rafmacalaba/datause-encoder-data huggingface dataset:
management.

Taken together, our findings suggest that interventions aimed at improving energy management should be tailored to firm circumstances. Whether generic or energy-centric management translates into energy expenditure savings varies depending upon a firm’s circumstances, such as the extent to which reducing energy use requires capital expenditures or equipment upgrades, or can be adjusted through, for instance, team- or employee-level incentives for conservation. A deeper investigation of the barriers (e.g., administrative costs) that prevent firms from adopting energy management practices could aid in the design of informational or financial interventions that could help to overcome the energy-efficiency gap. However, without policy interventions that alter fundamental relationships among input prices or increase the salience of energy costs and climate concerns, management is likely to play a limited role in reducing energy-related environmental impacts.

# **References**

Bloom, N., Brynjolfsson, E., Foster, L., Jarmin, R., Patnaik, M., Saporta-Eksten, I., and Van Reenen, J. (2019a). What drives differences in management practices? _American Economic Review_ , 109:1648–1683.

Bloom, N., Genakos, C., Martin, R., and Sadun, R. (2010a). Modern management: Good for the environment or just hot air? _Econ. J._ , 120(544):551–572.

Bloom, N., Genakos, C., Sadun, R., and Reenen, J. V. (2012a). Management practices across firms and countries. _Acad. Manage. Perspect._ , 26(1):12–33.

Bloom, N., Genakos, C., Sadun, R., and Van Reenen, J. (2012b). Management Practices Across Firms and Countries. _Academy of Management Perspectives_ , 26(1):12–33.

Bloom, N., Iacovone, L., Pereira-Lopez, M., and Van Reenen, J. (2019b). Management in Mexico: Market Size, Frictions and Misallocation.

Bloom, N., Lemos, R., Sadun, R., Scur, D., and Van Reenen, J. (2014). The new empirical economics of management. _Journal of the European Economic Association_ , pages 835–876. Bloom, N., Mahajan, A., McKenzie, D., and Roberts, J. (2010b). Why do firms in developing countries have low productivity? _American Economic Review_ , 100(2):619–623.

Bloom, N., Sadun, R., and Van Reenen, J. (2016). Management as a Technology? Bloom, N., Schweiger, H., and Van Reenen, J. (2012c). The land that lean manufacturing forgot? _Economics of Transition and Institutional Change_ , 20(4):593–635.

Bloom, N. and Van Reenen, J. (2007). Measuring and explaining management practices across firms and countries. _Q. J. Econ._ , 72(4):1352–1408.

Boyd, G. A. and Curtis, M. E. (2014). Evidence of an “Energy-Management Gap” in US 32
We add to this discussion with evidence from a randomized study of LEAP (Livelihood Enhancements and Associations among the Poor), a World Bank sponsored pilot of a selfhelp-group program in Siem Reap, Cambodia. Our main contribution is a more extensive set of social capital measures than have been used in this literature so far. Putnam (2000, p. 19) defines social capital as “... social networks and the norms of reciprocity and trustworthiness that arise from them.’’<sup>5</sup> Thus network and norms are the fundamental components of social capital and we designed our research to measure these components as directly as possible. We measured networks by carefully recording our subjects’ relationships with each other in a set of groups that extensive focus group research revealed to be the most important in the region. We used lab-in-the-field techniques to measure subjects’ norms by observing their behavior in a carefully incentivized, controlled laboratory setting rather than simply from their self-reported responses to a survey. We also conducted a standard survey in which we asked them, _inter alia_ , about their social activities.

We designed our evaluation to test three propositions: whether the program increased savings and access to credit, whether it produced income and livelihood enhancements and finally whether the program increased civic engagement and social capital among the poor. In line with previous evidence, we find some economic impacts. Yet these impacts have not translated into the accrual of social capital. We find that LEAP had a large and positive effect on savings in communities. Indeed, our estimates indicate that, in the program communities, poor members (on whom the program was targeted) outsaved non-poor members. We also found that incomes from sales of meat and fish rose in the program communities, which is consistent with LEAP’s creation of a relatively large number of poultry and pork producers groups.<sup>6</sup> Finally, while there is strong evidence that the program fostered participation in savings groups there is no consistent evidence that that participation in these savings groups generated the hoped-for spillovers in participation in

> 5 Rothstein (2005) defines it similarly. Also see the review in Portes (1998).

