rafmacalaba/datause-encoder-data huggingface dataset:
Table 6: Demonstration increases long-run adoption of improved seed, while decentralizing demonstration only increases medium-run adoption

||||Promot|ed crops|||
|---|---|---|---|---|---|---|
||One <br>post-|year<br>demo|Two <br>post-|years<br>demo|One a<br>years p|nd two<br>ost-demo|
||||Improv|ed seed|||
||(1)|(2)|(3)|(4)|(5)|(6)|
|Any demo in village|0.056|0.028|0.026|0.022|0.045|0.027|
||(0.017)|(0.020)|(0.012)|(0.014)|(0.013)|(0.015)|
||[0.001]|[0.167]|[0.026]|[0.106]|[0.000]|[0.068]|
|Shared demo in village||0.018||0.019||0.019|
|||(0.022)||(0.016)||(0.017)|
|||[0.422]||[0.236]||[0.278]|
|Decentralized demo in village||0.065||-0.006||0.035|
|||(0.027)||(0.014)||(0.018)|
|||[0.018]||[0.658]||[0.057]|
|Control mean|0.036|0.036|0.022|0.022|0.031|0.031|
|Wave FE|X|X|X|X|X|X|
|# of observations (Farmer-crop-wave)|3,289|3,289|2,244|2,244|5,533|5,533|
|# of clusters (Farmer group)|96|96|70|70|96|96|

_Notes:_ Selected regression coefficients from Equations 1 and 4 are presented above, with included control variables listed below. Robust standard errors clustered at the farmer group level are in parentheses, and p-values in brackets.

# **4.4 Decentralizing demonstration increases adoption, but these additional impacts vanish over time**

**Empirical strategy** We now leverage our multi-arm experimental design to estimate the additional impact of shared and decentralized demonstration relative to regular demonstration. Keeping similar notations as above, we now estimate:

Improved seed _hcvt_ = _αt_ + _β_ 1 _,t_ Any demo in village _v_ + _β_ 2 _,t_ Shared demo in village _v_ + _β_ 3 _,t_ Decentralized demo in village _v_ + _εhcvt_ (4) where Shared demo in village _v_ is a binary variable indicating that demonstration implemented in village _v_ followed a shared modality, while Decentralized demo in village _v_ indicates that a decentralized modality was followed in village _v_ . As we control for Any demo in village _v_ , _β_ 2 _,t_ and _β_ 3 _,t_ capture the additional impact of shared and decen32
_Source: TED (Tenders electronic daily). Supplement of the Official Journal of the European Union._ In these two auctions, we can perceive how much freedom the law provides regional administrations and at the same time how general or strict their auction requirements can be. In fact, although in both projects the quantitative and qualitative weights are assigned equally, the sub-criteria are very specific and binding for the competitors in Veneto, while the same sub-criteria are fairly general in the case of Lombardy, in which only the main variables are cited and the competitors are free to elaborate on different projects. In this case, we can clearly distinguish between a highly regulatory and a more operational approach to the concession call.

# **Highway Concessions**

Fifteen highways were concessioned after 1999 and currently 4,359.4 km of the 6,825.3 km national network are under concession to two private groups. These are Atlantia, headed by the Benetton family and grouping several Italian banks and entrepreneurs, and the Atm holding, headed by the Gavio family and grouping other private companies. These two manage, respectively, seven and eight highways each, although Atlantia is by far the largest company, as it has been controlling the whole Autostrade group since 1999 (which by itself includes concessions on 2,854.6 kilometers) and automatically obtained a dominant position in the market.

Each highway concession is managed by a specific on-purpose-created company. This is part of the larger group, either totally or partially because other private or public companies own its shares. Table 2 reports the average equity composition of the companies managing the highway concession and distinguishes between the shares possessed by the main group (the group column), those possessed by the regions or other national authorities (ministries or sub-regional ones), by ANAS or by public or private agents that own some shares but do not enjoy any decision-making power. The figures in Table 2 are reported both for the year 1992 (before the concession, when every highway was publically managed) and for 2005 (when 64 percent of the network was concessioned).

