rafmacalaba/datause-encoder-data huggingface dataset:
electronic scale, removing possible human errors or other dishonest practices that may have occurred at the mandis. With the price and weight known, the farmer then collects his full payment at the hub payment counter. At that time, the farmer is also reimbursed for transporting the crop, and receives a copy of the lab report and a receipt.

The result of the e-Choupal system has been a win-win for farmers and ITC. With greater information and understanding of prices, farmers have become more aware of what they should/can receive for their crop. When farmers sell to ITC through the e- Choupal, prices are 2.5 percent higher on average then if sold at the mandis (Annamalai and Rao 2003). And even though ITC is paying more for the produce and compensating farmers for transport, ITC is paying less than before (Prahalad and Hammond 2002). Because ITC cut out the intermediaries the mark up paid by ITC has decreased from 5 to 2.5 percent. ITC is not finished there, currently, there are 6,500 e-Choupals serving four million farmers; the plan is for a total of 20,000 e-Choupals serving 10 million farmers in the next five year (ITC website<sup>41</sup> ). In addition, ITC is starting to expand operations in the reverse direction, bring goods to rural areas, through structures called Choupal Saagars.

# **C. Contract farmer/outgrower scheme**

Contract farming or outgrower schemes are methods that firms employ to utilize the existing assets of small rural farmers. “Contract farming is a vertical coordination between a central processing or exporting unit on the one hand, and growers of agricultural products” (Al-Hassan et al. 2006). The coordination is based on a contract that outlines the purchase of the crop being grown, beforehand. In general, inputs (seeds, fertilizer, pesticides) and extension services are provided by the firm to the farm free or at a lower cost to the farmer, who in turn grows the crop and sells it to the firm at the previously agreed upon price (Kindness and Gordon 2001). Specific elements of the contract can vary, such as the extent of control over the farmer by the firm or if a certain amount of output was agreed upon, etc. There is great potential for both good and bad to come of this contract. A study of small Zimbabwean farmers asked what the motivation was for entering a contract; the top responses were market uncertainty, indirect benefits (i.e. knowledge), increased/secure income and intangible benefits (Masakure and Henson

> 41 ITC website: www.itcportal.com/rural-development/echoupal.htm (accessed March 19, 2009)

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record of the state of disputes. It is true that information about the outcome of disputes can be found in other fora as well.<sup>5</sup> We did take the liberty, however, to adjust some of the data re-produced in this document, as explained _supra_ , and also correct some of the information provided in the document by looking at the official record before the WTO Dispute Settlement Body (DSB).<sup>6</sup> Our classification of data is to some extent explained by the exogenous “legal constraint” that is imposed by the WTO Dispute Settlement Understanding (DSU).<sup>7</sup>

# **2.2 Implementation of WTO Obligations**

The outcome of many WTO disputes is uncertain. The reasons for this uncertainty vary. In some cases, we do not know if implementation actually occurred or not. One could speculate that the former is an unlikely event, since if the WTO Member at hand implemented in good faith its obligations, it would have little incentive not to notify the actual implementation. It may be that the parties failed to notify their MAS, or that the defendant failed to implement and no action by the complainant has been taken against such failure. These cases are of course relevant were one to measure the effectiveness of WTO remedies. It could, for example, be the case that the complainant fears that “pushing” the defendant could be counter-productive. It could also be that the complainant believes its countermeasures will not induce compliance. We prefer, 5 Horn, Mavroidis and Nordstrom (1999), for example, point to what they call a “third circle” of disputes that have never been notified to the WTO but concern cases of relevance to the world trading system whereby a change in trade policy has been requested through un-official channels.

> 6 Sometimes there is a disjoint between information provided to the DSB and that included in the OV document. We privilege the former since it reflects the official stance of the WTO Member concerned, whereas the latter is simply information compiled by the WTO Secretariat.

