rafmacalaba/datause-encoder-data huggingface dataset:
28 the case not only for small cities, but also for many large cities, which are often seen as “engines of growth” in developing countries (World Bank, 1999, 2009).

Furthermore, we can use our city-level measures of _production/consumption city-ness_ (PCC; Section 1.) as well as population data for each city to obtain the populationweighted average level of PCC for each country c. 2000 (see Panel A of Web Appx. Table D.8 for the full list). While some developing economies have high aggregated PCC values, for example China, Costa Rica, Lesotho, Mauritius and Vietnam, other developing economies have low aggregated PCC values, especially Colombia, Indonesia, Nigeria, Tanzania and the Rep ́ublica Bolivariana de Venezuela. Some countries such as Brazil, India, Malaysia, Mexico and Turkey have both production cities and consumption cities, as indicated by a (city population-weighted) Gini index of PCC calculated for each country (Panel B for the full list).<sup>37</sup> Either their urban system is chronically “dualistic” or it is transitioning, whether away from production cities as in Latin America or towards them as in Asia (Section 1.).

**Urban Human Capital.** These facts pose the question of whether having an urban hierarchy dominated by consumption cities might have economic implications for a country, “beyond” the economic structure of its cities. If consumption cities are not as growth-enhancing as production cities, are they doomed to remain so or is it possible that they evolve into production cities in the future? An important factor that can shed light on these questions is the stock and quality of _urban human capital_ . Indeed, cities with a bigger stock of high-quality human capital may be better able to adapt to future economic conditions and grow regardless of their current economic structure.

We assess how urban human capital correlates with our structural variables of interest by obtaining from IPUMS the average number of years of education of adults (ages 25 to 65) living in the urban areas of 53 out of the 116 sample countries for the year closest to 2000. We use the same cross-sectional regression as in our study of sectoral or informal employment (eq. (10)) and include MFGSERV (2000), NRXGDP (average in 1960-2020) and DEINDU (change in 1980-2000), as well as the urban share (2000). The results, shown in col. (1) of Panel A in Table 9, suggest that NRXGDP and DEINDU countries might have

> 37For each country one by one, and since we know the PCC and population of each city c. 2000, we can estimate the degree of statistical dispersion of the PCC measure – i.e., the Gini index – within the country.
# **Loss Aversion and Trade Policy**<sup>*****</sup>

Caroline Freund<sup>†</sup> and Çağlar Özden<sup>‡</sup>

## **Abstract**

We provide new survey evidence showing that loss aversion and reference dependence are important in shaping people’s perception of trade policy. Under the assumption that agents’ welfare functions exhibit these behavioral elements, we analyze a model with a welfare-maximizing government and with the lobbying framework of Grossman and Helpman (1994). The policy implications of the augmented models differ in three important ways. One, there is a region of compensating protection, where a decline in the world price leads to an offsetting increase in protection, such that a constant domestic price is maintained. Two, protection following a single negative price shock will be persistent. Three, irrespective of the extent of lobbying, there will be a deviation from free trade that tends to favor loss-making industries. The augmented models are more consistent with the observed structure of protection, and in particular, explain why many trade policy instruments are explicitly designed to maintain prices at a given level.

## World Bank Policy Research Working Paper 3385, September 2004

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

* We are grateful to Simeon Djankov, Kishore Gawande, Bernard Hoekman, Pravin Krishna, Sendhil Mullainathan, Marcelo Olarreaga, David Tarr, and participants at the Cornell/LSE/MIT conference on Behavioral Economics and at the World Bank trade seminar series for comments on an earlier draft. † World Bank. Mailing address: DECRG, World Bank Mailstop MC3-303. 1818 H Street NW Washington, DC 20433. Phone: 202-473-7102,  Email: <u>cfreund@worldbank.org</u>

‡ World Bank. Mailing address: DECRG, World Bank Mailstop MC3-303. 1818 H Street NW Washington, DC 20433. Phone: 202-473-5549, Email: <u>cozden@worldbank.org</u>
Beard J (2006) DDT and human health. Sci Total Environ, **355** : 78-89.

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Chen A, Rogan WJ (2003) Nonmalarial infant deaths and DDT use for malaria control. Emerg Infect Dis, **9** : 960-964.

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Cone M. Should DDT Be Used to Combat Malaria? Scientific American, May 2009 <u>http://www.scientificamerican.com/article.cfm?id=ddt-use-to-combat-malaria</u> Cox S, Niskar AS, Narayan KM & Marcus M (2007) Prevalence of self-reported diabetes and exposure to organochlorine pesticides among Mexican Americans: Hispanic Health and Nutrition Examination Survey, 1982–1984. Environ Health Perspect, **115** : 1747–1752.

