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

In a theoretical model, Amihud and Mendelson (1986) show that transaction costs cause a clientele effect in equity markets. Investors with longer holding periods choose to hold stocks with higher transaction costs in equilibrium. Amihud and Mendelson (1986) emphasize that this relationship between transaction costs and holding periods of investors represents a rational response by an efficient market. Counter to Amihud and Mendelson’s (1986) conjecture that investors understand and incorporate the impact of transaction costs, recent findings in the behavioral finance literature suggest that individual investors have bounded rationality and tend to ignore non-salient costs when making investment decisions. In this paper, we use the trading records of households in the US and in Finland, to investigate whether individual investors are cognizant of transaction costs when making trading decisions and hold illiquid securities longer as stipulated by Amihud and Mendelson (1986), or whether they ignore transaction costs consistent with the findings in the behavioral finance literature.

There is evidence that investors ignore non-salient costs as they relate to mutual fund fees. Barber, Odean and Zheng (2005) show that investors pay attention only to the salient costs of mutual funds, but ignore hidden operating costs. Consistent with these findings Gil-Bazo and Verdu (2008, 2009) document that there is a negative relation between mutual funds' before-fee performance and the fees they charge to investors. Surveys also suggest that retail investors do not understand all costs associated with investing in mutual funds (NASD Investor Literacy Survey 2003; Alexander, Jones, and Nigro, 1998).<sup>1</sup>

> 1 For example, only 21% of the retail investors that responded to NASD Investor Literacy Survey (2003) knew the meaning of a “no load” mutual fund.

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**Data Availability:** The data underlying this paper will be shared on reasonable request to the corresponding author, and is currently in the process of being uploaded to the SIEF date repository.

# **References:**

Adhvaryu, A., Molina, T., Nyshadham, A., & Tamayo, J. (2016). Helping children catch up: Early life shocks and the PROGRESA experiment. Retrieved from: <u>file:///C:/Users/ideas42/Downloads/HelpingChildrenCatchUpEarlyLifeSh_preview.pdf</u> Akwii, S., Kipchumba, E., Mohamed, A.S., Sulaiman, M. 2018. “Added on Benefits of Structured Peer to Peer Support on Unconditional Cash Transfer: Short-term Evidence from a Field Experiment in Baidoa, Somalia”, Save The Children.

<u>https://resourcecentre.savethechildren.net/node/14297/pdf/cash_plus_pss_final_report.pdf</u> <mark>Arriagada, A.M., Perry, J., Rawlings, L., Trias, J., and Zumaeta, M. (2018). Promoting early childhood development through combining cash transfers and parenting programs.</mark> _<mark>World Bank Working Paper Series</mark>_ <mark>,</mark> Washington, D.C.: World Bank Group.

Attanasio, O., Fernández, C., Fitzsimons, E., Grantham-McGregor, S., Meghir, C. & Rubio-Codina, M. (2014). Using the infrastructure of a conditional cash transfer program to deliver a scalable integrated early child development program in Colombia: Cluster randomized controlled trial. BMJ 349 (September): g5785.

<mark>Baird, S.,McIntosh, C., Özler, B. (2011). “Cash or Condition? Evidence from a Cash Transfer Experiment”.</mark> _<mark>The Quarterly Journal of Economics</mark>_ <mark>126(4): 1709–1753</mark> <mark>Baird, S., Ferreira, F.H.G., Özler, B. & Woolcock, M. (2014). Conditional, unconditional and everything in between: a systematic review of the effects of cash transfer programmes on schooling outcomes.</mark> _<mark>Journal of Development Effective</mark>_ <mark>ness, 6:1, 1-43, DOI: 10.1080/19439342.2014.890362</mark> Barrera-Osorio, F., Bertrand, M., Linden, L.L., & and Perez-Calle, F. (2011). Improving the design of conditional transfer programs: Evidence from a randomized education experiment in Colombia. _American Economic Journal: Applied Economics_ , 3(2):167-95.

Bastagli, F., Hagen-Zanker, J., Harman, L., Barca, V., Sturge, G., & Schmidt, T. (2016). Cash transfers: What does the evidence say? A rigorous review of programme impact and the role of design and implementation features. London: Overseas Development Institute.

Baumeister, R. F., Heatherton, T. F., & Tice, D. M. (1994). Losing control: How and why people fail at selfregulation. San Diego, CA, US: Academic Press.

Beegle, K.G.; Coudouel, A., & Monsalve, E. (2018). Realizing the Full Potential of Social Safety Nets in Africa (English). Washington, DC: World Bank Group.

