rafmacalaba/datause-encoder-data huggingface dataset:
same fund in other similar stocks with no affiliation. We find that PFAs are momentum traders on non-affiliated companies, purchasing (selling) stocks after positive (negative) returns.<sup>3</sup> On the contrary, we find that PFAs are not momentum traders on affiliated stocks. Instead, PFAs purchases (sells) on affiliated firms are followed by positive (negative) abnormal returns in the next month. The results are robust to the inclusion of firm and fund fixed effects, which account for time-invariant firm-specific characteristics that might affect returns, as well as fund managers’ constraints. The results are also robust to using a variety of alternative specifications. For example, since large shareholders are expected to have access to private information (e.g. Parrino et al., 2003), in addition to controlling for pension fund holdings in each firm, we restrict our sample to trading in related firms for which the fund manager is not among one of the largest shareholders (i.e. the PFA is not among the largest five shareholders in the company) and find similar results. Overall, the evidence suggests that group affiliation seems to be a source of private information for pension fund managers.

In an environment with information asymmetries, outside investors without adequate protection may be reluctant to invest, limiting trading activity.<sup>4</sup> In the second part of the paper, we investigate the effects of PFAs ownership concentration on secondary market liquidity. There are, however, at least two challenges to determine if in fact liquidity is affected by information asymmetries resulting from the ownership structure of the firm. First, if PFAs trade less than other investors, lower trading activity will increase transaction costs by spreading the liquidity provision costs over fewer trades. In such case, secondary market liquidity might be driven by the amount of trading of institutional investors and not by information frictions. Second, secondary market liquidity is potentially endogenous to the firm’s optimal choice of ownership structure, which might result form balancing the trade-offs between ownership control and the potential intervention from outsiders (Bolton et al., 1998).

> 3This is consistent with Raddatz and Schmukler (2012) who also document momentum trading for Chilean PFAs.

> 4According to the World Bank (2015), between 2001 to 2011, institutional ownership concentration in emerging markets was on average 60% larger than in developed economies, while turnover was 50% smaller.

3
Policy Research Working Paper 7265

# **Abstract**

In 2011 the Government of El Salvador implemented a reform to the gas subsidy that increased the welfare of households in all but the top two deciles of the income distribution. However, the reform turned out to be rather unpopular, especially among winners. This paper relies on ad hoc household surveys conducted before the implementation and in the following two and a half years to test which factors help explain the puzzle. The analysis uses probit and logit models to show that misinformation (a negativity bias by which people with limited information inferred negative consequences), mistrust of the government?s ability to implement the policy, and political priors explain most of the (un)satisfaction before implementation. Perceptions improved gradually—and significantly so—over time when the subsidy reception induced households to update their initial priors, although political biases remained significant throughout the entire period. The results suggest several implications with respect to policy reforms in cases where agents have limited information.

This paper is a product of the Central America Country Management Unit. 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://econ.worldbank.org. The authors may be contacted at ocalvogonzalez@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
socio-emotional and mental health development, food security, time use, engagement in risky behaviors, and social integration. These data allow us to examine the short to medium-term human development gaps faced by forcibly displaced minors and hosts in a comprehensive way.<sup>5</sup> It also enables the characterization of the variables that help reduce gaps between migrants and their hosts. Previous work, has mostly focused on the employment and earnings gaps between adult migrants and hosts (Borjas 1985, Abramitzky, Boustan and Eriksson 2020, Abramitzky et al. 2021) and the persistence of the economic status from first to second generations of immigrants in developed settings (Abramitzky, Boustan and Eriksson 2014). Focusing on children allows to diagnose gaps in human development that can have long-term consequences for immigrants’ economic success, including career choices and wages (Bleakley and Chin 2004, Bleakley and Chin 2010, Carlana, La Ferrara and Pinotti 2022). It can also inform the design of targeted interventions aimed at improving the life trajectories of both migrant and host populations, ultimately contributing to more informed and effective policy decisions.

