rafmacalaba/datause-encoder-data huggingface dataset:
68

- Singh, Ajit; Hamid, Javed; Salimi, Bahram and Nakano, Yoichi. "Corporate Financial Structures in Developing Countries," Washington, D.C.: International Finance Corporation Technical Paper 1, 1992.

- Slovin, Myron B.; Sushka, Marie E and Polonchek, John A. "The Value of<sup>Bank Durability:</sup> Borrowers as Bank Stakeholders," Joural of Finance, March 1993, 48(1), pp. 247-66.

- Smith, Adam. An Inquiry into the Nature and Causes of the Wealth of Nations.<sup>London: W.</sup> Stahan & T. Cadell, 1776.

- Stein, Jeremy C. "Takeover Threats and Managerial Myopia," Journal of<sup>Political Economy,</sup> February 1988, 96, pp. 61-80.

- Stem, Nicholas. "The Economics of Development: A Survey," Economics Journal. September 1989, 29(397), pp. 597-685.

- Stiglitz, Joseph E. "Credit Markets and the Control of Capital," Journal of Money, Credit and Banking, May 1985, 12(2), pp. 133-52.

- Stiglitz, Joseph and Weiss, Andrew. "Incentive Effects of Terminations: Applications<sup>to Credit</sup> and Labor Markets," American Economic Review, December 1983, 2D(5),<sup>pp. 912-27.</sup>

- Tilly, Richard. "Germany, 1815-1870," in Banking in the Early Stages of Industrialization: A Study in Comparative Economic History. eds. Rondo Cameron et al.<sup>New York: Oxford</sup> University Press, 1967, pp. 151-82.

- Tobin, James. "Money and Economic Growth," Econometrica. Oct. 1965, f(4), pp. 671-84.

- Townsend, Robert M. "Optimal Contracts and Competitive Markets with Costly State Verification," Journal of Economic Ther, Oct. 1979, 21(2), pp. 265-93.

- Tybout, James R. "Credit Rationing and Investment Behavior in a Developing Country," Review of Economic Statistics, November 1983, 65(4), pp. 598-607.

- Von Thadden, Ernst-Ludwig. "Long-Term Contracts, Short-Term Investment and Monitoring," Review of Economic Studies, 1995, X, pp. 557-75.

- Wachtel, Paul and Rousseau, Peter. "Financial Intermediation and Economic Growth: A Historical Comparison of the U.S., U.K., and Canada." in Anglo-American Finance: Financial Markets and Institutions in 20th-Century North America **and**<sup>the United</sup> Kingdom, eds. Michael D, Bordo and Richard Sylla, Homewood IL: Business<sup>One Irwin,</sup> 1995.
24 Table 8: Commission Structure

|Cumulative<br>Investment<br>Commission<br>Year<br>(% of salary)|Cumulative<br>Returns|Cumulative<br>Actual<br>Cumulative<br>Commissions<br>Commission/<br>Commission/<br>(% of assets<br>Normal<br>Returns<br>managed)<br>Commission|
|---|---|---|
|(a)|(b)|(a/b)<br>(c)<br>(a/c)|
|_5_<br>22.5|18.5|1.21<br>4.6<br>4.88|
|10<br>66.2|89.0|0.74<br>25.6<br>2.58|
|15<br>146.9|256.0|0.57<br>85.1<br>1.73|
|20<br>290.4|600.2|0.48<br>226.9<br>1.28|
|25<br>539.8|1250.6|0.43<br>536.2<br>1.01|
|30<br>965.7|2428.5|0.40<br>1173.0<br>0.82|
|35<br>1684.3|4500.1|0.37<br>2432.0<br>0.69|
|40<br>2885.3|8070.1|0.36<br>4859.6<br>0.59|
|Note: Based on an assumption that<br>14% of salary into a funded accoun<br>points go as commissions to pensio<br>Table 8 reveals many t<br>e pension systems. First<br>s in initial years by way<br>s exceeds one for severa<br>This is consistent with<br>ilean and Peruvian affili<br>Years until Asset-B<br>60<br>50|wages grow at 6% p.<br>t, of which I percen<br>n funds, and 10% of<br>endencies inh<br>, it shows that<br>of commissio<br>l years, the aff<br>the low or ne<br>ates for sever<br>Ch<br>ased Commissio<br>r<br>:<br>:;<br>|a., pension fund portfolios grow at 10% p.a., affiliates pay<br>tage point is paid towards insurance, another 3 percentage<br>salary is credited in the individual pension fund account.<br>erent in typical commission structure of<br>the pension funds take the lion's share of<br>ns. Second, as the ratio of commissions to<br>iliates make negative returns for several<br>gative net intemal rates of return realized b<br>al years.<br>art 1<br>ns Excede Salary-Based Commissions<br>:-<br>~~~~SelectedLevels of<br>Salary Based Commissions|
|40|||
|30 11]_ @0|||
|10<br>0|||
|1.00%|1.50%<br>2.0<br>Level of Asset B|0%<br>2.50%<br>3.00%<br>3.50%<br>ased Commisions|

