rafmacalaba/datause-encoder-data huggingface dataset:
# **4.3 Abnormal weather conditions**

Not only absolute rainfall and temperature levels matter, but also the extent to which these levels differ from long‐term normal climate conditions. Unusually wet, dry, hot and cold conditions can all have detrimental impacts, which depend on their timing and location. For example, more rain may be beneficial in a dry month and dry locations, but harmful in wet months and wet locations. Existing studies define unusual weather conditions as the deviation from the long‐term mean (Hidalgo et al., 2010; Baez et al., 2015; Noack et al., 2015). These studies put the calculated deviation into the context of the location‐ specific variability by normalizing by the location’s long‐term standard deviation (Lobell et al., 2011).

Figure 7 _Abnormal weather conditions in rural communes by climate zone in 2010, 2012 and 2014_

## _a. Wet months_

## _b. Hot months_

<!-- Start of picture text --> 2010, dry_cold 2010, dry_hot 2010, wet_cold 2010, wet_hot 2010, dry_cold 2010, dry_hot 2010, wet_cold 2010, wet_hot<br>2012, dry_cold 2012, dry_hot 2012, wet_cold 2012, wet_hot 2012, dry_cold 2012, dry_hot 2012, wet_cold 2012, wet_hot<br>2014, dry_cold 2014, dry_hot 2014, wet_cold 2014, wet_hot 2014, dry_cold 2014, dry_hot 2014, wet_cold 2014, wet_hot<br>0 1 2 3 4 5 6 0 1 2 3 4 5 6 0 1 2 3 4 5 6 0 1 2 3 4 5 6 0 1 2 3 4 5 6 0 1 2 3 4 5 6 0 1 2 3 4 5 6 0 1 2 3 4 5 6<br>number of months number of months<br>c. Dry months d. Cold months<br>2010, dry_cold 2010, dry_hot 2010, wet_cold 2010, wet_hot 2010, dry_cold 2010, dry_hot 2010, wet_cold 2010, wet_hot<br>2012, dry_cold 2012, dry_hot 2012, wet_cold 2012, wet_hot 2012, dry_cold 2012, dry_hot 2012, wet_cold 2012, wet_hot<br>2014, dry_cold 2014, dry_hot 2014, wet_cold 2014, wet_hot 2014, dry_cold 2014, dry_hot 2014, wet_cold 2014, wet_hot<br>0 1 2 3 4 5 6 0 1 2 3 4 5 6 0 1 2 3 4 5 6 0 1 2 3 4 5 6 0 1 2 3 4 5 6 0 1 2 3 4 5 6 0 1 2 3 4 5 6 0 1 2 3 4 5 6<br>number of months number of months<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>Density .20 Density .20<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>Density 0 Density 0<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br><!-- End of picture text -->

## _c. Dry months_

Notes: Histograms show the distribution of commune observations for each climate zone. Abnormal months are measured by the deviation of the current month’s value from the 30 years mean being greater than 1.5 standard deviations. Source: Author’s calculation based on VHLSS 2010, 2012 & 2014 and CRU data.

To measure the number of wet, dry, hot and cold months, this study calculates the current deviation from the long‐term mean for each month (Figure A.5). Wet conditions are defined as positive deviation from 15
where the owners themselves were not targeted employees ( _NE_ in the table A2 of the Annex) (2.4%, p<0.01). A similar effect in the same filing period is observed on the number of untargeted employees in firms where the owners themselves are not targeted employees (1.9% reduction, p<0.05). The reduction in overall employment originating from the soft tone letter continues in the March filing period, though only among non-owner employed firms (3.1% reduction, p<0.01). The impact of the soft tone letter on the total number of untargeted employees becomes positive towards the end of the series, where the treatment effect fluctuates between 12 and 13 percent from September through November (p<0.05).

As for the strong letter, impacts are only observed for select segments of firms and employees, and only beginning in the April filing period. Contrary to the soft tone letter, these impacts are positive throughout the series. For example, the number of untargeted employees reported increases by 9.5 percent in the April filing period for owner employed ( _OE_ in Table A2 in the Annex) firms (p<0.05). Among targeted employees across all firms, the strong letter leads to a net increase of between 1.1 and 2.7 percent in the period spanning April to December (p<0.05 in April and May, p<0.01 thereafter). These impacts appear to be driven primarily by non-owner employed firms ( _NE_ in the table). What this suggests -given the null effect of the strong tone letter on the overall firm payroll -is that firms exposed to the strong tone letter (relative to no letter at all) maintained more of their targeted employees on the payroll but without increasing the average wage of these (expanded upon below).

Figure 3. Dynamic treatment effects on employment report by the firm <!-- Start of picture text --> Soft letter Strong letter<br>Filing period Filing period<br>.06 .06<br>.05 .05<br>.04 .04<br>.03 .03<br>.02 .02<br>.01 .01<br>0 0<br>-.01 -.01<br>-.02 -.02<br>-.03<br>-.03<br>Marginal effect on employment (w.r.t control) Marginal effect on employment (w.r.t control)<br>-.04<br>-.04<br>-.05<br>-.05<br>Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec<br><!-- End of picture text --> Source: Authors' calculations using historical tax declaration records. Coefficients from random effects model plotted.

