rafmacalaba/datause-encoder-data huggingface dataset:
18 features from whether the website is functioning, fast to load, available in multiple languages, and optimized for mobile viewing; to whether it includes marketing features such as customer testimonials, appealing aesthetics, a clear statement of what differentiates their product or service, and includes a clear call to action towards contacting the company or making a purchase. The final measure is based on the company’s business Facebook page, and scores the company on 15 features including whether they actively post, provide clear descriptions of what the company does, include customer reviews, highlight new products or services, offer promotions, and include attractive images.

In Appendix B, we use these digital presence data to test whether the firms that did not answer the endline survey differ systematically in these follow-up outcomes from the firms that did. The attriting firms have similar google, website, and Facebook scores, and, coupled with the balance on baseline observables for the responding sample, provide us with additional confidence that attrition is not likely to be causing systematic bias.

# **3.2 Estimating Treatment Effects**

To estimate the impact of the program on the outcomes of interest, we use the following regression for firm _i_ in randomization stratum _s_ : This regression includes dummy variables for each of the randomization strata, implicitly controlling for the combination of baseline diagnostic score, country, and whether they have a digital product. Robust (Eicker-White) standard errors are used. The coefficient of interest _β_ corresponds to the intention-to-treat effect (ITT), which is the effect of being offered the full intensive training and one-on-one consulting program, compared to just being offered the short webinars in the control group.

# **3.3 Impacts on Digital Presence, Business Practices and Pitching to Judges**

Table 2 tests whether firms implemented changes in their digital presence and business practices that had been recommended in the training, and whether they were able to pitch more effectively to judges. Columns 1 through 3 examine the impact on digital presence. We see positive and significant impacts on the Google score and Facebook score in columns 1 and 3, and a small and statistically insignificant impact on the website score. Table 3 unpacks these changes in Google score and Facebook score to see where the improvements are coming from. We see the improvement in the Google score is driven by firms having a higher page rank, and being 11 percentage points more likely to show up in the first 13
cooperation. Using the same argument (while in the case of the GLM estimates ((1) and (3)), the Maddala R<sup>2</sup> is 0.284 and 0.295 respectively) we will improve the overall explanation of the GLM estimates by adding several control variables. Tables 2 and 3 introduce control variables that improve the level of explanation while keeping the significance of the results intact.<sup>viii</sup> Table 2 presents the results of the Logistic runs, estimating the likelihood of forming a treaty. Equations (1)-(3) pertain to the precipitation variability where as equations (4)-(6) pertain to the runoff variability. The estimates of the precipitation variables suggest that it affects the likelihood of forming a treaty in a U-shaped pattern. The coefficients of the basin precipitation variables were found significant at a 10 percent level while the coefficients of the basin runoff variables were found significant at 5 percent to 10 percent in 2 equations and not significant in equation (6). Moving to the democracy and governance variables the Freedom variables yielded the best results in terms of significance level, across the two climate variables—precipitation and runoff. The other variables used, Voice and Polity of each of the riparian states provide consistent signs, but not always significant coefficients. The two dummy geography variables were not significant in this table. The trade variables are highly significant across all 6 equations and with the expected sign, suggesting that as in the case of the climate variables (precipitation and runoff), trade has an inverted U-shape effect on treaty cooperation. Diplomatic relations have positive and significant coefficients in all but one equation, suggesting that higher levels of the diplomatic engagement between the countries lead to increased likelihood for treaty cooperation. The Economic power variable has negative and significant coefficients, suggesting that power asymmetry in the basin would work against treaty cooperation. All 6 regressions yield stable estimates with Log Pseudo Likelihood that ranges between -58.89 and -60.45. The Wald χ2 values are significant at a level of 1 percent and better. The Pseudo R2 values are around 0.25 and much improved compared to Table 1.