De Jager C, Farias P, Barraza-Villarreal A, Avila MH, Ayotte P, Dewailly E, Dombrowski C, Rousseau F, Sanchez VD & Bailey JL (2006) Reduced seminal parameters associated with environmental DDT exposure and _p_ , _p_ ′-DDE concentrations in men in Chiapas, Mexico: a cross-sectional study. J Androl, **27** : 16–27.

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Eriksson P, Talts U. (2000). Neonatal exposure to neurotoxic pesticides increases adult susceptibility: a review of current findings. Neurotoxicology, **21:** 37-47.

Eskenazi B, Marks AR, Bradman A, Fenster L, Johnson C, Barr DB & Jewell NP (2006) In utero exposure to dichlorodiphenyltrichloroethane (DDT) and dichlorodiphenyldichloroethylene (DDE) and neurodevelopment among young Mexican American children. Pediatrics, **118** : 233–241.

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17
“ backbone candidates”. Backbone teachers are those identified by either the county Bureau of Education or the provincial Department of Education as excellent teachers; such teachers generally receive additional training beyond what the general teacher gets and are usually accorded additional responsibilities within their schools. Backbone candidates are those who have been identified as potential backbone teachers, but who have yet to go through the requisite training to qualify as full backbone teachers. Supplementing the classroom observations was a teacher beliefs survey and training history questionnaire given to all 36 observed teachers.

The breakdown of teachers (see table 2), while not large enough to form a representative sample, allows us to judge whether CLASS is useful in the Chinese context, as well as to examine teacher performance from several angles: teacher experience, overall academic performance, rural versus urban,<sup>3</sup> and by subject.<sup>4</sup>

**Table 2: Breakdown of Teacher Observation Sample**

||**Prim**|**ary (4th gra**|**de)**|**Junior Se**|**condary (8th**|**grade)**||
|---|---|---|---|---|---|---|---|
|||**Rural**|||**Rural**|||
||**Urban (in**<br>**township)**|**(out of**<br>**township)**|**Total**<br>**primary**|**Urban (in**<br>**township)**|**(out of**<br>**township)**|**Total**<br>**Jr. Sec.**|**Total**|
|**Overall sample**||||||||
|**Total schools**|**3**|**3**|**6**|**3**|**3**|**6**|**12**|
|**Total teachers**|**9**|**9**|**18**|**9**|**9**|**18**|**36**|
|_New teacher_|_3_|_3_|_6_|_3_|_3_|_6_|_12_|
|_Backbone candidate_|_3_|_3_|_6_|_3_|_3_|_6_|_12_|
|_Backbone teacher_|_3_|_3_|_6_|_3_|_3_|_6_|_12_|
|**Sample within a single**<br>**county**||||||||
|**Schools withina county**|**1**|**1**|**2**|**1**|**1**|**2**|**4**|
|**Teachers per school **|**3**|**3**|**6**|**3**|**3**|**6**|**12**|
|_New teacher_|_1_|_1_|_2_|_1_|_1_|_2_|_4_|
|_Backbone candidate_|_1_|_1_|_2_|_1_|_1_|_2_|_4_|
|_Backbone teacher_|_1_|_1_|_2_|_1_|_1_|_2_|_4_|
|**Sample by subject**||||||||
|Math|3|3|**6**|3|3|**6**|**12**|
|Language (Chinese)|3|3|**6**|3|3|**6**|**12**|
|English|3|3|**6**|3|3|**6**|**12**|

> 3 While all schools observed are in Guangdong’s poorer counties, the conditions in each of these counties vary depending on whether one is in the countryside or in the county seat. For this study, half of the schools observed are in county seats, while half are in the countryside. Despite this distinction between urban and rural in our data set, it is likely that _all_ schools observed would be classified as rural relative to true urban centers such as Guangzhou, Shanghai, or Beijing.

> 4 The only divergence between initially planned criteria and the final breakdown was the selection of “new” teachers. While new teachers were originally requested to have less than three years of experience, the final sample included “new” teachers with up to 19 years of experience. Follow-up discussions with the government suggest that they consider teachers with more than three to five years of experience who are not backbone candidates as “general” or “common” teachers, rather than new teachers.

3
including Adana) and four subregions of eastern Anatolia (Erzurum, Agri, Malatya, Van). These nine subregions make for a more homogeneous sample of Turkey (see Ceritoglu et al. 2015 for extensive evidence highlighting the common levels and trends in labor market outcomes for this sample). This is especially important since in the full sample we were not able to entirely rule out the existence of some pre-trends, for some subgroups of the Turkish population, that are correlated with refugee flows (see Section 5.1 above).

