rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 7:  Regression Results: Determinants of PM10 Concentrations In Kitchens and Living Areas**<sup>**a**</sup>

||Kitchen &<br> Living|Kitchen|Kitchen|Living|
|---|---|---|---|---|
|Living Room Dummy|-40.057<br>(3.52)**||||
|Mud Walls|252.921<br>(9.84)**|261.472<br>(6.67)**|253.896<br>(6.53)**|229.729<br>(6.39)**|
|Mud Walls,|-158.160|-121.130|-124.058|-163.725|
|Detached Kitchen|(3.99)**|(1.72)|(1.76)|(3.83)**|
|Thatch Roof|-100.357|||-70.898|
|(Living Room)|(5.17)**|||(6.01)**|
|Kitchen Windows, Doors|-32.016|-39.906|||
|Open After Midday Meal|(2.25)*|(1.79)|||
|Detached Kitchen|-46.711<br>(4.25)**|-40.672<br>(2.48)*|-37.599<br>(2.44)*|-57.381<br>(4.86)**|
|Open-Air Kitchen|-64.134<br>(4.31)**|-88.337<br>(4.11)**|-79.887<br>(3.77)**|-80.504<br>(5.90)**|
|Jute|-41.136<br>(3.45)**|-40.645<br>(1.97)|-45.233<br>(2.20)*|-41.225<br>(3.27)**|
|Kerosene|-89.758<br>(8.15)**|-103.172<br>(6.59)**|-106.729<br>(7.46)**|-76.197<br>(6.68)**|
|Lpg/Lng|-102.597<br>(4.16)**|-113.334<br>(3.16)**|-112.523<br>(3.40)**|-89.441<br>(3.04)**|
|Piped Natural Gas|-136.411<br>(12.09)**|-144.226<br>(9.27)**|-155.285<br>(10.22)**|-135.870<br>(10.07)**|
|Constant|289.830<br>(16.43)**|287.410<br>(11.47)**|258.563<br>(17.47)**|235.342<br>(19.94)**|
|Observations|424|207|234|246|
|R-squared|0.46|0.41|0.40|0.54|

a Huber-White robust t statistics in parentheses Robust t statistics in parentheses significant at 5%; ** significant at 1% 21
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balanced repeated replication (BRR) weights provided in the data to obtain sampling errors. In addition, we estimate measurement errors by estimating variation among point estimates obtained for different plausible values. As up to PISA 2018 databases contain only five plausible values and 10 since 2018, we use only the first five for 2018 and 2022 data. The final standard errors are obtained by the so-called Rubin’s formula, the same way as in the official OECD (2022) publication.

# **4. Results**

The overall learning loss estimates are presented in Table 2 (column 1), along with estimates depending on weeks of closures for four different measures of the length of school closures (columns 2-5). The overall decline in mathematics associated with pandemic-era school closures is 12 points. In 2022, the average within-country SD of mathematics is 86.2. Thus, the decline in achievement is equal to 14.2 percent of a SD. Considering that one year of learning is equivalent to roughly 20 points on the PISA scale (Avvisati and Givord 2023), then this means that on average across around 70 countries students lost an equivalent of more than 7 months of learning.

Results in columns (2) to (5) show that one week of school closures is associated with an additional decline in student achievement. The estimates per week of closures vary across the measures of the length of closures and are not directly comparable as they reflect different definitions of closures. The clearest definition is the full closure defined by UNESCO; thus, this parameter (-0.44) will be used further to estimate the global welfare losses. We compare results calculating the learning loss for different percentiles of each measure of the length of closures. Figure 1 compares the estimated learning loss for the countries with the shortest (10<sup>th</sup> percentile of the length of closures in each of the four measures), short (25<sup>th</sup> ), average (50<sup>th</sup> ), long (75<sup>th</sup> ) and the longest school closures (90<sup>th</sup> ).

11
participatory approach based on mutual trust and valuing local expertise is important in undertaking such analysis.

As this approach becomes more widespread, PE analysis should probably be integrated in standard documents, such as sector studies and public expenditure reviews, which are already being used to support existing operational activities. This would allow updating them on a regular basis, which would be particularly useful when dealing with long-term operations. It would also help target a wider audience, which is important particularly when outputs are not disseminated and team members are not necessarily aware of existing PE studies.

