rafmacalaba/datause-encoder-data huggingface dataset:
- Fischer, M. A., Mazor, K. M., Baril, J., Alper, E., DeMarco, D., & Pugnaire, M. (2006). Learning from mistakes. _Journal of General Internal Medicine_ , _21_ (5), 419-423.

- Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. _Review of Educational Research, 74_ (1), 59-109.

Frenzel, A. C., Goetz, T., Pekrun, R., & Watt, H.M.G. (2010). Development of mathematics interest in adolescence: Influences of gender, family, and school context. _Journal of Research on Adolescence, 20_ (2), 507-537.

<mark>Fullan, M., (2007) The new meaning of educational change, Routledge, New York.</mark> <mark>Furrer, C. J., Skinner, E. A., & Pitzer, J. R. (2014). The influence of teacher and peer relationships on students’ classroom engagement and everyday motivational resilience.</mark> _<mark>National Society for the Study of Education</mark>_ <mark>,</mark> _<mark>113</mark>_ <mark>(1), 101-123.</mark> Gasser, L., Grütter, J., Buholzer, A., & Wettstein, A. (2018). Emotionally supportive classroom interactions and students' perceptions of their teachers as caring and just. _Learning and Instruction, 54,_ 82-92. <u>http://dx.doi.org/10.1016/j.learninstruc.2017.08.003</u>

- Gelman, S. A., & Raman, L. (2003). Preschool children use linguistic form class and pragmatic cues to interpret generics. _Child Development, 24,_ 308–325.

- Gest, S. D., & Rodkin, P. C. (2011). Teaching practices and elementary classroom peer ecologies. _Journal of Applied Developmental Psychology_ , _32_ (5), 288-296.

- Gill, B., Shoji, M., Coen, T., and Place, K. (2016). The content, predictive power, and potential bias in five widely used teacher observation instruments. (REL 2017–191). Washington, DC.

- Glaser, C., & Brunstein, J. C. (2007). Improving fourth-grade students' composition skills: Effects of strategy instruction and self-regulation procedures. Journal of Educational Psychology, 99(2), 297-310.

- Goldenberg, C. (1992). Instructional conversations: Promoting comprehension through discussion. _The Reading Teacher_ , _46_ (4), 316-326.

- Good, T. L., Biddle, B. J., & Brophy, J. E. (1975). _Teachers make a difference._ New York: Holt, Rinehart and Winston.

- <mark>Good, T. L., & Grouws, D. A. (1977). Teaching effects: A process-product study in fourthgrade mathematics classrooms.</mark> _<mark>Journal of Teacher Education</mark>_ <mark>,</mark> _<mark>28</mark>_ <mark>(3), 49-54.</mark>

Good, T. L., & Grouws, D. A. (1979). The Missouri Mathematics Effectiveness Project: An experimental study in fourth-grade classrooms. _Journal of Educational Psychology_ , _71_ (3), 355.

Greenberg, M. T., Kusche, C. A., Cook, E. T., & Quamma, J. P. (1995). Promoting emotional competence in school-aged children: The effects of the PATHS curriculum. _Development and Psychopathology_ , _7_ (1), 117-136.

49
of each profile attribute level, or the overall probability of selecting a profile given the presence of that version of the attribute, averaging over other attributes. Marginal means are calculated using the _cregg_ package in R (Leeper 2022).<sup>18</sup> I focus on discussing when marginal mean estimates are statistically distinguishable from 0.5 (50%), which is the marginal mean in expectation in the forced-choice conjoint design when a respondent has no preference over levels of an attribute.

18. I present the marginal means, rather than the commonly used average marginal component effects (AMCEs), in light the greater suitability of the marginal mean for discussions of respondent preferences (particularly for subgroup analyses) and the greater ease of interpreting them (Leeper, Hobolt, and Tilley 2019). AMCE estimates are included in the Appendix; the results are substantively similar.

