rafmacalaba/datause-encoder-data huggingface dataset:
der its quota. US ROOs regarding apparel products are governed by Section 334 of the Uruguay Round Agreements Act. For the purpose of tari¤s and quotas, an apparel product is considered as originating from a country if it is wholly assembled there.<sup>10</sup> No local fabric requirement is necessary. Thus, the products of a Bangladeshi ...rm are not penalized if the ...rm chooses to use imported fabrics. Bangladesh did not have any trade preferences in the US and had to compete with producers from other countries such as India and China. However, since there were quotas on other exporters as well, competition among supplying countries was limited.<sup>11</sup> In wovens, 65-75% of Bangladeshi exports to the US in value terms are under quota. For the subgroup we focus on this number is close to 100%. These quotas are bilateral and product speci...c, so ...rms have no choice but to meet origin.<sup>12</sup> Note that quotas in the US have a license price associated with them. As these quotas are binding, it is positive. A ...rm survey in Bangladesh conducted by the World Bank in 2004 gave the quota license price to be about 7%.<sup>13</sup> The EU Environment In 2004, the EU had a MFN tari¤ rate of 12%-15% on woven apparel. However, Bangladeshi ...rms could avoid paying this tari¤: under the “EverythingBut-Arms”(EBA) initiative in 2001, Bangladesh together with 48 other LDCs was allowed to export duty and quota free to the EU, provided that ROOs were satis...ed. The EBA initiative e¤ectively removed any inklings of a quota and granted a 100% preference margin for garment exports of Bangladesh to the EU. It signi...cantly improved the market environment in which Bangladeshi garment exporters operated.

EU ROOs on apparel products were considerably more restrictive than the US ones. As such, an item exported to the US may be considered as a product of Bangladesh and imported under its quota allocation. However, the same item may fail to meet EU ROOs

> 10For details, please see the online Appendix available at the authors’web-sites.

> 11Note that less competitive countries are at less of a disadvantage in the US than they would be in the absence of the quota. The quota in e¤ect guarantees them a niche as long as they are not too ine¢ cient. Their ine¢ ciency reduces the price of their quota licenses, while the quota licenses of a very competitive country would be highly priced.

> 12The ...ll rate of the quota is close to 80% suggesting the quotas are binding.

> 13In the survey administered by H.L. Kee to a sample of Bangladeshi woven ...rms the average cost increase from having to buy a license was 7%, which is in line with estimates in Mlachila and Yang (2004) for 2003.

9
The civil war is the second crucial event in this period. In October 1990, a group of rebels consisting of Tutsi refugees who left Rwanda during the 1959 to 1962 revolution and their offspring attacked Rwanda from Uganda. What followed was a civil war between the Rwandan armed forces (FAR) and the rebel army (Rwandan Patriotic Front or RPF) in which civilians in the northern provinces of Byumba and Ruhengeri were the main victims. In January 1991, the rebels mounted a surprise attack against the city of Ruhengeri to liberate political prisoners from the government prison (Prunier, 1995). A report by an international group of human rights experts documents the disruption in agricultural production and in the lives of northern villagers (Federation International des Droits de l’Homme, 1993).<sup>4</sup> Battles between both armies were paralleled by peace negotiations and third party interventions. Before the war, relative to other regions in Rwanda, these northern provinces were areas of surplus production providing potatoes to the rest of the country. There are no existing household level data on the extent of the disruption to crop production and income generating activities in the north due to the war.

In a manner analogous to the crop failure measure, we use these reports to create a variable indicating which Rwandan regions experienced armed conflict during this period. The civil war area included parts of the northern provinces of Byumba and Ruhengeri (see Figure 1).

# _2.3 Data_

In January 1992, UNICEF financed a survey on the health status of young children and mothers in Rwanda (UNICEF, 1992). The UNICEF survey was nationally representative and collected detailed information on the exact date of birth and height of every child under age five in 2,496 rural households, yielding 1762 children under age five with complete information in these

> 4  In addition, between 1990 and 1993, the Rwandan government organized small-scale, local massacres that targeted Tutsi in regions far from the war front (Fédération Internationale des Droits de l’Homme, 1993).

