rafmacalaba/datause-encoder-data huggingface dataset:
some 40 percent of female entrepreneurs report that their (male) spouses are responsible for all of the business decisions, and, on average, women involved in businesses spend less time in managerial decisions than their male counterparts. This suggests low decision-making power among women business owners. Indeed, business creation in households of women offered business training and assigned to be winners of the lottery increases, but only when the female client is not directly involved in the new business. Instead, their spouses or other household members use the proceeds of the loan to start the business.<sup>4</sup> One could argue that households could capitalize on the training that a woman receives by letting her take on more business decisions. However, husbands may be reluctant to share control of the business if this leads to a longer-term shift in the wife’s decision-making power.<sup>5</sup> Business training also increased the probability of borrowing, as did being assigned to be a lottery winner. Being assigned a lottery winner also increased average loan size, perhaps unsurprisingly, but it had little effect on household welfare, suggesting perhaps that the limit on the current loan size already meets the demands of most borrowers. Put differently, we find little evidence of a technology-based poverty trap, at least among the sample of microfinance clients that we study.

Also consistent with the model, we find that business training improved financial decision-making and labor allocation. In particular, among men offered business training, those with low entrepreneurial ability (proxied by a high ex-ante probability of loan default or low growth in expenditures between baseline and follow-up), were less likely to borrow during the lottery and devoted less time to the business. This suggests that attending business training helped individuals realize how successful they really were as entrepreneurs, validating the assumption made by many business training programs around the world that use training as a screening device to later provide additional services, such as credit or mentoring.

> 4 A recent report by the World Bank (2012) conducted extensive focus groups with clients of several microfinance institutions and reports that women in Pakistan are not the final users of loans but rather the conduits of their husbands.

> 5 Udry (1996) finds evidence of this rejection of efficiency in the intra-household allocation of resources.

4
# TABLE 6.11

<!-- Start of picture text --> OI P & f d_ CObDOUw proUin.<br>FuntiOns - Dependt V - AL o g f 4ns Prilce Vahie Added<br>(Values in parenthes are standard eorn)<br>Based on Value-Added Based on Value-Added<br>in Domestic PriCeS in World Prices<br>1 2 3 4 5 6<br>InterCept -1269 8.231 0227 -1.213 8595+ 40.083<br>(% Senegalese Private) (3.480) (4.462) (1287) (3.488) (4.468) (1-292)<br>Ownershin:<br>% Senegalese State -0.876+ Om82 4.851' 0.706<br>(0453) (0575) (0A54) (0.576)<br>9% French 0.569 '.261 0.565 -131S<br>(0552) (0822) (0553) (0.23)<br>% SYrO-Lebane5C 0.204 -1.089 0.160 -1.128'<br>(0.349) (0.486) (0.349) (0.487)<br>% Other Foreign -5298 .0.341 -5587 -0383<br>(2.180) (4517) (2.185) (4524)<br>Trade Variables:<br>Effective Rate of 0.318 -0504 .0-23S -x282 -1.129 4.881<br>Protection (0335) (0560) (0.103) (0336) (0561) (0.103)<br><!-- End of picture text -->
# TABLE 7: Composition of the Constrained International Portfolio [Emerging Markets 10%, GSCI = 10%]

|Rate of<br>Retum<br>(Re)|USA|UK|JAP|GER|SWZ|FRA|HKG|TAI|KOR|MEX|TiI|BRZ|VWD|GSCI|**Sum**of<br>Weights|Portfolio<br>S*andard<br>Deviation|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|1.00|.061||.500||.279||||||||.060|.100|1|4.23|
|1.25|.378||.266||.156||||||||.100|.100|1|3.52|
|1.31*|.439*||.218*|.014*|.119*||.010*||||||.100*|.100|1|3.50*|
|1.50|.452||.171|.030|.035||.113||||||.100|.100|I|3.58|
|1.75|.423||.080|||.074|.223|.011|||||.089|.100|1|3.89|
|2.00|.359|||||.120|.320|.015||.022|||.063|.100|1|4.39|
|2.25|.216|||||.144|.440|.003||.066|||.031|.100|1|5.05|
|2.50|.062|||||.165|.573|||.100||||.100|I|5.84|
|L2.75||I|||||..<br>799|||.100||||.100|1|6.84|

NOTE: Weights may not sum exactly to I because of rounding up.