> 7 The reader might be surprised to see a number of very old disputes included in Annex Table 2. These disputes are present because there is no maximum limit in the DSU restricting the duration of bilateral consultations. The right to a panel can be exercised following the passage of statutory deadlines (see, for example, Art. 4.3 and 4.7 DSU), but there is no legal obligation to submit a dispute to a panel. Parties can

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used local information had lower rates of delinquency; in Bangladesh, Sharma and Zeller (1997) show that groups that were formed through self-selection had better repayment rates. However, both of these studies may suffer from omitted variable biases or selection problems. Other studies provide little empirical evidence for the theory: Wydick (1999) finds that in group lending in Guatemala, social ties have limited impact on repayment and that borrowers are in fact more tolerant with their friends; Ahlin and Townsend (2007) use Thai data to show that repayment rates are negatively associated with social ties.

Only a handful of studies examine the merits of group liability relative to other contract structures. Fischer (2009) conducts a series of lab experiments with actual microfinance clients and provides evidence that contract structure affects project selection. Specifically, he finds that group liability increases risk-taking, relative to individual liability contracts, as borrowers free-ride on the insurance provided by their partners. When project approval is required, however, risk taking under group liability is lower than both individual liability and an equity-like contract in which income is shared equally.

The most relevant study on repayment rates under different loan liability structures is Gin ́e and Karlan (2009), who report evidence from a series of field experiments in the Philippines. In the first, borrowers who had signed up under a group liability structure were converted to individual liability, thus the authors can independently identify the peer monitoring effect under group liability since both joint and individual liability groups previously underwent the same screening. However, they cannot identify or rule out any impacts of screening with this methodology. In addition, the group repayment and monitoring mechanisms may already be entrenched and difficult to undo even with an indivdual liability structure. Their second experiment randomly introduced either group or individual liability lending to new borrowers. However, the experiment was at the loan center level and take-up was quite uneven between group and individual loan centers, resulting in potential statistical power concerns. In both instances, they find default rates are invariant to contract structure.

Our paper complements Gin ́e and Karlan (2009) by examing optimal contract structure in an alternative setting. While the original experiment in Gin ́e and Karlan (2009) focuses on moving from group to individual liability contracts, we explore the reverse; that is, the shift from individual to group liability. As such, we are able to study the dynamics of group formation and find evidence 6
limited.

# **<<A>>V. Conclusions**

In this paper, we have documented empirical patterns of correlation in the adoption of innovation and contractual practices among manufacturing firms in Ethiopia and Sudan. Our empirical analysis is based on network data that indicate whether any two firms in our sample do business with each other, buy inputs from a common supplier, or sell output to a common client. We also exploit data on firms’ locations to investigate whether firms that are near each other tend to be more similar or dissimilar than firms that are geographically far apart.

Our results can be summarized as follows: (i) for Sudan, but not for Ethiopia, there is some evidence that network proximity is associated with similar innovation strategies; (ii) for both countries, there is relatively strong evidence that firms that are located close to each other differ more with respect to innovation than firms that are far apart; (iii) there is no evidence that network proximity is associated with greater similarity in training decisions or labor management across firms; (iv) there is some evidence that firms that are located close to each other differ more with respect to training decisions than firms that are geographically far apart; (iv) for Sudan, but not for Ethiopia, there is some evidence that network proximity is associated with similar contractual practices; (vi) differences in contractual practices across firms are only weakly related to geographical proximity; (vii) there is no evidence that network proximity is associated with greater similarity in the perceived consequences of disputes; (viii) there is some evidence that geographical proximity is associated with greater differences in the perceived consequences of disputes; and (ix) except for supplier-based links in Sudan, differences in firm performance are only weakly related to geographical and network proximity. Overall, the strongest results are for innovation.