Note: Based on an assumption that wages grow at 6% p.a., pension fund portfolios grow at 10% p.a., affiliates pay 14% of salary into a funded account, of which I percentage point is paid towards insurance, another 3 percentage points go as commissions to pension funds, and 10% of salary is credited in the individual pension fund account.

Table 8 reveals many tendencies inherent in typical commission structure of private pension systems. First, it shows that the pension funds take the lion's share of the returns in initial years by way of commissions. Second, as the ratio of commissions to returns exceeds one for several years, the affiliates make negative returns for several years. This is consistent with the low or negative net intemal rates of return realized by the Chilean and Peruvian affiliates for several years.

Second, the salary based commissions exceed asset based commissions for 25 years. The extent of "prepayment" is naturally related to the level of asset based charges we assume and would be lower if asset-based fees were higher. Chart 1 compares commissions of 2-4% of salaries against asset-based fees ranging from 1-3.5% p.a.. It shows that within these ranges, affiliates would have "prepaid" for future services from
2012), female labor force participation (Azam, 2012) and household consumption (Ravi and Engler, 2015). We complement this literature and examine the effects of the program on schooling outcomes. Our paper does not refute these clear benefits of the program but rather shows that there is an opportunity cost.

There are two studies that are closely related to our paper. Afridi et al (2012) find that MNREGA increases mothers income and through this channel improves childrens educational outcomes. Unlike Afridi et al (2012), we do not find an improvement in schooling outcomes. There are a number of differences between our paper and their study. First, Afridi et al (2012) focus on one state, whereas we use data from the entire country from 2005 to 2008. Hence, our design allows us to understand nation wide effects of the program. Second, they use data from 2007 to 2009. By 2007, MGNREGA was already implemented in the poorest parts of the country, and was being implemented in the rest of the districts. Hence, their study only makes post introduction comparisons and uses the intensity of exposure for identification. In contrast, the strength of this study design is that it uses the roll-out timing for identification and compare outcomes pre- and post-implementation. We also examine a very rich set of schooling outcomes, whereas they focus on time spent in school. Importantly, we show that the program has an unintended effect on child labor increasing likelihood of being employed. In a subsequent study, Shah and Steinberg (2015) use test score data and evaluate the impact of MGNREGA on human capital. Their paper reinforces our findings. However, there are important dissimilarities. Their study uses the Annual Status of Education Report (ASER) data on test scores<sup>5</sup> and finds that test scores worsen for children in early roll-out districts. They focus on gender based age specific differences in labor outcomes. Our study, on the other hand, uses school level data and highlights the heterogeneity in outcomes by school type, quality of private schools and age. There are two major differences that underscore the contributions of our paper. First, a novel result

> 5This data is collated by an NGO Pratham and is publicly available.

4
second one is basically to leave abnormal returns unaggregated; that is, one has to deal with abnormal returns security by security (for details, see MacKinlay, 1997, 27).

In the next section, we present results obtained from using the single-index model (constant mean return model).<sup>15</sup>

# **4. Dataset and Results**

The initial dataset comprises 96 environmental news (details appear in Appendix 1). These events were examined to identify whether or not other events (positive or negative) were observed during the identified window. These confounding events could impact the results from the event-study methodology. As a result, 9 events were eliminated from the dataset.<sup>16</sup> Thus, the dataset used for the analysis of stock market reaction comprises 87 environmental news involving 57 publicly traded firms over the period 1993 to 2000. All events are of a negative nature. Of the 87 events, 57 (65.5%) pertains to the violation of emissions standards, while 17 (19.5%) pertain to the failure to operate pollution abatement equipment.