Our research complements and extends the findings of Demirci, Foster and Kirdar (2022), who investigated health and nutrition disparities between native children and Syrian refugee children aged 0 to 5 years in T ̈urkiye, using data from the Demographic and Health Survey. The authors document find no significant differences in infant or child mortality rates between refugee children born in T ̈urkiye and their native counterparts, it did reveal that refugee infants have lower birth weights and age-adjusted weights and heights compared to native infants. Our work broadens the scope of analysis beyond anthropometric indicators to encompass a holistic assessment of child development. By incorporating measures of physical, cognitive, socio-emotional, and mental health development, along with factors such as food security, time use, risky behaviors, and social integration, we offer a more comprehensive understanding of the developmental challenges faced by displaced minors. Additionally, our study includes a wider age range, 5Chiovelli et al. (2021) examine the effects of forced displacement on separated sibling in the long-term.

8
# **<u>WEB APPENDICES</u>**

**_Demography, Urbanization and Development: Rural Push, Urban Pull... and Urban Push?_**

## **by Remi Jedwab, Luc Christiaensen and Marina Gindelsky**

— `WEB THEORY APPENDIX` — Urban Natural Increase and the Speed of Urbanization A.2 — `WEB DATA APPENDIX` — Data Description, Variable Construction and Sample Selection A.4 — `WEB APPENDIX FIGURES` — Figure 1: Urban Crude Birth Rates and Urban Total Fertility Rates (2000) A.9 Figure 2: Urban Crude Death Rates and Urban Child Mortality Rates (2000) A.9 Figure 3: Urban Crude Rates of Birth, Death and Natural Increase (2000) A.10 Figure 4: Family Planning Effort Index and Log Per Capita GDP (1970) A.10

|—`WEB APPENDIX TABLES`—||
|---|---|
|Table 1: Natural Increase Source Information by Country|A.11|
|Table 2: Decomposition of Urban Growth for Industrial Europe (1800-1910)|A.13|
|Table 3: Decomposition of Urban Growth for the Developing World (1960-2010)|A.14|
|Table 4: Urban Birth and Fertility Rates and Women of Reproductive Age (2000)|A.15|
|Table 5: Alternative Identifcation Strategies (1960-2010)|A.15|
|Table 6: Urban Birth Rates, Religiosity and Family Planning Effort (1960s)|A.16|
|Table 7: Family Planning, Religion and Urban Growth, Cross-Section (1960s)|A.16|
|Table 8: Auto-Regressive Distributed Lag Model (1960-2010)|A.17|
|Table 9: Birth Rates and Growth of the Largest City, Cross-Section (1960-2010)|A.17|
|Table 10: Urban Natural Increase and Urban Primacy (1960-2010)|A.18|
|Table 11: Urban Natural Increase and Urbanization, Robustness (1960-2010)|A.18|
|Table 12: Urban Natural Increase and Slums, Cross-Section (1960-2010)|A.18|
|Table 13: Urban Natural Increase, Rural Growth & Urban Congestion (1960-2010)|A.19|
|Table 14: Urban Natural Increase, Rural Growth & Dependency (1960-2010)|A.19|
|Table 15: Rural Natural Increase, Urban Growth & Rural Congestion (1960-2010)|A.20|
|Table 16: Rural Natural Increase, Urban Growth & Dependency (1960-2010)|A.20|

A. 1
_** Net labor income gains encompass both the losses incurred from job displacements and the earnings increases resulting from higher wages for those who retained their employment. Since it is assumed that all dismissed workers move to unemployment or inactivity, this represents a lower bound of the net gains (or an upper bound of the earning losses). If workers move to formal or informal employment and have a positive labor income, net labor gains could be higher._ _*** This column presents information on the share of full-time employees who become unemployed or inactive after the minimum wage increase. 95% confidence intervals are provided in parentheses._ _Source: Own simulations based on administrative tax data._