Finally, the approach and methodology of how PE studies are carried out is important. Sector task teams have to be involved at all the stages of this analysis. This is extremely important for buy-in and subsequent follow through. Without this, the usefulness of a political economy analysis is likely to be low.

Overall, even though PE studies are useful, PE analysis should not be regarded as a “magic bullet” that will unleash a process of reform through some miraculous process. Besides providing knowledge about the political realities on the ground, PE analysis should also come up with practical recommendations for approaching change, which ultimately would result in adjustments in the design and implementation of projects. For this reason, task teams should invest time up-front in developing a very clear idea about what questions they would like the political economy analysis to answer and then ensure that the analysis is carried out in a manner that is participatory and inclusive.

While it might become feasible to publish the findings of such analysis at some point in the future, given the sensitivities associated with such analysis, a published report should not be the end goal for such analytical work. Rather, the goal should be to inform staff and management on how sensitive policy and implementation issues should be approached, given the political environment, with the clear objective to maximize development impact.

15
causality running from exporting to innovation as well the reverse. They find no evidence of innovation causing firms to export and instead find that exporting causes firms to innovate more. Other studies that suggest that exporting may lead to higher propensity to innovate include for example, Salomon and Shaver (2005), Yang and Chen (2012), Gorodnichenko et al. (2010), Girma et al. (2008), and Criscuolo et al. (2010).

Available evidence suggests that increasing the supply of human capital in the country through expanded university programs and/or relaxed immigration rules is likely to have positive impact on R&D effort and innovation (Bloom et al. 2019, Toivanen and Lotta Väänänen 2016, Valero et al. 2019, and Hausman 2018). Studies at the firm-level also report higher skillendowment of firms to be associated with greater innovation activity (Leiponen 2005, Piva and Vivarelli 2009). Cohen and Levinthal (1989, 1990) claim that highly qualified employees are a firm’s primary vehicle for absorbing external knowledge and, consequently, for enhancing its absorptive capacity. This argument can be extended to the management of the firm (see for example, Koellinger 2008).

The quality of the business environment and institutional development can impact R&D activity in several ways. The business environment affects how firms appropriate the outcomes of R&D efforts, the rate of return from such efforts, and the ability of firms to exploit technological opportunities (OECD/Eurostat 2018). Several elements of the business environment are at play such as the level of corruption, rule of law and the quality of courts, macro-economic stability, patent protection, investor protection, and so on (see for example, Alam et al. 2019 for an overview of the related literature). For instance, Murphy et al. (1993) argue that innovators are particularly vulnerable to expropriation by government officials due to their inelastic need for government services such as permits and licenses, resulting in high risks, uncertainty and vulnerability. Xu and 17
- Dussaux, Damien (2020). _The joint effects of energy prices and carbon taxes on environmental and economic performance: Evidence from the French manufacturing sector_ . Paris: OECD.

- Earnhart, Dietrich H., Madhu Khanna, and Thomas P. Lyon (2014). “Corporate Environmental Strategies in Emerging Economies”. _Review of Environmental Economics and Policy_ 8. Publisher: The University of Chicago Press, 164–185.

- Elliott, Robert J. R. et al. (2019). “Estimating the direct and indirect impact of typhoons on plant performance: Evidence from Chinese manufacturers”. _Journal of Environmental Economics and Management_ 98, 102252.

- Fan, Haichao et al. (2019). “Going Green in China: Firms’ Responses to Stricter Environmental Regulations”.

- Fan, L. W. et al. (2017). “Does energy efficiency affect financial performance? Evidence from Chinese energy-intensive firms”. _Journal of Cleaner Production_ 151, 53–59.

- Fogleman, Maxwell, Layla Fakhrzadeh, and Thomas E. Bernard (2005). “The relationship between outdoor thermal conditions and acute injury in an aluminum smelter”. _International Journal of Industrial Ergonomics_ 35, 47–55.

- Fowlie, Meredith and Robyn Meeks (2021). “The Economics of Energy Efficiency in Developing Countries”. _Review of Environmental Economics and Policy_ 15, 238–260.

