rafmacalaba/datause-encoder-data huggingface dataset:
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42
# **1. INTRODUCTION**

Social interactions allow for information dissemination about technologies, products, job opportunities, and beliefs (Jackson, 2010; Jackson, 2011). In finance, substantial evidence shows that information sharing with peers plays an important role for investment decision making, including stock market participation and portfolio choice (e.g., Duflo & Saez, 2003; Hong, et al., 2004; Bursztyn, et al., 2014; Li, 2015; Hvide & Ostberg, 2015). In social settings, however, individuals are selective about the information they want to share with others. For example, people may tend to boast about good stock trades, favoring the transmission of appealing but inaccurate ideas about active trading (Shiller, 1995; Shiller, 2015), or they may try to convince others that they are wealthier or more intelligent than what they really are.<sup>3</sup> Despite extensive research on the effects of social interactions on investment choices, the role of selective communication in the diffusion of trading strategies remains largely unexplored.<sup>4</sup> In this paper, we use data from a large-scale financial training program to study the effects of noisy word-of-mouth communication on stock market participation. We show that biased signals from peers disproportionally attract uninformed individuals to equity trading. These investors overestimate the value of active trading and generate inferior returns once they enter the stock market.

Canonical models of social learning assume that peer choices are perfectly observed by members of the social network (Banerjee, 1992; Bikhchandani, et al., 1992). In practice, peer preferences and outcomes are not always observed directly, and such information is often transmitted via informal conversations. When negative outcomes (e.g., trades that generated low or negative returns) are filtered out to present a positive self-view to others, less information is available to those who are forming an opinion

> 3 This behavior has long been recognized and studied in sociology and psychology. See for example, Schlenker (1980), Leary & Kowalski (1990), and Gonzales & Hancock (2011). Although people often avoid lying given their preference for being seen as honest (Abeler, et al., 2019), they might selectively omit information that is unfavorable, or that may give the impression that they are not successful. Bénabou & Tirole (2002) present a general economic model in which agents protect their self-esteem by engaging in self-deception through selective memory awareness.

> 4 Two notable exceptions are Kaustia & Knupfer (2012) and Han, et al. (2020). Hirshleifer (2020) argues that _social transmission bias_ – “the systematic directional modification of ideas as they pass from person to person” – is a key yet underexplored concept in social economics and finance.

2
|Bovespa<br>Mais|Separate board|CVM|Not directly. Some benefits<br>and exemptions of fees are<br>cross-subsidized by the stock<br>exchange.|
|---|---|---|---|
|BIST ECM|Separate board|Capital Markets Board of<br>Turkey and Borsa Istanbul|Yes. ECM utilizes trading,<br>clearing, and settlement<br>infrastructure of main board.|
|WSE<br>NewConnect|Separate market<br>organized and<br>operated by the WSE<br>as an ATP|WSE|No|
|GreTai|Separate exchange|Financial Supervisory<br>Commission|No|

_Source:_ WBG compilation of exchange information. _Note:_ ATP = Alternative Trading Platform.

The legal structure of SME exchanges is often linked to factors outside the realm of market efficiency. Exchanges such as GreTai, Shenzhen Stock Exchange, and NASDAQ in the United States began life focused on SME listings or as alternative markets to other national blue chip segments. Such examples are mostly found in highly developed equity cultures that can support multiple listing venues. The legal structure of exchanges has significant strategic impact; for example, stand-alone exchanges are less likely to prioritize their largest listed companies “graduating” to a different marketplace or competitor.

# 2. Branding the Exchange

A key question in considering if and when to develop an SME exchange is which SMEs are appropriate for exchange funding and listing, and whether a particular subsector should be targeted. That is, should the exchange target:

- SMEs in general

- A certain type of company, and if so:

1. Only high-growth SMEs

2. Highly innovative companies such as technology, hardware, or product innovators 3. Other This decision is linked to how the exchange brands itself, that is, as an SME exchange, a growth market, or something else. The exchanges in the working group had mixed approaches.