**<mark>Overall, the simulation shows a relatively small reduction in labor income inequality due to the minimum wage increase,</mark> as it** **<mark>narrows the wage gap between low-wage and higher-wage workers, promoting a more equitable income distribution.</mark>** <mark>Table 6 presents the impact of a minimum wage (MW) increase on various income inequality indicators. The baseline values represent the conditions before the MW increase, and subsequent columns show simulations after the increase. The impact of labor inequality depends on the final labor market status of the dismissed workers, which can affect their new earnings (or lack of earnings).  When dismissed workers move to unemployment or inactivity (Table 6, column 3), we observe moderate declines in labor income inequality driven by a compression in the income distribution. The Gini coefficient, a measure of income inequality, decreases from 0.3560 to 0.3498. The Theil Index and Mean log deviation, which also gauge income inequality, follow a similar trend, indicating a reduction after the MW increase. The income shares for different population percentiles demonstrate minor shifts, with a slight increase in the bottom 50% income share and marginal decreases in the middle 40% and top 10% income shares. On the other hand,  dismissed workers can also move to other employment opportunities in the formal or informal sector, becoming new wage earners. If their new wages are, on average, higher than the original, the Gini index can decline even more. When new wages are, on average, smaller, the Gini reduction can be reduced or muted. The extreme scenario occurs when the new wages are negligible or near zero, leading to a higher dispersion of the income distribution (Table 6, column 2). In this case, the reduction in labor income inequality is minimal. This represents the lower bound of inequality reduction.</mark>

**Table 6. Simulated Distributional Effect of Minimum Wage Increase in Scenario 1 (Inflation-Linked Adjustment)**

(13.1% of Full-Time Employees were Affected by the Raise)

|Indicator|Baseline (Before MW<br>increase)<br>(1)|Simulation<br>(After the MW increase,<br>sample of wage earners<br>andjob losers*(2)|<br>Simulation<br>After the MW increase,<br>sample of wage earners<br>only**(3)|
|---|---|---|---|
|Gini coefficient|0.3560|0.3557|0.3498|
|Theil Index|0.2504|0.2502|0.2443|
|Mean logdeviation|0.2037|0.2034|0.1965|

37
# Table 11: Action plan gendered task division

|<br>Couples' Training|Planter/<br>spouse in<br>opposite sex<br>dominant<br>tasks<br>0.215*|Planter/<br>spouse in<br>same sex<br>dominant<br>tasks<br>0.087|Planter in<br>female-<br>dominant<br>tasks<br>0.045|Planter in<br>male-<br>dominant<br>tasks<br>0.015|Spouse in<br>male-<br>dominant<br>tasks<br>0.170|Spouse in<br>female-<br>dominant<br>tasks<br>0.072*|Planter/<br>spouse in<br>opposite sex<br>dominant<br>tasks<br>0.303***|Planter/<br>spouse in<br>same sex<br>dominant<br>tasks<br>0.149|Planter in<br>female<br>dominant-<br>tasks<br>With sole r<br>0.044|Planter in<br>male<br>dominant-<br>tasks<br>esponsibility<br>0.075|Spouse in<br>male<br>dominant-<br>tasks<br> <br>0.258**|Spouse in<br>female<br>dominant-<br>tasks<br>0.075***|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(0.127)|(0.153)|(0.056)|(0.143)|(0.116)|(0.041)|(0.115)|(0.126)|(0.037)|(0.122)|(0.111)|(0.027)|
|Observations|927|927|927|927|927|927|927|927|927|927|927|927|
|Mean for<br>individualtraining|0.345|0.535|0.116|0.486|0.229|0.049|0.151|0.339|0.069|0.327|0.082|0.011|

Robust and clustered standard errors in parentheses

* p<0.1 ** p<0.05 *** p<0.01

Note: All tasks variables are action plan tasks. Individuals who did not take part in the training and did not create action plans are recoded as zeros. Baseline control variables in all regressions: Age of household head, Household size, Number of adults, Number of children, Dependency ratio, Married planter, Polygamous relationship, Matrilineal ethnicity, Planter has IGA, Asset index, Credit constrained, Share of plots owned, Average distance to plots (minutes), Number of plots, Average plot size.