Table 3 presents the results of the GLM and POISSON regressions, where the treaty cooperation is estimated using the number of treaties (including no treaties) as the dependent variable. A total of 8 equations are presented. Equations (1)-(4) use precipitation and equations (5)-(8) use runoff as the climate variables. The climate coefficients perform as expected in terms of sign and significance level, but the estimated coefficients in the runoff equations are more significant than those in the precipitation equation. The polity variables (both the Polity Dummy and the Polity Index) perform also as expected in terms of sign and significance level. They are 22
The authors propose a transfer payments mechanism to solve this problem, that is to compensate the losses of the countries with increasing costs function. For each State, the function to maximize now is: _n_ with the constraint ∑ _i_ = 1<sup>_T_</sup> _i_ = 0 (feasibility condition). Transfer payments are introduced to solve a problem that cooperation induces, that is the violation of the individual rationality condition. They describe also the transfer payments mechanism, which takes into account the abatement cost sharing parameter introduced by Chander and Tulkens (1991).

> This mechanism provides also coalitional rationality: “ _...the cooperative game theoretical property of inducing an imputation in the core of local games associated with each point of the solution...The cooperation thus achieved is claimed to be stronger because it is not only individually rational but also coalitionally rational or group rational_ ”.

The authors offer also another extension of the first model, introducing a non-linear damage function that considers the possibility of increasing marginal damages: where<sup>π</sup> _i_<sup>are the marginal damage costs of country</sup><sup>_i_. With this new assumption, “</sup><sup>_total_</sup> _reductions are less important than those obtained with linear damage functions, as one could expect_ ”.

The paper proposes an application with real data to the problem of acid rain involving Finland, Russia and Estonia and uses their economic data to implement the computation.

> The main result of the paper is that, as the authors say, “ _local information is sufficient for determining a succession of cooperative emissions abatement programs that converge to the international economic Pareto optimum_ ”.

Another dynamic approach is given in Mäler and de Zeeuw (1998). In this paper the authors consider the problem of acid rain taking into account the critical loads of the soil. The critical load is defined, from Nilsson (1986), as the maximal exposure to some pollutant an ecological system can adjust to without suffering long term damage. The acid rain problem affects many countries in this way: the soils have a natural buffer stock that can avoid the acidification problem, but this capability is not infinite and the buffer stock can be depleted. This depletion can be irreversible or reversible depending on the critical load, which is a feature of each soil. “ _The more and the longer depositions exceed_ 47
The share of income from nonagricultural manual labor falls from nearly 30 percent among the poorest to only around 10 percent among the richest. But, in the segmented model, the wage paid to such labor is much less responsive to food prices than in the model with perfect intersectoral mobility. In short, ruling out labor market segmentation empirically does have consequences for how we think about the distributional effects of food price increases.

# **4.3 Public Distribution System**

Assessing the role of the PDS in buffering price shocks provides a useful comparator as well as being of interest in its own right. Under the PDS, eligible households (generally, those below the poverty line) can purchase fixed rations of either rice, wheat, or sugar in “Fair Price Shops” at below-market (but non-zero) prices. If PDS prices rise at the same rate as non-PDS prices, then the above formula for _ε_ applies. However, insofar as PDS prices rise more slowly than non-PDS prices, or do not rise at all, we need to adjust by where _θj_ = 1 _−_<sup>∆</sup><sup>_<u>p</u>_</sup> ∆<sup>_<u>j,</u>_</sup> _p_<sup>_P DS_</sup> _j_ and _νj,PDS_ is the expenditure shares for PDS purchases of commodity _j_ evaluated at the PDS price. Thus, to take the extreme case, if all PDS prices stay fixed while market prices rise, then instead of _νA_ in the calculation of _ε_ , one would use the food share _net_ of the PDS expenditure share. To get _θj_ , I take the state mean unit values from the NSS-CES for the respective commodity-types (PDS or non-PDS) in each successive round (61st and 66th). Thus, my estimate of _θj_ is the ratio of percentage changes between 2004-05 and 2009-10 of state mean unit values for PDS and non-PDS purchases of commodity _j_ . Median values of _θj_ for rice, wheat, and sugar across the 18 major states are all close to unity, which means that PDS prices were indeed quite stable over this five-year period.