*Starred values represent return, weights and standard deviation of the minimum-risk portfolio.

Data sources: See Tables I and 2.
**Figure 2: Impacts of oil price increase on biofuel penetration at the global level (%)**

<!-- Start of picture text --> Baseline 25% 50% 100%<br>14%<br>12%<br>10%<br>8%<br>6%<br>4%<br>2%<br>0%<br>2010 2015 2020<br><!-- End of picture text --> Table 3 delivers further insights by presenting the penetration of biofuels in different countries and regions under alternative scenarios of oil price increase. As expected, higher oil price would further increase penetration of biofuels in Brazil, where biofuels currently accounts for around 10% of the total liquid fuel consumption, in terms of energy unit, in the transport sector (excluding jet fuels and heavy fuel oils). The share of biofuels in Brazil would reach around 28%% in 2020 if oil price doubles from its baseline level. Other countries that would experience significant penetration of biofuels if oil price doubles from its baseline level include the United States, Malaysia, India and Russia, where biofuel penetration would exceed 15%.

15
tion 4 _′ Yidt_ = _φ × HighExposured × PowerCrisist_ + **Xidt**<sup>_α_+</sup><sup>_γWd× t_+</sup><sup>_θd_+</sup><sup>_δt_+</sup><sup>_λY OB_+</sup><sup>_εijct_(4)</sup> where _Yidt_ is the employment status of individual _i_ , in district _d_ , surveyed in year _t_ . _HighExposure_ is a measure of a district’s dependence on electricity before the crisis. It is defined as an indicator variable equal to 1 if electricity access rate in the district at the baseline<sup>41</sup> is higher than the median access rate in the country and 0 if otherwise. _PowerCrisis_ is an indicator variable equal to 1 for the period between 2013 and 2016 when the power crisis was at its peak. **Xidt** is a vector of individual controls. I also control for trends in observable determinants of local economic development by including district level characteristics (during the baseline) interacted with linear time trend. District and year fixed effects are represented by _θd_ and _δt_ respectively. I also include birth-year fixed effects ( _λY OB_ ) to account for age-cohort effects. Standard errors are clustered at district level.

The coefficient of the interaction between _HighExposure_ and _PowerCrisis_ represented by, _φ_ , measures the difference in employment outcomes between high and low access (electricity dependent) districts in the crisis and non-crisis periods. The intuition behind the identification strategy is that economic activities in districts with high electricity access are plausibly highly reliant (dependent) on electricity for economic activities relative to low access districts. As a result, an exogenous shock to electricity supply is likely to affect high access districts relative to low access districts. The validity of this research design relies on the assumption that absent the electricity crisis, trends in the outcome variable (employment) between low and high access (exposed) districts are parallel. In other words, the change in employment rate between the treated and control districts is uncorrelated with underlying (observable/unobservable) trends prior to the treatment.

41I use data from the 2000 population and housing census to compute the access rate in each district.

28
Table 2. Conflict and nighttime light

|||log (s|um of lightspe|r area)||
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Conflictt-1|-0.0110**|-0.00833*|-0.00825*|-0.00826*|-0.00821*|
||(0.00488)|(0.00499)|(0.00499)|(0.00500)|(0.00496)|
|Conflictt-2|-0.0203***|-0.0173***|-0.0173***|-0.0172***|-0.0172***|
||(0.00659)|(0.00632)|(0.00632)|(0.00631)|(0.00631)|
|Conflictt-3|-0.0103|-0.00742|-0.00751|-0.00744|-0.00749|
||(0.00764)|(0.00676)|(0.00676)|(0.00675)|(0.00675)|
|Conflictt-4|-0.00835|-0.00616|-0.00621|-0.00616|-0.00619|
||(0.00792)|(0.00713)|(0.00712)|(0.00712)|(0.00710)|
|Troopst-1||0.0211***||0.0212***||
|||(0.00455)||(0.00453)||
|ISAFt-1|||0.0217***||0.0215***|
||||(0.00476)||(0.00473)|
|ANAt-1|||-0.00943||0.00251|
||||(0.0758)||(0.0763)|
|AIDt-1||4.83e-07***|4.91e-07***|||
|||(1.85e-07)|(1.84e-07)|||
|Securityt-1||||1.12e-05**|1.10e-05**|
|||||(4.81e-06)|(4.81e-06)|
|Other aidt-1||||4.40e-07**|4.45e-07**|
|||||(1.85e-07)|(1.83e-07)|
|District Fixed Effects|Yes|Yes|Yes|Yes|Yes|
|Time Fixed effects|Yes|Yes|Yes|Yes|Yes|
|Observations|5,970|5,970|5,970|5,970|5,970|
|Adj. R2<br>Robust standard errors in paren<br>*** p<0.01, ** p<0.05, * p<0.1|0.994<br>theses|0.994|0.994|0.994|0.994|