Table 2: Average equity composition and concessionaire network.

|**Concessionaire**|**Network**|**Aver**|**age % own**|**ership in**|**2005**|**Aver**|**age % ow**|**nership in**|**1992**|
|---|---|---|---|---|---|---|---|---|---|
|**(concessions)**|**in km**|||||||||
|||Group|Regions|ANAS|Other|Group|Regions|ANAS|Other|
|**Atlantia Group (7)**|3402.3|74.3|6.7|0|0.8|67.7|6.9|14|3.9|
|**Atm Group (8)**|957.1|74.5|14.9|4.5|0.3|12.7|37.6|5|2.52|
|**Direct Public**|1252.8|0|81.8|0|14.3|0|82.7|0|3.3|
|**Control (7)**||||||||||
|**ANAS**|1213.1|0|0|100|0|0|0|100|0|

_Source: Benfratello et al. (2006). Group refers to the concession holder; regions refer to regional and other local public authorities involved in the highway ownership; ANAS is the public company managing highways and other public streets owned 100 percent by the Ministry of Economy and Finance; and other refers to_ 6
8 (b) lhs s tibility of the vector pcilatioa in the target arve to the prcpsed p-sticide. The target vector ne to be evaluated to determine if resistance has already develmed to the prcpoed c x d. Res my hMe oxred by prior ecposure to the prcqosed coqxxvid or cars-resistance to a related o. The prcposed ocqxcrd may already be ineffective for vector control in the target area because of _prevics_ aWlications in _agricultural_ proram. Where resistance already exists to a given dcemical group, no use shcx ld be mde of ccuIpomfs ,whidh are closely related chemically or cross-resistance might occur.

**(c)** The safety of pecple exsed to the prcuwosed pesticide. The tcodcity of the given corpound should be low ena4gh to ensue the safety of the spray persorwel applying it and of the inhabitants of the houses or area in whidh it is being applied. Oral, dermal and inhalation toxicity, as well as bo&h acute and dhronic toxicity must be taken into acomnt. lb. latter my be especially iqportant for the spray persmnnel Who are under prolcnged exposure to the _cqmpcund,_ frequently under climatic conditians Whidh _disccurage_ the use of protective clothing. Adequate training of spray persannel and, if necessaryf, provisimn of protective e*qipment (clothes, hats, masks) dhould be c **nsidered.** Reference should be **m** to the World Hbalth Organization classification of pesticide oral and drma
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systematically revised with the 2011 ICP. Therefore, although $1.44 would buy the same bundle of goods in the US in 2011 as $1.25 did in 2005, the relative shift in prices means that $1.44 in 2011 would buy significantly less in most poor countries (i.e, it lowered significantly the real value of the poverty line for most poor countries of the world). The Dykstra et al. line very dramatically changed the goal posts, and not surprisingly, the resulting estimates radically reduced the global poverty count.

Nevertheless, a useful point that came from Dykstra et al. is that it forces one to think about what we mean when we say we want to keep the real value of the $1.25 line the same, but express it in 2011 PPPs. Their $1.44 line did maintain the real value of the $1.25 line, but only for the US (or more accurately, only for countries with the � defined in equation (2) with values close to 1.15).

In our view, the goal should be to try to find a line that would keep the definition of the line as consistent as possible, and on average buy the same bundle of goods as the $1.25 line did, but in poor countries - such as those used to construct the $1.25 line.<sup>51</sup> This approach has some attractive features, including from a communications standpoint. It is relatively easy to state that the definition of the new poverty line is unchanged, and that, on average, it buys the same bundle of goods that could have been purchased in 2005 with $1.25 in the fifteen countries that define the international poverty line.