7
Figure III plots the “raw” differences in HAZ for children under 60 months between the treatment and the control groups.<sup>28</sup> The figures are consistent with the hypothesis that differences in children’s heights are moderated by exposure to the program. Most strikingly, we see a very large difference in HAZ between the UCT and the control group during the program, which steadily declines, disappears by the end of the program, and even turns negative during the final epoch (Panel C). This pattern is consistent with the substantive but transient improvements in the nutritional status and mental health of UCT beneficiaries. In contrast, no significant differences in child height are apparent between the CCT and the control groups during the program – also consistent with the fact that most mothers of children born in this period would have dropped out of school as a result of their pregnancies, thus forgoing any cash transfers (Panels A and B). Column 1 in Tables VII and VIII reports the raw differences in HAZ by epoch, for baseline dropouts and baseline schoolgirls respectively, and confirms these patterns.

These impacts may combine extensive margin selection effects (such as the types of women who became pregnant, the types of partners they chose, and the age at birth) with a ‘direct’ casual effect of the program on the children actually observed. Unlike many such applications in the natural experimental literature, it is entirely plausible that _all_ of the observed impacts on HAZ arise from the selection effect of unwanted children being delayed by the receipt of the UCT.<sup>29</sup> Following the methodology laid out in Appendix C, we can then

> (Haddad et al. 2014). Child assessments (MDAT and SDQ) are also objectively measured outcomes of cognitive and socio-emotional development, but the target age group for these assessments (36-59 months) makes them unsuitable for analysis by epoch of exposure to the program because only children born during the first year of the program (less than 200 in the baseline schoolgirl stratum with less than 30 in the UCT arm) were eligible for assessment. 28 We construct these figures by running a locally weighted treatment effects regression across the distribution of child age (Fan 1992) and plotting the resulting time-specific treatment effects and 95% confidence intervals.

> 29 In the study of a negative shock, the most likely extensive margin impact is an increase in mortality among the weakest fetuses and children, thereby pushing upwards the average outcome among surviving cohorts exposed to the shock. The large set of papers studying negative shocks such as pollution (Chay and Greenstone 2003, Black et al. 2014, Adhvaryu et al. 2016), disease (Almond 2006), and hunger (Almond and Mazumder 2011) can therefore typically argue that any negative effects found on surviving children are actually conservative. Because we study a

30
Table 2. Robustness tests.

|Dependent variable: annual sum of<br>earthquake deaths<br>Propensity:><br>5<br>(Richter)|Propensity:><br>6.5<br>(Richter)|Propensity: largest<br>quake|Only quakes > 6<br>(Richter)|Executive years in<br>office incl.|
|---|---|---|---|---|
|Model 1|||||
|quake propensity<br>-0.000248***|-0.0139***|-0.00114***|-0.00387***|-0.00343**|
|(0.0000688)|(0.00382)|(0.000273)|(0.000981)|(0.00166)|
|Model 2|||||
|propensity in developing countries<br>-0.000207***|-0.0118***|-0.000997***|-0.00332***|-0.00275*|
|(0.0000698)|(0.00378)|(0.000240)|(0.000908)|(0.00160)|
|propensity in developed countries<br>-0.000740***|-0.0316***|-0.00172***|-0.00940***|-0.0103***|
|(0.000116)|(0.00527)|(0.000333)|(0.00154)|(0.00187)|
|Model 3|||||
|propensity in autocracies<br>-0.000202*|-0.0114**|-0.00104***|-0.00311**|-0.000981|
|(0.000121)|(0.00556)|(0.000267)|(0.00150)|(0.00230)|
|propensity in democracies<br>-0.000286***<br>(0.000109)|-0.0173***<br>(0.00541)|-0.00134***<br>(0.000313)|-0.00472***<br>(0.00141)|-0.00752***<br>(0.00161)|
|Model 4|||||
|propensity in autocracies<br>0.000213|0.00920|0.00245**|0.00201|0.00143|
|w. low institutionalization<br>(0.000220)|(0.00935)|(0.00122)|(0.00254)|(0.00303)|
|propensity in autocracies<br>-0.000184**|-0.0104***|-0.000548**|-0.00254**|-0.00207**|
|w. high institutionalization<br>(0.0000724)|(0.00375)|(0.000232)|(0.00101)|(0.000985)|
|propensity in young democracies<br>-0.000295***<br>(0.000103)|-0.0188***<br>(0.00594)|-0.00110***<br>(0.000404)|-0.00476***<br>(0.00119)|-0.00440**<br>(0.00175)|
|propensity in old democracies<br>-0.000568***<br>(0.0000870)|-0.0300***<br>(0.00418)|-0.00173***<br>(0.000303)|-0.00800***<br>(0.00110)|-0.00809***<br>(0.00103)|
|Model 5|||||
|propensity in corrupt countries<br>-0.000173|-0.00596|-0.000949**|-0.00225|0.00303**|
|(0.000205)|(0.0100)|(0.000415)|(0.00242)|(0.00149)|
|propensity in non-corrupt countries<br>-0.000564***|-0.0243***|-0.00133***|-0.00718***|-0.00381*|
|(0.000169)|(0.00749)|(0.000301)|(0.00190)|(0.00216)|