24
# Table 15 Welfare by Income and Expenditure Components

|I Edi||1989|||1991|||1992||
|---|---|---|---|---|---|---|---|---|---|
|ncome ar xpenture||||||-||||
|<br>Components|<br>% Contrlb.<br>of comp<br>to total<br>waifare|Wolfare<br>EBasticity<br>of each<br>cOmp|Progress<br>Index of<br>each<br>COMD|96 Contrlb.<br>of camp to<br>total<br>welfare|Welfare<br>Elasticity<br>of each<br>c|Progress<br>Index of<br>each<br>omc|96 Contrlb.<br>of comp to<br>totd<br>welfare|Whfare<br>Elasddtty<br>of each|Pragress<br>Index of<br>each<br>car|
|Salary Income|52|0.52|-0.09|47.3|0.47|-0.09|41.6|0.42|-0.1|
|Collective Farm Income|10.1|0.1|-0.03|7.2|0.07|0.04|15.5|0.16|0.14|
|Govt Cash Benefits|17.5|0.18|0.37|24.5|0.25|0.29|11.7|0.12|0.12|
|Smallholding Income|10.2|0.1|0.04|10.7|0.11|-0.07|20.5|0.21|0.05|
|Other sources|10.2|0.1|0.01|10.2|0.1|-0.04|10.9|0.11|0.07|
|Total Income|100|1|0|100|1|0|100|1|0|
|||||<br>Expe|<br>nditue Compo|<br>nents||||
|Food|37|0.37|-0.13|42.3|0.42|-0.11|42.9|0.43|-0.12|
|Non food|29.3|0.29|0.01|31.6|0.32|0.02|31.5|0.32|0.01|
|Alcohol|2.4|0.02|0.02|2.1|0.02|0.01|2.1|0.02|-0.01|
|Social Expenditure|9.3|0.09|-0.06|6.4|0.06|-0.03|3.8|0.04|-0.03|
|Taxes|-8.4|-0.06|0.12|-4.4|-0.04|0.13|-4.2|-0.04|0.2|
|Other Expenditures|7.4|0.07|0.13|6.4|0.06|0.09|6.4|0.06|0.14|
|Savings|8.3|0.08|0.23|6.9|0.07|0.26|8.9|0.09|0.21|
|Total Expenditure|87.3|0.86|0|100|_<br>1|0|100|1|0|
# 4. ESTIMATION STRATEGY AND RESULTS

# 4.1 ATTRITION AND BALANCE

Table 1 investigates whether there are differences in the attrition rates between the treatment groups under investigation in this paper. Columns (1) & (2) regress a binary indicator for inclusion in the Round 1, 2, and 3 data on the main treatment indicators, respectively for the two cohorts analyzed in this paper: _baseline dropouts_ and _baseline schoolgirls_ . The regression results show that the CCT did not have a significant effect on the probability of being interviewed in the two follow-up rounds in either of the two cohorts. Nor were _baseline schoolgirls_ who were offered a UCT or who were assigned to the spillover group (i.e. girls living in treatment EAs who did not receive treatment) more or less likely to be interviewed at follow-up survey rounds. Together, these results suggest that the findings of program impact are unlikely to be biased by differential attrition.

In all regressions described below, we include baseline values of the following variables as controls: age dummies, a dummy for mother‟s education beyond primary school, a dummy that indicates if a girls lives with her mother, a dummy for having been ill during the two weeks prior to the interview, a dummy variable for ever having had sex, and an indicator for the highest grade attended. These variables were chosen because they are predictive of mental health and, as a result, improve the precision of the impact estimates.<sup>15</sup> Following Bruhn and McKenzie (2009) we also include indicators for the strata used to perform block randomization – Zomba Town, rural within sixteen kilometers of the town, and rural beyond sixteen kilometers.