Behrman, J. R., and Hoddinott, J. (2005). Program evaluation with unobserved heterogeneity and selective implementation: The Mexican Progresa impact on child nutrition. _Oxford Bulletin of Economics and Statistics_ 67(2): 547–569.

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13 The results for the introduction of IRAs are even more puzzling. For the period covered by our sample the presence or absence of an IRA did affect the access rate in the water sector. However, it was associated with a statistically significant _reduction_ in access rates in electricity and in telecoms (line 15).

Finally, whatever the impact of either reform, the coefficient on their interaction (line 3) suggests that together they exert beneficial effects in electricity and telecoms but do not interact in the water sector.

**Table 5 Econometric results for access, 1990–2002**

|||**(1)**|**(2)**|**(3)**|
|---|---|---|---|---|
|||**Ln energy use (kg of**<br>**oil equivalent per**<br>**capita)**|**Ln telephone**<br>**subscribers/1,0**<br>**00 people**|**Ln access to improved**<br>**water sources (percent**<br>**of population)**|
|**(1)**|IRA|0.016<br>[0.55]|-0.063<br>[0.76]|-0.425<br>[1.16]|
|**(2)**|Privatization|-0.066**<br>[2.16]|0.055<br>[0.79]|0.234**<br>[1.98]|
|**(3)**|IRA * privatization|0.031*|0.214***|-0.243|
|||[1.66]|[5.57]|[1.28]|
|**(4)**|Corruption index<br>(0=low 1=high)|-0.092***<br>[6.23]|0.548***<br>[9.80]|-0.476***<br>[3.72]|
|**(5)**|IRA * corruption|-0.111**|-0.290**|1.156|
|||[2.16]|[2.22]|[1.30]|
|**(6)**|Privatization *|0.095*|0.023|-0.416|
||Corruption|[1.75]|[0.23]|[1.01]|
|**(7)**|Ln GDP per capita<br>(2000 US$)|0.195***<br>[11.58]|1.884***<br>[33.24]|0.342***<br>[5.03]|
|**(8)**|Ln Urbanization|-0.123*<br>[1.92]|1.625***<br>[8.03]|-0.068<br>[0.75]|
|**(9)**|Time trend|0.013***<br>[10.25]|0.102***<br>[24.64]||
|**(10)**|Ln agricultural value<br>added (percent of GDP)|||0.191**<br>[2.50]|
|**(11)**|Constant|-19.610***<br>[7.93]|-222.553***<br>[28.79]|1.571**<br>[2.31]|
|**(12)**|Observations|929|1108|122|
|**(13)**|R–squared within|0.64|0.96|0.54|
|**(14)**|No. of countries in sample|80|92|72|
|||**_Marginal effe_**<br>|**_cts_**<br>||
|||-0035***|-0148***|0061|
|**(15)**|IRA|.<br>[3.77]|.<br>[5.30]|.<br>[0.58]|
|**(16)**|PRIV|-0.007<br>[0.58]|0.149***<br>[5.53]|-0.017<br>[0.29]|
|**(17)**|CORRUPTION|-0.107***<br>[6.58]|0.443***<br>[7.18]|-0.181<br>[0.74]|

Absolute value of t statistics in brackets. * significant at 10 percent, ** significant at 5 percent, *** significant at 1 percent. For water, we include the agricultural value added as percentage of GDP ( _Agri-VA_ ) to capture the strong demands on water supply for irrigation purposes, thus significantly improving the explanatory power of the regression. Ln stands for neperian logarithm
pure democracy, by which I mean a society consisting of a small number of citizens, who assemble and administer the government in person, can admit of no cure for the mischiefs of faction." Similarly, Albert Hirschman(1976) has argued that deliberation may be manipulated by an "articulate minority". There is, however, a lack of credible evidence testing whether deliberative processes can result in domination by a faction (Fishkin and Lushkin (p. 294)).

