rafmacalaba/datause-encoder-data huggingface dataset:
estimates by over 200 percent (table 2). Although there were no new orders after the TMI accident in 1979, utilities sought to complete more than 40 nuclear power projects already under

**Table 2  Projected and Actual Construction Costs for U.S. Nuclear Power Plants**

|**Constructio**|**n Starts**|**Averag**|**e Overnight Costs**<br>**a**||
|---|---|---|---|---|
|**Year Initiated**|**Number**<br>**of**<br>**Plants**<br>**b **|**Utilities' Projections**<br>**(Thousands of dollars**<br>**per MW)**|**Actual (Thousands of**<br>**dollars per MW)**|**Overrun**<br>**(Percent)**|
|1966 to 1967|11|<br>612|1,279|109|
|1968 to 1969|26|<br>741|2,180|194|
|1970 to 1971|12|<br>829|2,889|248|
|1972 to 1973|7|<br>1,220|3,882|218|
|1974 to 1975|14|<br>1,263|4,817|281|
|1976 to 1977|5|<br>1,630|4,377|169|
|**Overall**|||||
|**Average**|**13**|<br>**938**|**2,959**|**207**|

_Source_ : CBO (2008).

way. For those plants, construction cost overruns exceeded 250 percent (CBO, 2008; Joskow, 2006a).<sup>9</sup> Prior research and more recently discussions with more than 30 industry members across the nuclear supply chain, have identified several explanatory factors for the observed uncertainty and miscalculation/escalation of construction costs (Zimmerman, 1982; Cantor and Hewlett 1988; Arthur D Little, 2011):

- incorrect understanding of economies of scale—early cost projections tended to ignore the potential diseconomies of scale due to the increased complexity and greater management requirements of larger nuclear plants;

> 9 The overruns in overnight costs did not include additional financing costs that were attributable to post-accident construction delays.

18
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impute a labor productivity impact. In particular, we use data from a survey we ran in 2011 of 113 firms in the broader textile industry around Mumbai (see details in Appendix A2), in which we collected data on physical production, employment, and looms. Using this, we show in Appendix Table A4 and Figure A2 that there is a strong correlation between labor productivity (output per worker), and looms per worker in both the cross-section and the panel. Taking the fitted coefficient of 0.734 from column (4) of Table A4, we impute labor productivity from looms per employee for our experimental firms. The average imputed increase in labor productivity since 2011 is then 19.0% (exp(0.237*0.734)), and the long-run impact is 35.3% (exp(0.412*0.734)). These impact figures are remarkably similar to the 15.3% and 31.2% 1-year and 10-year productivity impacts respectively reported for management interventions in post-war Italy reported in Table 3 of Giorcelli (2017).<sup>12</sup> In column (4) we asked the plants if they had used any consultants since 2011, and if so how many days. Many of these firms had, and indeed, as column (5) shows, this use of consultants was significantly higher in the treatment plants. These consultants were local firms offering very practical advice on loom-changing practices, fabrics, human resources, or textile marketing, rather than the types of expensive international-firm management consulting provided by our intervention. We interpret this as a revealed preference indicator that treatment firms found the intervention useful and were more willing to pay for commercial consulting in the future. This was more likely to occur once some time had passed since their previous consulting experience in our project (panel B).

Finally, in column (5) we look at the adoption of marketing practices. Marketing practices were not part of our initial intervention, and so this enables us to examine whether changes in the specific practices on which our intervention focused are accompanied by broader management changes. Our measure is a score given for the adoption of seven practices: (1) does a director regularly attend trade shows; what is the frequency of systematically analyzing markets, products and prices to assess policies (and make changes wherever necessary) ((2), (3) and (4)); (5) does the firm have a dedicated brand; (6) does the firm have a sales and marketing professional; and (7) does the firm use any e-commerce (for sales) and social media (for advertising). Panel A shows that treatment firms are significantly more likely to adopt these marketing practices. Discussions with firms highlighted their attempts to be more systematic in 12 The results are also similar to the 1-year impact of 17% reported in Bloom et al. (2013).