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Table 6: Marriage penalty by decisionmaking

|Dependent variable||Working|||
|---|---|---|---|---|
|Sample<br>SAR|BGD|IND|MDV|NPL|
|(1)|(2)|(3)|(4)|(5)|
|_Panel A: No _|_child sample_||||
|Post-marriage<br>-0.139***|-0.180***|-0.131***|-0.234**|-0.084*|
|(0.013)|(0.039)|(0.015)|(0.117)|(0.050)|
|Post-marriage _×_ Decisionmaking<br>0.060***|0.047**|0.023|0.064|0.086|
|(0.012)|(0.019)|(0.024)|(0.073)|(0.064)|
|No. of obs.<br>8812|1712|4847|440|1805|
|R-squared<br>0.524|0.327|0.490|0.558|0.413|
|_Panel B: Ignor_|_e child samp_|_le_|||
|Post-marriage<br>-0.176***|-0.188***|-0.155***|-0.208*|-0.152***|
|(0.008)|(0.026)|(0.009)|(0.122)|(0.039)|
|Post-marriage _×_ Decisionmaking<br>0.045***|0.041***|-0.041**|-0.026|0.066*|
|(0.008)|(0.012)|(0.016)|(0.067)|(0.037)|
|No. of obs.<br>10884|2163|5887|578|2249|
|R-squared<br>0.564|0.353|0.530|0.576|0.422|

Note: Standard errors in parentheses clustered at the cohort level. All estimates weighted by cohort size. All models include birth cohort and country-by-year fixed effects. SAR - South Asia Region, BGD - Bangladesh, IND - India, MDV - Maldives, NPL - Nepal. No child sample refers matching post-marriage observations in the sample without children. Ignore child sample refers matching post-marriage observations in the full sample. Cohort FE refer to birth cohort. Decision making is measured as the sum of indicator variables for 5 DHS questions on whether the respondent is involved in decision making individually or jointly, averaged *** ** * by cohort, with larger values indicating greater decision making involvement. _p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1.

28
biased in favor of skilled workers. Luo (2017) shows that as Europe was experiencing significant technological progress during the 500 years before the First World War, the wage gap between skilled and unskilled workers declined by more than 50%. In our empirical model we will control for both demand and supply side determinants of the wage gap. Our contribution is to add as potential demand driver the growth in online international trade.

The literature has also looked at the importance of trade as a determinant of the wage gap. Some trade economists have stressed the fact that the timing of rising wage inequalities in developed and developing countries coincided with a rapid increase in international trade flows (see Feenstra and Hanson, 2001; Harrison and McMillan, 2011, for surveys). Feenstra and Hanson (1999) find that imports of intermediate inputs account for about 15 percent of the increase in the relative wage of skilled workers in the United States over the period 1979-1990. SBTC is twice as important and accounts for about a third of the increase. Evidence from developing countries is more scarce, but also suggests that wage inequalities have been driven by episodes of trade liberalization in the 1980s and 1990s (Goldberg and Pavcnik, 2007). Lower tariffs imply that many developed economies outsourced part of their production process to lower income countries, which as emphasized by the “trading tasks” models (Feenstra and Hanson, 1996; Grossman and Rossi-Hansberg, 2008) can lead to increased wage inequalities in both developed and developing countries as long as the tasks being outsourced to developing countries are considered as unskilled in the rich country and skilled in the low-income country. Autor et al. (2013) find that trade with China hurts more unskilled workers in the United States, and Autor (2014) and Autor et al. (2015) argue that the negative impact of imports from China on wage and employment of unskilled workers in the United States is much larger than the impact of technological change.

More recently, income inequality has been declining in many developing countries, especially in Latin America (De la Torre et al., 2015). Lustig et al. (2013) review some of this literature and conclude that the decline in overall inequalities in Latin American countries is to a large extent the consequence of a decline in the wage gap, income redistribution associated with government transfers, and the boom in commodity prices (Gasparini et al., 2011).