In this paper we examine the mechanism of deliberation in Indian village governments. Our data consisting of transcripts of open village meetings, gram sabhas, empowered by the Indian constitution to make important decisions for the village, linked with household-level preferences, enable us to examine the relationship between individual preferences and the preferences that emerge during deliberations. We ...nd that the preferences of the landed class are more likely to be mentioned in the meeting and are also taking up more time in the meetings. Equally important, the voices of disadvantaged castes, while not dominating the meeting, are also heard. The transcript data allow us to distinguish between o¢ cials’and villagers’talk, as well as between men’s and women’s talk. Using these partitions, we are able to more accurately pinpoint the source of these e¤ects. We ...nd that the land dominance e¤ect does not stem from the o¢ cials favoring the landed in their talk but rather from the landed being more vocal among villagers. In addition, we ...nd that the preferences of the disadvantaged castes are more likely to be mentioned in the o¢ cials’talk but not in the villagers’ talk. Within villagers’ talk we also notice that the preferences of Muslims are taking up less time, relative to the those of Hindus. This ...nding suggests that the Muslim minority, which does not bene...t from the a¢ rmative action measures o¤ered to disadvantaged castes, is marginalized in these meetings. Another notable ...nding is that within women’s talk the preferences of women take up more time. This ...nding is particularly important 5
firm, for a total number of observations in their study of 76,903.<sup>10</sup> This study is an important contribution to the literature, since there are no studies of the impact of services sector _liberalization_ on firm level productivity using firm level data across several countries. The closest study to this one is the study by Arnold, Mattoo and Narcisco (2008). Arnold et al. (2008) found that improved services sector _performance_ (as opposed to services liberalization in the Klishchuk and Zelenyuk study) increased total factor productivity in 1,000 firms in 10 SubSaharan African economies. The Klishchuk and Zelenyuk study is an advance of the literature for several reasons. First, Klishchuk and Zelenyuk examine the impact of services sector liberalization, rather than performance, on firm level productivity. Second, it uses panel data that allows the use of fixed effects in the empirical work; fixed effects allow the authors to capture firm heterogeneity that is not captured by other variables in the model, and this is important in the results of their paper. Third, the large sample size in this study justifies the asymptotic distribution theory in some of the estimators they employed. Finally, it is the first such study to be conducted on transition countries, and provides support for the applicability of the casual link between services liberalization and productivity on data from transition countries.

The authors use seven separate categories of services performance from the EBRD Transition Indicators as their measures of services sector performance. The EBRD performance measures they use are for the following sectors are: banking, non-bank financial services, telecommunications, railways, electric power, roads, water and waste water. They control for liberalization of the trade and foreign exchange regime, and several other variables of importance to firm level productivity. They estimate labor productivity as a function of these many variables in both a pooled ordinary least squares regression and in a model with fixed effects for each firm in the sample. Their statistical tests indicate that the fixed effects model is the preferred model.

> 10 Their data source is a randomly selected subset of the Orbis dataset supplied by Bureau van Dijk.

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over the last 10 years prior to the year the ES was conducted in the country. Last, we show that our results hold if we use the number of days of paid maternity leave instead of its log value.

# 3.5 _Variables used for heterogeneity analysis_

Our first heterogeneity test relates to who pays for maternity leave in the country. To this end, we first define a dummy variable equal to one if the maternity leave is 100 percent paid by the government and zero otherwise. We use average value of this dummy over the last five years prior to the year the ES was conducted ( _Govt. Funded_ ). Data source for the variable is WBL. The mean value of _Govt. Funded_ equals 0.65 and the standard deviation is 0.48.

The next heterogeneity test analyzes how the impact of maternity leave on female workers varies with the share of females in the country that are in the childbearing age group. We assume this group to include all women who are 20-29 years old. Thus, we compute the number of females that are in the 20-29 age group in the country expressed as a percentage of the country’s total population. We take average values of the variable over the last 5 years prior to the year the ES was conducted ( _Females: 20-29 yrs. old (%)_ ). Data source for the variable is WDI, World Bank.

The last heterogeneity we analyze is with respect to the skill level of women workers. The ES does not provide information on employees’ skill level or education. Thus, we use two proxy measures defined at the firm-level. The first is (log of) total real wage bill (in constant 2009 USD) divided by the number of employees at the firm ( _Wage rate_ ). The second measure is (log) labor productivity of the firms as defined above. The underlying assumption is that firms that pay lower wages and have lower labor productivity have proportionately more low-skilled workers, including women workers, than firms that pay higher wages and have higher labor productivity. In separate regressions, we interact each of these proxy measures with maternity leave. These interaction terms 22
is more than 10 percent in seven out of nine cases.