- Gagliardi, Luisa, Giovanni Marin, and Caterina Miriello (2016). “The greener the better? Job creation effects of environmentally-friendly technological change”. _Industrial and Corporate Change_ 25, 779–807.

- Garg, Teevrat, Matthew Gibson, and Fanglin Sun (2020). “Extreme temperatures and time use in China”. _Journal of Economic Behavior & Organization_ 180, 309–324.

- Garg, Teevrat and Ajay Shenoy (2021). “The Ecological Impact of Place-Based Economic Policies”. _American Journal of Agricultural Economics_ 103, 1239–1250.

- Giglio, Stefano, Bryan Kelly, and Johannes Stroebel (2021). “Climate Finance”. _Annual Review of Financial Economics_ 13, 15–36.

42
# **Table 2. Results for the Traditional Gravity Model, Excluding Non-Reporters**

||OL|S|PP|ML|Feasibl|e GLS|Tob|it|Weighte|d Tobit|E|K-H|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Coeff|t-stat|Coeff|t-stat|Coeff|t-stat|Coeff|t-stat|Coeff|t-stat|Coeff|t-stat|
|Log exporter's GDP|0.94|80.6|0.73|27.3|0.96|86.9|1.49|94.6|1.50|99.1|1.49|91.3|
|Log importer's GDP|0.80|71.7|0.74|26.9|0.83|78.4|1.15|75.3|1.17|80.0|1.15|73.6|
|Log exporter's GDPC|0.21|12.5|0.16|2.9|0.21|13.1|0.27|12.1|0.25|11.6|0.24|9.6|
|Log importer's GDPC|0.11|6.4|0.13|3.0|0.11|7.0|0.21|9.3|0.19|8.5|0.18|7.6|
|Log distance|-1.17|-34.4|-0.78|-14.3|-1.15|-37.0|-1.74|-36.3|-1.62|-36.4|-1.41|-24.0|
|Contiguity|0.32|2.2|0.20|1.9|0.10|0.9|-0.50|-2.4|-0.69|-4.1|-0.69|-3.0|
|Common language|0.68|10.6|0.74|5.5|0.70|11.4|1.02|11.8|0.95|11.3|0.78|7.1|
|Colonial tie|0.40|5.8|0.03|0.2|0.38|5.9|0.76|8.3|0.80|9.0|0.82|7.4|
|Landlocked exporter|-0.06|-0.9|-0.86|-5.5|-0.09|-1.5|-0.11|-1.2|-0.14|-1.6|0.00|-0.1|
|Landlocked importer|-0.66|-10.5|-0.69|-4.9|-0.65|-10.4|-0.74|-8.8|-0.78|-9.1|-0.62|-7.6|
|Exporter’s remoteness|0.47|6.0|0.65|4.9|0.46|6.3|0.65|5.9|0.61|5.8|0.39|3.5|
|Importer’s remoteness|-0.21|-2.6|0.56|4.7|-0.13|-1.8|-0.31|-2.7|-0.26|-2.4|-0.30|-2.6|
|FTA|0.48|4.5|0.18|2.1|0.17|1.9|0.32|2.0|-0.30|-2.3|-0.35|-2.5|
|Openness|-0.17|-3.5|-0.12|-0.9|-0.16|-3.3|0.20|3.1|0.19|2.9|0.34|4.3|
|Constant|-28.51|-26.2|-32.13|-15.4|-30.50|-30.2|-49.36|-32.2|-50.86|-35.3|-49.6|-31.9|
|Observations|9602||16290||9602||16290||16290||16290||
|R<sup>2</sup>|0.66||0.86||0.72||0.24||0.26||||
|Hetero, ln|||||||||||||
|_a_|2.21|48.0|2.8|115.1|||||||2.8|94.3|
|_b_|-0.11|-18.4|-0.098|-19.8|||||||-0.11|-19.8|
||||_Mean_|_SD_|||||||_Mean_|SD|
|Ln Predicted Trade|||8.4|2.7|||||||4.2|4.8|

Source: As for Table 1.