16
# **V.   CONCLUSIONS**

The global financial crisis has led to widespread calls to reform bank regulation and supervision. Changes in bank capital regulation have been at the heart of the resulting policy discussions led by global banking regulators. In redesigning prudential standards to incorporate lessons from the recent turmoil, the Basel committee of supervisors has grappled with two important questions in particular: what type of capital should banks hold to ensure that they can better withstand periods of economic and financial stress? And should a simple leverage ratio be introduced to reduce regulatory arbitrage and improve transparency?

Our paper sheds light on both of these questions by investigating whether banks’ stock returns were affected differently depending on whether banks entered the financial crisis with a better capital position. Specifically, we use a quarterly panel of bank data for 12 countries for 20062009 to study the impact of bank capital and its different definitions and components on changes in market valuation of banks. Using the crisis period that started in August 2007 as an unexpected negative shock, we explore whether market participants perceive different capital definitions to be effective measures of banks’ ability to withstand stress.

We find that before the crisis, differences in initial capital – whether risk-adjusted or not, however defined – did not consistently affect subsequent bank stock returns. The effect becomes evident only during the crisis period, and even then it is significant and robust just for the largest banks in our sample. This is consistent with the implication that a negative relationship between risk and capital should be stronger for undercapitalized banks, which is the case for larger banks in our sample. Our results also suggest that during the crisis stock returns of large and undercapitalized banks were much more sensitive to leverage ratios as opposed to risk-adjusted capital ratios. This may be because market participants viewed risk- adjusted ratios as much less informative given they were more easily subject to manipulation and therefore less transparent. Finally, the positive association with subsequent stock returns is stronger for higher quality capital (Tier 1 leverage and common equity), but these findings are not as robust across different specifications.

Our findings have potential policy implications for the on-going process of regulatory reform. First, for undercapitalized and larger banks, we find better capitalization is associated with greater resilience in dealing with shocks, consistent with the spirit of capital regulation. Second, our results provide support for introducing a leverage ratio as a way to strengthen bank capitalization, as properly measuring risk exposure is very difficult especially for large and complex financial organizations. Finally, our tests provide some support to the view that greater emphasis on Tier 1 capital and common equity is likely to be effective.

13
Getting more specific, adopting what has been called a “labor protections” optic,<sup>11</sup> the first group, the informal salaried, can be defined as those wage employees whose employers do not comply with the legal requirements. This, however, does not tell us whether these workers are working for small informal firms, or for large generally formal firms. In the absence of data on compliance with labor regulations the ILO traditionally recommended classifying informal as workers in small establishments of fewer than 5-10 employees, which tend to be informal along different dimensions. Though there is substantial overlap in these definitions (see Henley et al., 2006 and Perry et al., 2007), conceptually the distinction can be important for our analysis. For instance, a transition from informal to informal under the “labor protections” definition might reflect purely a worker being granted benefits after a period of time but without a corresponding change in position and accompanying job search. Similarly, under the size definition, a measured transition may simply be capturing the growth of the firm, but no change in actual job. Relatedly, an emerging literature discusses firm decisions about whether or not to comply with legislation, and how a growing firm may choose to register and formalize its workers. Formalization along this margin has been studied by De Soto (1989), Rauch (1991), De Paula and Sheinkman (2007) among others. Again, a job “transition” defined as a change in protection status may simply reflect a firm becoming formal.

We classify on the basis of lack of compliance with labor legislation for issues of data comparability. While the Mexican survey reports the establishment size in which the respondent works, the Brazilian does not. Furthermore, compliance is relatively straightforward to capture in these surveys. In Mexico employers have to satisfy the contributions to the social security agency IMSS (or the equivalent for civil servants IMSTS) for their employees. Similarly, employers in Brazil are obliged to register their employees by issuing them a working permit or _carteira_ the signing of which guarantees them access to formal labor protections. Therefore those wage employees not registered with the social security agency (in Mexico) or _sem_ (without) _carteira_ in Brazil are consider informal salaried.