The proportion of rice, wheat, and sugar consumption accounted for by the PDS generally increased between 2004-05 and 2009-10 (Appendix Figure C.2). No doubt, this reflects the 25
# **Figure 8**

## **A: Olley-Pakes Decomposition of ln(TFP), by Year**

<!-- Start of picture text --> 6.00<br>Aggregate<br>Avg. Technical<br>5.00 4.76<br>Allocative<br>4.00<br>2.95 2.99<br>3.00<br>2.52<br>2.00 1.78<br>1.00<br>0.43<br>0.00<br>2003 2010<br>ln(TFP)<br><!-- End of picture text -->

## **B: Olley-Pakes Decomposition of lnTFP, by Year and Geography**

<!-- Start of picture text --> 6.00<br>Aggregate<br>5.11<br>Avg. Technical<br>5.00<br>Allocative<br>4.00<br>3.51<br>3.10<br>2.94 2.95<br>2.85<br>3.00<br>2.56<br>2.46<br>2.01<br>2.00<br>1.00 0.66<br>0.49<br>0.39<br>0.00<br>2003 - Small City 2010 - Small City 2003 - Large City 2010 - Large City<br>ln(TFP)<br><!-- End of picture text --> 32
This article has provided micro-level evidence of the importance of the price signal as a guide for institutional change for reservoir water savings. At the same time, we also have shown that when the price is high enough, a further price increase does not contribute to reservoir water savings. If this failure in water savings is rooted in the free-riding problem in the WUGs under no institutional change as our data imply, it entails social costs. The evaluation of social costs would be a worthwhile topic for further research.

In addition to price, we find that the initial conditions affect the paths of change; if the conditions are not supportive, the chance of upholding informal management increases. Our regression results suggest the following interventions for smooth institutional changes. First, to facilitate the formal appointment of a water manager, simpler topography would make water management easier. However, from a practical perspective, the cost to change the landscape may be too high to implement. Second, improving social capital among nearby farmers would make the formation of a new smaller group more attractive. However, with China’s rapid economic growth and the resulting dissolution of rural communities, improving social capital may be a cumbersome option. An investigation of intervention costs and water savings benefits could be another topic for future research. Although work is still needed, our view from micro-level data, in the context of the Chinese case, is nevertheless a useful complement to views from macrolevel and historical data, providing more detailed mechanisms of institutional change and expanding our insights for future research.

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_Differences by Firm Size_ . Having access to credit might be more important for small firms than for large firms. If small firms are less likely to have access to non-bank financing than large firms, they might be especially vulnerable to drops in demand and therefore bank credit might be especially important to them.

To see whether this is the case, we interact the dummy indicating that the firm has credit with the dummies indicating firm size (see column 1). To assess the impact on small firms, we look at the coefficient on the dummy variable indicating that the firm has a loan. To assess the impact on medium-sized and large firms, we need to sum the coefficients on the dummy variable indicating the firm has a loan and the relevant interaction terms.

The coefficients on the interaction terms are statistically insignificant. That is, we are unable to reject the null hypothesis that bank credit improves the survival rates for small, medium-sized and large firms to similar degrees. Moreover, the sum of the coefficients is negative for both medium-sized and large firms. For medium-sized firms, however, we are unable to reject the null hypothesis that the sum is zero, suggesting a more ambiguous relationship for medium-sized firms than for large or small firms.

_Omitted variables that might be related to firm performance._ If banks only provide credit to the best performing firms and these firms are more likely to survive, this could introduce a spurious correlation between survival and access to bank credit. Although the regressions include multiple controls for enterprise performance, it is possible that an omitted variable problem might remain.