2
<!-- Start of picture text --> Chart 6. Changes in G-3 Import Demand<br><!-- End of picture text --> <!-- Start of picture text --> Percent<br>32° ~ Fore<br>20/<br>10<br>-20<br>72 74 76 78 80 82 84 86 88 90 92 94 96<br>| - USA -------- Japan Germany<br>Source: Intemational Economics Department, World Bank, August 1995.<br><!-- End of picture text -->

Desynchronized business cycles were reflected in a more stable pattern of overall OECD import demand (charts 6 and 7), which grew on average by 1.8 percent a year in 1991-93, compared to declines of more than 11 percent in 1975 and 2.7 percent in 1981-82. Although the contribution of the OECD high-income countries to world import growth fell to about 30 percent in 1991-93 from over 80 percent in 1986-90 (see Chart 8), at least it was not sharply negative, as in the previous two recessions. Europe provided a floor for trade in 1990-199 1, when US and Japanese import growth was falling; then the United States supported world import demand from 1992 onward, and Japan started to contribute vigorously to imports in late 1993. In the last case, import growth has rebounded even before the recovery of the economy as a result of yen appreciation and an accelerated process of market deregulation. The desynchronization of business and import cycles in the OECD is likely to continue in the near term, although in a much more attenuated way, with growth expected to gradually slow in the United States in 1995 and 1996 while it moves into higher gear in Europe (Chart 6). Japan's import demand is projected to expand at a robust rate in spite of its sluggish recovery (Chart 6), due to continuous market opening triggered by appreciation of the yen and heightened consumer awareness.' Nevertheless, with import demand in all three OECD areas expanding together in 1994-96, the OECD high income region's contribution to world import growth is expected to rise to over 60 percent, and this will be an important factor in the projected surge in overall world trade growth to 8.7 percent a year in 1994-96.

> 7 See Appendix B, "Prospects for Japan's Trade: Market Opening?"

9
_Recognition and Measurement_ . The effect is that “insurance entities” will have to follow these broader standards except for the accounting for the “insurance contracts”. Where an insurer issues insurance policies that do not meet the definition then they would be treated as investment contracts and accounted for under a deposit type approach.

**Figure 7: A Definition of an Insurance Contract**

An insurance contract is “a contract under which one party (the insurer) accepts significant risk by agreeing with another party (the policyholder) to compensate the policyholder of other beneficiary if a specified uncertain future event (the insured event) adversely affects the policyholder of other beneficiary.” <u>Source: IASB Project Summary.</u> The tentative position of the IASB on matters relating to recognition and measurement of assets and liabilities, improvements to existing practices, and disclosure will lead to change in many jurisdictions to a greater or lesser extent as the IFRSs are adopted.

For example,

- While current practices for recognition and measurement can continue under phase I, it will not be possible for an insurer to adopt a new accounting practice that perpetuates practices that are unlikely to survive into phase II. To this end, moving from a discounted to an undiscounted approach to liability valuation, creating or increasing margins in valuations of insurance liabilities that represent an effort toward “deliberate overstatement”, implicitly valuing future asset based fees at an amount above their “fair value”, or permitting subsidiaries to use non-uniform accounting methods should not be adopted as a new practice.

- Catastrophe provisions of a more general nature (not related to the risk of a particular closed book of business) and equalization provisions should not be permitted.

- Offsetting reinsurance assets against insurance liabilities should be prohibited – that is, the use of net provisioning should not be adopted.

- Loss-making contracts, when evaluated on the basis of current estimates of future cash flows, should have that loss recognized immediately.

- Liability valuations should recognize the liability under a contract at all times, until that liability has expired.

20
Number of settlements 293 293 District Fixed Effects Yes Yes Time Fixed Effects Yes Yes Standard errors in parenthesis, *** p<0.01, ** p<0.05, * p<0.1 Note: Regressions control for population in settlement in 2017, the share of settlement income derived from agricultural activities in 2020, and a constant.