We apply the event-study methodology to each of these 87 events. The study uses an estimation period of 210 days before the event window and an event window of 7 days (3 days prior the event, the day of the announcement, and 3 days after the event).

> 15  The single-index model is a particular case of the market model described above. Where market returns were available, we also obtained results using the market model. Results were similar to those presented here. In fact, Henderson (1990) points out that the three estimating methodologies yield results of similar nature.

> 16 These are (by code number): 210 (3/4/99), 2270 (12/1/99 and 11/3/99), 2580 (8/31/96), 4800 (8/27/99), 5300 (7/1/94), 9830 (12/30/96), 11780 (1/30/97), and 15760 (8/1/96).

17
|**Table 4 – Regres**|**sion result**<br>_(1)_|**s**<br>_(2)_|_(3)_|_(4)_|_(5)_|_(6)_|
|---|---|---|---|---|---|---|
|y|gr<br>-0.172<br>(6.64)***|gr<br>-0.161<br>(5.78)***|Gr<br>-0.171<br>(5.23)***|gr<br>-0.171<br>(6.02)***|gr<br>-0.186<br>(7.46)***|gr<br>-0.175<br>(7.64)***|
|gini<br>gini2|-1.191<br>(0.71)<br>1.808|0.126<br>(1.13)|-2.048<br>(1.17)<br>3.094|0.096<br>(0.84)|||
||(0.74)||(1.22)||||
|gi_hyredu|<br>0.238|||0.089|0.099|0.349|
||(1.33)|||(2.46)**|(2.90)***|(3.24)***|
|gi_hyredu2|-0.115<br>(0.91)|||||-0.192<br>(2.53)**|
|n_hyredu|0.009|0.001|0.003|0.008|0.011|0.010|
|n_spyredu|(1.71)*<br>0.018|(0.21)<br>0.016|(0.61)<br>0.015|(1.51)<br>0.017|(2.54)**<br>0.015|(2.38)**<br>0.018|
||(4.20)***|(3.91)***|(3.31)***|(4.01)***|(3.94)***|(4.56)***|
|hage|<br>-0.001|<br>-0.000|<br>-0.001|<br>-0.000|<br>-0.001|<br>-0.001|
||(1.03)|(0.52)|(0.88)|(0.64)|(1.71)*|(1.87)*|
|hfem|0.049|0.057|0.055|0.046|0.047|0.053|
||(2.77)***|(3.45)***|(2.73)***|(2.90)***|(2.97)***|(3.37)***|
|aesize|-0.006<br>(3.16)***|-0.005<br>(2.60)***|-0.006<br>(2.42)**|-0.006<br>(3.03)***|-0.007<br>(3.35)***|-0.007<br>(3.49)***|
|pchild|-0.046|-0.042|-0.035|-0.044|-0.056|-0.063|
|<br>Constant|(1.98)**<br>1.795|(1.91)*<br>1.550|(1.48)<br>2.034|(2.02)**<br>1.593|(2.39)**<br>1.785|(2.70)***<br>1.604|
||(3.96)***|(5.08)***|(3.96)***|(5.20)***|(7.13)***|(7.03)***|
|Observations|719|719|719|719|719|719|
|R-squared|0.92|0.90|0.91|0.91|0.91|0.92|
|<br>p(Hausman Chi-sq)|0.07|0.00|0.01|0.00|0.15|0.44|
|p(Sargan Chi-sq)<br>Notes: Coefficient est<br>* significant at 10%;|0.10<br>imates on dis<br>** significant|0.06<br>trict dummies<br>at 5%; *** si|0.16<br>omitted. Abs<br>gnificant at 1%|0.05<br>olute value of<br>|0.04<br>t statistics in|0.20<br>parentheses.|

is after variance correction related to the imputation of the growth and expenditure variables). The last specification (column 6) includes education inequality squared: the coefficient has a negative sign and is significant at the five percent level. We conclude that education inequality should be entered in both linear and quadratic form. Although growth thus appears to be an inverted U-shaped function of education inequality, ceteris paribus, we should not overstate the non-monotonicity: the maximum of the parabola is found where gi_hyredu equals 0.91. In our data, more than 95 percent of sub-counties has lower education inequality. In other words, only for extremely high values of education inequality is the effect on growth negative.

