rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

The Northern Mali conflict started in January 2012. It was caused by several secessionist groups and led to a coup d’état in March 2012 and to the occupation of the three regions of the North – Gao, Kidal and Timbuktu – by rebels and Islamist factions. These territories were regained following a military intervention by a coalition composed of the Malian Army, French troops and the ECOWASled African-led International Support Missions to Mali (AFISMA) in June 2013 (David, 2013). A Peace Accord was finally signed in May and June 2015 between the government and different actors involved in the rebellion. Nevertheless, in spite of the Accord, the regions of Gao, Kidal and Timbuktu remain in a state of prolonged crisis, with high levels of insecurity and weak governance.

According to the UN Refugee Agency (UNHCR, 2016c), in June 2016 the number of Malian refugees was almost 135,000, while there were more than 36,000 Internally Displaced People (IDPs) and around 23,000 returnees (about 49,000 according to the Mali government (UNHCR, 2016b)). Among these refugees, more than 60,500 were in Niger, 41,000 in Mauritania and 32,000 in Burkina Faso. Including returnees, the population of concern has reached the staggering figure of 570,000 (UNHCR, 2016a). Despite these figures and the (scarce) media coverage (WorldPost, 2016), the UN operations in the regions have been constantly underfunded (UNHCR, 2016d).

The scope of this paper is to analyze some important characteristics of refugees, IDPs and returnees. Furthermore, we investigate the willingness to return to their place of origin for refugees and IDPs after the conflict in Mali. By making use of a unique data set interviewing the head of household (or another member) for as many as 12 consecutive waves, we managed to gather information on welfare and subjective well-being, as well as on willingness to return. Using mobile phones for interviews allowed us to obtain information about individuals who tend to be neglected in traditional surveys since they are difficult to reach given their high mobility, precarious living conditions and their proximity to conflict zones. In this instance, we managed to follow individuals _while_ they were experiencing a crisis and while on the move, thus avoiding issues common in the literature on violent conflicts (Brück, Justino, Verwimp, Avdeenko, & Tedesco, 2016). Still, the attrition rate was exceptionally low even if respondents changed locations and were difficult to contact in person. We note that the high-frequency, mobile phone based, panel structure used for this research is well-suited for situations where respondents live in highly-volatile environments.

Our goal is to provide as much information as possible on the conditions of people following displacement, whether they are in a refugee camp or in another locality in Mali or outside the country. The place where respondents lived after the conflict might be related to the willingness to return, or 2
process. A second contributing factor may have been members’ initial lack of familiarity with the new dispute settlement system. In some instances countries initiated cases but did not follow through, perhaps to “test” the system to see if they could get something for nothing. In other instances complainants were forced to (re)initiate the same dispute, again suggesting that part of the high volume may be attributable to learning of the new procedures. The final and perhaps most important factor is the acceleration of export growth that began around 2001. As Figure 2 shows, 2001-2006 was a period of substantial export growth across much of the WTO membership. Broad acceleration of exports would have undercut members’ ability to establish a loss of expected market access attributable to a trading partner’s imposition of a WTO-inconsistent policy, and thus reduced the likelihood of complainant success.

In the remainder of section 2, we examine the WTO dispute-initiation data more closely, looking in turn at the distribution of dispute initiation across developing member countries, across sectors of commercial activity, and across types of trade barriers at issue.

# **2.1 DSU involvement of WTO members**

Table 1 documents the frequency with which individual WTO members have been involved in WTO disputes as complainants, respondents, and interested third parties during this period.<sup>6</sup> Although the U.S. and EC were the most frequent litigants, other industrialized countries (Japan, Canada and Korea) and some developing countries (Argentina, Brazil, Chile, India, Mexico, and Thailand) each initiated ten or more disputes during this era.

Figure 3 breaks down the data on dispute-settlement initiation by category of complainant country over the two time periods. Together the U.S. and EC initiated an average of 20 new disputes per year during the 1995-2000 period—as many as all other WTO members combined. However, the U.S. and EC together averaged fewer than six newly initiated disputes per year during the 2001-2008 period, which was less than a third of their yearly average for 1995-2000. Although dispute initiation also

> 6 Throughout this paper we sometimes choose to separate data associated with U.S. and EC use of WTO dispute settlement from that of “other industrialized countries.” When it is not necessary to do so, we refer to group of countries including the U.S., EC, and other industrialized countries collectively as “developed countries.”

