missing. Furthermore, sales of firewood are rarely reported, so collection is a good proxy for consumption. The NLSS‐III data set reports firewood collection in loads of firewood ( _bharis_ in Nepali).<sup>5</sup> We then convert _bharis_ to kilograms as follows. Households were asked how many kilograms a _bhari_ of wood contained, but not all firewood‐collecting households answered this question. We used the conversion factor reported by households when it was available. This was the case for 3,389 households. We imputed the conversion factor for the remaining 631

> 5 A bhari is a load of firewood, the amount one person usually carries on one trip. Very few households collected firewood using carts and the appropriate conversion factor was used in those cases.

6
OECD. 2014. PISA 2012 Results: What Students Know and Can Do (Volume I, Revised edition). Student Performance in Mathematics, Reading and Science. Paris: OECD Publishing.

OECD. 2016. PISA 2015 Results (Volume 1): Excellence and Equity in Education. Paris: OECD Publishing.

Organización de las Naciones Unidas para la Educación la Ciencia y la Cultura (UNESCO). 2009. Aportes para la Enseñanza de las Ciencias Naturales: Segundo estudio Regional Comparativo y Explicativo (SERCE). Santiago de Chile: OREALC/UNESCO.

Organización de las Naciones Unidas para la Educación la Ciencia y la Cultura (UNESO). 2016. Informe de resultados TERCE. Logros De Aprendizaje. Santiago de Chile: OREALC/UNESCO. http://unesdoc.unesco.org/images/0024/002435/243532S.pdf. UNESCO 2015.

Sailors, M., and L. Price. 2010. “Professional Development That Supports the Teaching of Cognitive Reading Strategy Instruction.” Elementary School Journal 110: 301e323.

Sailors, M., and L. Price. 2015. “Support for the Improvement of Practices Through Intensive Coaching (SIPIC): A Model of Coaching for Improving Reading Instruction and Reading Achievement.” Teaching and Teacher Education 45: 115–27.

Serra, J.C. 2001. “La política de Capacitación Docente en Argentina: La Red Federal de Formación Docente Continua (1994–1999). Buenos Aires: Argentine Ministry of Education http://repositorio.educacion.gov.ar/dspace/bitstream/handle/123456789/96628/EL000689.pdf Sloan, H. A. 1993. “Direct Instruction in Fourth and Fifth Grade Classrooms.” Dissertation Abstracts International 54 (8): 2837A.

Stanger-Hall, K. F. 2012. “Multiple-Choice Exams: An Obstacle for Higher-Level Thinking in Introductory Science Classes.” CBE-Life Sciences Education 11 (3): 294–306.

Urquiola, M. 2006. “Identifying Class Size Effects in Developing Countries: Evidence from Rural Bolivia.” Review of Economics and Statistics 88 (1): 171–77.

Vegas, E., A. Ganimian, and M. S. Bos. 2014. América Latina en PISA 2012: ¿Cuántos Estudiantes Tienen Bajo Desempeño? Washington, DC: InterAmerican Development Bank (IADB). https://publications.iadb.org/handle/11319/701.

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Yoon, K. S., T. Duncan, S. W. Y. Lee, B. Scarloss, and K. L. Shapley. 2007. “Reviewing the Evidence on How Teacher Professional Development Affects Student Achievement.” Issues & Answers Report, REL 2007-No. 033, US Department of Education, Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance, Regional Educational Laboratory Southwest, Washington, DC.

37
48

# 6. CONCLUSIONS

Our analysis has shown that both the GOV and PDVSA are exposed to oil price volatility. Oil price volatility is responsible for a large part of the volatility of GOV's total (oil and non-oil) revenues. Under the current oil tax system, PDVSA's exposure increases as oil prices fall.

Comparisons of price volatility esdmates show that crude oil price volatility is higher than refined product price volatility, but not by much. Also, as product prices are closely correlated with crude oil prices, diversification into down-steam activides provides little risk reduction. Regarding the issue of basis risk, Venezuelan crude oils have a relatively high basis risk vis-avis WTI as compared to other, similar quality Latin American crudes. This relatively high basis risk is mainly attributed to the pricing policies for Venezuelan crudes. While a thorough evaluation of the basis risk in refined products was not pursued, preliminary analysis indicates that it is not high. To the extent basis risk exists, it may be due to Venezuela's inefficiencies in pricing these products.

The analysis raises concerns regarding the establishment of an OSF. It was argued that international financial markets are not as incomplete as perceived. In addition, the random walk hypothesis for oil prices cannot be easily challenged based on recent empirical work. Finally, adjustment costs deriving from price/revenue instability cannot alone be a sufficient reason to establish an OSF when the private sector can hedge. A jusdfication based on adjustment costs relies on the lack of access of the private sector to external hedging instruments, something which cannot be taken for granted as the existence of capital flight shows.
# **1.  Introduction**

The Poverty Reduction Strategy (PRS) of the Government of Rwanda, published in 2002, identifies rural development and agricultural transformation as the top priority (out of six) for promoting private sector-led development in that country<sup>1</sup> . This high focus on the rural economy is justified by the fact that agriculture contributes at least 40 percent of GDP and provides a livelihood for about 90 percent of the population. Therefore, growth in the agriculture is key to reducing the poverty rate of 60 percent of the population, based on a poverty line of 64, 000 RWF (about US $140) per person per year<sup>2</sup> .