A key starting point is that all SME exchanges should focus on growing companies. SMEs that are not growing will not need the capital an exchange can provide and may not be inclined to use the exchange for advertising either. They may resist sharing information with outsiders; the drive for funding will not be strong enough to overcome this reluctance. The focus should be on “gazelles” from the start. These should be companies that do not meet the requirements of the main market or board.

16
and province, and time fixed effects, shows that those in a strictly green job earn nearly 43 percent higher wages than those who are in jobs without any green tasks. When adding the full set of demographic, industry, and occupation controls, which are often correlated with wages, the green premium is still a positive and statistically significant 2.7 percent.<sup>35</sup> If we include the broad definition of a green job, the wage premium disappears. The coefficient o _n green_broad_ is negative. Workers in broadly green jobs earn 14.9 percent less than those in non-green jobs. The gap narrows to 9.9 percent when including the full set of demographic, occupation, and industry controls. This follows from the descriptive section where we showed that once we expand the definition of a green job to include broadly green occupations, mid-level and elementary occupations (1-digit ISCO codes 7, 8, and 9) are more heavily weighted in the broadly green sample relative to the non-green sample.

Table 6: Wage regression, using the strict and broad definition of green jobs

|Models|StrictlyG|reen Jobs|BroadlyG|reen Jobs|
|---|---|---|---|---|
|Variables|Baseline|Full|Baseline|Full|
|green_strict (green_broad)|0.432***|0.027***|-0.149***|-0.099***|
||(0.011)|(0.010)|(0.005)|(0.005)|
|Female||-0.263***<br>(0.004)||-0.281***<br>(0.004)|
|Age||0.032***||0.032***|
|||(0.001)||(0.001)|
|Age2||-0.000***||-0.000***|
|||(0.000)||(0.000)|
|base=less than matric|||||
|Matric||0.287***||0.283***|
|||(0.005)||(0.005)|
|Other tertiary||0.480***<br>(0.009)||0.479***<br>(0.009)|
|Degree||0.821***||0.811***|
|||(0.009)||(0.009)|
|base=black/African|||||
|Coloured||0.054***<br>(0.007)||0.054***<br>(0.007)|
|Indian||0.110***<br>(0.015)||0.108***<br>(0.015)|
|White||0.270***<br>(0.008)||0.271***<br>(0.008)|
|Urban|0.478***|0.200***|0.466***|0.198***|
||(0.005)|(0.005)|(0.005)|(0.005)|
|Constant|8.117***|7.234***|8.201***|7.287***|
||(0.012)|(0.038)|(0.012)|(0.038)|
|Observations|419,148|415,553|419,148|415,553|
|R-squared|0.070|0.345|0.067|0.346|
|Robust standard errors inpar|entheses ***|p<0.01,**p<|0.05,*p<0.1||

35 Doan et al. (2022) carry out a similar conditional estimate and do not find a wage premium for green jobs relative to non-green jobs.

23
products that are exported. The dom estic subsidy is granted to an industry on all of its production of a product, regardless of whether that products are exported. There is a big literature to show the impacts of subsidies on trade and welf are. Some subsidies can correct market failures and enhance econom ic welfare; other subsidies can distort resource allocation and reduce economic welfare.<sup>6</sup>

**Export Subsidy.** Export subsidies take on m any forms in the real world. These include tax rebates conditioned on export (excluding VAT rebate), subsidized loans to exporters or foreign purchasers, insurance guarantees, gu arantees against losses, and direct g rants or subsidized loans.<sup>7</sup> The econom ic effects of export subsidies are symmetrical with those of import tariffs. Just as tariffs cau se import-competing sector to expand, export subsidies lead to an expansion in the exportable production.

We now assume that there are two large countries trading a particular product in a partial equilibrium model. The expor ting country A is assum ed large enough to influence the world price in this particular product. Initially, consumption and production in country A 0 0 0 0 are qd , qs , and consumption and production in country B are q* d , q*s , respectively. After the exporting co untry introduces a per-unit export subsidy, it will lead to an increase in the supply of export, which redu ces the world price of the good and the price in the importing country from Pw0 to Pw1, and raise the price of the good in the exporting 1 0 country Pd relative to the free trade price Pw , as more products are exported rather than sold domestically for consumption. Consumers in exporting countries lose. See Figure 1.