In contrast, the coresidency bias in the IGC estimates are again much smaller, in only one out of nine cases the bias is more than 10 percent in Bangladesh (10 _._ 9 percent when father’s age is the indicator of parental education and the age range for children is 1350 years). The average bias in the IGC estimate for Bangladesh is only 4.6 percent, less than one-third of that in the IGRC estimates (15.56 percent). The estimates for India are similar; in three out of nine cases the bias is more than 10 percent, the highest being 12 percent, and the average is 9 percent, significantly smaller than the corresponding average for IGRC (14.7 percent).<sup>23</sup> Table 5 reports estimates for the father-son and mother-daughter persistence in schooling attainment for different age ranges of children. As to be expected, the magnitudes of the estimates vary across different age ranges, but the main conclusions of the paper remain valid. The coresidency bias in the IGRC estimates is very high in the estimates for Bangladesh; the lowest bias is 15 percent and the highest 46 percent, with an average of 27 percent, a very high bias by any standard. The bias estimates for India are smaller in magnitude consistent with its higher coresidency rates. However, the average bias in IGRC is still more than 10 percent (10.6 percent). _More important for the research on intergenerational mobility in developing countries constrained by the coresident samples is the clear evidence that in all 12 cases reported in Table 5, the bias in the IGC estimate is much smaller than that in the corresponding IGRC estimate. The average bias in the IGC estimates is only 7.5 percent in Bangladesh (27 percent for IGRC), and 4.5 percent in India (10.6 percent for IGRC)._

# **5.2 Coresidency Rates and the Extent of Bias**

An interesting aspect of the results presented above is that there is significant variation

> 23Note that there are two negative estimates of bias out of a total of 36 estimates in Table 4, implying that the estimate from full sample is smaller than that from the coresident sample. It is, however, important to underscore that, in both of these cases, the numerical magnitudes of estimates from full and coresident samples are extremely close; the IGRC estimates are 0.83 (full sample) and 0.84 (coresident sample), and the IGC estimates are 0.53 (full sample) and 0.54 (coresident sample).

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**Table 4b: Pre-Reform Yearly Changes in Individual Level Variables**

||Early adopters<br>average|Late adopters<br>average|Difference in<br>averages|Coefficient on<br>quarter of<br>implementation|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Outcome Variables|||||
|Employed change|-0.0045|0.0148|-0.0193*|0.0046***|
||(0.0220)|(0.0331)|(0.0097)|(0.0017)|
|Low-risk employed change|0.0047|0.0201|-0.0154|0.0035|
||(0.0236)|(0.0452)|(0.0124)|(0.0022)|
|High-risk employed change|-0.0092|-0.0053|-0.0040|0.0011|
||(0.0130)|(0.0282)|(0.0075)|(0.0013)|
|Wage earner change|-0.0082|0.0044|-0.0126|0.0022|
||(0.0170)|(0.0279)|(0.0079)|(0.0016)|
|Low-risk wage earner change|-0.0006|0.0100|-0.0106|0.0016|
||(0.0229)|(0.0296)|(0.0091)|(0.0018)|
|High-risk wage earner change|-0.0076|-0.0056|-0.0020|0.0005|
||(0.0140)|(0.0289)|(0.0078)|(0.0014)|
|Registered business owner change|0.0016|0.0017|-0.0001|0.0005|
||(0.0145)|(0.0124)|(0.0046)|(0.0007)|
|Low-risk registered owner change|0.0025|0.0014|0.0012|0.0001|
||(0.0147)|(0.0105)|(0.0044)|(0.0008)|
|High-risk registered owner change|-0.0010|0.0003|-0.0013|0.0004|
||(0.0047)|(0.0035)|(0.0014)|(0.0003)|
|Log monthly income change|0.1044|0.1379|-0.0334|0.0057|
||(0.0455)|(0.1034)|(0.0274)|(0.0046)|
|Background Variables|||||
|Primary education change|0.0029|-0.0102|0.0131**|-0.0013|
||(0.0176)|(0.0177)|(0.0061)|(0.0010)|
|Secondary education change|0.0086|0.0032|0.0053|-0.0013|
||(0.0217)|(0.0166)|(0.0066)|(0.0012)|
|High school education change|-0.0037|0.0144|-0.0181***|0.0017|
||(0.0182)|(0.0190)|(0.0064)|(0.0011)|
|University education change|0.0018|0.0023|-0.0005|0.0001|
||(0.0183)|(0.0116)|(0.0053)|(0.0009)|
|Female change|0.0036|-0.0081|0.0117*|-0.0029**|
||(0.0157)|(0.0199)|(0.0062)|(0.0013)|
|Age change|0.3067|-0.0508|0.3575*|-0.0244|
||(0.4665)|(0.7032)|(0.2047)|(0.0453)|
|Married change|-0.0069|-0.0049|-0.0020|0.0001|
||(0.0240)|(0.0175)|(0.0072)|(0.0012)|