17
where X is a set of household characteristics that determine the participation process outcomes. To test our hypothesis we include a “political access” index as a proxy for household access to local elected politicians where it takes the value 1 if the household has access to either the _Union Parishad_ or Council member or chairman, two if the household has connection with both, and zero if it does not have any links with these individuals.<sup>13</sup> We also include dummy variables to reflect household access to administrative officials at the Upazila level as well as access to a powerful relative.

We estimate four separate equations: (i) _a simple participation_ regression to assess whether the “political access” index is a significant determinant of participation in EGPP; (ii) _a knowledge regression_ to assess whether the “political access” index is a significant factor that determines a household’s ability to know about EGPP; (iii) _an application regression conditional on household knowledge_ of EGPP to assess whether “political access” is significant for lodging an application to EGPP; and (iv) _a selection regression conditional on household knowledge and application to the program_ to assess whether political access also plays a role in the final selection into EGPP. The same equations are run to estimate all four regressions. This allows us to understand the relative significance of the factors that affect these three participation stages, and thus help us understand why and how the “political access” index matters for EGPP participation. We estimate these equations using the baseline survey only.<sup>14</sup>

# **3.2 Assessing Impact of Participation in EGPP on Poverty**

Participation in EGPP, in principle, is supposed to provide employment to those households engaged in casual labor in agriculture as they are likely to face unemployment during agricultural lean seasons. Thus, participation in EGPP is expected to mitigate a temporary drop in consumption resulting

> 13 We also conduct some robustness checks with two dummy indicators, one taking the value 1 if the household had access to the UP chairman, and another one taking the value 1 if the household had access to a UP member. These results are provided in the Appendix.

> 14 The baseline has a balanced sample of participants and non-participants as it was constructed according to the sampling strategy. Two third of the participants became non-participants in the endline survey therefore not making it an appropriate sample for addressing any questions on participation.

11
enterprises in our survey sample. The results for male managers are quite similar to the pooled data for nonfarm analysis. Financial services, transportation, electricity, and facing payment disputes are positively associated with enterprise performance. In addition to all the above factors, distance to markets is positively correlated with performance, and management innovation is negatively correlated. As for management innovation, a negative sign can be interpreted as the entrepreneurs with better performance having not even considered improvement or innovation, but entrepreneurs with worse performance had to improve or innovate for survival, but all by their own ideas and sources. Receiving business services is not statistically significant. The availability of business services is extremely limited in rural areas. According to the RIC survey, marketing services are available in 20 percent of the communities, and business services are available in 25 percent of the communities. However, the utilization of business services is even worse. Only 1.0 percent of farm entrepreneurs and 2.5 percent of nonfarm entrepreneurs used management-related services, such as consulting, marketing, and accounting, during the past year.

# **4.3 Rural Labor Migration**

Migration, more move-in than move-out, may reflect an increase in economic activities in the community. The survey data on migration are shown in Table 4-3.

**Table 4.3 Migration (% communities)**

|Governorate|Sana’a|Lahj|Dhamar|Total|
|---|---|---|---|---|
|More Move-in|50.0|16.7|16.7|27.8|
|More Move-out|33.3|66.7|16.7|38.9|
|The same move-in & move-out|0|8.3|50.0|19.4|
|No move-in & move-out|16.7|8.3|16.7|13.9|

_Source:_ 2010 Yemen RIC Survey

# **4.3.1** **_Migration Model_**

The migration model is used to analyze which household characteristics and community level RIC components may affect migration workers to flow into the community. In other words, the hypothesis of the migration model is to find out which household characteristics and community level RIC components affect migration. The Probit model is also applied for the dependent variable “more move-in” with household characteristics and community-level RIC variables.

_Mi= 0+ 1 (_ **_H_** _i) + 2 (_ **_C_** _j) + 3 (_ **_D_** _j) + εi_ (6) where _M_ denotes a dummy that equals 1 when more people moved into the communityduring the past three years.

The regression results are shown in Table 4.4.