Notes: Standard errors clustered on countries. * significant at p<.1 ** at p<.05 *** at p<.01.

34
Table A2.2. Balancing test

|**Vibl**|**Unmatched/**|**Me**|**an**|**% bi**||**t‐t**|**est**|
|---|---|---|---|---|---|---|---|
|**arae**|**Matched**|**Treated**|**Control**|**as**|**% bias reduction**|**t**|**p>|t|**|
|Log income per capita|U|3.47|3.81|−53.6||−55.8|0.00|
||M|3.46|3.45|0.0|100|0.0|0.99|
|Proportion of Female|U|0.73|0.38|141.4||156.1|0.00|
||M|0.53|0.53|0.5|99.6|0.6|0.58|
|Education of PA||||||||
|6−11 years|U|0.52|0.61|−18.9||−20.7|0.00|
||M|0.62|0.62|0.0|100|0.0|1.00|
|More than 12 years|U|0.15|0.19|−9.9||−10.7|0.00|
||M|0.16|0.16|0.0|100|0.0|1.00|
|Marital Status||||||||
|Married with spouse in the HH|U|0.53|0.13|94.2||109.9|0.00|
||M|0.69|0.69|0.0|100|0.0|1.00|
|Widowed|U|0.25|0.01|75.2||97.4|0.00|
||M|0.12|0.12|0.0|100|0.0|1.00|
|Single or engaged|U|0.10|0.17|−18.7||−19.7|0.00|
||M|0.04|0.04|0.0|100|0.0|1.00|
|Divorced|U|0.06|0.01|29.1||36.8|0.00|
||M|0.02|0.02|0.0|100|0.0|1.00|
|Elderly in the HH|U|0.12|0.06|22.5||26.0|0.00|
||M|0.03|0.03|0.0|100|0.0|1.00|
|Disabled in the HH|U|0.07|0.11|−15.5||−16.3|0.00|
||M|0.05|0.05|0.0|100|0.0|1.00|
|Receives income|U|0.01|0.03|−13.6||−13.8|0.00|
|from wages|M|0.00|0.00|0.0|100|0.0|1.00|
|Receives income|U|0.08|0.03|20.7||24.3|0.00|
|from remittances|M|0.05|0.05|0.0|100|0.0|1.00|

_Note:_ Omitted categories: less than 6 years of education, married with spouse in the household.

34
# **5. Concluding remarks**

This paper investigates the impact of Internet job search on employment in Egypt, a country with a large population, high unemployment rates, and fast-growing Internet adoption rates. Given that in Egypt and more generally in the MENA region poor labor market prospects for the youth have contributed to large-scale protest movements and in some cases political instability, it is especially important to understand whether Internet job search could help reduce unemployment rates. The question is all the more relevant given that unemployment is particularly pronounced among the youth and that the younger cohorts are also the most likely to use the Internet.