Another desirable attribute is that it is a relatively simple adjustment to make. This decision ensures that the concept (poverty lines of the same 15 poor countries); method (simple average across these same 15 countries); and data (the same 15 national poverty lines) are unchanged. The only element that is changing is the PPPs used to convert the national lines to estimate an international line. Table 6 provides the 2005 and 2011 PPP values of these national poverty lines, as well as the PPPs and CPIs used for the conversion.<sup>52</sup> Taking the average of the same 15 national poverty lines that yielded the $1.25 line in 2005 PPPs, now gives $1.88 per day in 2011 PPPs, which is rounded to $1.90 for ease of communication. Because this average is based on the exact same national lines as the $1.25 line, we believe the $1.90 line reflects the

> 51 This approach has risks, in particular if the change in relative prices for those 15 countries is radically different from the change in relative prices for most poor countries but, as we will see, this is not the case for the present update.

> 52 In effect, this conversion applies the conversion factor �� �����/����� to the 2005 PPP-adjusted dollar �����/����� value of the national poverty lines to get the 2011 PPP-adjusted dollar value.

30
# **Figure 2: Actual performance or coincidence?**

In Section 2.4.1 we described the need for benchmark models to be able to produce benchmark distributions. These distributions are centered around the benchmark outcome and help us understand if an observed outcome and its associated KPI could be due to chance (within the buffer zone) or constitutes a significant signal for additional performance beyond any reasonable doubt. The 50% and 95% cutoffs are illustrative and can be adjusted in practice.

<!-- Start of picture text --> Outcome is Outcome is<br>worse significantly better<br>than benchmark than<br>95% confidence Outcome<br>interval variable<br>50% confidence<br>interval Very strong KPI<br>Weak negative KPI<br>Strong negative KPI<br>Very strong negative KPI<br><!-- End of picture text --> Source: Authors.

interval) or significant (outside the 95% confidence interval).5 A KPI that falls inside the 50% confidence interval does not make a strong case for either financial reward or penalty because its signal is too weak to rule out the possibility of chance. When a KPI falls outside the 50% confidence interval, it is much harder to attribute the result to chance. Hence, a step-up or step-down may be warranted. A KPI that falls outside the 95% confidence interval is a statistically significant signal for over- or underperformance beyond a reasonable doubt. A step-up or step-down is certainly warranted. Table 1 shows a possible incentive structure, where the size of the step-up or step-down scales with the strength of statistical evidence for performance.

# **2.5 Sustainability performance target**

An SLB reaches its SPT when the cumulative benchmarked KPIs cross a specific threshold (see lower panel of Figure 1). Targets, therefore, share the same unit as benchmarked KPIs and are expressed in relation to the benchmark outcome. Note that while benchmarked SPTs are set in absolute numbers, they are not interpreted in absolute terms since they are a sum of benchmarked KPIs.6 The key challenge is how to set targets that are ambitious and feasible. We return to this question in Section 5.

**Example** 10,000 km<sup>2</sup> of avoided primary forest loss above benchmark over the next five years.

> 5While confidence intervals of 50% and 95% are common reference points in statistical analysis, the precise boundaries may differ for each SLB issuance and change during the structuring process.

> 6Some financial products may evaluate social or environmental performance on a yearly basis, combined with a yearly payout structure. SPTs would then also be annual, rather than cumulative. In practice, most instruments will define periodic evaluation windows that are less frequent than annually which necessitates expressing SPTs as cumulative benchmarked KPIs.

7
# Table 6 Heavily Indebted Countries: Private Invedtment and Private Externl **Meets. 1-1988 Il**