17
polygamy ( _m_ ), and unobserved type ( _μ_ ).<sup>10</sup> The utility function also incorporates a nonpecuniary cost (or benet) associated with being pregnant. This cost includes a stochastic preference shock as well as a deterministic age-dependant element ( _λ_ 4 _,t_ ). The cost of pregnancy changes if a woman was pregnant in the previous period ( _λ_ 5 ). The cost of consecutive births is allowed to change with the age of women ( _λ_ 6 ).

The specication of the model implies perfect control over conception and contraception. I could have instead introduced a cost of contraceptives, a probability of conception conditional on not wanting to become pregnant and a probability of not conceiving conditional on trying. Using data on births only, however, I cannot separately identify these elements. Instead, these elements will be absorbed into the cost of pregnancy (both deterministic and stochastic) and the preference for children. For example, having the cost of pregnancy change with age captures physiological factors which vary the propensity to conceive during dierent stages of a woman's life-cycle. The added cost of consecutive births enables the model to generate patterns of birth spacing.

# 2.3 Income and Consumption

It is assumed that households cannot borrow or save, which implies household consumption ( _C_ ) equals the household's income ( _Y_ ). The household's income is exogenous and stochastic. I specify a parsimonious household income function that is appropriate for the context of subsistence agriculture. I assume that the logarithm of income is distributed as Income depends on region of residence (Balaka, Mchinji), size of household's land plot (Land High), the age of the woman ( _t_ ), the number of children ( _N_ ) , and a time-varying income

> 10The majority of respondents in each of the three sites of the MDICP dataset are of dierent tribal groups. The tribes dier in their practices of lineage and residence after marriage which might aect preferences for children.

9
All of the countries found to have a comprehensive framework for data governance have included cybersecurity requirements in these regulations (Table 14). These include administrative and technical controls to protect against accidental loss or destruction or unauthorized access of the data. <mark>While most countries only require the adoption of an internal policy or the performance of internal controls, some countries have additional requirements. France and Korea feature the most inclusive requirements, calling for data encryption under certain circumstances to prevent unauthorized access to the data; appointment of a data manager; procedures to assess the threats and risks to personal information; and notification of the data subject and/or the authorities in case of a data breach.</mark> <mark>Also, most countries that regulate data privacy governance have a supervisory authority that monitors data processing activities. Although Kyrgyzstan’s Law on Personal Data makes reference to the “authorized body”, such body is not currently identified. About half of the countries that have a supervisory body require certain administrative procedures to lawfully process personal data. Data processors in Colombia and Kyrgyzstan must register with the supervisory authority. In Moldova, Senegal, and Tunisia, controllers must notify the authority of their intent to process personal data. In Armenia, notification requirements only apply to sensitive data or by request of the authorized body.</mark>

# MENA

The framework for cybersecurity in the region follows the same pattern of limited development as data governance (Table 15). Even for those countries that have adopted broader privacy regulations, provisions on cybersecurity tend to be vague, requiring data processors to implement measures to protect the data from unauthorized access or damage, without any clear specifications on the nature of the measures. Israel and Qatar are the only countries featuring a comprehensive framework on cybersecurity, imposing obligations on data processors to adopt internal data protection policies, conduct risks assessments,, notify data subjects and/or authorities of any data breaches, maintain sensitive data protected encrypted (Israel), and appoint a Data Manager responsible for data security in the firm (Qatar) (Table 14).

|**Table 15: Cybe**|**rsecurity measur**<br>**Encryption**|**es in MENA**<br>**Internal**<br>**policy**|**Data**<br>**manager**|**Risk**<br>**assessment**|**Breach**<br>**notifications**|**Supervisory**<br>**authority**|
|---|---|---|---|---|---|---|
|**Algeria**||**●**||||**●**|
|**Bahrain**||**●**|**●**|||**●**|
|**Iran, Islamic**|||||||
|**Rep.**|||||||
|**Israel**|**●**|**●**||**●**|**●**|**●**|
|**Kuwait**||**●**|||||
|**Lebanon**||**●**|||||
|**Morocco**||**●**||||**●**|
|**Oman**|||||||
|**Qatar**||**●**|**●**|**●**|**●**|**●**|
|**Saudi Arabia**|||||||
|**Tunisia**||**●**||||**●**|
|**United Arab**|||||||
|**Emirates**|||||||