> 11 See Perry et al. (2007)

8
Pindyck, Robert S. (2000), Irreversibility and the Timing of Environmental Policy. _Resource and Energy Economics_ , 22, 233-259.

Pindyck, Robert S. (2002), Optimal Timing Problems in Environmental Economics. _Journal of Economic Dynamics and Control_ , 26, 1677-1697.

Pindyck, Robert S. (2007), Uncertainty in Environmental Economics. _Review of Environmental Economics and Policy_ , 1, 45-64.

Shalizi, Zmarak and Franck Lecocq (2009), Economics of Targeted Mitigation Programs in Sectors with Long-Lived Capital Stock. Background paper for the World Development Report 2010.

Strand, Jon (2011a), Inertia in Infrastructure Development: Some Analytical Aspects, and Reasons for Inefficient Infrastructure Choices. _Journal of Infrastructure Development_ , 2, 2011, 51-70.

Strand, Jon (2011b), Implications of a Lowered Damage Trajectory for Mitigation in a Continuous-Time Stochastic Model. Unpublished, the World Bank.

Strand, Jon and Sebastian Miller (2010), Climate Cost Uncertainty, Retrofit Cost Uncertainty, and Infrastructure Closedown: A Framework for Analysis. Policy Research Working Paper 5208, the World Bank.

Strand, Jon, Sebastian Miller, and Sauleh Siddiqui (2011), Infrastructure Investment with the Possibility of Retrofit: Theory and Simulations. Policy Research Working Paper 5516, World Bank.

Vogt-Schilb, Adrien, Guy Meuner, and Stephane Hallegatte (2012), How Inertia and Limited Potential Affect the Timing of Sectoral Abatements in Optimal Climate Policy. Policy Research Working Paper WPS6154, Development Research Group, World Bank.

25
with the inundation zone and then summing to a country total. Exposure indicators, such as land surface, urban extent, agriculture extent and wetlands were measured in square kilometers.

For exposure indicators such as land area, population and GDP, which have measured country “coastal zone” totals available, the exposed value is adjusted to reflect its real value by using the following formula: where: _Vadj_ : Exposed value adjusted; _Vcal_ : Exposed value calculated from exposure grid surfaces; _CTmea_ : Country “coastal zone” total obtained based on statistics; _CTcal_ : Country “coastal zone” total calculated from exposure grid surface.

Due to the relatively high resolution of some of these datasets, summary statistics are derived tile by tile, and a master table of countries is updated as each tile is processed. In the update, new values are added to existing values so that values in the final country table represent the sum of all tiles in which a country falls.

All processing, once again, was done by 5o x 5o tile, using aml (ArcInfo Macro Language) for automation. Output is in the form of a database table. Further manipulations are done in MS Excel.

# **(ii) Datasets**

Summary statistics were calculated for each zone using the following datasets; 9
individuals perceive themselves to be in control of factors that affect their lives.<sup>8</sup> In addition, in each village, key informants were interviewed about physical and social infrastructure in the village, and access to government programs.

We supplemented these quantitative surveys with qualitative research in purposively selected villages in six districts in north and south Bihar (Gaya, Khaimur, Kishanganj, Muzaffarpur, Purnea and Saharsa) during February and August 2009.<sup>9</sup> We drew on the qualitative work in designing both the intervention and follow-up surveys and in interpreting some of our quantitative findings.

The RCT for the information campaign was conducted in February-March 2010 in 40 villages randomly selected from the BREGS baseline sample of 150 villages. This took the form of a movie on BREGS rights and entitlements, tailored to Bihar’s context and program guidelines. We return to the movie in Section 3.

The follow-up survey repeated the baseline survey, but with some extra questions. This was done 2-4 months after the intervention. As always, the choice of the follow-up time period is important. Given the timing of the seasons and the nature of the program, our expectation is that if the intervention had impact it should be evident within this time period, given that this coincided with the lean season, when demand for BREGS should be high. Around the time that many people would be in need of extra work, the intervention tells them how to go about getting that work. We can never rule out the possibility of longer lags or nonlinearities (such as due to threshold effects in knowledge), but we would still expect at least some impacts to be evident within this time period if learning about rights is the key constraint.