|Ai|1980|**1981**|**1ea**|**19U**|**1984**|**1906**|**19s**|**1987**|**188**|
|---|---|---|---|---|---|---|---|---|---|
|rgentna<br>Private External Aserte|14.0|23.2|31.2|31.1|33.0|35.2|33.1|30.7|36.4|
|Private Investment|13.0|9.4|8.9|7.9|6.9|8.8|0.0|6.8||
|Bolivia||||||||||
|Private External Assets||||||||||
|Private Investment|7.3|3.8|5.0|2.0|1.0|3.0|4.0|4."||
|Brazil||||||||||
|Private External Assets|7.2|9.6|6.9|5.8|12.3|8.2|16.7|31.9|23.0|
|Private Investment|14.5|12.0|11.7|9.7|O.5|10.5|12.3|12.8||
|Chile||||||||||
|Private Extemal Assets|1.2|-1.4|-1.3|0.0|-0.7|-0.9|-2.0|-3.2|-0.0|
|Private Investment|15.6|17.5|6.5|4.9|7.3|0.8|7.0|10.0|9.5|
|Colombia||||||||||
|Private External Assets|0.5|-0.1|-0.4|0.0|1.6|3.0|4.7|6.6|8.9|
|Private Investment|11.4|12.0|11.1|11.0|10.0|9.4|0.3|11.1||
|Ecuador||||||||||
|Private External Assets|3.8|3.0|2.8|3.0|3.8|3.8|4.0|4.1|3.8|
|Private Investment|14.1|11.7|13.0|8.6|9.0|9.5|9.8|14.0||
|Mexico||||||||||
|Private External Aeets|16.3|22.9|24.8|36.2|36.3|37.7|42.0|52.3|35.6|
|Private Investment|13.9|14.7|12.7|10.0|10.8|12.2|13.3|13.5||
|Nigeria||||||||||
|Private Extemal Assets|6.2|3.3|0.8|3.4|4.2|8.0|13.1|21.0|22.0|
|Private Investmen'||||||||||
|Peru||||||||||
|Private External Assots|3.5|2.3|3.6|2.4|2.7|4.2|5.5|0.8|6.4|
|Private Investment|9.7|11.4|11.6|7.0|6.9|6.8|0.3|10.0||
|Philippines||||||||||
|Private External Assets|0.7|7.0|8.7|5.4|4.8|6.7|10.4|10.9|10.1|
|Private Investment|18.4|18.5|18.1|18.8|14.5|11.4|9.7|10.6|12.5|
|Uruguay||||||||||
|Private External Assets|-0.7|-1.1|-0.9|0.3|0.0|0.5|0.2|0.3|-0.3|
|Private Investment|11.4|10.7|7.9|6.0|5.2|4.5|4.4|5.4||
|Venezuela||||||||||
|Private External Assets|15.2|16.3|15.7|31.9|30.9|30.5|24.8|20.0|23.3|
|Private Investment|13.0|9.8|7.7|4.5|6.6|0.6|6.8|6.6||

I/Private external assots are in millions of US dollars, while private investment is the share of GOP.

Source: Data on private extemal assets are from Dooley and Stone (1901). Private mivestment data are from Pfefferman and Madarasy (1990).
costs (e.g. Feyrer 2009), (ii) information frictions (e.g. Chaney 2011), or (iii) trust frictions.<sup>2</sup> In order to appreciate what is driving the distance-reducing effect brought about by eBay, we need to isolate these frictions.

We start by controlling for bilateral shipping costs, which are included in our eBay dataset. Somewhat surprisingly, we find that adding shipping costs barely affects the distance coefficient. As seen in Figure 4, this is because shipping costs are uncorrelated with distance. Furthermore, ad-valorem shipping costs seem higher online than offline, probably as there are less bulk-shipping scale economies for online shipments. It is thus highly unlikely that a reduction in shipping costs is driving the online death of distance.

To explore the trust and information channels we interact the distance coefficient with indicators of corruption and information frictions at the country level. We find that the distance-effect reduction is largest for exporting countries with high levels of corruption and which are relatively unknown to consumers, as measured by Google search results. This suggests that online markets reduce the distance effect by providing both trust and information.<sup>3</sup> We obtain the same result when we interact distance with indices of information intensity at the product level, namely Broda and Weinstein’s (2006) trade elasticities as well as indices we build using the WIPO Global Brand Database and eBay’s trademark-infringement alert system. We also test whether the distance effect is reduced by the eBay seller-rating mechanism, which increases importer trust in exporters. As predicted, we find that distance matters significantly less for sellers with higher ratings. This confirms that distance captures trust and information frictions which are reduced by technology.