> 6 There are some perplexing patterns in the data, discussed in greater detail below, that raise questions about the validity of this finding, however.

3
underlying the debate on the future of nuclear power relates to the continuing lack of consensus on what will be the costs of new nuclear generating plants (Joskow and Parsons, 2009). These cost uncertainties have been highlighted by the turbulent experience of Finland‘s Olkiluoto plant.

The costs of nuclear power consist of four major components:

- _Capital or construction costs_ —are those incurred during the planning, preparation and construction of a new nuclear power station;

- _Operations and maintenance_ —relate to the management and upkeep of a power station (labor, insurance, security, spares, planned maintenance, and corporate overhead costs);

- _Fuel costs_ —reflect the cost of fuel for the power station;

- _Back-end costs_ —are those related to the decommissioning of the plant at the end of its operating life and the long-term management and disposal of radioactive waste.

# **Figure 4 Cost profile of nuclear and gas-fired generation**

<!-- Start of picture text --> 80 Nuclear<br>CCGT<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Capital O&M Fuel Back-end<br>Source : DTI (2007).<br><!-- End of picture text --> Much of the uncertainty surrounding nuclear power‘s future costs relates to construction cost—the most important component (roughly two-thirds) of total generating costs (figure 4; Nuttall and Taylor, 2009).

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such as their education level, assets, and whether they have been accused of any crimes. A shortcoming of this affidavit data is that it is only available for post-DPEP years (2004-2018).

Finally, we utilize data from the 1991 Census of India to compare districts that were eligible for the DPEP treatment with districts that were ineligible.

# **4.1 Descriptive Statistics**

In Table 1, we present some descriptive statistics on constituency-level state election outcomes during 1974-2018, and also on candidate characteristics for elections during 2004-2018. We report these statistics for districts that lie within a 12-point female literacy rate bandwidth of 39.3 percent in the 1991 Census, as this is the approximate optimal bandwidth in our main estimates.

Panel A reports the statistics for election data aggregated at the district-by-year level. An average seat is contested by ten candidates, and these candidates are overwhelmingly male (with only 0.5 female candidates per seat, on average). Roughly half of the contesting candidates are affiliated to a political party, and the other half are independents. Not all candidates are competitive, however, as the “effective” number of parties per constituency on average is only three, suggesting that a large fraction of the candidates, who are largely male, are marginal to the electoral contest.<sup>10</sup> The low rate of entry of female candidates is also visible in the share of elections with at least one female candidate, which is only 0.34. Female candidates also have only a 5 percent chance of winning a constituency. Candidates that win a constituency are aligned with the leading party in the state in 57 percent of elections and only 30 percent of winners are from the incumbent party in power in that constituency.

Panel B shows that 93 percent of contesting candidates have completed primary school, and 78 percent of them have completed secondary school. These completion rates are broadly comparable to the primary and secondary school completion rates in the Indian population of 94 percent and 85 percent, respectively, in 2017 (UNESCO Institute for Statistics). However, contesting candidates are significantly wealthier than the average population, with declared assets of INR 20.38 million per candidate on average. In contrast, per capita net national income in India was substantially lower at INR 1,26,406 during 2018-19.<sup>11</sup> Contesting candidates also have a high rate of criminality, with 16 percent of candidates having been accused of a criminal case, and 8 percent of candidates facing serious charges such as murder, kidnapping, or arson.

> 10The effective number of candidates is calculated as the inverse of the sum of squares of each candidate’s vote share.

> 11This income estimate is from the Ministry of Statistics and Program Implementation in India, available at `https://shorturl.at/CEIU1` .

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Notice that not all non‐ad valorem tariffs can be converted into an ad valorem equivalent rate. This is the case for technical duties imposed on some products (see Table 2). Nonetheless, such duties represent only 1.7 percent of the country‐pair‐product observations in the database.