In total, 3,000 households and approximately 5,000 individuals were interviewed in both rounds. The balanced panel comprises 2,728 households and 3,749 individuals. The overall attrition rate for households between the two rounds was 8% and was not concentrated in any particular stratum. There were relatively few refusals; two-thirds of the attrition was because a household was away temporarily when the survey team visited the village. There is virtually no

> 8  The original scale consists of a 7-item scale developed by Pearlin et al. (1981). Each item is a statement about the respondent's perception of self, and they are asked how strongly they agree or disagree with each statement with four potential categories. We have transformed the answers into “yes/no,” which proved to be a better approach in our setting, based on our field tests. Our scale is then created by adding up the answers and so ranges from 0 to 7. 9  The qualitative results are reported in Development Alternatives (2009), Indian Grameen Services (2009) and Sunai (2009).  Dutta et al. (2013) provide summaries of the findings and their implications.

7
after controlling for potential confounds, heavy schistosome infection was associated with lower performance on tests of short-term memory, reaction time, and information processing. A double-blind medical trial by Nokes, _et al._ (1992) found that the administration of albendazole led to immediate gains in memory skills in a population of Jamaican schoolchildren infected with whipworm and roundworm, and an experimental de-worming study with Tanzanian schoolchildren in the same region as the 2002 observational study also found cognitive gains in response to de-worming (Grigorenko, _et al._ 2006).

That I find effects mainly on reasoning–and to some extent, vocabulary–rather than memory speaks to the differences between slowed cognitive development and the more immediate cognitive impairments brought about by concurrent disease. Memory improves with age, but seems to depend less on health in early child development. Reasoning, however, shows a long-term response to improved health in early childhood. That stature is not affected suggests that worms do not cause a severe nutritional deprivation in early childhood; the low intensity of worm infections at this age is in accord with this reasoning.

## **5.4 Attrition**

One could imagine differential health or academic performance inducing different attendance rates among those whose communities initially experienced deworming at different times. Such a pattern could bias coefficient estimates if different treatment groups attrit differentially from the sample via absenteeism. However, comparing the number of students on the schools’ enrollment rosters to the number of students interviewed by enumerators reveals no empirical evidence of differential attendance rates by treatment group, so I do not view this as a serious threat to the analytical design.

# **6 Cost-effectiveness**

The policy implications of a program’s impact depend on its cost-effectiveness. As has been well-established, the costs of deworming a child directly are quite low, at around $0.59 per pupil per year (as discussed by Baird, et al., 2014). This paper examines only spillover impacts, so the effects documented here may be thought of as additional benefits beyond 18
# Table 1. The Malagasy Tax System

|Source|Type|Tax|Rates|1988 reve<br>(% of tax|nue' and<br>revenue)|
|---|---|---|---|---|---|
|D|||45% for commerce|22.7|(6)|
|0|D|Profits (IBS)|35% for other activities|22.2|(6)|
|M|I|Individuals<br>(IGR)|Progressive max. rate:<br>45%|||
|E|R|Property|(see text)|5.1|(1)|
|S<br>T|E<br>C|Other|(see text)|6.5|(2)|
|I<br>C|T|||||
|T|I<br>N|Value-added<br>(TUT)|15% except for exports,<br>necessities and|40.3|(11)|
|A|D||agricultural products|||
|x|I|||||
|E|R|||||
|S|E<br>C<br>T|Consumption<br>tax (TC)|5% to 10% for most of<br>300 products (see text)|28.8|(8)|
|T<br>R|I<br>N|Customs duty (DD)|5% to 45% (see text)|18.3|(5)|
|A|D|||||
|D<br>E|I<br>R|Import tax (TI)|5% to 80% (see text)|73.1|(20)|
|T|E<br>C|Other import taxes|(see text)|11.2|(3)|
|A<br>X<br>E|T|Export taxes|Specific tax on vanilla,<br>cloves, coffee (see text)|43.8|(12)|
|S||Value-added<br>(TUT)|15% on imports|54.1|(15)|

a. In billions of FMG..

Source: Authors' elaboration ffom Guillaumont et al. (1990).