rafmacalaba/datause-encoder-data huggingface dataset:
Table I.5. Robustness Analysis for the Rights and Services Index

|_Panel A. Polynomial of order zer_|_o_||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|1[PEP_i_ = 1]|4.235***|4.107***|4.287***|4.291***|3.826***|4.415***|4.235***|4.107***|4.287***|4.291***|3.826***|4.415***|
||(0.561)|(0.626)|(0.614)|(0.490)|(0.502)|(0.517)|(0.561)|(0.626)|(0.614)|(0.490)|(0.502)|(0.517)|
|Obs. left|452|427|415|452|427|415|452|427|415|452|427|415|
|Obs. right|872|766|741|1,214|1,054|1,162|872|766|741|1,214|1,054|1,162|
|Mean values (Non-RAMV)|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|_Panel B: Polynomial of order one_|||||||||||||
||(13)|(14)|(15)|(16)|(17)|(18)|(19)|(20)|(21)|(22)|(23)|(24)|
|1[PEP_i_ = 1]|4.424***|4.324***|4.417***|4.620***|4.429***|4.607***|4.053***|4.190***|3.897***|4.249***|3.751***|4.073***|
||(0.917)|(1.089)|(0.989)|(0.555)|(0.581)|(0.583)|(0.767)|(1.013)|(0.842)|(0.561)|(0.718)|(0.597)|
|Obs. left|363|348|365|363|348|365|363|348|365|363|348|365|
|Obs. right|655|563|661|1,597|1,355|1,559|655|563|661|1,597|1,355|1,559|
|Mean values (Non-RAMV)|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|_Panel C: Polynomial of order tw_|_o_||||||||||||
||(25)|(26)|(27)|(28)|(29)|(30)|(31)|(32)|(33)|(34)|(35)|(36)|
|1[PEP_i_ = 1]|4.642***|4.554***|4.692***|4.525***|4.147***|4.585***|4.359***|4.110***|3.897***|4.081***|3.715***|3.972***|
||(1.132)|(1.222)|(1.212)|(0.694)|(0.798)|(0.734)|(0.859)|(1.076)|(0.842)|(0.702)|(0.682)|(0.748)|
|Obs. left|375|367|379|365|367|362|375|367|365|365|367|362|
|Obs. right|691|677|706|1,583|1,651|1,522|691|677|661|1,583|1,651|1,522|
|Mean values (Non-RAMV)|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|Bandwidth type|mserd|mserd|mserd|msetwo|msetwo|msetwo|cerrd|cerrd|cerrd|certwo|certwo|certwo|
|Kernel|triangular|uniform|epanechnikov|triangular|uniform|epanechnikov|triangular|uniform|epanechnikov|triangular|uniform|epanechnikov|

_Notes:_ All columns include department (Antioquia, Atl ́antico, Bogot ́a, and Norte de Santander) and sampling-city fixed effects. Individual controls include: age, gender, and years of education before migration. Labor history in Venezuela controls include: ever worked [=1], type of job, had a written contract [=1], and gap between last job and migration. Household controls in Venezuela include: number of children; household size; if had energy, water, and sewage [=1]; owner of dwelling [=1]; and had smartphone [=1]. Migration decisions controls include: had family/friends in Colombia before migrating [=1], knew of job opportunities before migrating [=1], if the head migrated for health reasons [=1], and time of settlement in Colombia. Standard errors are reported in parentheses and False Discovery Rate (FDR) q-values are reported in brackets.<sup>_∗∗∗_</sup> significant at the 1%,<sup>_∗∗_</sup> significant at the 5%,<sup>_∗_</sup> significant at the 10%.
There are often numerous tariff bands in the tariff schedules of MENA countries. Jordan, for example, has 33 different rates, ranging from 0 to 320 percent. In Egypt, rates generally range from 0 to 70 percent, although tariffs on motor vehicles may rise to 160 percent. Tariffs in Morocco and Tunisia are lower, in the former country ranging from 0-35 percent, and in the latter varying between 10-43 percent. Annex Table 1 compares average unweighted tariffs for a number of MENA countries with those of countries in other parts of the world that have pursued liberalization programs since the mid-1980s. Average rates for the four MENA countries reported are in the 25-30 percent range, and have declined less than in many comparator countries in other regions. While lower than the average rate in the reported South Asian countries, they are substantially higher than the average tariff that is now applied in many Latin American countries. Moreover, they have been declining more slowly. This is perhaps the most important conclusion to be drawn, as levels of nominal tariffs are of course difficult to compare across countries.

Table 1 reports data on collected tariff revenue as a share of imports. This is more revealing than the average statutory rate, as it better reflects the actual tax burden on trade by allowing for the existence of exemptions and possible biases in customs classification and valuation. The unweighted average burden in MENA countries is about 17 percent, abstracting from the countries in the Gulf region who pursue a zero or low tariff policy (Bahrain, Oman, UAE). This is more than 3 times higher than the average for the seven comparator countries listed.