Relying on panel data from the 2012 and 2018 rounds of the ELMPS, this paper thus examined the impact of Internet job search on the employment prospects of unemployed indivduals. Focusing on unemployed individuals in 2012, the results suggest that men who used the Internet for job search had a higher likelihood of finding a job within a five year timeframe compared to those who did not use the Internet or to those who used the Internet for purposes other than job search. While Internet job search seems to be an effective search method for men, as it increases men’s employment, we find that Internet job search is associated with an increase in women’s labor force participation but does not have an effect on women’s employment. Accounting for unobserved heterogeneity through first-difference and fixed effects models yields consistent results. Meanwhile, placebo regressions confirm that the results are not driven by pre-existing trends in labor market outcomes between individuals who use the Internet for job search and those who do not or between neighborhoods with high versus low Internet adoption rates.

Finally, this paper also investigates whether Internet job search among employed job seekers is associated with wage growth and tackles the question of digitalization at the workplace and its effect on earnings growth. The findings of this paper suggest that Internet job search does not have any significant effect on wage growth for employed job seekers. Even though digitalization and Internet penetration have been associated in other contexts with job creation, productivity gains, and wage growth, in the case of Egypt I do not find any effect on wage growth, or at least not in the short term.

# **References**

16
25 and above) gender (identified as male or female), income (identified as poor for adults living in the poorest 40 percent of households and rich for adults living in the richest 60 percent of households), employment (identified as wage employed, self-employed, unemployed, or out of the workforce), and ethno-religious groups. To explore the religious and geographic complexities in our data, we assign adults to one of four categories, based on self-reported religious identification and place of residence: Shia, Sunni, Kurds, and Baghdad. See Appendix A for additional information.

The Gallup World Poll conducts annual surveys covering about 150,000 people in more than 140 economies around the world. The data for Iraq is available for years 2010-2019 for 1,000 adults annually (age 15 and above). The surveys were conducted over the phone in Arabic and Kurdish for all years, except 2010, 2011, 2012, 2018 and 2019 where they were conducted face-to-face. We also bring complementary data from the Arab Barometer, which has conducted public opinion surveys in the Middle East and North Africa (MENA) since 2006. The most recent Arab Barometer survey of Iraq was carried out in 2018 and surveyed 2,461 adults (age 18 and above). The surveys were conducted face-to-face.

This paper only makes use of quantitative data sets, which are limited in their ability to provide additional insight into the complex relationships highlighted in many ethnographic and qualitative studies done on the shifting roles and power dynamics over the last couple of decades in Iraq. Its main goal is to support additional research by providing a quantitative background on vertical and horizontal inequalities.

# **3. Quality of Life**

Basic needs such as food and shelter, happiness, and belief that the future can be better than the present, affect the degree of social cohesion between different groups—and a lack of social cohesion and trust can trigger conflict.

# **_3.1 Food and shelter_**

Food and shelter are proxies for basic survival needs. Gallup constructs a simple average to determine if there were times in the past 12 months when there was not enough money to afford food or shelter. The period between 2010-2017 saw this average decline by nearly 32 points implying that large shares of Iraqi 4
Though we found that foreign banks themselves seem to charge higher fees than other banks, in foreign dominated banking systems fees are lower (perhaps because of greater competitive pressures) and it is easier to open bank accounts. On the other hand, in systems that are predominantly government-owned, customers face lower fees but also face greater restrictions in terms of where to apply for loans and the time it takes to have applications processed is longer. Finally, banks in countries with more concentrated banking systems are less likely to allow customers to open deposit accounts outside headquarters but impose lower minimum amounts for SME loans, are faster at processing loan applications and charge lower ATM fees.

As recent empirical work has shown that competition is not a linear and unambiguous function of banking sector structure (Claessens and Laeven, 2004), we also explore the relationship between banking barriers and two indicators of an economy’s competitiveness. First, we use the index on Banking Restrictions from the Heritage Foundation, a composite index of whether foreign banks are able to operate freely, how difficult it is to open domestic banks, what degree of regulations there are on financial market activities, the presence of state-owned banks, whether the government influences allocation of credit, and whether banks are free to provide customers with insurance products and invest in securities. Second, we use the cost of starting a formal enterprise, as share of income per capita, as an indicator of the ease of entry into the economy (World Bank, 2006a).<sup>16</sup> Less competitive economies have banks that impose higher barriers to banking. We find that banks in economies with more restrictions to banking freedom are less likely to allow that accounts are opened outside the headquarters, demand higher minimum balances to open a checking or savings account, impose higher fees on checking accounts, require more

> 16 We also tried regulatory indicators of bank entry, but these refer to regulatory requirements rather than the cost of setting up banks.