Of the MENA countries found to have general data governance framework, the Islamic Republic of Iran stands out as the only one with no cybersecurity requirements and no supervisory 25
# **Table A24: Second stage: Effects of refugee presence on household welfare disaggregated by initial occupation, with analysis at cluster level and the distance weighted -** **<u>refugee index at 100km from the clusters (Panel 2009 2012)</u>**

|||||L|og of welfar|e (consumption|aggregate per ad|ult equivalen|t)||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Log of refugees (100km) predicted by Log of<br>Distance IV (mean border distance)|0.12465*<br>(0.06995)|0.12617*<br>(0.07035)|0.14639**<br>(0.07320)|0.13621**<br>(0.06243)||Panel A (Subs|istence farming)||||||
|Log of refugees (100km) predicted by Log of<br>Distance IV (square of mean border distance)|||||0.16012**<br>(0.07739)|<br>0.16217**<br> <br>(0.07783)|0.17679**<br>(0.08000)|0.16928**<br>(0.06971)|||||
|Log of refugees (100km) predicted by Log of<br>Distance IV (square-root of mean border<br>distance)|||||||||0.10722<br>(0.06740)|0.10754<br>(0.06763)|0.12783*<br>(0.07094)|0.11700*<br>*<br>(0.05947)|
|Observations|2,850|2,849|2,532|2,532|2,850|2,849|2,532|2,532|2,850|2,849|2,532|2,532|
|Kleibergen-Paap rk Wald F|15.31|15.31|14.12|13.31|17.91|17.91|16.31|15.32|13.79|13.79|12.60|11.92|
|Root MSE|0.534|0.531|0.515|0.367|0.535|0.533|0.517|0.369|0.533|0.531|0.514|0.366|
|Log of refugees (100km) predicted by Log of<br>Distance IV (mean border distance)|0.94310<br>(1.06472)|0.74418<br>(0.90921)|0.55849<br>(0.93491)|0.39234<br>(0.82826)||Panel B (Com|mercial farming)||||||
|Log of refugees (100km) predicted by Log of<br>Distance IV (square of mean border distance)|||||0.94215|0.76856|0.56876|0.41883|||||
||||||(1.04851)|<br>(0.90004)|(0.93709)|(0.84295)|||||
|Log of refugees (100km) predicted by Log of<br>Distance IV (square-root of mean border<br>distance)|||||||||0.78204<br>(0.90783)|0.61503<br>(0.79301)|0.42804<br>(0.81904)|0.26943<br>(0.74543)|
|Observations|148|148|133|133|148|148|133|133|148|148|133|133|
|Kleibergen-Paap rk Wald F|12.41|12.45|10.38|10.11|11.83|11.78|9.910|9.771|13.02|12.80|10.15|9.607|
|Root MSE|0.432|0.406|0.373|0.345|0.432|0.407|0.373|0.346|0.426|0.401|0.369|0.342|
|||||||Panel C (Wag|e employment)||||||
|Log of refugees (100km) predicted by Log of<br>Distance IV (mean border distance)|0.05862<br>(0.18544)|0.05894<br>(0.18623)|-0.03598<br>(0.20406)|0.04346<br>(0.18940)|||||||||

39
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Table 7: Effects of LEAP on economic networks

||(1)<br>Exchange<br>hands|(2)<br>Buyand sell|(3)<br>Employee|(4)<br>Coworker|(5)<br>Average<br>effect|
|---|---|---|---|---|---|
|_Full sample_||||||
|LEAP|0.24*|-0.11|0.02|0.05|0.09|
||(0.12)|(0.12)|(0.13)|(0.13)|(0.19)|
|Constant|0.48**|0.75**|0.41**|0.48**||
||(0.09)|(0.08)|(0.10)|(0.09)||
|N|526|526|526|526||
|_ID Poor 1 and_<br>_2_||||||
|LEAP|0.23*|-0.09|0.03|0.07|0.12|
||(0.12)|(0.12)|(0.13)|(0.12)|(0.19)|
|Constant|0.48**|0.72**|0.40**|0.49**||
||(0.09)|(0.09)|(0.10)|(0.09)||
|N|351|351|351|351||
|_Non-poor_||||||
|LEAP|0.24*|-0.15|-0.00|0.01|0.03|
||(0.13)|(0.12)|(0.14)|(0.14)|(0.21)|
|Constant|0.47**|0.79**|0.41**|0.46**||
||(0.09)|(0.08)|(0.10)|(0.10)||
|N|175|175|175|175||