The converse of the high tariff burden is that import duties account for a much higher share of government revenue in MENA countries than in countries that pursue a more outward-oriented economic strategy. The average share of revenue generated by trade taxes (excluding stamp duties) in the sample of MENA countries is 20 percent, that for the comparators only 6 percent. Alternative, less distorting sources of revenue must be developed in the context of further efforts to liberalize trade flows. The expected weakness of oil-related revenues in the medium term--given sluggish demand and the anticipated increase in supply following the recovery/re-entry of producers such as Iraq and Russia (World Bank, l995)--may perversely induce a greater reliance on trade taxes. This should be resisted, priority instead being given to increasing indirect taxes, broadening the tax base, 4
Table D.1 presents results on the origin nationality of foreigners living in Venezuela in 1990 and on the host countries of relatives living abroad as reported by Venezuelan households interviewed in the ENCOVI between 2017 and 2021. The overwhelming majority of foreigners living in Venezuela in 1990 had been living in Colombia five years earlier, drawn by demand for labor during Venezuela’s oil boom that began in the 1970s. Their subsequent return to Colombia, combined with the networks they left behind, provided informational and logistical support that lowered displacement costs for Venezuelans seeking to relocate after 2013. Consistent with this, Venezuelan households reported that most of their relatives abroad were also living in Colombia. Our measure of foreign settlements therefore captures the strength of network connections to Colombia across municipalities, connections that facilitated forced displacement after the crisis began. We validate this mechanism in Section 4 by examining if Venezuelans arriving in Colombia after 2013 were more likely to originate from municipalities in Venezuela with historically higher foreign settlement shares.

Tables D.2 and D.3 characterize foreigners living in Venezuela in 1990. In addition to predominantly Colombian nationality, these individuals were mostly employed, older, and more educated than Venezuelans (Table D.2). On average, they also had incomes three times greater than those of the Venezuelan population in 1990 (Table D.3).<sup>12</sup>

# **3.2 Remote-Sensing Data**

To create a proxy of Venezuela’s economic growth and inequality, we construct a municipalityyear-level longitudinal dataset of satellite night-light density for 1992–2021. This information is processed by the National Oceanic and Atmospheric Administration (NOAA) using images collected by the U.S. Defense Meteorological Satellite Program Operational Linescan System (1992–2013) and the Visible Infrared Imaging Radiometer Suite remote sensor (2012–

> 12Since the main specification follows a difference-in-difference design, any imbalance in the outcome levels between high and low shares of foreign settlements in municipalities does not threaten the empirical strategy because they are absorbed by the municipality fixed effect in the empirical specification. Nevertheless, we rule out the concern that these characteristics could trend differently by controlling for time trends in socioeconomic baseline characteristics.

17
# **The ABCs of Financial Education: Experimental Evidence on Attitudes, Behavior, and Cognitive Biases**

Fenella Carpena, Shawn Cole, Jeremy Shapiro, and Bilal Zia<sup>1</sup> _JEL Codes:_ C93, D14, G21, O12 1 University of California at Berkeley (fenella@econ.berkeley.edu), Harvard Business School (scole@hbs.edu), Princeton University (jeremyshapiro@gmail.com), and the World Bank (bzia@worldbank.org), respectively. We are very grateful to Saath Microfinance for their constant support. We thank Stuti Tripathi, Bhakti Shah, and the Center for Microfinance at the Institute for Financial Management and Research (IFMR) for excellent field work and research assistance. We also thank Anamaria Lusardi, David McKenzie, Xavier Gine, Arie Kapteyn, and seminar participants at the World Bank, Harvard, and the AEA meetings for comments, and the World Bank Development Impact blog and the World Bank All About Finance blog for coverage.
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32
3 small economies and therefore had a catastrophic effect on their output. Hence dismantling them was expected to induce economic recovery. Second, the economic stagnation generated during the period of Import Substitution Industrialization (ISI) was believed to be so significant, that even without assuming endogeneous technological change, the move to a free trade steady state was expected to take long enough that trade liberalization would lead to higher output growth for a decade or more. Since most empirical exercises in this literature typically considered 10 to 15 years after reform, trade liberalization should result in positive and significant estimates of its impact on growth, even if the technological change was exogeneous. Finally, increased trade is likely to bring higher growth to all countries under endogeneous growth models if we assume the existence of technological spillovers (Feenstra, 2004), and economists supportive of an outward orientation expected that the acceleration in exports, imports of intermediate inputs and foreign direct investment, prompted by trade policy openness, would lead to such spillovers.