To try to correct for this possible omitted variable bias, we present results from a robustness check, using an instrumental variable approach. The instrument is the average probability that other firms in the same size group, industry and region have bank credit.<sup>27</sup> This instrument should control for unobserved heterogeneity at the firm level (i.e., it does not depend on characteristics of the firm itself). The results from this analysis are similar to the previous

> 27 Note that the  ̳leave-one-out‘ average omits the firm itself (i.e., for each firm the average is only for other firms). These  ̳leave-one-out‘ averages are commonly used in firm level studies as instruments or in place of potentially endogenous variables.  See, for example, Aterido and others (2011); Clarke (2009); Dollar and others (2005); Fisman and Svensson (2007); and Svensson (2003).

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37. Recently, however, the investigators have discovered<sup>that about 100 of the schools</sup> in the sample have only one sixth grade teacher. Thus,<sup>in these schools there is no ambiguity</sup> about the link between the teacher and the students.<sup>Moreover, these schools are of special</sup> interest to policy makers. These are small, rural schools,<sup>precisely those schools that provide</sup> the greatest challenge to policy aimed at improving<sup>teaching. Because of the availability of</sup> the nationally representative sample of schools, it is<sup>possible to locate these schools quite</sup> accurately in terms of the national distribution of a variety<sup>of important contextual variables.</sup> 38. Another advantage of using these schools is that,<sup>because our goal is to evaluate</sup> instruction during sixth grade rather than to estimate<sup>school effects over the entire course</sup> of primary schooling, it is now possible to utilize<sup>grade point average at fifth grade as a</sup> covariate for predicting student achievement. This<sup>is a powerful covariate with quite large</sup> differences between schools, probably because<sup>grades in Thailand are linked in part to</sup> regional and national examination scores.

39. After deletion of missing data, the obtained sample<sup>for the present analysis includes</sup> 103 sixth grade classrooms in 103 schools and a total<sup>of 2111 students. This compares to the</sup> national sample which has 411 schools, 3808 teachers,<sup>and 9768 students.</sup>

# **Instruments**

40. Below we describe the outcome variables (student<sup>achievement and instructional</sup> quality), the key predictors (intensity of inservice<sup>training and supervision), and the key</sup> 15
**Figure 14**

Forecasted share of nuclear energy in t+30 by energy project and according to year of publication Note: US scenarios are depicted as large squares <!-- Start of picture text --> 50<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>1970 1975 1980 1985 1990 1995 2000 2005 2010<br>Department of the Interior EUOGEP Energy Research and Development Agency European Union / DGTREN<br>Ford Foundation Greenpeace Harvard Business School IEA<br>IIASA IPCC OECD PEW<br>Shell International BV Union of Concerned Scientists WEC WEC and IIASA<br>Nuclear share (%x100)<br><!-- End of picture text --> 23
while the _LATEac_ measures how well the new CPS program fares in comparison with the existing offer. Measuring the impacts on both subLATEs is therefore crucial to understanding how the program affects outcomes, and therefore for proving policy recommendations.

We study the reduced form effects for each treatment group using midline (1 year after construction) and endline (2 years after construction) data, but also the effects on both sub-groups of compliers. The reduced form effects are straightforward to derive, empirically valid, and policy relevant as they reflect the program’s impacts in a typical Cambodian preschool environment. The subLATE parameters are not identified but the _LATEhc_ can be bounded. In addition, under heavier assumptions, the subLATEs can be estimated using additional instruments interacted with the treatment variable.

We have four main findings. First, the construction of CPS—the implementation of which closely followed the program and experimental design—increased preschool participation. Children in villages with a newly built CPS were about 52 percentage points (pp) more likely to have ever attended a CPS by the time they were between four and six years old (or 11 pp to have ever attended any preschool). Put differently, they were enrolled, on average, about 4.5 months longer than children in the control group.