16
<mark>These messages were delivered via two outreach channels over a period of one week: i) individually delivered pamphlets by key individuals in the community, or “norm entrepreneurs (NEs)” (Treatment 1); or ii) mass communication campaign involving community events, posters at key locations around the village (entry, exit, schools, community halls and markets), as well as NE‐delivered pamphlets (Treatment 2). While T2 ensured maximum outreach, T1 utilized personalized delivery through individuals who are well connected or highly central to the village social network, and who could play an influential role in the creation of a new social norm around latrine use and act as the positive norm enforcers within their respective communities (see Appendix 1 for detailed theory of change).</mark> <mark>Treatment was randomly assigned at the village level. Two of the five baseline villages were assigned to T1 and the remaining three to T2—covering a total of 225 households with latrines across all five villages. In addition, 10% of the households (25) in the T1 villages were randomly selected to receive no intervention (and serve as a control group).</mark>

# **<mark>Results</mark>**

<mark>— The interventions targeted change in latrine use behavior through two pathways change in beliefs about the importance of using latrines via its association with cleanliness, and updating empirical expectations about the norms of latrine use in the community. A follow‐up survey was conducted in all baseline villages following the weeklong interventions to evaluate the impact of the interventions on latrine use behavior and related personal and normative beliefs. Of 204 households surveyed at baseline, 156 (76%) were included in the follow‐up survey. The attrition was due to the enumerators’ inability to resurvey the baseline respondents within the short follow‐up survey period. (Since beliefs and behaviors are subjective, baseline respondents could not be replaced with other members of the household.) The control group for this pilot was also very small given the small sample size of the overall study. In addition, because control group households were sampled from T1 villages, there was high risk of spillover due to information diffusion from treatment to control households and presence and activity of norm entrepreneurs in the villages. As a result, comparisons between the treatment and control groups could not be made in our analysis.</mark> <mark>The endline data show relatively large changes in both behavior and attitudes in favor of latrine use, relative to the baseline. Given the small sample size, our study was underpowered to detect significant effects, but a simple difference in means test between baseline and endline outcomes shows large and statistically significant differences in both behavior and beliefs related to latrines across both treatment arms (see Appendix 2 for detailed results).</mark>

**<mark>Figure 1.</mark>**

15
As part of the reform process, a customer complaints system (ODECO) was established to allow users to express dissatisfaction with the service and to seek redress from the utility. First round complaints can be made to the customer service center operated by each of the utility companies. Moreover, if the dispute is not adequately resolved, the customer has the option of raising a second round complaint with the regulatory agency (SIRESE). Most recently, the Superintendencia de Electricidad has introduced a virtual complaints system with a view to extending its reach throughout the country. Using the ‘virtual ODECO’ terminals stationed in major urban centers, use ISDN lines to provide an interactive multi-media link to the Superintendency’s offices in La Paz including the possibility of having a video-conference consultation with a customer service representative in La Paz.

It is interesting to ask to what extent this customer complaint system succeeds in reaching customers from lower income groups. While it is not possible to answer this question directly, some indirect inferences can be made by comparing customer complaints statistics across the cities of La Paz and El Alto (Table 5.1). The results show that (relative to their total numbers) consumers in La Paz are very much more likely to lodge complaint as consumers in El Alto. In the case of complaints made directly to the utility company, the differential is a factor of three in the case of water and sewerage. In the case of complaints made directly to the Superintendency, the differential is around _[add water]_ , a factor of 3 for electricity, and 6 for telecommunications. The data for telecommunications show that less than 6% of first round complaints, resulted in a second round complaint to the Superintendency.

**<u>Table 5.1: Customer complaints, 1997-2000</u>**

||Elect<br>La Paz|ricity<br>El Alto|Telecommunications<br>La Paz<br>El Alto|Water an<br>La Paz|d Sewerage<br>El Alto|
|---|---|---|---|---|---|
|_First round complaints_<br>No. of complaints|||6,773|14,317|5,784|
|Complaints per ‘000 customers|||129.3|92.0|36.5|
|_Second round complaints_||||||
|No. of complaints|472|99|375<br>12|||
|Complaints per ‘000 customers|2.9|1.0|2.9<br>0.5|||
|% of first round complaints|||5.7|||

# **6. Conclusions**

The broad picture that emerges from the analysis of the impact of utility service reforms in La Paz and El Alto is one of expanding access in the context of rising prices and improved accountability for quality of service. There is significant evidence that low income households benefited considerably from the growth of coverage. The cost of both long distance and cellular telephony fell by almost 70%. Tariffs for all other services increased significantly. Nonetheless, owing to concomitant reforms in tariff structure, many small consumers would actually have experienced _falling_ bills.