Table 2: Examples of technical duties

|**Importing**<br>**country**|**National Product**<br>**Code**|**Product description**|**Custom duty as**<br>**reported**|
|---|---|---|---|
|**_Yemen_**|22043000|Wine of fresh grapes, including fortified wines;<br>grape must other than that of heading 20.09;<br>othergrape must.|**Prohibited**|
|**_Russian_**<br>**_Federation_**|8703329093|Motor cars and other motor vehicles principally<br>designed for the transport of persons (other<br>than those of heading 8702), including station<br>wagons and racing cars; Other vehicles, with<br>compression‐ignite.|**2.2 euro per cm**<sup>**3**</sup>**of**<br>**engine volume**|
|**_New Zealand_**|95081000|Roundabouts, swings shooting galleries and<br>other fairground amusements; travelling circuses<br>and travellingmenageries;travellingtheatres|**The rates applicable to**<br>**the separate**<br>**components**|
|**_United States_**|91091010|Alarm clock movements, complete and<br>assembled, electrically operated, with<br>optoelectronic displayonly|**3.9% on the movement**<br>**+ 5.3% on the battery**|

Source: ITC Market Access Map To make the tariffs comparable across countries and sectors, AVEs are aggregated from the NTLC to HS6 by calculating the simple average of all underlying NTLC rates. If there is more than one preferential tariff under a given NTLC for a partner country, then the ‘minimum’ rate is selected. The most favored nation (MFN) tariff or the general tariff is used if no tariff preference is applicable.

The resulting aggregated database includes information on the ad valorem equivalent at the 6‐ digit HS product level for both the maximum applied rate (MFN rates) and preferential tariffs for a total 199 reporters and 239 partners. Among the 199 reporters, 141 countries have data for 2016, 7 for 2017, 20 for 2015 and 13 for 2014. For the remaining 18 countries, most recent information is available between 2006 and 2013 (see Figure 2).<sup>6</sup> In terms of products, information is reported on all 5,203 HS6 level products (HS 2012 nomenclature).

> 6 Out of these 18 countries, only Panama and Trinidad and Tobago have signed agreements entering into force after the date for which tariff information is available (see Appendix Table A. 2).

5
The literature does include a few isolated accounts of attempts to use correspondence courses for vocational and technical training. A smallscale auto-mechanics pilot study undertaken in Indiana in 1974 illustrates some of the issues. Unsurprisingly, most vocational education administrators involved with the project felt that a lack of "hands-on" tuition limited the scope of this mode for skill development, and. a lack of access to equipment was also cited as a matter of concern. Accordingly they felt that the mode was best suited to imparting education related to occupational training, and in particular, vocationally-related mathematics, blueprint reading and some science subjects like electricity, electronics and refrigeration.

In view of this, an obvious possible application oi the mode is to supply the training-related education component of an apprenticeship course in places where the geographical dispersion of the population makes conventional institution-based education uneconomic. Nanayakkara (1981) reports that this approach has been adopted for 24 of the trade apprenticeships regulated by the National Apprenticeship Board in Sri Lanka, but no evaluation appears to be available. An earlier very small-scale U.S. experiment, the Isolated Apprenticeship Program in New Mexico (New Mexico State Department of Education, 1972) failed after rapid director turnover. The British Open Tech experiment, a government initiative intended to foster the creation, without a central organization, of occupational training courses serviced by units in polytechnics and similar institutions (Tinsley, 1985), is still in its infancy but may eventually provide material for evaluation.

# 8.9 Training brokers

It is beyond the information-processing capacity of many enterprises to evaluate the extraordinarily heterogeneous sources of external training available nationally and internationally. As a consequence an efficient exploitation of training resources depends on the mediation of an agency equipped to play the role of a training broker, that is, to advise enterprises in the light of their needs, drawing on a comprehensive knowledge of relevant training provision. There is no systematic literature on training brokers and the examples given below are merely illustrative of the contribution that they can make to cost-effective training.

# International training brokers

The highest profile type of international training broking is undertaken by donor agencies assisting in the choice of appropriate destinations for individuals selected for aid-assisted training, either under the umbrella of a technical assistance programme or as a component of projectrelated training.

Closer to true mediation is the broking service provided by the ILO International Center for Advanced Technical and Vocational Training, an example being the organization in 1985 and 1986 of two training study tours in coal-mining safety in the Federal Republic of Germany, Italy and the United Kingdom for Indian coal mine staff.