Figure 1. Partial equilibrium model of an export subsidy <!-- Start of picture text --> Country A (exporter) World Country B (importer)<br>ES 0 S*<br>Pd1 S σ<br>Pw0 ES 1<br>Pw1<br>D*<br>ED 1 ED0<br>D<br>qd1 qd0 qs0 qs1 X 0 X 1 q * s1 q*s0 q * d0 q * d1<br><!-- End of picture text --> Source: Kathy Baylis, 2005.

> 6 Alan O. Sykes. 1989. “Countervailing Duty Law: An Economic Perspective.” _Columbia Law Review_ , V. 89 No. 2.

> 7 Husted, Seven, and Michael Melvin. 2004. _International Economics_ , sixth edition. Pearson AddisonWesley.  VAT rebate is considered trade-neutral, see Grossman 1980.

9
A Duration Analysis of CONALEP

# Summary

Mexico’s National Technical Professional School (CONALEP) is the largest technical education system in the country. CONALEP serves low-income students at the upper-secondary school level in Mexico. Using graduate tracer surveys from CONALEP, this paper analyzes the impact of modular courses and reform programs implemented by CONALEP in 1991-1992 on CONALEP graduates’ labor market outcomes. Results indicate that graduates from the prereform program had to search longer for a job compared with those of the postreform program. Graduates from the post-reform program have 45 percent higher probability of finding a job than those from the pre-reformed program. However, the pre-reform program cohorts earned higher hourly wages than those from the post-reform program.

2
Union over 3.4%, Japan – 4.6%, the United States – 3.4% and Canada – 3.2%. Countries in Sub-Saharan Africa (SSA) and the Middle East and North Africa (MENA) are the least affected, and under the global and amplified global pandemic scenarios, the estimated loss of GDP is estimated to be around 3%.

Under the amplified global pandemic scenario, global exports decline 4.6 %. Several countries that experience larger than global average losses of exports are in the EAP region such as Hong Kong SAR, China (9.8%), Cambodia (7.4%), Singapore (8.5%), Lao PDR (7.3%), Thailand (6.8%), but also the Russian Federation and the Philippines see losses up of 6.4%, while Canada, Europe, and the United States see declines of around 4.5%. With the amplified global spread of the virus, all countries see their total exports decline, but the least integrated regions through trade and tourism, such as MENA, SSA, and Latin America and the Caribbean, are the least affected. Some EAP countries tend to be relatively less affected in this scenario than others, but all countries’ exports decline the most under the amplified global pandemic scenario, e.g., Vietnam, Japan, and Korea.

Our estimates are broadly in line with previous studies. Annex 2 reviews several analyses by OECD, Brookings and S&P quantifying the potential impacts of the COVID-19 outbreak. The studies use a variety of tools, with OECD relying on a macroeconomic model and Brookings applying a hybrid CGE/DSGE model with rational expectations. Most estimates on the impacts on China range from 0.5% to 2% of GDP. World GDP is expected to decline between 0.1% and 1.5 %, while global trade is expected to decline between 0.2% and 3.75 %. The biggest impacts are reported in the extreme scenarios by McKibbin and Fernando (2020), with Chinese GDP declining by up to 6%, with GDP declines in the United States and Japan reaching, respectively, 8% and 10%.

- 8 -
capture the difference between the number of mortality during the Great Recession and that before the Great Recession. Hence the dependent variable measure is ������,����������� ������,����������. Table 3.2 indicates that on average, mortality increased about 1.38% between the two periods.