To sum up, the existing literature has concluded that both trade and skill biased technological progress play a role in explaining changes in the wage gap between skilled and unskilled workers. Importantly, these changes substantially contribute to the evolution in income inequality. However, gave strong support to the skill-biased technology change hypothesis (Krueger, 1993; Berman et al., 1994; Autor et al., 1998; Acemoglu, 2002).

3
summary, we observe positive but small to moderate correlations, which is consistent with earlier literature on the topic.

**Table 6** : Concurrent Correlations

||Teach<br>Score|IRT<br>rescaled|
|---|---|---|
|**Teacher Characteristics**|||
|Subject Content Knowledge|.06*|.08*|
|Pedagogical Content Knowledge|.05*|.07*|
|Teacher education|.14*|.17*|
|(Completed college/university)|||
|Teacher Experience (>2 years)|.08*|.09*|
|Temporary Teachers|-.20*|-.22*|
|**Classroom Facilities**|||
|Classroom Hygiene|.06*|.07*|
|Chalk and Blackboard|.07*|.08*|
|Teacher: Desk|.16*|.19*|
|Teacher: Chair|.10*|.11*|
|Student: Chair and Desk|.03*|.05*|
|Student: Textbook|.07*|.06*|
|Student: Pen and Pencil|.08*|.07*|
|Student: Exercise Book|.08*|.07*|

Note: *p<0.05. We defined Classroom Hygine equal to 1 if the classroom is extremely or reasonably clean as evaluated by the observer; Chalk and Blackboard are equal to 1 if they are both present; Teachers: Desk is equal to 1 if present; Teacher: Chair is equal to 1 if present; Student: Chair and Desk is equal to 1 if all students have them; Student: Textbook is equal to 1 if 80% or more students have them; Student: Pen and Pencil is equal to 1 if 80% or more students have them; Student: Exercise Book is equal to 1 if 80% or more students have them.

Table 7 presents regression estimates with a set of student, teacher, and school controls. A unit increase in _Teach_ score is associated with .13-.24 (.07-.14 using IRT rescale score) standard deviation increase in student test score.

**Table 7** : _Teach_ Associations with Student Learning

|Dep. Variable|(1)<br>OLS<br>Total<br>Score|(5)<br>OLS<br>Total<br>Score|(6)<br>OLS<br>Math<br>Score|(7)<br>OLS<br>Urdu<br>Score|(8)<br>OLS<br>English<br>Score|
|---|---|---|---|---|---|
|_Teach_|0.14<sup>***</sup>|0.21<sup>***</sup>|0.15<sup>***</sup>|0.13<sup>***</sup>|0.24<sup>***</sup>|
||(.050)|(.048)|(.046)|(.042)|(.048)|
|Constant|-0.4<sup>***</sup>|-0.7<sup>***</sup>|-0.7<sup>***</sup>|-0.3|-0.7<sup>***</sup>|
||(0.141)|(.211)|(.198)|(.183)|(.214)|
|Observations|18,243|14,947|14,947|14,842|14,842|

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This discussion suggests that it is possible that both the benefits and costs of WTO disputes may differ for developing countries, and as a result we might not see a lot of developing country-initiated zeroing disputes. Because the failure to initiate a dispute is not clear evidence that there has been no harm, whether the U.S. zeroing process is also likely to adversely impact developing country exporters is therefore an empirical question.

For our purposes we limit ourselves to the question of whether zeroing likely has a significant effect on any potential duty imposed on developing countries. To get a sense of the possible extent of zeroing’s impact on developing countries we gathered U.S. import data for some of the most prominent products subject to U.S. ADD scrutiny over the past decade.<sup>36</sup> Two factors influenced what products we included in our sample. First, we wanted to capture cases that were economically “important” for developing countries and were in products most likely to be subject to AD examination. Second, we wanted to focus on products where we had strong independent evidence that there had been a WTO zeroing violation. With respect to the first criterion, we included cases where there was both significant AD activity and also substantial import supply by developing countries. With respect to second criterion, we included products for which there already had been WTO disputes.