7
|<br>(other blackouts per mio accounts)|<br> <br>(41.6)||(25.1)||(734.6)|<br>|
|---|---|---|---|---|---|---|
|Power accounts|<br>0.046|***|0.023|*|0.036||
|<br>(in ‘000s)|<br> <br>(0.017)||(0.013)||(0.031)|<br>|
|Heat frequency x|||-0.080||0.275||
|<br>Grid density|<br> <br>||(0.329)||(0.448)|<br>|
|Heat frequency x|||6.4||-386.0|*|
|<br>Fragility|<br> <br>||(10.0)||(210.0)|<br>|
|Heat frequency x|<br> <br>||0.014|***|0.015|***|
|<br>Power accounts (in ‘000s)|<br> <br>||(0.003)||(0.003)|<br>|
|Power generation|||||-6E-04||
|<br>(annual, in MWh)|<br> <br>||||(0.0011)|<br>|
|GDP per capita|<br>||||-126.3||
|<br>(relative to US average)|<br> <br>||||(128.0)|<br>|
|Forest coverage|||||-225.6|*|
|<br>(% of state area)|<br> <br>||||(130.9)|<br>|
|Year FE|||||YES||
|Constant|38.5<br>*<br>-89.8|*|-14.8||283.0||
||(21.1)<br> <br>(49.9)||(30.3)||(172.3)||
|N (state-years)|911<br> <br>900||900||833||
|R-squared|0.00<br>0.03||0.04||0.07||

_Note: Pooled OLS, standard errors clustered on state level in parenthesis. Significance levels: *** 0.01, ** 0.05, * 0.10. Source: authors_

# 4.2. Evidence from Bangladesh

This study also shows that the resilience of an energy system to natural shocks ultimately is closely associated with its overall stability and service reliability. Even in the absence of natural shocks, electricity consumers in Bangladesh are used to unreliable service. According to the 2012 Enterprise Survey conducted by the World Bank (World Bank, 2018), 73% of firms report experiencing regular power outages, with an outage frequency of 85 times per month and a duration of 70 minutes on average. In addition, a significant share of the population still does not have access to electricity. In 2016, about 76% of the Bangladeshi population were estimated to be connected to the grid, though access rates differ considerably across divisions (World Bank, 2016). The Household and Income Expenditure Survey conducted in 2010 by the Bangladesh Bureau of Statistics constitutes one of the few sources of sub24
**Table 4: Composition of Exports in the Post-USBTA Period**

|**Dependent variable: Ln**<br>**(Exports as a fraction of**<br>|**(1)**|**(2)**|**(3)**|
|---|---|---|---|
|**output)**|**OLS**|**FE (industry-**<br>**level)**|**FE**<br>**(dirty/clean)**|
|Air Pollution Intensity|-0.422***|0.00|-0.417***|
||[2.84]|[.]|[2.78]|
|Water Pollution Intensity|0.093|0.00|0.09|
||[1.36]|[.]|[1.30]|
|Toxic Pollution Intensity|0.325**|0.806**|0.356*|
||[2.01]|[2.22]|[1.91]|
|Labor Intensity|10.125***|2.854|9.903***|
||[3.17]|[0.22]|[3.02]|
|Capital Intensity|0.182|-0.029|0.288|
||[0.31]|[0.02]|[0.44]|
|Air Pollution * Trade|0.00|0.00|0.00|
|Liberalization dummy||||
||[0.59]|[0.63]|[0.54]|
|Water Pollution * Trade|0.00|0.00|0.00|
|Liberalization dummy||||
||[0.48]|[0.11]|[0.46]|
|Toxic Pollution * Trade|0.00|0.00*|0.00|
|Liberalization dummy||||
||[0.00]|[1.78]|[0.01]|
|Constant|-2.790***|4.962|-3.110**|
||[2.91]|[1.39]|[2.30]|
|Observations|132|132|132|
|R-squared|0.21|0.09|0.21|
|Number of Dirty_dum|||2|
|Number of VSIC||22||