16
|This table presents panel estimati<br>indexes, exchange rates and EM so<br>Late is a dummy indicating the 2<br>including in the estimations. All est<br>Bloomberg, MSCI, GEM World Ba|**Th**<br>ons of monthly dl<br>vereign credit spre<br>005-2010 period. <br>imations include c<br>nk, US Treasury.|20002010<br>**Tabl**<br>**e financial reco**<br>og(EM Asset) vs <br>ads. Global assets<br>2000-2010 (w/o C<br>ountry fixed effect|**e 7**<br>**upling hypothe**<br> dlog(Global Ass<br>are the S&P, the<br>risis) indicates t<br>s. *,**, and ***|20002010<br>**sis**<br>et) during the 200<br>Broad Dollar Inde<br>hat the period dur<br>denotes significane|0s. EM assets a<br>x and the US HY<br>ing 2008M03-20<br>at the 10, 5 and|20002010<br>re MSCI country<br>corporate spread.<br>09M03 were not<br>1% level. Source:|
|---|---|---|---|---|---|---|
|Period|2000-2010|-<br>(w/o Crisis)|2000-2010|-<br>(w/o Crisis)|2000-2010|-<br>(w/o Crisis)|
|Group Country|EM|<br>EM|EM|<br>EM|EM|<br>EM|
|**Variable**|**Equity**|**Equity**|**Spreads**|**Spreads**|**FX**|**FX**|
|S&P|0.763***|0.765***|||||
||(0.0479)|(0.0481)|||||
|S&P*Late|0.260***|0.157**|||||
||(0.0650)|(0.0645)|||||
|US HY|||0.555***|0.550***|||
||||(0.0428)|(0.0876)|||
|US HY*Late|||0.250***|0.130**|||
||||(0.0625)|(0.0623)|||
|Broad Dollar Index|||||0.648***|0.640***|
||||||(0.0876)|(0.0876)|
|Broad Dollar Index*Late|||||0.601***|0.314***|
||||||(0.125)|(0.120)|
|Constant|0.00927***|0.0110***|-0.00607**|-0.00967***|0.00119*|0.000244|
||(0.00127)|(0.00135)|(0.00256)|(0.00265)|(0.000639)|(0.000628)|
|Observations|3036|2737|2112|1904|2508|2261|
|R-squared|0.289|0.215|0.281|0.195|0.175|0.097|
The PMP for good _1_ of the _nth_ firm (produced in the oligopolistic market) is _l l h h l h_ From good _2_ ’s PMP, we know it is true that _w_  _a_ , _w_  _a_ and _q_ 1 _n_  _q_ 1 _n_  _q_ 1 _n_ , which implies that _N N l N h_  _n_  1 _q_ 1 _n_   _n_  1 _q_ 1 _n_  _n_  1 _q_ 1 _n_ . So, the PMP in (B.7) becomes Treating  1( _N_ , _Q_ 1) as a fixed amount equal to<sup>ˆ</sup> 1 , we find that good _1_ ’s PMP in (B.8) becomes Solving for the Cournot equilibrium, we obtain the first order condition for the _nth_ firm in the market of good _1_ in the noncompetitive market as Assuming a symmetric equilibrium, we find the quantity of good _1_ produced by firm _n_ and the total quantity of good _1_ as a function of _N_ are and the corresponding equilibrium price and profits as a function of _N_ are 21
Barrera-Osorio, F., and L. L. Linden. 2009. The Use and Misuse of Computers in Education: Evidence from a Randomized Controlled Trial of a Language Arts Program. Cambridge, MA: Abdul Latif Jameel Poverty Action Lab (JPAL) 20. http://www.leighlinden.com/Barrera-Linden%20Computadores_2009-03-25.pdf Bassi, M., C. Meghir, and A. Reynoso. 2016. “Education Quality and Teaching Practices.” NBER Working Paper No. w22719, National Bureau of Economic Research, Cambridge, MA. http://www.nber.org/papers/w22719 Beede, D. N., T. A. Julian, D. Langdon, G. McKittrick, B. Khan, and M. E. Doms. 2011. “Women in STEM: A Gender Gap to Innovation.” Economics and Statistics Administration, Issue Brief No. 04-11 Washington, DC: U.S. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1964782.