As highlighted earlier, different demographics could also be driving the different distance effects. To control for these differences, all our specifications include importer and exporter fixed effects that are specific to online and offline flows. Yet it could be argued that country characteristics that drive the selection of eBay traders are correlated with the distance effect. 2Horta ̧csu et al. (2009) attributed the remaining effect of distance on eBay trade to trust, arguing that people trust their neighbors more than people further away. An alternative explanation is provided by Blum and Goldfarb (2006) who showed that gravity holds in the case of website visits and argued this was because distance proxies for taste similarity.

> 3We also find that the distance differential is highest for country pairs that do not share a language, i.e. when information and trust frictions are high.

4
**_Energy demand models for policy_** **_<u>formulation</u>_**

- Murphy, R., N. Rivers and M. Jaccard, 2007, Hybrid modelling of industrial energy consumption and greenhouse gas emissions with an application to Canada, Energy Economics, 29, pp. 826-46.

- Nakata, T., 2004, Energy-economic models and the environment, Progress in Energy and Combustion Science, 30, pp. 417-78.

- Narayan, P. K. and R. Smyth, 2005, The residential demand for electricity in Australia: an application of the bounds testing approach to cointegration, Energy Policy, 33, pp. 467-74.

- Nasr, G.E., E.A. Badr and G. Dibeh, 2000, Econometric modeling of electricity consumption in post-War Lebanon, Energy Policy, 22, pp. 627-40.

- Newell, R.G. and W. A. Pizer, 2005, Carbon mitigation costs for the commercial sector: discrete-continuous choice analysis of multifuel energy demand, RFF-DP-05-13, Resources for the Future, Washington, D.C.

- O’Doherty, J. nad R,S.J. Tol, 2007, An environmental input-output model for Ireland, Economic and Social Studies, vol. 38(2), pp. 157-190.

- OEF, 2006, Research on output growth rates and Carbon Dioxide emissions of the industrial sectors of the EU-ETS, Oxford Economic Forecasting, Oxford.

- OTA, 1991, Energy in Developing countries, OTA-E-486, Office of Technology Assessment, U.S. Congress, U.S. Government Printing Office, Washington, D.C.

- Ozlap, N. and B. Hyman, 2006, Energy end-use model of paper manufacturing in the US, Applied Thermal Engineering, 26 (5-6), pp. 540-48.

- Pachauri, R.K. and L. Srivastava, 1988, Integrated Energy Planning in India: A Modelling Approach, in The Energy Journal, vol. 9, No. 4, October.

115
**Table 3a: Growth in real per capita sectoral GVA and GDP by distance to major city**