Note: Columns 1-4 report OLS estimates with village-clustered standard errors. Column 5 reports the standardized average effect, as described in Kling et. al. (2004) and Clingingsmith et. al. (2009), across the measures in columns 1-4

* = significant at the five percent level, **=significant at the one-percent level

37
Table 5, a large non-linear effect is found: the jointly significant coefficients of the timeto-adoption variable suggest that households on the extremes – those with the earliest predicted adoption and those with the latest (e.g. likely those who would never adopt<sup>30</sup> ) – are the ones relatively better off, supporting the idea that the biggest gains from snowpeas were to be achieved only through early adoption (or, by foregoing adoption altogether).<sup>31</sup>

**Table 5:** Impact of adoption on per capita consumption

|_232 observations_|_Years befo_<br>_Adoptio_|_re_<br>_n_|_Years of_<br>_Cultivation_|
|---|---|---|---|
|Time to adopt|-1275|**|-8.83|
|Time to adopt, squared|128||0.054|
|Time to withdraw|||-45.3|
|Time to withdraw, squared|||140|
|Household size|87.5||-6.77|
|Head's education|-81.4||4.14|
|Head's education, squared|26.1||138|
|Head's age|-19.4||-2.291|
|Period household formed|-38.7||-14.71|
|Period household formed,<br>squared|1.06||-1.193|
|Initial land|106||-104.8|
|Initial land, squared|-7.58||2670|
|Land quality|-218|**||
|Constant|7910|**||

** indicates significance at 95% or higher.

To contrast this finding to the results that would be obtained without using the predicted timing of adoption, in Figure 4 we map consumption either as a function of predicted time (estimated from the coefficients in the first column of Table 5) or actual time of adoption (estimated from a similar model as in Table 5). The comparison suggests that the actual time of adoption understates the impact of very early adoption but then overstates the benefits for much of the remaining time-to-adoption spell. That is, it appears that continued adoption, after the narrow early window of opportunity in

> 30 It must be noted that households with the longest pre-adoption spell include the censored observations in the duration model, i.e. the ones who have not yet adopted at the time of the survey. 31 The estimated U-shaped curve turns at about 5 years..

33
**Table 3:  Daily Water Flow and Sediment Load in Treatment Watersheds**

||**Total**|**Enkulal**|**G/Mechaw**|**Guale**|**Tikur wu**|**Toma**|
|---|---|---|---|---|---|---|
|**Flow in m**<sup>**3**</sup>**/s**<br>|||||||
|2010|0.339|0.101|0.530|0.404|0.305|0.353|
|2011|0.213|0.089|0.380|0.097|0.071|0.425|
|2012|0.160|0.098|0.141|0.167|0.052|0.341|
|2013|0.201|0.082|0.234|0.163|0.064|0.460|
|2014|0.277|0.135|0.311|0.026|0.147|0.765|
|2015|0.253|0.104|0.201|0.110|0.240|0.749|
|**_Mean_**|**_0.241_**|**_0.102_**|**_0.300_**|**_0.161_**|**_0.147_**|**_0.516_**|
|**Daily sediment load in t/d**|||||||
|2010|2.809|1.731|3.460||2.942|3.104|
|2011|1.308|0.408|1.858||0.757|2.210|
|2012|0.882|0.677|0.728||0.476|1.646|
|2013|0.973|0.619|0.930||0.294|2.049|
|2014|1.398|0.632|1.079||0.390|3.490|
|2015|1.380|0.563|0.852||1.368|3.416|
|**_Mean_**|**_1.458_**|**_0.772_**|**_1.485_**||**_1.038_**|**_2.653_**|

_Source:_ Project data.

18