_Note:_ Standard errors in parentheses. Changes are changes in ENE municipality averages from 2000-IV to 2001-IV. Column 4 presents the coefficients of a separate regression for each variable on quarter of implementation. Significance levels: * 10%, ** 5%, *** 1% 38
predicted drop in prices and the increase in the number of users will generate an average relative welfare gain of 1.09 percent among current users and 0.28 percent among new users, for a total combined relative welfare gain of 1.37 percent. The combined effect on poverty is a reduction by 0.31 percentage point and a change in inequality of 0.23 Gini point.

Besides these potential direct welfare gains of expanding competition in the ICT market in Ethiopia, the potential marginal effects on adoption may have additional indirect welfare implications because mobile phones are transitioning from simple communication tools to stand-alone service delivery platforms and because digital technologies have the potential of significantly reducing the costs of economic activities (Goldfarb and Tucker 2019; Rennhoff and Routon 2016). Recent research on Africa, for example, has shown the importance of internet access in poverty reduction, increasing household consumption, and improving labor market outcomes (Bahia et al. 2020; Hjort and Poulsen 2019; Masaki, Granguillhome Ochoa, and Rodríguez-Castelán 2020; Masaki, Raja, and RodríguezCastelán 2020). Furthermore, if complementary reforms in other sectors are undertaken, such as the financial sector, mobile devices would have the potential to enable poor people to lower transaction costs, raise access to credit, and apply risk mitigation safeguards, leading to a reduction in vulnerability and poverty (Jack and Suri 2014; Wieser et al. 2019).

The results on Ethiopia are also a useful illustration for policy makers on the potential welfare gains of competition-enhancing policies. This case study shows that breaking up monopoly power in a key sector of the economy, such as mobile services, may have substantial direct positive effects on welfare, especially among lower-income households that are often priced out of sectors that are key to boosting household productive capacity. The analysis presented in this paper can thus be easily replicated in any country in the case of other industries affected by high market concentration.

As in most simulation methods, there are areas of improvement in this proposed approach. First, the simulation is based on partial equilibrium analysis. So, it focuses on the first-order effects of competition on consumer welfare through the price channel, potentially missing other impacts through alternative channels, such as labor markets, asset accumulation, or the diversity of alternatives available in purchasing. Second, by using household budget surveys, which typically do not include detailed information on the quality or brand of the goods and services consumed, the analysis may not capture the potential welfare effects brought about by changes in quality or the effects of alternative pricing schemes. This work may be the subject of future research to improve the simulation approach, which has the potential of becoming a powerful tool to motivate competition reforms around the world.

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# **1 Introduction**

Banking crises often uncover weaknesses in the design and implementation of bank regulation and supervision. The latest 2007-2009 global financial crisis (GFC) was no different, and it has sparked a heated discussion on the lessons to be learned and how to design efficient and safer banking systems. One clear outcome of the GFC has been a period of intense regulation, with several initiatives put in motion to address the flaws that were revealed during the crisis. To analyze the progress in regulatory reforms and the determinants of these changes, we rely on the fifth update of The World Bank - Bank Regulation and Supervision Survey (henceforth the 2019 BRSS or ‘round 5’), which we also introduce and describe in detail in this paper.

There is widespread agreement that the GFC was caused by excessive risk-taking by financial institutions and thin capital cushions to cover for unexpected financial losses (see, for instance, The World Bank (2012) and references therein). Excessive leverage was facilitated by a disproportionate reliance on wholesale funding. Excessive risk-taking was also fueled by lower lending standards, inaccurate credit ratings, and complex structured instruments. Moreover, the prospect of bank default did not deter speculative investments because of the expectation that failing institutions would be rescued in case of trouble (Caprio et al., 2010). By the same token, poorly designed and managed deposit insurance schemes enabled banks to take on excessive risk (Anginer and Demirgüç-Kunt, 2018). Limitations in supervisory enforcement measures and in market discipline (i.e., the capacity and incentives of market participants to monitor and influence bank health) exacerbated the issue by not providing the external checks needed to affect bank risk-taking behavior. To compound matters, misaligned incentives in the supervisory framework undermined the effectiveness of supervisory monitoring due to lack of enforcement of the existing rules (Barth et al., 2012). Market discipline may have also weakened because of distorted incentives of market players and lack of available information to objectively assess the risk and value of financial institutions and instruments.

1