25
marginally significant.<sup>22</sup> In contrast, none of the broader, more recent agreements has a positive, significant elasticity, and two – for ECOWAS and COMESA – have perverse signs.<sup>23</sup> For our baseline impact simulations, we use the results in column (5), which control for membership in the three strongest trade agreements.

# **4.3  Calibration of the Inter-City Gravity Model**

For each city pair in the network, gravity model estimation of trade flows requires four pieces of information: the economic scales of the two cities, the network distance between them, and the quality of the connecting roads. To proxy economic scale, we multiply the population of each city by national GDP per capita at purchasing power parity for 2001 (the most recent year for which comprehensive Sub-Saharan income data are available). We calculate inter-city distances in the network using our spatial network analysis model (see Figure 2), and index inter-city road quality using the minimum quality for transit countries. Then we use the parameter estimates from column (5), Table 5 to estimate trade volumes for 3,403 city pairs:<sup>24</sup> where Tij = Trade volume between exporter i and importer j K = A valuation constant

- Aij = Membership in WAEMU, CEMAC or EAC (1=exporter and

- importer both members of the same agreement)

- Ei = Exporter economic scale

Mj = Importer economic scale

- qij = Quality index for the road joining cities i and j dij = Road distance between cities i and j

> 22  Formally, rejection of zero effect with only 90% confidence.

> 23  The negative, significant result for COMESA appears in the reported regression, which is based on minimum inter-city road quality.  The result is still negative, but insignificant, in the corresponding regression that is based on average inter-city road quality.

> 24  This involves 6,806 calculations, since each city plays two roles (exporter, importer) in each pair.  Two sets of calculations are necessary because parameter estimates for importers and exporters are not identical. We exclude 6 countries from the analysis because data for trade and other variables are missing: Botswana, Lesotho, Namibia, Swaziland, Somalia and Liberia.

17
## **2.1 Using standard tests to detect the problem**

Though the problem is apparent when shown this way, a natural question is whether existing tests would have detected it. The answer requires two decisions. First, the standard RNG testing suite, TestU01, is designed for uniform distributions between zero and one. To transform a random variable into a uniform one, the usual choice (though not the only one) is to apply the variable’s cumulative distribution function to the values. Having done this, the second choice is how to test multiple parallel streams of numbers; TestU01 is designed to test only a single stream. However, I follow the suggestion of the TestU01 authors L’Ecuyer and Simard (2007) on how to handle parallel streams, taking a fixed number of draws, _L_ , after each re-initialization with a new seed. I use _L_ = 20, though other values would also work.

With these choices, a relatively small number of draws are necessary for several of the TestU01 tests to reject that the resulting draws were independent and uniformly distributed on (0,1). One of the simplest tests—and a parallel to Figures 3 and 4—compares the empirical distribution of sample means to the theoretical distribution via a KolmogorovSmirnov test. With 1000 sample means of 20 consecutive draws each, the test rejects the null hypothesis with a p-value less than 10<sup>_−_16</sup> . In contrast, the same test does not reject the null for a number of alternative RNGs, including those that result from applying the same re-initializing procedure to Stata’s `uniform()` function or to the `rnormal()` function after the March, 2011 Stata update.<sup>2</sup>

# **3 Frequent re-initialization versus advancing the state**

Though the problem could have been detected this way, and has now been repaired, frequently re-initializing the seed is not the intended use of the RNG, and may be harmful to RNGs in general. To illustrate, I examine the well-known algorithm Stata uses to generate uniformly distributed draws.

Stata’s uniform random number generator, using Marsaglia’s KISS algorithm, only re-

> 2 In C, this test uses the TestU01 function as follows: `svaria_SampleMean(gen, NULL, 1000, 20, 0);` Further details on this and a number of other TestU01 tests are shown in Table 1.

5