Berg, A., and F. M. Mensah. 2014. "De-marginalizing science in the elementary classroom by coaching teachers to address perceived dilemmas". Education Policy Analysis Archives, 22 (57): 1-57 Berlinski, S., and M. Busso. 2017. "Challenges in Educational Reform: An Experiment on Active Learning in Mathematics". Economics Letters, 156 (C): 172-175.

MET Project. 2012. "Asking Students about Teaching: Student Perception Surveys and Their Implementation. Policy and Practice Brief". Bill and Melinda Gates Foundation.

http://www.metproject.org/downloads/Asking_Students_Practitioner_Brief.pdf.

Brown, M. W. 2009. “The Teacher-Tool Relationship: Theorizing the Design and Use of Curriculum Materials.” In Mathematics Teachers at Work: Connecting Curriculum Materials and Classroom Instruction, edited by J. T. Remillard, B. A. Herbel-Eisenmann, and G. M. Lloyd. Pp. 37-56. New York: Routledge.

Bruns, B., and J. Luque. 2014. Profesores Excelentes. Docentes Excelentes: Cómo Mejorar el Aprendizaje en América Latina y el Caribe. Washington, DC: Banco Mundial.

http://www.bancomundial.org/content/dam/Worldbank/Highlights%20&%20Features/lac/LC5/Spanishexcellent-teachers-report.pdf.

Cilliers, J., and S. Taylor. 2017. “Monitoring Teachers and Changing Teaching Practice: Evidence from a Field Experiment.” McCourt School of Public Policy, Georgetown University. Department of Basic Education, South Africa. Working Paper. https://sites.tufts.edu/neudc2017/files/2017/10/paper_110.pdf.

Christophel, D. M. 1990. “The Relationships Among Teacher Immediacy Behaviors, Student Motivation, and Learning.” Communication Education 39 (4): 323–40.

Cristia, J. P., P. Ibarrarán, S. Cueto, A. Santiago, and E. Severín. 2012. “Technology and Child Development: Evidence from the One Laptop per Child Program.” Working Paper No. IDB-WP-304, InterAmerican Development Bank, Washington, DC.

Darling-Hammond, L., R. C. Wei, A. Andree, N. Richardson, and S. Orphanos. 2009. Professional Learning in the Learning Profession. Washington, DC: National Staff Development Council. https://learningforward.org/docs/default-source/pdf/nsdcstudy2009.pdf 33
through financial shocks in Ecuador. However, parental migration may also have negative consequences if the child must now fill the role of the absent parent by working more at home, or if there is loss of parental involvement. In Mexico, McKenzie and Rapoport (2007) report a decline in school attendance and attainment for boys aged 12– 18 and girls aged 16–18 with a parent abroad. Similarly, in China, de Brauw and Mu (2011) find that the status of underweight children aged 7–12 is positively correlated with parental migration.

Type of migration and migrants affect welfare outcomes. Macours and Vakis (2010) find that maternal seasonal migration positively affected early cognitive development in Nicaragua. Gibson et al. (2011) show that children who migrate with parents experience improvements in diet reflected in improved weight-for-age and height-for-age, while children who remain have worsened diets and declining weight-forage and height-for-age.

In examining health outcomes, a household survey could be combined with anthropometric indicators to examine issues of nutritional status and food security as related to migration (see Antón, 2010; and Carletto et al., 2011). Similarly, for education outcomes, purpose-specific surveys as well as general household surveys with relevant questions can be used. Information specifically on remittance income is also needed.

Last, but not least, migrant selectivity is again a key concern. Health and education outcomes are closely related to income levels, which have an endogenous relationship with migration decisions, as discussed above. Furthermore, in many surveys, migrants state that they choose to migrate specifically to provide the resources for their children’s education and family’s health expenditures. Thus a natural endogenous 29
# **_Ex post –ex ante comparison studies since 2000_**

In 2006 OIRA updated and extended the HMN analysis as part of its Annual _Report to Congress_ (OMB 2006). This study includes a few analyses overlooked by HMN as well as work that appeared after its publication, nearly doubling the sample size to 47 case studies, including studies of regulations by agencies that were not part of the original HMN tally. The new analyses included studies of vehicle safety rules promulgated by the National Highway Traffic Safety Administration (NHTSA), none of which make it into HMN; results of a study of OSHA workplace safety rules by Seong and Mendeloff (2004); a study of vehicle emission rules by Anderson and Sherwood (2002); and a study of EPA pesticide rules (Gianessi 1999)<sup>4</sup> ; a study of Department of Energy (DOE) rules for energy efficiency in household appliances (Dale et al. 2002) and two nuclear plant safety rules issued by the Nuclear Regulatory Commission (NRC).