> 15 To select this set of baseline covariates, we ran a regression of mental health status among the control group in Round 2 and Round 3 on a set of baseline characteristics suggested by our review of the literature. The other baseline characteristics included in the regression were: a dummy for father‟s education beyond primary school, a dummy that indicates if a girls lives with her father, and an asset index (the first principal component of 25 durable goods). None of these characteristics correlates significantly with the mental health statuses of respondents in the control group in Round 2 or Round 3.

12
power of the upstream firm, the larger is its share of the RSI-induced surplus. It also stands to lose more if it does not make the appropriate level of RSI and thus the disciplining effect of its large share of future surplus makes trade credit less needed as a commitment device. Thus, _ceteris paribus_ , Dass et al. (2015) predict less trade credit from suppliers with greater bargaining power but for a different reason than in other papers. And, if trade credit is used as a commitment mechanism when there is uncertainty about the level of RSI, its use should be less prevalent when information frictions between supplier and customer are less severe.

Because RSI are not easily observable or measurable within a large sample of firms, Dass et al. (2015) follow previous literature which measures RSI using proxies such as firms’ research and development expenses (R&D), patent citations and advertising expenses. The intuition is that a firm that conducts research that yields well-cited patents is more likely to be making RSI and producing output that is specialized input for its downstream customer. Previous research also suggests that firms that make RSI and produce unique products are also more likely to advertise than firms that produce commodities (Titman and Wessels, 1988).

Using data on all available firms in Compustat over the period 1997–2008, Dass et al. (2015) find a significant positive relationship between R&D expenditures and the amount of trade credit extended by upstream suppliers, one which is stronger if the supplier patents its research or cites patents of downstream industries in its own patent applications. In addition, they confirm that trade credit is less prevalent when information frictions between supplier and customer are less severe and thus credit is less necessary as a commitment device. Specifically, suppliers extend less trade credit when their relationship with the customer is longer, they are in closer geographic proximity to the customer, or they have a listing on the New York Stock Exchange (and thus information about them is readily available). Moreover, those authors also control for the market power of suppliers (using price-to-cost margins as reflected in the Lerner Index and measures of industry concentration) in their regressions and find that those with greater market power extend less trade credit to downstream customers (in line with the findings, e.g., in Fabbri and Klapper, 2016). Thus, Dass et al. (2015) provides evidence in support of both the equity stake and the market power hypotheses of suppliers’ trade credit behavior within the same theoretical and empirical models.

In sum, Chod et al. (2019) and Dass et al. (2015) illustrate the importance of data that capture more details of the relationships between suppliers and their customers, which have 14
million metric tons of annual carbon emission avoidance. Likewise, using in-use emission data collected by remote sensing, Harrington (1997) demonstrated a strong association between better fuel economy and lower emissions of carbon monoxide (CO) and hydrocarbon (HC), which gets even stronger as vehicles age.

Despite the considerable amount of research done on the effects of CAFE on fuel consumption and other related factors, there is no universal consensus on the effects of the CAFE program on the fuel economy of the U.S. vehicle fleet, the overall safety of passenger vehicles, the health of the domestic automobile industry, employment in that industry, and the well-being of consumers (NSC, 2002). Greene (1998) estimated that CAFE standards have led to about a 50 percent increase in on-road fuel economy for light duty vehicles during the period 1975-1995. Improvement in fuel economy forced by the CAFE standards has resulted in an overall decrease in motor fuel expenditure. This means that consumers, in the late1990s, spent over $50 billion per year less on fuel than what they actually would have spent at 1975 mpg levels. By contributing to increased fuel economy, the CAFE program has reduced dependence on imported oil, improved the nation’s terms of trade, and reduced CO2 emissions relative to what otherwise would have been (NSC, 2002).