Second, we find significant positive ITT impacts one year after the program started but these effects disappear after two years. More specifically, children in treatment villages scored about 0.04 SD higher on an index of cognitive development and about 0.07 SD higher on an index of socio-emotional development, one year after the program’s implementation. These ITT impacts are consistent with moderate-to-large impacts on children who would have stayed at home absent the program ( _LATEhc_ ). We show that this subLATE parameter can be bounded between 0.14 SD and 0.46 SD on our cognitive development measures using light assumptions; with additional assumptions the bounds can be narrowed to between 0.14 and 0.35 SD and, under even more restrictive assumptions, the _LATEhc_ can be estimated to be around 0.19 SD. We also estimate the effect on children who would have attended another preschool absent the program ( _LATEac_ ) to be consistently close to zero, confirming that the quality of education provided in the newly built preschools was not fundamentally improved. These results are consistent with direct measures of school and teacher quality that indicate that the CPS construction program improved preschool quality mostly in dimensions that tend to be weakly correlated with child performance (e.g. equipment, materials, quality of the building–which we refer to as ”structural quality”) while it did not improve the quality of educational processes (e.g. teacher quality or teaching practices–which we refer to as ”process quality”). After two years (at endline), however, these impacts were no longer statistically significant for the full sample. As discussed below, the 3
their companies’ business strategies, policy barriers, operational obstacles, and investments in the host economy. Information was collected on general characteristics and investments of the company, the importance and effect of global megatrends on the company’s business operations, FDI’s contribution to the host economy, and the importance of investment policy factors and operational obstacles faced by foreign-owned affiliates.

The survey was designed to generate results that are representative at the country level and comparable across countries. It targeted a statistically representative sample of existing foreign-owned affiliates across the 10 surveyed MICs. The target was to reach 125 interviews per sector (manufacturing and services). Each country sample comprises roughly 250 affiliates with at least some level of foreign equity ownership and at least five employees. The only exception is Nigeria, where, due to sampling frame limitations, the sample comprises 164 respondents (55 manufacturing and 109 services). Thus, across the 10 target countries, more than 2,400 responses were collected.

# **4. DESCRIPTIVE STATISTICS**

The GIC 2019 Survey attempted to draw a representative sample of foreign investors across the 10 surveyed emerging markets. This section outlines the profiles of the 2,424 survey respondents.

_Sector and subsector:_ Survey respondents operate across a wide range of sectors. By design, about half of surveyed affiliates were in the manufacturing sector, and about half were in services. Within each sector, the sample covers many subsectors: _Machinery and Equipment_ comprise the largest subsector within manufacturing, and _Wholesale and Retail Trade_ is the largest subsector within services in our sample. However, no one subsector accounts for more than 11 percent of the overall sample (see Table 1).

**Table 1. Respondents by sector and subsector**

|**Sector and Subsector**|**N**|**Share of total sample**|
|---|---|---|
|**MANUFACTURING**<br>|**1,190**|**49.1%**|
|Machinery and equipment<br>|151|6.2%|
|Metals and metal products|124|5.1%|
|Automobiles, other motor vehicles, and transport|116|4.8%|
|Rubber and plastic products|108|4.5%|
|Chemicals and chemical products|84|3.5%|
|Information technology and telecommunications|72|3.0%|
|Agroprocessing, food products, and beverages|71|2.9%|
|Electrical and electronic equipment and|52|2.1%|
|Textiles, apparel, and leather|51|2.1%|
|Wood products, paper, and printing|45|1.9%|
|Pharma|18|0.7%|
|Refined petroleum products, coke, and nuclear fuel<br>|8|0.3%|
|Other or unclassified manufacturing|290|12.0%|
|**SERVICES**|**1,234**|**50.9%**|
|Wholesale and retail trade|270|11.1%|
|Business services|116|4.8%|
|Logistics, transport, and storage|101|4.2%|
|Computer and software services|85|3.5%|
|Administrative and support services|59|2.4%|
|Construction|58|2.4%|

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