Table 11 presents the IV estimates of the impact of refugees on total, formal and informal employment when we drop the Gaziantep subregion (Panel A) and when restrict the sample to nine subregions of southeastern and eastern Anatolia (Panel B). We show results separately for the whole sample and by gender. Our findings are highly robust to the particular choice of sample.<sup>36</sup> The estimates in both Panels A and B are very similar to our main results (see Tables 5b and 6). Specifically, as in our main results, we find large-scale displacement in informal employment and job growth in formal employment. The net impact on total employment is negative and statistically significant. The impacts by gender also closely reflect our main results. Women experience particularly pronounced displacement in the informal sector and no formal job gains. In contrast, for men displacement in the informal sector is fully offset by employment growth in formal sector, with no net job losses. Clearly, our main findings do not depend on the particular sample of subregions we analyze, and importantly are robust to restricting the analysis to a more homogenous group of regions.

# **6. CONCLUSIONS**

This paper combines newly available data on the 2014 distribution of 1.6 million Syrian refugees across subregions of Turkey and the Turkish LFS, to assess the impact on Turkish labor market conditions. The Syrian refugees in Turkey are overwhelmingly employed informally, since they were not issued work permits, and so their arrival was a well-defined supply shock to informal labor. Consistent with economic theory our IV estimates, which also control for distance from the Turkish-Syrian border, suggest large-scale displacement of natives in the informal sector. At the same time, consistent with occupational upgrading, there are increases in formal employment for the Turkish. This increase though only occurs among men without completed high school education. The employment patterns of women and the high-skilled mean they are not in a good position to take advantage of lower cost

> 36 Results are also robust to dropping all subregions with close to no refugees.

28
Incentives for Mayors to Improve Learning: Evidence from state reforms in Ceará, Brazil<sup>_∗_</sup> Ildo Lautharte<sup>_†_</sup> Victor Hugo de Oliveira<sup>_‡_</sup> André Loureiro<sup>_†_</sup>

**Keywords:** Education Incentives for Mayors, Results-Based Financing, Technical Assistance. _JEL Classification: H52, I21, I22._

> _∗_ We would like to thank Halsey Rogers, Pedro Cerdan _−_ Infantes, Renata Lemos, Emanuela DiGroppelo, Caio Piza, Leandro Costa, Bruno Ottoni, and David Evans for the detailed comments. The authors gratefully acknowledge financial support from the REACH Trust Fund at the World B ank. We also thank seminar participants at the World Bank, Instituto IDados, Todos Pela Educação and Brazilian Society of Econometrics for valuable feedback on early drafts of this manuscript. The views expressed by the authors do not reflect the views of IPECE or the World Bank. All errors are, naturally, our own.

The authors gratefully acknowledge financial support from the REACH Trust Fund at the World Bank.

> _†_ Education Global Practice, The World Bank Group

> _‡_ Instituto de Pesquisa e Estratégia Econoômica do Ceará
# **1 Introduction**

The internet has had a transformative impact on society, promoted trade, and generated growth (Freund and Weinhold, 2004; Litan and Rivlin, 2001). Yet, it has also enabled various forms of crime (Bhuller et al., 2013; M ̈uller and Schwarz, 2021), reduced confidence in government in most countries (Miner, 2015; Guriev et al., 2020), and long raised concerns about its use by violent extremists (Gartenstein-Ross, 2017; Conway, 2017). The Islamic State in Iraq and the Levant (ISIL, also known as Daesh) has been the epitome of tech-savvy terrorism and has attracted an estimated 30,000 foreign recruits from 89 countries to Syria and Iraq (Benmelech and Klor, 2018), about 6,000 of whom came from Tunisia (Berger and Morgan, 2015; Soufan Center, 2015; Benmelech and Klor, 2018), the largest country contingent in per capita terms. – While Daesh’s reliance on social media to spread propaganda, secure financing, plan attacks, proselytize, and recruit– is well-documented (FATF, 2015; Greenberg, 2016; Bloom et al., 2017; Conway et al., 2019; Mitts et al., forthcoming), the internet can also hamper its transnational terrorist recruitment efforts; it can enhance the efficiency of law enforcement or of repression by the state (Rød and Weidmann, 2015; Baum and Potter, 2019; Gohdes, 2020; Mastrobuoni, 2020), positively reshape public views of government (Gunitsky, 2015; King et al., 2017; Qin et al., 2017; Guriev and Treisman, 2019), and increase the opportunity cost of joining the terror group by improving labor market prospects (Autor, 2001; Hjort and Poulsen, 2019). The overall extent to which the internet has enabled the international recruitment of jihadists is, therefore, theoretically ambiguous.

This paper addresses this question empirically by using a unique dataset on Tunisians who joined Daesh in Syria and Iraq between early September 2013 and late 2014. The data contain information on recruits’ ages, education levels, and delegations (district) of residence before traveling abroad, allowing us to exploit variation in enrollment both between and _within_ geographical areas, and between age and education groups.<sup>1</sup> These granular data are matched with equally granular socio-economic information obtained from the 2014 Tunisia Census of Population and Housing. Thus, for each delegation-education-age cell, we are able to associate

> 1Tunisia’s smallest administrative units are the communes, which form the country’s 264 delegations, which in turn aggregate into 24 governorates.