Although the overall goal of CAFE regulation has shifted from reducing fuel consumption in a period of high oil prices to reducing harmful emissions, positive environmental gains resulting from CAFE standard has drawn flak from various quarters (Goldberg, 1998). Dowlatabadi et al. (1996) demonstrated that enhanced CAFE standards might have little or no effect on urban air pollution and a less than proportional reduction in GHG emissions. They argued that CAFE is not the most cost effective way of lowering nitric oxide (NO), volatile organic compounds (VOC) and GHG emissions. Portney et al. (2003) asserted that by reducing gallons/mile, the CAFE standards make driving cheaper, which might lead to an overall increase in pollution.

Crandall (1992) ranked the effectiveness of a carbon tax, a petroleum tax, and CAFE standards in terms of their ability to reduce greenhouse gases. He considered a carbon tax 11
such households and contributes significantly to poverty reduction in rural areas. Evidence from Africa shows that higher non-farm income leads to faster growth in income and consumption (Barrett et al. 2001), while evidence from rural Asia shows that household access to non-farm income is one of the main drivers of poverty reduction (Estudillo and Otsuka 2010). Hence a well functioning non-farm sector to which poor households have access is, next to agriculture, important for income generation and poverty reduction in rural areas.

The question that arises for policy is whether agricultural and rural non-farm sector activities are complementary or substitutes. If, for example, agriculture has little potential in certain regions (a view consistent with ―agro-pessimism‖), policies might need to be more tailored toward non-farm activities. Both consumption and (upward and downward) production linkages exist between agriculture and the non-rural sector, although the strength of these linkages varies across time and space.<sup>12</sup> The existence of linkages does not mean that a vibrant agricultural sector is a necessary condition for a successful non-farm sector. There are examples of agricultural growth limiting the tradable sector in rural areas—see, e.g., the study by Foster and Rosenzweig (2003) cited above. But what follows from the existence of linkages between agriculture and the rural non-farm sector is that policies targeted at one or the other should not be made in isolation but integrated in an overall framework (Lanjouw 1999). Although the traditional interpretation of the rural non-farm economy (Mellor 2001, Adelman 1984) was that it emerged though linkages with agriculture, new empirical studies have opened important new perspectives for poverty reduction and shown that more attention should be paid to small towns (as the India study by Himanshu et al. shows). Agricultural growth stimulates the growth of the rural non-farm economy through forward, backward and final demand linkages, located in rural areas due to the advantages of proximity to agriculture (Byerlee, de Janvry and Sadoulet 2010).

_Changing Approaches to Rural Development_ Of 5.5 billion people living in developing countries, 3 billion live in rural areas and 75 percent of today‘s poor are rural (World Bank 2007). The rural sector has contributed more than

> 12 Lanjouw and Lanjouw (1995, 2001) provide quantitative estimates of these linkages.

30
ordinary least squares (OLS) and fixed-effect models using employer-employee matched data shows correlations between personality traits and wages both within and across formal sector firms.

This analysis aims to make three main contributions to the literature on non-cognitive skills and labor market outcomes. First, we present findings regarding the association between personality traits and labor market outcomes among labor force participants in Bangladesh. To the best of our knowledge, this is the first comprehensive assessment of personality traits and their relationship to wages in the South Asian context.<sup>2</sup> With an estimated population of 1.7 billion in 2014,<sup>3</sup> and with the labor force set to grow by 1 million to 1.2 million new workers every month over the next two decades,<sup>4</sup> South Asia presents unique challenges to researchers and policy makers striving to understand the characteristics and skill sets of the region’s rapidly growing workforce. Bangladesh presents an especially salient example of the changing employment dynamics in South Asia, as the share of the labor force employed in agriculture shrank from 51 percent in 2000 to 45 percent in 2013.<sup>5</sup> Moreover, the Government of Bangladesh has made skills development a core priority, as described in its 2011 National Skills Development Policy.

Second, we use employer-employee matched data taken from the 2012 Enterprise Skills Survey (ESS), which sampled 500 formal sector employers and employees. Unlike household-based labor force surveys, employer-employee matched data allow an estimation of employee characteristics based on firm-level fixed effects, revealing correlations between different non-cognitive skills and labor market outcomes. Using firm-level fixed effects also reduces the likelihood of omitted variable bias. A comparison of OLS and firm-level fixed-effect models reveals the relationships between wages and personality traits within and across firms.