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|**Authors &**<br>**date**|**Countries, data**<br>**unit, and time**<br>**period**|**Policy variables**<br>**tested**|**Impact variables**|**Other determining**<br>**variables**|**Estimation**<br>**method**|**Principal results**|
|---|---|---|---|---|---|---|
|19.<br>Sarr 2015|58 developing<br>countries 1990 ‐<br>2005|Regulation|Generation per<br>capita<br>GDP per unit of<br>energy used<br>T&D losses|GDP per capita<br>Population<br>Openness<br>GFCF<br>FDI<br>Oil rents/GDP<br>Polity score<br>privatization<br>dummy|Mean<br>comparisons<br>for 5 years<br>before and 5<br>years after<br>introduction<br>of regulation.<br>Propensity<br>score<br>matching|T& D losses decline significantly<br>for both unmatched and matched<br>samples.<br>Generation per capita also<br>increases significantly. There is no<br>significant increase in energy<br>efficiency post regulation.|
|20.<br>Sen &<br>Jamasb<br>2012|19 States in India<br>1991‐2007.|Privatization<br>Regulation<br>Unbundling<br>Open access to T&D<br>network<br>Presence of IPPs|Plant load factor<br>T&D losses<br>Gross generation<br>capacity<br>Average price of<br>electricity<br>Ratio of industrial<br>to domesticprice|State GDP per<br>capita<br>Population<br>Hydro generation<br>in state|Least squares<br>dummy<br>variable bias<br>corrected.<br>Three stage<br>least squares<br>to del with<br>endogeneity.|Many indicators revealed initial<br>worsening of performance then<br>followed by later improvements.|
|21.<br>Sen, Nepal<br>& Jamasb<br>2016|17 Asian<br>countries 1990‐<br>2013|IPPS<br>Regulation<br>Unbundling<br>Corporatization<br>Open access<br>Distribution<br>privatization<br>Interaction terms|T&D losses<br>GDP per capita<br>Electricity trade per<br>capita<br>Human<br>Development Index<br>Gini coefficient|Electric power<br>consumption per<br>capita<br>Transparency index<br>Installed capacity<br>per capita<br>Hydro capacity per<br>capita<br>Political freedom<br>index<br>Civil liberties index<br>Population|Panel using<br>instrumental<br>variables.|Only corporatization is<br>significantly negatively linked to<br>losses; corporatization and<br>distribution privatization are<br>significantly positively linked to<br>GDP; reforms are not significantly<br>linked to electricity trade;<br>regulation is negatively linked to<br>Gini, while distribution<br>privatization is positively linked.|

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> <sup>- 26 -</sup>

**Table 13 Contributions to personal and occupational pension schemes in 2003 by Income**

|||**Group**<br>|||
|---|---|---|---|---|
|Pension scheme|Personal|Pensions|Occupation|al Pensions|
|Income (DKK)|Fraction|Amount|Fraction|Amount|
||(per cent)|(DKK)|(per cent)|(DKK)|
|Up to 150,000|19|20,600|14|12,700|
|150,000-249,999|33|11,500|70|19,300|
|250,000-349,999|35|15,700|82|31,300|
|350,000-449,999|35|20,600|84|45,900|
|450,000-549,999|36|28,500|84|59,100|
|550,000-649,999|39|38,700|81|76,200|
|650,000 and above|44|52,300|80|147,700|
|Total|31|16,400|60|29,700|

Note: The fraction shows how many persons in the age of 25-64 in a certain group of income who have contributed to a personal or an occupational pension scheme in 2003. The amount is the average amount for those who contributed to a pension scheme in 2003. Source: Ministry of Economic and Business Affairs (2005).
source of difference. Diving deeper into this issue, we find that Fernandes et al. (2014) estimate extremely small average effect sizes for their sample of 15 RCTs. Our broader sample of randomized experiments, however, leads to a much more positive assessment. In line with this observation, the effect size of financial education on financial behavior documented in RCTs seems to increase over time, indicating a positive time trend in effect sizes: a regression of effect size on year of study publication results in a statistically highly significant coefficient ( _b_ =0.014, _SE_ =0.004). This moderate, positive time-trend is an important element in explaining our positive result about the effect of financial education on financial behavior.