Absolute value of t statistics in brackets significant at 10%; ** significant at 5%; *** significant at 1% Finally, an interesting aspect of trade liberalization in Vietnam in that last few years has been the amount of FDI that has come in. A variant of the trade-environment debate argues that in developing countries, foreign investors are more likely to invest in pollution intensive sectors to take advantage of the cost difference in environmental 22
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- Jensen, R. (2010). “The (Perceived) Returns to Education and the Demand for Schooling”, _Quarterly Journal of Economics_ 125(2): 515-548.

- Lavecchia, A.M.; Liu, H.; and Oreopoulos, P. (2016). “Behavioral Economics of Education: Progress and Possibilities”, _Handbook of the Economics of Education_ 5(ch.1): 1-74.

- Lybbert, T.J.; and Wydick, B. (2018). “Poverty, Aspirations, and the Economics of Hope”, _Economic Development and Cultural Change_ 66(4): 709-753.

- Oreopoulos, P. (2007). “Do Dropouts Drop Out too Soon? Wealth, Health, and Happiness from Compulsory Schooling”, _Journal of Public Economics_ 91: 2213-2229.

- Oreopoulos, P.; Brown, R.S.; and Lavecchia, A.M. (2017). “Pathways to Education: An Integrated Approach to Helping At-Risk High School Students”, _Journal of Political Economy_ 125(4): 947-984.

- <mark>Sutter, M.; Kocher M.G.; Glätzle-Rützler D.; and Trautmann S. T.. (2013). “Impatience and Uncertainty: Experimental Decisions Predict Adolescents' Field Behavior”,</mark> _<mark>American Economic Review</mark>_ <mark>, 103 (1): 510-31.</mark>

- Wilson, S.J.; and Tanner-Smith, E.E. (2013). “Dropout Prevention and Intervention Programs for Improving School Completion among School-Aged Children and Youth: A Systematic Review”, _Journal of the Society for Social Work and Research_ 4(4): 357-372.

**<u>Annex A</u>** <u>:</u> **<u>Differences between PODER schools and Federal Schools in CDMX and Estado de México</u>**

||VARIABLE|PODER<br>schools|SE|Obs.|Mean Dep.<br>Control<br>Group|SD Dep.<br>Control<br>Group|
|---|---|---|---|---|---|---|
||Male students 1yr|-79.485|(454.508)|133|333.798|2040.313|
|School|Female students 1yr|-161.328|(649.19)|133|410.586|2914.601|
|year|Total students 1yr|-240.813|(1103.34)|133|744.384|4953.277|
|2015-<br>|Male students|54.062|(579.739)|133|589.506|2602.265|
|2016|Female students|-0.815|(842.525)|133|728.067|3781.702|
||Total students|53.247|(1421.11)|133|1317.573|6378.756|
||Num. students graduated|186.906*|(58.93)|133|191.266|274.475|
||Students w/scholarship|301.837*|(154.498)|133|470.206|703.5|
||Admission requests male|-139.405|(621.829)|133|441.896|2791.64|
||Admission requests female|-212.772|(817.634)|133|514.921|3670.928|
|School<br>|Admission requests total|-352.177|(1439.09)|133|956.817|6460.869|
|year<br>2016-|New students 1yr|-204.124|(1157.25)|133|832.436|5194.959|
|2017|Students enrolled in 1yr|-277.016|(1312.93)|133|897.288|5894.129|
||Groups 1yr|-28.957|(76.08)|133|37.953|341.677|
||Students enrolled|-22.955|(1751.66)|133|1553.73|7862.4|
||Number ofgroups|-27|(93.522)|133|57.939|419.91|
||Male teachers|10.451|(17.844)|133|29.003|80.198|

24
and lending. Then, if higher world interest rate leads to a fall in financial inflows into Paraguay, we should see that such interest rate increases are associated with improvements in the current account (proxied by trade balance here).