||**Manufacturing**|**Construction**|**Agriculture**|**Services**|**GDP**|
|---|---|---|---|---|---|
|2004-2006 Expansion|2.98**|-1.44|7.85***|1.49***|2.11***|
||[2.44]|[-0.81]|[2.94]|[3.51]|[2.91]|
|2008-2009 Recession|-12.30***|-28.83***|10.48***|-7.62***|-6.80***|
||[-11.87]|[-18.09]|[2.97]|[-18.85]|[-11.20]|
|2010-2011 Rebound|9.39***|2.95|8.24***|-2.61***|3.63***|
||[4.79]|[1.16]|[3.37]|[-6.24]|[4.94]|
|2013-2017 Expansion|2.76**|-6.30***|7.61***|-2.63***|0.37|
||[2.43]|[-2.93]|[3.52]|[-7.58]|[0.75]|
|2018-2019 Recession|-1.09|-40.19***|7.74***|-5.27***|-4.58***|
||[-0.67]|[-13.51]|[3.44]|[-9.11]|[-7.80]|
|2004-2006 Expansion X Distance|0.24|0.58|0.45|-0.09|0.09|
||[1.05]|[1.21]|[0.76]|[-1.09]|[0.71]|
|2008-2009 Recession X Distance|0.60**|0.80*|-0.41|0.13*|0.24*|
||[2.43]|[1.75]|[-0.57]|[1.84]|[1.79]|
|2010-2011 Rebound X Distance|-0.07|1.77***|0.60|0.25**|0.44**|
||[-0.16]|[3.03]|[1.04]|[2.41]|[2.55]|
|2013-2017 Expansion X Distance|0.09|-0.03|0.09|0.15*|0.03|
||[0.27]|[-0.05]|[0.20]|[1.88]|[0.33]|
|2018-2019 Recession X Distance|-1.09**|0.89|-0.12|0.42***|0.37***|
||[-2.22]|[1.23]|[-0.27]|[3.13]|[2.91]|
|Constant|4.54***|13.08***|-6.04***|6.22***|3.66***|
||[9.75]|[16.54]|[-5.08]|[42.07]|[14.06]|
|Observations|1,134|1,134|1,134|1,134|1,134|
|R-squared|0.23|0.47|0.04|0.42|0.39|
|Number ofprovinces|81|81|81|81|81|

Notes: t-statistics based on robust standard errors (clustered by province) in parenthesis *** p<0.01,

** p<0.05, * p<0.1. All regressions use data on all 81 provinces and employ province fixed effects. Distance to major city is computed as the shortest distance between the province centroid and the centroids of the provinces with the two largest cities (Istanbul and Ankara) using the Vincenty method. This is measured in units of 100 km.

20
response of government, both central and state, to the food-price crisis of 2007-08, as PDS coverage was expanded (Khera, 2011), though take-up of the existing program may have also increased. Since 2004-05 is the base year for the welfare analysis, I initially compute _νj,PDS_ from NSS61-CES data to reflect PDS coverage circa 2004-05. However, to address PDS coverage expansion, I also calculate for each PDS commodity _j_ and decile of per-capita expenditures _q_ within each state _s_ the ratio _Rj,s,q_ of average PDS expenditures in 2009 to average PDS expenditures in 2004. Adjusting for 2009-10 PDS coverage is, then, just a matter of multiplying _νj,PDS_ in equation (15) by the _Rj,s,q_ corresponding to that household’s state and decile.<sup>33</sup> The quantitative implications of these adjustments are shown in Figure 5. PDS coverage circa 2004-05 leads to a substantial “improvement” in the welfare elasticity for the poorest two or three deciles. When higher 2009-10 PDS coverage is factored in, the elasticity profile shifts up again ever so slightly, but only for the bottom two deciles. Although the PDS evidently plays a significant role in protecting the welfare of the rural poor, even the distributional effects of this giant safety net scheme are dwarfed by those of the rural wage channel, as we will see next.

# **4.4 General versus partial equilibrium**

Partial equilibrium welfare analysis _assumes_ that _ψ_ = _δ_ = 0 so that, from equation (13), Ω= _λ_ Π _/αK − νA_ . Since _λ_ Π _/αK_ is equivalent to the ratio of crop revenue to total consumption,<sup>34</sup> Ω _−νA_ is just the net consumption ratio, Deaton’s (1989) justly celebrated workhorse. Figure 6 compares the partial equilibrium welfare elasticity profile (dashed curve) to the general equilibrium case already discussed. In partial equilibrium, the poorest rural households in India would experience a welfare loss of around 0.3 percent for a 1 percent uniform increase in agricultural prices. But the distributional gradient is very steep, so much so that the top

> 33To be clear, while NSS66-CES provides household-level data on PDS consumption from 2009-10, these are not the same households as in the NSS61-CES of 2004-05. Thus, to keep 2004 as the base-year, I need to impute to this latter sample of households what their 2009 PDS coverage would have been.

> 34This is because _λ_ Π _/αK_ = Π _AKA/yαK_ = _PAYA/C,_ where _C_ is household consumption.

26