Once we selected the products to review, we then calculated the monthly price variation during the 12 months during the year prior to the filing of the case, a time

> 36 List of cases included in the analysis is given in the appendix.

– 45 –
Abstract. This paper examines the relationship between international migration and source country fertility. The impact of international migration on source country fertility may have a number of causes, including a transfer of destination countries’ fertility norms and an incentive to acquire more education. We provide a rigorous test of the di¤usion of fertility norms using original and detailed data on migration. Our results provide evidence of a signi...cant transfer of fertility norms from migrants to their country of origin: a one percent decrease in the fertility norm to which migrants are exposed reduces home country fertility by about 0.3 percent for origin countries. JEL classi...cation: J13, J61, O11.

Keywords: International migration, endogenous fertility, human capital, social norms.

Acknowledgement. This paper was written while Michel Beine was visiting the World Bank, DECRG Trade Unit. Thanks are due to Chris Parsons for providing us with the data and to Assaf Razin, Hillel Rapoport, Caglar Ozden, Ramón Lopez, Paola Conconi, Antonio Spilimbergo and participants of the OECD-CEPII conference on Brain Drain in Paris and of the TOM conference in Louvain for their useful comments. The views in this paper are those of the authors and do not necessarily re‡ect those of their respective institutions, the World Bank Executive Directors and the governments they represent. The second author acknowledges ...nancial support from the Belgian Federal Government (PAI grant P6/07 Economic Policy and Finance in the Global Equilibrium Analysis and Social Evaluation) and from the Marie-Curie research and training network TOM (Transnationality of migrants).

2
**Figure 3. Projections of Income per Capita, EU11 and EU15, Smoothed Distribution**

<!-- Start of picture text --> 8<br>2020<br>2030<br>7<br>2040<br>2050<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>0 0.2 0.4 0.6 0.8 1 1.2<br>Ratio of average income per capita in EU11 to average income per capita in EU15<br>Frequency<br><!-- End of picture text --> _Source:_ Authors’ calculations based on Crespo Cuaresma, Havettová and Lábaj (2012) _Note:_ The figure plots the (smoothed) histogram of the 144 projections for 2020, 2030, 2040, and 2050. It shows average income in EU11 as a share of EU15 income. As the projection horizon increases, the histograms become "fatter" because of uncertainty about the long-term development of income per capita. As time progresses, the center of the smoothed histogram moves to the right, with a larger share of observations around 0.8–1, which suggests close to full convergence.

**Human capital accumulation dynamics are the key factor in explaining why some EU11 countries are more likely to be successful in closing the income gap with EU15 in the long term.**<sup>9</sup> Differences in income convergence speed within EU11 can be attributed to the sizable gap with EU15 regarding human capital accumulation. To close these differences, further investment in human capital could significantly accelerate income convergence. Investment in human capital can even pay off where educational attainment is already relatively high if demand is rising for skilled workers and employers are running into specific skills shortages (see World Bank 2012). To the extent that human capital investments (in terms of both quantity and quality) affect an economy’s ability to innovate, the benefits for income convergence from policies directed to improving skills may be particularly important.

**In terms of accumulation of production factors, the growth rate of physical capital in the EU11 region has been systematically above that in EU15.** Redirecting investments to more productive sectors seems to be an income convergence priority in the region. The recent crisis has also had a negative effect on innovation policies in EU11, which diverge in innovative capabilities from the rest of the region (Archibugi and Filippetti 2011). Such a

> 9 Recently, a number of scholars have emphasized the prominent role of human capital accumulation as a driver of income growth and convergence in Europe in studies of the determinants of economic growth and income convergence patterns in Europe (see LeSage and Fischer 2008; Crespo Cuaresma, Doppelhofer; and Feldkircher 2012; and Crespo Cuaresma and Feldkircher 2012).

7