**Figure 1.3: Impulse Responses of Output to Various Shocks**

<!-- Start of picture text --> tot shock r_us shock Log(yf) shock<br>.002 .002 .006<br>0 0 .004<br>-.002<br>.002<br>-.002<br>-.004<br>0<br>-.004 -.006<br>Log(gc) shock Log(inv) shock tby shock<br>.006 .004<br>.001<br>.002<br>.004<br>0 0<br>-.001 .002 -.002<br>-.002 0 0 5 10 15 20<br>quarters<br>r shock Log(y) shock<br>.002<br>.006<br>.004<br>0<br>.002<br>-.002 0<br>0 5 10 15 20 0 5 10 15 20<br>quarters quarters<br>95% CI IRF<br><!-- End of picture text --> <!-- Start of picture text --> Source : authors.<br><!-- End of picture text --> Figure 1.4(a) plots the trade balance-to-GDP ratio in Paraguay together with the U.S. real interest rate. The correlation between the two variables is positive equal to 0.30. Of course, this unconditional correlation, while supportive of our hypothesis, may be the result of shocks other than the world interest rate shocks. SVAR allows us to control for those. Figure 1.4(b) plots the response of trade balance to GDP ratio following a positive unanticipated shock to the world interest rate. It is easy to see that trade balance improves on impact, supporting the idea that world interest rate lead to some portfolio rebalancing by foreign investors.
# FOREWORD

This paper is the author's second examination of the extension service in Cameroon's North West Province and its unusual success in reaching the province's women farmers. The first study, Making Extension Work With Women: The Effortg of MIDENO in Cameroon, was commissioned by the Equity Policy Center and was based on data collected in September 1986. That report described the initiatives of the 'Mission de Developpement de la Province du Nord-Ouest" (MIDENO) to improve the extension service and the success these initiatives were having in reaching women.

The report came to the attention of the staff of the World Bank's Women in Development Division early in 1988. The division felt that while MIDENO's efforts clearly exemplified innovative programming for women in agricultural extension, a broader and more up-to-date case study would be of greater value. As a result, the author was contracted, chrough the International Development Management Center (IDMC) of the University of Maryland, to prepare an expanded and more current description of MIDENO's I terventions and its ability to deliver services to women farmers. The UNDP - Regional Program for Africa financed this work, which forms an integral part of ongoing research: Raising the Productivity of Women Farmers in Africa being managed by PHRWD and funded by the UNDP Regional Program. This report is the result.

The field work for this report was conducted in August 1988 and included interviews with staff of: the Ministry of Agriculture at the national, provincial and local levels; HIDENO and its executing agencies; donors; and others involved in agricultural development in Cameroon. Discussions were also held with three women's farming groups and numerous other individual farmers. Raw data were also collected from MIDENO, the Provincial Delegation of Agriculture, and the FONADER agency in Bamenda. These data serve as the basis for the tables and analysis in Chapter II. It should be noted that all of the information and data presented in this paper was current as of August 1988. However, given the fluid environment that presently prevails in Cameroon, the situation is certain to have changed.

The author would like to thank Katrine Saito of the Women in Development Division for her able management of the entire consultancy and the staff of IDMC for their tireless support. In Cameroon, the fieldwork was made so much eas'er because of the assistance of the staff of MIDENO and the North West Provincial Delegation of Agriculture, particularly Samuel Mbonchom, Chief of Service for Extension and Training and Jonathan Tame, Technical Coordinator of MIDENO's Planning, Evaluation,. and Honitoring Division.

Helpful comments on an earlier draft of this paper were provided by Irene Tinker, Mary Beth Wertime, Paula Williams, and Judy Bryson. Daphne Spurling provided the Executive Summary. Their suggestions and insights have made this a better report.
percent increase in income levels. The Kenyan episode that we investigate lacks the quasiexperimental characteristics of the Azteca study, because the increase in bank branch penetration was not random. However, the sheer magnitude of the increase in financial sector participation makes this an episode worthy of study. Moreover, similar in spirit to Burgess and Pande (2005), we exploit a shift in a bank branching strategy and use an IV approach to mitigate the potential endogeneity problems emerging from nonrandom branch expansions.