Turning to the result concerning the treatment effect of financial education on _financial literacy_ (measured through knowledge assessments), we estimate the average impact of financial education on financial literacy to be _g_ =0.263 ( _p_ =0.000, _n_ =190). Thus, our analysis of a comprehensive sample of studies ( _n_ =67) leads to a positive assessment of the effectiveness of financial education on financial literacy. This education explains 1.7% of the variance in financial knowledge and, thus, appears only slightly less effective than educational interventions in other domains, such as math and science instruction (cf. Fernandes et al. 2014, p.1867). Our positive result is in remarkable contrast to Fernandes et al. (2014, p.1867), who find that financial education only explains 0.4% of the variance in financial literacy and state accordingly that, “financial education yields surprisingly weak changes in financial knowledge presumed to cause financial behavior.” However, this result seems a bit fragile as it is based on only 12 studies and cannot, obviously, be replicated in our larger sample of studies (cf. Fernandes et al. 2014, p. 1867).

# **References in Appendix S2**

Fernandes, D., Lynch, Jr., J.G., and Netemeyer, R.G. (2014). Financial literacy, financial education, and downstream financial behaviors _. Management Science_ , 60(8): 1861–1883.

Miller, M., Reichelstein, J., Salas, C., and Zia, B. (2015). Can you help someone become financially capable? A meta-analysis of the literature. _World Bank Research Observer_ , 30(2): 220–246.

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ASER-like test scores and TSC trends for older children whose critical developmental periods were before the TSC.

# **6.1 Census literacy data, 1991-2001**

Were trends in district-level literacy different before the TSC for districts that did and did not experience improvements in sanitation by 2003? If so, it may not be reasonable to assume that districts that were exposed to the TSC in the period we study would have evolved similarly to districts without the program, if the TSC had not happened. India conducts a census that includes literacy every ten years. In this section, we match district literacy rates to our administrative records on TSC implementation.

Figure 5 presents the result. Panel (a) plots the empirical cumulative distribution function of the percentage point increase in literacy from the 1991 census to the 2001 census, separately for districts that did and did not receive any TSC latrines by 2004. The two CDFs cross and touch at several points, making clear that there is no important difference between the distributions. Indeed, the average change in literacy between these two pre-program censuses is less than 0.1 percentage points smaller in the districts that received some TSC latrines by 2003, a gap with a _t_ -statistic of 0.18.

Panel (b) shows that there is also no association between pre-program literacy trends and program intensity, measured as TSC latrines per capita reported built by 2003. If anything, a one standard deviation increase in TSC intensity is associated with a 0.2 percentage point smaller pre-program literacy trend, but again this is not statistically significant ( _t_ = 0 _._ 97).

# **6.2 Age-cohort trends in literacy, rural IHDS**

A further test of parallel trends is to compare the age gradients of literacy, in districts that did and did not receive TSC latrines in the period we study. The IHDS was conducted in – 2005 after the 2001-2003 period of the TSC that we study. However, children who are six 22
Figure 3: Event study: relationship between pre-determined measure of German comparative advantage and the acquisition of Polish firms.

<!-- Start of picture text --> .01<br>.005<br>0<br>Coefficient<br>-.005<br>-.01<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br><!-- End of picture text --> Notes: Authors’ calculation based on the Orbis database. The plot is created by regressing a dummy variable indicating German ownership on a full set of event time indicators interacted with the pre-determined measure of German comparative advantage and controlling for firm and year fixed effects (equation (3)). The omitted baseline is the last pre-treatment year, 2003. The vertical bars indicates 95% confidence interval. Results include only firms established before 2004. Germany’s comparative advantages are measured by average sector-level export shares over the period 1994-2004. Overall difference in differences results are shown in Table A.5.

Figure 4: Event study: the effects of Germany’s specialization over time on productivity <!-- Start of picture text --> .2<br>.1<br>0<br>Coefficient<br>-.1<br>-.2<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br><!-- End of picture text --> Notes: Authors’ calculations based on survey data from the Central Statistical Office of Poland. The plots are created by regressing firm-level TFPR on a full set of time indicators interacted with Germany’s initial export share and controlling for firm and year fixed effects (equation (5)). The omitted baseline is the last pre-treatment year, 2003. Estimates are based on the sample of firms with more than 50 employees. TFPR is estimated using the method in Levinsohn and Petrin (2003).

.

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