For some rules in the OMB sample, either benefits or costs are not quantified. For a number of OSHA safety rules there are no cost estimates, because the source of information is a paper by Seong and Mendeloff (2004), which focuses on comparing expected change in fatalities estimated in the RIAs with estimates of fatality reductions following implementation. For Department of Energy (DOE) appliance efficiency standards no benefit estimates are cited. Again, in the source document (Dale et al. 2002), the focus is on the estimates of the cost of energy efficiency. In principle, it should have been possible to determine the effects of the rule, because the change in the energy efficiency of the appliances should be known. In any event, for both these types of regulations it was assumed that the missing estimate (costs or benefits) were accurate for the purposes of calculating a change in benefit-cost ratio. Pesticides represent a slightly different case since the rules in question were bans on pesticide uses for certain crops in particular regions.

OMB scoring criteria are slightly different from HMN, especially for assessing the effectiveness of rules. Whereas HMN stopped at examining the “effects” of regulation in physical terms, OMB used a hierarchy. The most desirable benefit criterion is the total monetized effects of the regulation, if available in both the _ex ante_ and _ex post_ study. If this metric is not available, the benefits are the quantitative effects of the regulation, just as in HMN. OMB’s preferred metric for assessing accuracy is the change in benefit-cost ratio. If it is overestimated—that is, higher in the _ex ante_ versus the _ex post_ estimate, that means the performance of the regulation is not as good as was predicted in the _ex ante_

> 4 The Gianessi study of EPA pesticide cancellations (for particular crops in designated areas) was sponsored by RFF and was ongoing at the time the HMN manuscript was in preparation. We took results from the six finished case studies. In all, 31 case studies were completed, but after excluding pesticide rules for which farmers applied for and received emergency or permanent exemptions, 26 remain..

13
estimated the number of poor people by calculating the number of individuals below the 2019 Guinean poverty line of 5,006,362 Guinean francs or $1,385 international dollars (purchasing power parity (2019); $) per year.<sup>[12]</sup> We considered total consumption expenditure as the wealth of individuals, and total consumption expenditure minus OOP health expenditure as the impact of health expenditure on the wealth of individuals. We conducted the analyses for the entire population and by quintile. We calculated quintiles before and after health expenditures. For already poor individuals (individuals in quintiles 1 and 2), the change in poverty was equal to their total consumption expenditures.<sup>[9]</sup> For CHE and IHE, we considered all health expenditures as <mark>OOP</mark> health <mark>payments b</mark> ecause of the small proportion (3%) of individuals with some form of prepayment mechanism.

# **Identification of CHE and IHE drivers and estimation of the potential FRP of subsidies**

We defined the drivers of CHE and IHE as the expenditure categories resulting in the highest prevalences of CHE and IHE cases (previously calculated).

We estimated the FRP associated with THE and expenditure categories as the equivalent of the prevalence of CHE and IHE cases they induce. We estimated FRP for the entire population and by quintile. For IHE, quintiles were calculated before and after health expenditures.

Statistical analyses were performed using STATA software (StataCorp LLC, College Station, TX, version 16.1). We report the number of individuals at the national level rather than the number of respondents to the LSMS. Expenditure data were converted to 2019 international dollars using the rate 1 international dollar = 3,614 Guinean francs.<sup>[12]</sup>

# **Results**

# **Characteristics of the study population**

The LSMS included 41,449 individuals. We excluded 15 (0.04%) individuals who reported no consumption expenditures. The study population therefore included 41,434 individuals, representing the 12 million people in Guinea at the time of the LSMS (in 2018 and 2019). Three percent of the total population 7