In addition to the emergence of Equity Bank, the Kenyan financial landscape has witnessed another innovation over the last decade that has gained global attention: the introduction of M-PESA, which operates through mobile telephones and greatly facilitates money transfer and remittance by the poor. With the same household surveys that we use, Mbiti and Weil (2011) show that M-PESA is primarily used for transferring money from individual to individual rather than as a vehicle for saving, and that its use increases the probability of being banked. M-PESA can, therefore, be seen as being complementary to the fuller set of financial services offered by large financial institutions, especially Equity Bank.

The remainder of the paper is organized as follows. Sections II and III describe the data and the methodologies, respectively. Section IV presents the empirical results stemming from baseline specifications and Instrumental Variables (IV) analyses, and a set of robustness checks and a discussion of those results. Finally, Section V concludes.

# **II. Data**

Our dataset on bank presence is based on branch-level information that we aggregate to construct a district-level panel on the number of branches by bank. This data was constructed from multiple sources, including phone calls, official websites, banks’ annual reports and government publications. The panel data set covers 45 commercial banks that operated in 65 Kenyan districts in the period between 2006 and 2009. This new dataset allows us to explore the relationship between bank expansion and access to banking services over time and to exploit within-district variation in bank presence.<sup>3</sup> In addition, this dataset set allows us to characterize the expansion of the whole banking

> 3 Although Kenya is currently divided into 46 districts, we take advantage of a more disaggregated country division as of the 1999 census. In that census, Kenya comprised eight provinces (Central Coast, Eastern, Nairobi, North Eastern, Nyanza, Rift Valley, and Western) that were subdivided into 69 districts; the survey data cover 65 of those districts.

6
Recent Perspectives on Trade and Inequality Ann Harrison<sup>�</sup> John McLaren<sup>y</sup> Margaret McMillan<sup>z</sup> June, 2010 Abstract The 1990’s dealt a blow to traditional Heckscher-Ohlin analysis of the relationship between trade and income inequality, as it became clear that rising inequality in lowincome countries and other features of the data were inconsistent with that model. As a result, economists moved away from trade as a plausible explanation for rising income inequality. In recent years, however, a number of new mechanisms have been explored through which trade can a¤ect (and usually increase) income inequality. These include within-industry e¤ects due to heterogeneous ...rms; e¤ects of o¤shoring of tasks; e¤ects on incomplete contracting; and e¤ects of labor-market frictions. A number of these mechanisms have received substantial empirical support.

> �University of California, Berkeley and World Bank.

> yDepartment of Economics, University of Virginia, P.O. Box 400182, Charlottesville, VA 22904-4182. Email: jmclaren@virginia.edu.

> zTufts University and IFPRI.
11 later year over an earlier one. Instead, money-metric social welfare was unambiguously higher in 1976 than in 1985, as indicated above. Indeed, all poverty measures reported for both of our lines (and for 0 = 1.0) are higher in 1985 than in 1976.10 This is conspicuously not the case for a comparison between 1976 and 1996. Whereas poverty measures very sensitive to the poorest are higher for 1996, poverty incidence for 'higher' lines fall from 1976 to 1996, suggesting a crossing of the distribution functions. Figure 3 shows this crossing, by plotting the Pen parades (F-'(y)) - truncated at the 60' percentile - for all four years analyzed. Note that whereas 1976 lies everywhere above 1985, all other pairs cross. In particular, 1976 and 1996 cross somewhere near the **17 t** percentile.

||*1976|1981|1985|1996|
|---|---|---|---|---|
|1976|||F||
|1981||||L|
|1985||||L|
|1996|||||

# [See **Figure 3 in Appendix 4]**

Before we turn to the model used to decompose changes in the distribution of household incomes, which will shed some light on all of these changes, it will prove helpful to gather some evidence of the evolution of educational attainment, as measured by average effective years of schooling, and for labour force participation, for different groups in the Brazilian population, partitioned by gender and ethnicity. Table 4 presents these statistics.

As can be seen, there was some progress in average educational attainment in urban Brazil over this period. Average effective years of schooling, as reported in Table 1, rose from 4.2 to 5.3. In fact, this piece of good news will prove of vital importance in having prevented a more ' Note that this first-order welfare dominance is not robust to a change in 0 to 0.5.