The effects of the reform differed significantly across sectors, reflecting different adjustments to the regulatory framework. The impact appears to have been least 26
the same firms (organizational structures) in our 1991-96 sample.7 The obtained price data allowed us to calculate input and output price indices at the firm level. Those were used in the analysis in the next sections.<sup>8</sup> The interviews covered eighteen manufacturing firms and three former trading companies. The trading companies had turned into private holding companies in 1992 and had managed to buy majority stakes in most of the companies whose products they had marketed including some of the firms on the Ministry list. The interviews with their managers contained information on both the parent holding company and its subsidiaries. While the statistical analysis in the next section is based on the overall financial performance of the holding companies, most restructuring measures (improving quality standards, new product lines, foreign partnerships) are traced back to their subsidiaries.

The case study method normally has significant downsides. Most important is the lack of representativeness of case study findings, i.e., their performance may not be indicative of economywide trends. Fortunately, we have comprehensive data on all large industrial firms in Slovakia and can therefore link the smaller sample of case studies to the broader trends in the manufacturing sector (see Figure 1 above). Another potential problem is the subjective narrative of managers/owners regarding the causes of (and constraints to) restructuring. We have, however, only used the interviews to complement our analysis of the financial performance of firms in getting a better understanding of the many elements of a successful restructuring strategy.

- 7 In two cases, firms were still undergoing a split-up of the former conglomerate in1991. Since both firms were independent plants (located away from other plants in the conglomerate and with their own general managers) even before the split-up, we obtained financial and other (including employment) data from their managers pertaining to their respective plants _only._ For further analysis of the impact of conglomerate split-ups on restructuring in Czechoslovakia, see Lizal et al., (1996).

- 8 The possibility of obtaining firm-specific price data is one of the main merits of the case study methodology.

10
database. In the short run, we assume that both the level and the mix of output from the household’s farm firm are fixed, for consistency with Deaton (1989).

The actual calculation of the production elasticities is done in the same way as the calculation of the wage-price elasticities at the national level presented earlier—by representing the production of each household using the same structure as the GTAP model (e.g. substitution elasticities of inputs and factors, value-added and input shares in output costs, transformation elasticities of factors). Expressing all necessary equations for household production from the GTAP model (e.g. substitution between inputs and value-added, substitution between factors in the value-added composite, transformation of land between activities, zero profits) with the appropriate parameters, allows us to solve for all endogenous variables as functions of the variables that are exogenous to the household (output and input prices, and factor endowments) to obtain a production elasticity matrix μ specific to each household. To highlight the most important supply elasticities at the commodity level among the households in our country sample, we present their average values for the most important production commodities in for the medium and long run in Appendix tables 4 and 5. A striking feature of this table is just how high the long run elasticities of supply are, with elasticities of 5 and above being quite common.

As an example of our approach, we present the case of one of the households included in the Bangladeshi 2005 survey, whose production we observe in the data. In the case of this household, we present the matrix representation of the household model (transposed [Me|Mx]) for the long-run assumption of an exogenous wage rate in Appendix table 7. As can be seen from the variables included in the matrix, this household produces both wheat and rice, as do 862 other households in our Bangladesh sample. While the key input composition is determined by the GTAP database (see for example entries in the column for the equation determining value added zero profits in wheat production). The key information that comes from the household survey is represented, albeit indirectly, by the separation of common inputs and factors across rice and wheat production (consider for example the entries in the row corresponding to the variable capturing the price of capital in equations determining zero profits for value-added in rice and wheat production.)

The actual process of obtaining the set of elasticities for output and input use involves separating from the original model matrix of size 24×28 the four columns corresponding to the exogenous variables (price of labor, price of inputs, price of wheat and price of rice) and forming 11