Many have questioned the conclusions that trade reforms have led to better growth performance, and some have even gone ahead to argue that trade liberalization has been detrimental to the long-run growth of countries, especially in Africa. Some have suggested also that trade reforms have led to the “deindustrialization”. Thus the question of what happened to countries after their trade reforms continues to be an important issue in the policy discussions of many developing countries. A key issue about this debate is that, almost all developing countries have liberalized their trade regimes over the last few decades (IEG, 2006). Thus, the issue of what happened to these countries is of interest beyond the question of whether or not all the changes can be attributed solely to the trade reforms.

By correcting several methodological deficiencies in existing event studies of trade reforms, the descriptive analysis and fixed effects estimations undertaken in this paper confirm that trade liberalization indeed has been followed by improved economic performance and a significant increase in economic growth compared to a counterfactual that considers other growth determinants. Furthermore, this improvement applies to most groups of developing countries. Section 2 briefly
I TABLE 10: SIMULATION OF "RESIDUAL" LO8SE8 POST IPM ADOPTION

----------------------------------------------------------------

_

||DEG<br>LOW|REE OF LOS<br>|S<br>|
|---|---|---|---|
||<br>|MEDIUM|HIGH|
|EXTERNALITY RS. CRS|0.1|0.25|0.4|
|<br>||RS.CRS.<br> <br>|<br>|
|LOW<br>33.4|3.34|<br>8.35|<br>13.36|
|MEDIUM<br>66.8<br> <br>|6.68|<br>16.7|<br>26.72|
|HIGH<br>133.6|13.36|<br>33.4|<br>53.44|
|--------------------------------------------------------------||||
|CONTINUING LOSSES INCL|UDING CATA|STROPHIC|LOSSES (RS.CRS.)|
|LOW<br>33.4<br> <br>|6.54<br>|<br>11.55<br>|<br>16.56|
|MEDIUM<br>66.8<br> <br>|9.88<br>|<br>19.9|<br>29.92|
|HIGH<br>133.6|16.56|36.6|<br>56.64|
|IMPLIED INCREASE IN CO|STS OF COT|TON CULTI|VATION (%)|
|LOW|5.41|<br>9.55|<br>13.70|
|MEDIUM<br>|8.17<br>|<br>16.46<br>|<br>24.75|
|HIGH|13.70|<br>30.27|<br>46.84|
|--------------------------------------------------------------||||
|IMPLIED UPWARD SHIFT I|N THE SUPP|LY CURVE|(RS./QTL.)|
|LOW|25.49|45.02|64.55|
|MEDIUM|38.51|<br>77.57|116.63|
|HIGH|64.55|142.66|220.78|
|--------------------------------------------------------------||||
|IMPLIED NET SHIFT IN T|HE SUPPLY|CURVE (RS.|/QTL.)|
|LOW|-154.81|-135.28|-115.75|
|MEDIUM|-141.79|-102.73|-63.67|
|HIGH|-115.75|-37.64|40.48|

_________________________________________________________________-
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averaged over 1979-85.

P8: Changes in P8 are proxied by the _Degree of Urbanization_ (percentage of total department population living in urban areas) in 1985. The degree of urbanization is a continuous variable. Therefore Ps (0 s Ps5 100) is a continuous variable. I also experimented with a rural-urban dummy variable (P. - p,v, where p and u are the price of schooling a child in rural and in urban areas respectively). This is done by estimating male and female schooling regressions separately for rural and urban schooling levels across departments. However, income per capita, Y, and the potential work opportunities by sector, ti, are still department-level: department GDP figures are not available separately for each region. Thus it is assumed that ti do noc vary across regions within a department, but Ps does. This assumes t-hat, within a department, it is costless for _an_ adult to migrate from rural to urban urban regions (or vice versa), but the cost of sending a _child_ to school from rural to urban regions is very high.

Z : The vector of department-level infrastructural variables Z include _Roads per Square Kilometer,_ and _Post Offices per Square Kilometer._ I also experimented with _Telegraph Offices per Square Kilometer, Telephone Lines per Capita,_ and some other variables. Since household specific variables Z2 are only available in a household-level survey, this section only employs department-level infrastructural variables (subset Z ) in the schooling regressions.

37
Policy Research Working Paper 9329

# **Abstract**

The worsening of Ecuador’s socioeconomic conditions and the rapid inflow of Venezuelan migrants demand a rapid government response. Representative information on the migration and host communities is vital for evidence-based policy design. This study presents an innovative methodology based on the use of big data for sampling design of a representative survey of migrants and host communities’ populations. This approach tackles the difficulties posed by the lack of information on the total number of Venezuelan migrants—regular and irregular—and their geographical location in the country. The total estimated population represents about 3 percent of the total Ecuadoran population. Venezuelans settled across urban areas, mainly in Quito, Guayaquil, and Manta (Portoviejo). The strategy implemented may be useful in designing similar exercises in countries with limited information (that is, lack of a recent census or migratory registry) and scarce resources for rapidly gathering socioeconomic data on migrants and host communities for policy design.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at solivieri@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team