Third, we compare different methodologies for calculating personality traits. These include a raw index of personality traits and the Rasch rating scale model (Andrich, 1978). The Rasch modeling technique allows us to incorporate latent abilities hidden in the answer patterns for each question and provides a more robust statistical justification for combining various items within the non-

> 2 Similar questions were administered in Sri Lanka via the STEP survey (Dundar _et al_ . 2014) but we could not find any analysis of the relationship between non-cognitive skills and employment outcomes. 3 http://data.worldbank.org/region/SAS.

> 4 http://blogs.worldbank.org/category/tags/south-asia-workforce.

> 5 Bangladesh Labor force surveys.

3
and can be measured is whether blogging about a research paper leads to more people viewing and downloading that paper.

The typical economics working paper gets very few readers, especially after its first couple of months of release. For example, a random sample of papers released in the NBER working paper series in January 2010 shows that the median paper in this prestigious series received 21 abstract views and 12 downloads through RePEc services in the first two months, and then an average of 6-7 abstract views and 2-3 downloads per month through RePEc over the next year. Given these low readership levels, blog posts that draw attention to such research can potentially have large relative impacts on readership.

# _2.1 Descriptive Figures_

Several economics blogs regularly link to working papers. However, two issues arise in trying to measure the impact of these links on downloads. First, many of these links are to the web pages of the individual authors or to working paper series for which download statistics are not publicly available. To resolve this, we restrict our analysis to blog posts which link directly to papers in the RePEc – the largest database of economics papers. Monthly abstract views and download statistics are publicly available from this site. The second issue is that, in some cases, papers are linked to when they are first released in a working paper series. It is harder to form a counterfactual in these cases, since there are often several avenues of dissemination when papers are first released which might also drive download statistics, and heterogeneity in topics amongst papers would make comparison to other papers in the same series or by the same author not necessarily a good counterfactual.

We therefore focus on blog postings to papers which have been out several months at the time of a blog posting, and which are listed in RePEc. Figure 1 then provides a particularly striking illustration of the phenomenon we wish to 6
growing economies in Asia<sup>5</sup> . In contrast, producers in India and most of SSA still devote more than 60 percent of their production to crushing for oil and meal. The overall declining use of groundnut for oil and meal reflects the increasing availability of cheaper, low-fat vegetable oils such as soybeans and rapeseeds, as substitutes. Similarly groundnut meal competes with meal from other oilseeds and with cereal-based products such as maize gluten.

**Figure 2. Utilisation of Groundnut (Percentage of World Production)**

<!-- Start of picture text --> 70<br>60<br>50<br>40 Food<br>Oil<br>30<br>Feed<br>20<br>10<br>0<br>Percent<br>1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000<br><!-- End of picture text --> Source: Oil World.

# **Global Trade and Market Shares Dynamics**

Among the different groundnut products, shelled edible groundnuts, prepared groundnuts and groundnut oil are the most traded products in international markets. Table 3 shows the trade balance of each of these products for the major countries involved in groundnut trading in groundnut markets in 1996-2000. Countries with net exports over US$100 million are China, Argentina, and the U.S. The bulk of these countries’ exports is constituted of edible groundnuts. India’s trade balance reached US$94 million while Senegal, the largest African net exporter in the period registered a US$53 million groundnut trade surplus. Ninety two percent of Senegalese net export was groundnut oil.

The EU, Canada and Japan are the major edible groundnut markets. The EU is the world’s largest groundnuts market, accounting for 43 percent of world groundnut imports. EU’s total value of net groundnut imports was just below US$500 million in 1996-2000. The other important groundnuts markets include Canada (9 percent of world imports) and Japan (8.2 percent).

> 5

8