# C.2. Quadratic impact of displacements flows on NTL growth

Table 6. _Regression of NTL on net inflows of persons in settlement (cumulated over 2018-2020)_

|Y = NTL level|||
|---|---|---|
|VARIABLES|(1)<br>Linear|(2)<br>Quadratic|
|β1;Lagged NTL|0.744***|1.162***|
||(0.00805)|(0.0220)|
|β4;(Lagged NTL)<sup>2</sup>||-0.337***|
|||(0.0140)|
|β2;Net Inflow|-5.40e-07***|-2.10e-06***|
||(9.38e-08)|(4.65e-07)|
|β3;(Lagged NTL)*Net Inflow|1.88e-06***|6.36e-06***|
||(1.97e-07)|(1.62e-06)|
|β5;(Lagged NTL)<sup>2</sup>* Net Inflow||4.44e-06***|
|||(1.05e-06)|
|Observations|8,497|8,497|
|Number of settlements|293|293|
|District Fixed Effect|Yes|Yes|
|Time Fixed Effects|Yes|Yes|

Standard errors in parenthesis, *** p<0.01, ** p<0.05, * p<0.1. Note: The settlements are spread across seven districts. Regressions control for population in settlement in 2017, the share of settlement income derived from agricultural activities in 2020, and a constant.

The quadratic regression displays a concave relationship between displacement flows and NTL growth, which suggests diminishing marginal returns of displacement flows on NTL. When comparing settlements of equal population size in 2017, settlements that show the largest impact of displacement flows on NTL expansion are those that received the lowest cumulated levels of displacement inflows. There exist several situations whereby a marginal increase in the _net_
Table 1a: Establishment size distribution for manufacturing

||India 1989|India 1994|India 2000|India 2005|US 1997|
|---|---|---|---|---|---|
|A. Establishment count distribution|1989|1994|2000|2005||
|Unorganized tiny (1-9 employees)|0.959|0.914|0.937|0.935|0.512|
|Unorganized micro (10-19 employees)|0.034|0.066|0.048|0.047|0.156|
|Unorganized small (20+ employees)|0.000|0.012|0.010|0.012||
|Organized tiny (1-9 employees)|0.001|0.001|0.001|0.001||
|Organized micro (10-19 employees)|0.002|0.002|0.002|0.002||
|Organized small (20-39 employees)|0.002|0.002|0.001|0.001|0.124|
|Organized medium (40-99 employees)|0.001|0.002|0.001|0.001|0.111|
|Organized large (100-499 employees)|0.001|0.001|0.001|0.001|0.084|
|Organized mega (500+ employees)|0.000|0.000|0.000|0.000|0.013|
|Total unorganized share|0.994|0.992|0.994|0.994|0.668|
|Total organized share|0.006|0.008|0.006|0.006|0.332|
|B. Employment weighted distribution||||||
|Unorganized tiny (1-9 employees)|0.731|0.593|0.649|0.618|0.040|
|Unorganized micro (10-19 employees)|0.076|0.142|0.126|0.128|0.046|
|Unorganized small (20+ employees)|0.003|0.054|0.053|0.061||
|Organized tiny (1-9 employees)|0.002|0.003|0.003|0.003||
|Organized micro (10-19 employees)|0.010|0.011|0.011|0.011||
|Organized small (20-39 employees)|0.019|0.018|0.014|0.015|0.074|
|Organized medium (40-99 employees)|0.029|0.037|0.025|0.029|0.148|
|Organized large (100-499 employees)|0.049|0.061|0.054|0.071|0.366|
|Organized mega (500+ employees)|0.081|0.081|0.064|0.064|0.326|
|Total unorganized share|0.810|0.789|0.828|0.807|0.086|
|Total organized share|0.190|0.211|0.172|0.193|0.914|

Notes: Indian descriptive statistics taken from Annual Survey of Industries and National Sample Statistics. U.S. descriptive statistics taken from 1997 Census of Manufacturers. Unorganized/organized shares for United States use establishment sizes to mimic Indian definitions.