Table <u>3.2: Summary Statistics:</u>

||N|Mean|SD|10<sup>th</sup><br>percentile|90<sup>th</sup><br>percentile|
|---|---|---|---|---|---|
|Tradable employment/Employment, 2007|3079|0.1456|0.1065|0.0314|0.2880|
|log(��,����)-log(��,����)<br><br>|3135|0.0727|0.1124|-0.4629|0.1924|
|log(��,����<br>��������)- log(��,����<br>��������)|3048|0.1903|0.4073|-0.1328|0.6092|
|Bartik|3128|0.0271|0.0267|0.0035|0.0602|
|Mortality Rate, 2005-07|3132|0.0100|0.0026|0.007|0.0132|
|Changein log of Mortality|3130|0.0138|0.0868|-0.083|0.107|
|Housing supply elasticity|868|2.5090|1.3488|1.0591|4.0038|
|Fraction white|3135|0.8700|0.1502|0.6583|0.9883|
|Median Household Income|3135|35,597|9,147|26,312|46,608|
|Median Home Value|3135|82,862|45,629|45,378|127,121|
|Home Owner|3135|0.7406|0.0754|0.6432|0.8182|
|Education - Less than High school|3135|0.2257|0.0871|0.1258|0.3497|
|Education - High school diploma|3135|0.3471|0.0657|0.2640|0.4290|
|Unemployment Rate|3135|0.0582|0.0273|0.0300|0.0907|
|Poverty rate|3135|0.1415|0.0645|0.0726|0.2261|
|FractionUrban|3135|0.3932|0.3088|0.0000|0.8461|

Finally, we use Mian and Sufi (2014)’s data in several of our regressions. Most notably, we use several variables as pre-crisis controls: household leverage, fraction of white population, median household income, fraction of homes occupied by owners, fraction of population with less than high school diploma, fraction of population with only high school diplomas, unemployment rate, poverty rate, and fraction of urban population.

11
5 interest rate prevaileci. The invariable outcome is the allocation of some of the available credit f)r suboptimal projects while a larger number of high-return projects are denied credit.

There is another relationship between trade liberalization and the financial, markets that should be noted. One of the purposes of trade liberalization is to raise the return on investments in the tradable sector. However, the sectors or firms to which the government channels credit are not necessarily associated with trade strategy priorities. Moreover, the cost of borrowing in a financially repressed economy can differ from sector to sector and may do so in a way that reduces the efficiency of resource allocation. Consequently, if the financial market is repressed, the anticipated reallocation of resources that should follow trade liberalization may not take place or may take place in a less effective way.

There are two ways in which a controlled financial reg.me can thwart the employment and income distribution effects of trade liP eralization. Credit rationing at artificially low interest rates, together with real exchange rate overvaluation and the preferential treatment accorded to capital goods imports in a controlled trade regime, encourages capitalintensive activities at the expense of labor-intensive activities (Krueger 1983). Furthermore, credit rationing may squeeze out small borrowers who cannot pay the high costs of obtaining information, who lack adequate collateral, or who are not likely to achieve economies scale. Small borrowers may also be denied credit because of the natural inclination of commercial private banks to favor only the largest and safest borrowers. (Gonzalez-Vega, 1984 and 1985). Reducing or eliminating credit rationing can therefore be expected to improve employment prospects and income distribution, since labor-intensive activity wiil benefit and a larger number of borrowers will have access to formal cr

# 2.3 Some Qualifications

The hypotheses above focus only on the operations of che organized (formal) money market (OMM). In most developing countries, however, there is also an active but unorganized money market (UMM) often referred to as the "curb market" or "informal market." The volume of the lending transacted on this market may be at least as great as, and perhaps many times greater than, that of the OMM.4 Because of this, it has been argued that "financial liberalization is an exceedinglv chancy proposition" in terms of whether it will produce financ:al deepening (Buffie. 1984:318, see also van Wijnbergen, 1982 and 1983: Buf'ie. 1984: Taylor. 1983: andl Edwards, 1988). Claims in the informal market may be an important alternative to holding transaction balances '-ashi and time deposits with commercial banks. These claims are me-t l; in ehp form of dire-t borrnwinp by households and thus can be termed "1Q0 percent intermediation.' By contrast. reserve requirements constituta a leakage in the financial intermediation done through fommercial banks. ecB(-ause of offi-ial roservP 4. Ghate (1988) provides a comprehensive suiv-., f the literature on thiphenomenon.
This version: 15 Feb 2024 55 ________________. 2020a. "Ethnic inequality and poverty in Malaysia since May 1969. Part 1: Inequality," _World Development_ , 134(C).

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Ravallion, Martin, and Shaohua Chen. 2003. ““Measuring Pro-Poor Growth,” _Economics Letters_ , 78(1): 93-99.

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