1
# **5 Experimental Design**

All informal settlement property owners in the utility database were first called to confirm contact details and receive the base intervention. Eligibility criteria into the study included: (i) property owners were able to be contacted and their contact details could be updated; (ii) their payment accounts were in arrears and (iii) property owners did not hold multiple accounts (multiple-property owners). All eligible property owners then received the basic information intervention and contact details were updated. Figure 3 describes the experimental design and sample selection. Starting from the group of 5,091 property owners that completed the phone survey in August 2018, just over 50% (2,584) indicated that they had tenants residing in the property. These 2,584 accounts were randomly assigned into a group of 1,292 who received the engagement treatment and an equally sized control group. The engagement intervention was successfully implemented in 885 (69%) of the 1,292 accounts assigned to the treatment group. Reasons for non-compliance included not being able to find the property, tenants being unavailable at the time of visit, and incorrect recording of the compound as having tenants when this was not the case.

For the enforcement intervention, we started with the same 5,091 accounts used for the engagement intervention and removed two informal settlements because these settlements are characterized by multi-story apartment blocks where individual disconnections pose a technical challenge. The remaining sample of 3,253 accounts from 4 settlements (which included compounds with and without tenants) were then clustered by street using GPS and address data. This generated 147 distinct street clusters. We then randomized 73 clusters consisting of 1,584 accounts into the treatment group, while the remaining 74 clusters (1,669 accounts) were left as pure controls. Within the treatment clusters there were 649 compounds eligible for disconnection, and 327 of these were randomly assigned to receive the disconnection notifications, following the protocols described in Section 4. The remaining 322 control compounds in the treatment clusters, as well as the 674 disconnection-eligible compounds in pure control clusters were exempted from the policy for the period of the study. Figure 4 illustrates how the two-stage randomization was applied in one settlement to allow us to test for direct and spillover effects associated with the enforcement intervention. The disconnection notices were ultimately delivered to 299 compounds (91.4%). In the remaining cases (28), compounds were found to have already had their services disconnected by the utility in which case no notice was delivered. The number of compounds actually disconnected was 96 or 2.9% of the 3,253 compounds eligible for disconnection in the study.<sup>20</sup>

> 20In six additional cases, despite property owners not coming to an agreement with the utility, enforcement was not possible because of of the technical complexity of the connection.

13
# **Table 4: Panel: Tobit Analysis**

|**Dependent variable: log(1+a**|**id) donor ****_j _provid**<br>|**es aid after disaster **<br>|**_i_**<br>||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
||Pooled-Tobit|Pooled-Tobit FE|RE-Tobit DOFE|Tobit EMFE|
|_Impact measures_|||||
|Log(Nr.affected)|1.269***<br>|1.271***<br>|1.271***<br>||
||(0.153)|(0.140)|(0.140)||
|Log(Nr. Killed)|2.497***|2.483***|2.483***||
||(0.179)|(0.163)|(0.163)||
|_Socio economic indicators:_|||||
|Log(Population)|-2.488***|-2.327***|-2.327***||
||(0.316)|(0.308)|(0.308)||
|Pop. density|-0.000|-0.000|-0.000||
||(0.004)|(0.004)|(0.004)||
|Log(GDP per capita)|-1.982***|-1.994***|-1.994***||
||(0.532)|(0.488)|(0.488)||
|Oil dummy|4.023***|4.136***|4.136***||
||(0.850)|(0.777)|(0.777)||
|_Policy performance indicators_|||||
|Trade openness|-0.015|-0.013|-0.013||
||(0.017)|(0.016)|(0.016)||
|ELF index|-0.423|0.077|0.077||
||(1.427)|(1.323)|(1.323)||
|Gastil index|0.034|0.035|0.035||
||(0.123)|(0.115)|(0.115)||
|_Bilateral relation indicators_|||||
|Geograph. Distance|||||
||-0.235**|-0.384***|-0.384***|-0.451***|
|Affinity index|(0.099)|(0.089)|(0.089)|(0.078)|
||-16.636***|-13.586***|-13.586***|-12.952***|
|Former colony|(1.152)|(2.729)|(2.729)|(3.579)|
||6.600***|7.077***|7.077***|7.089***|
||(1.180)|(1.172)|(1.172)|(0.983)|
|Donor FE|NO|YES|YES|YES|
|Disaster FE|NO|NO|NO|NO|
|Observations|5153|5153|5153|5153|
|Pseudo R-squared|0.07|0.12|0.12|0.20|

_Notes:_ Coefficients reported; standard errors in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% All specifications include year, regional and disaster type fixed effects. The donors included are.Austria, Australia, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Japan, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, UK and the US 27