Agriculture also contributes significantly to Rwanda’s trade with the rest of the world. In 2005, agricultural products accounted for just over 60percent of total exports in goods. Tea and coffee, the main cash crops, accounts for about 56 percent of these exports, and more than half of Rwanda’s export revenue. Over the past 5 years, tea has remained, on average, the second major export crop (after coffee), although tea exports in some years (notably 2000 and 2001) have exceeded coffee exports. The sector is the largest employer in the country and directly generates close to 60,000 jobs.

Despite the growth in the sector following the genocide, several key problems limit the potential for this sector to generate foreign exchange and to contribute toward increased welfare of the population. The key problems constraining potential in the sector include: (i) agronomic conditions related to the location of factories and the quality and type of surrounding soil; (ii) low capacity of factories related to years of inadequate investment; (iii) differences in fertilizer application. With regard to the latter point, there have been reports that managers of the government owned factories apply less than optimum levels of fertilizer in order to ensure that production levels do not surpass the capacity of the tea processing factories. These problems have resulted in poor outcomes for the sector that are manifested in low producer prices, and low average yields. Based on these poor indicators, and the inefficiency of the government owned factories and plantations, the Government of Rwanda initiated a reform program based on

> 1 The other five priorities include: human development, economic infrastructure, governance, private sector development and institutional capacity development.

> 2  This is based on data from the 2001 Household Living Conditions Survey, also known as _Enquête Intégrale sur les Conditions de Vie_ (EICV) _des Ménages au Rwanda_
39

**Table SI-13:** Migration impacts of storms with paired counties

||Impact on o|ut-migration|Impact on|net migration|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|1(Storm year)=1|-0.018<sup>_∗∗_</sup>|-0.023<sup>_∗∗_</sup>|0.022|0.028|
||(0.009)|(0.010)|(0.016)|(0.018)|
|1(Storm year), t-1=1||-0.017||0.018|
|||(0.010)||(0.014)|
|1(Storm year), t-2=1||-0.007||0.013|
|||(0.009)||(0.015)|
|1(Storm year), t-3=1||-0.008||0.013|
|||(0.011)||(0.013)|
|1(Storm year), t-4=1||0.002||0.000|
|||(0.010)||(0.011)|
|1(Storm year), t-5=1||0.014||-0.022<sup>_∗∗_</sup>|
|||(0.012)||(0.010)|
|N|2,908,370|2,369,765|2,377,137|1,936,912|
|Σ<sup>5</sup><br>_i_=0<sup>_Stormt−i_</sup> <sup>= 1</sup>||-0.040||0.049|
|||(0.037)||(0.059)|
|Fixed Efects:|||||
|County pair|Yes|Yes|Yes|Yes|
|Sending County|Yes|Yes|Yes|Yes|
|Year|Yes|Yes|Yes|Yes|
|State_×_Year|Yes|Yes|Yes|Yes|

Notes: All columns report the results of a fixed effects specification with the IHS transformation of the number of out-migrants from a county as the outcome in columns 1-2 and the IHS transformation of net migration as the outcome in columns 3-4. Net migration is defined as in-migration minus out-migration so that negative net migration indicates a declining population. In all regressions we control for storms in the receiving counties. Counties are defined as exposed to a storm if at least one hurricane resulted in a flood warning during the year or the county experienced wind speeds of at least 21 m/s - the speed at which structural damages begins to occur - during a hurricane in the year. 1(Storm year), t-# indicates that the county experienced a storm # years previously.<sup>_∗_</sup> p=0.1, _∗∗_ p=0.05, _∗∗∗_ p=0.01.
specific scale parameters that are employed. Our results are robust to different measures of poverty, income expectations, reference groups, functional forms, and various other specifications.

This paper consists of seven sections. We briefly review the literature in the next section, before discussing our empirical strategy in Section 3. We subsequently describe the data in section 4, and present estimation results in section 5. We offer a wide range of robustness checks and further extensions in Section 6 before finally concluding in Section 7.

# **2. Brief Literature Review**

A number of studies estimate equivalence scales using panel subjective well-being data, but these studies mostly investigate data on life and income satisfaction and focus on richer countries that have panel data such as Germany or the United Kingdom (Charlier, 2002; Schwarze, 2003; Falter, 2006; Bollinger _et al._ , 2012; Biewen and Juhasz, 2017; Borah _et al.,_ 2018). Our brief overview of these studies, shown in Table A.1, Appendix A, offers several findings. First, although the magnitude of the estimated equivalence parameters differs considerably across studies, all the four studies for Germany find a lower weight for children than that of an additional adult. Only one study by Bollinger _et al._ (2012) finds that children in the United Kingdom are associated with diseconomies, but this result mostly applies to the first child. Second, although most studies suggest larger returns to scale than the (old or modified) OECD equivalence scales, non-parametric scales recently estimated for Germany by Biewen and Juhasz (2017) are fairly close to “squareroot” equivalence scales.<sup>5</sup> Third, equivalence parameters depend on the types of subjective data/questions used for analysis. For example, analyzing life satisfaction or minimum income data

> 5 The old OECD scale assigns a value of 1 to the first household member, 0.7 to each additional adult, and 0.5 to each child. The corresponding figures for the modified OECD scale are 1, 0.5, and 0.3. We discuss the definitions of the square root and other scales in Section 3.

5