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Figure 8 _Extreme weather conditions in rural communes by climate zone in 2010, 2012 and 2014_

## _b. Maximum temperature_

## _a. Maximum rain_

<!-- Start of picture text --> dry_cold dry_hot wet_cold wet_hot dry_cold dry_hot wet_cold wet_hot<br>2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014<br>c. Minimum rain d. Minimum temperature<br>dry_cold dry_hot wet_cold wet_hot dry_cold dry_hot wet_cold wet_hot<br>2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014<br>40<br>1,500<br>35<br>1,000<br>rainfall in mm<br>500 temperature in C 30<br>0 25<br>100 30<br>25<br>50<br>rainfall in mm<br>temperature in C 20<br>0 15<br><!-- End of picture text -->

## _c. Minimum rain_

Notes: Box plot shows the distribution of commune observations for each climate zone. The boxes illustrate the 25 to 75 percentile with the median value represented by the line in the box. The whiskers indicate the lowest and highest adjacent value. Maximum rainfall is measured by the rainfall level in the wettest month. Minimum rain is measured by the rain level in the driest month. Maximum temperature is measured by the mean of maximum temperature in the hottest month. Minimum temperature is measured by the minimum temperature in the coldest month. Source: Author’s calculation based on VHLSS 2010, 2012 & 2014 and CRU data.

# **4.5 Weather events**

Variables describing the occurrence of weather events, such droughts, floods and storms allow to measure the impact of weather shocks and natural disasters. A recent study for Vietnam uses monitored data on riverine floods and cyclones to identify natural disasters and assess their welfare impacts (Thomas et al., 2010). Notwithstanding the limitations of self‐reported data, which often suffers from subjective judgements and is highly correlated with welfare outcomes, other studies from Vietnam use self‐reported data to assess the welfare impacts (Arouri et al., 2015; Bui et al., 2014).

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|Access to ICT|||||||0.004<br>|0.009<br>|-0.003<br>|0.001<br>|0.006<br>|0.008<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||||||||(0.027)|(0.030)|(0.059)|(0.027)|(0.030)|(0.058)|
|Log of distance to nearest<br>|||||||0.009|0.010|0.010|0.006|0.005|0.018|
|Agrarian Service Center|||||||(0.008)|(0.009)|(0.022)|(0.008)|(0.009)|(0.023)|
|Incidence of natural calamity|||||||-0.032|-0.014|-0.058|-0.028|-0.002|-0.085|
||||||||(0.024)|(0.026)|(0.059)|(0.025)|(0.027)|(0.060)|
|Constant|9.034***|8.795***|9.057***|8.771***|8.773***|8.536***|5.157***|5.137***|5.132***|4.967***|5.025***|4.618***|
||(0.015)|(0.140)|(0.263)|(0.247)|(0.243)|(0.230)|(0.188)|(0.208)|(0.471)|(0.189)|(0.208)|(0.456)|
|District dummies||X|X|X|X|X|X|X|X|X|X|X|
|Number of observations|5613|5613|5613|5613|5613|5613|5613|4408|1205|5683|4449|1234|
|R<sup>2</sup>|0.011|0.185|0.250|0.350|0.353|0.437|0.669|0.668|0.670|0.759|0.771|0.706|

Source: Authors’ estimation using HIES 2016.

_Note:_ The dependent variable for all regressions except (10-12) is the log of productivity. Productivity is measured by yield, i.e., gross real monthly output divided by the area cultivated (Rs/acre). The dependent variable for regression (10-12) is the log of earnings (Rs). Gross real monthly output is used as a proxy for crop earnings. The regressions are estimated using ordinary least squares (OLS).

Except in regression (1-2), ethnicity dummies, sector dummies (urban, rural, estate), and district dummies are included as control variables.

*, **, and *** indicate that the coefficients are significant at the 10, 5 and 1% level, respectively.

Robust standard errors are in parentheses.

> a. <mark>Agricultural inputs include seeds, fertilizer, chemicals, hired labor, transport cost, agricultural equipment/rental, and others.</mark>

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draw. The two set-ups share the property that not any “idea” is profitable enough to become a firm, although they differ in how costly it is to set it up. Further evidence allowing to more concretely identify the properties of the process of firm creation would be very valuable for all models where the number of producers is determined endogenously.

# **References**

- Asker, J., Collard-Wexler, A., and De Loecker, J. (2014). Dynamic inputs and resource (mis) allocation. _Journal of Political Economy_ , 122(5):1013–1063.

- Bartelsman, E., Haltiwanger, J., and Scarpetta, S. (2013). Cross-country differences in productivity: The role of allocation and selection. _American Economic Review_ , 103(1):305–34.

- Basu, S., Pascali, L., Schiantarelli, F., and Serven, L. (2012). Productivity and the Welfare of Nations. NBER Working Papers 17971, National Bureau of Economic Research, Inc.

- Bernard, A. B., Redding, S. J., and Schott, P. K. (2010). Multiple-product firms and product switching. _American Economic Review_ , 100(1):70–97.

- Bernard, A. B., Redding, S. J., and Schott, P. K. (2011). Multiproduct firms and trade liberalization. _The Quarterly Journal of Economics_ , 126(3):1271–1318.

- Broda, C. and Weinstein, D. E. (2006). Globalization and the gains from variety. _The Quarterly Journal of Economics_ , 121(2):541–585.

- Buera, F. J. and Shin, Y. (2013). Financial Frictions and the Persistence of History: A Quantitative Exploration. _Journal of Political Economy_ , 121(2):221–272.

- Camacho, A. and Conover, E. (2010). Misallocation and productivity in colombia’s .

- manufacturing industries. _IDB working paper series N0. IDB-WP-123_

- Casacuberta, C. and Gandelman, N. (2009). Productivity, exit and crisis in uruguayan manufacturing and services sectors. _mimeo_ .

- Chen, K. and Irarrazabal, A. (2015). The Role of Allocative Efficiency in a Decade of Recovery. _Review of Economic Dynamics_ , 18(3):523–550.

34
with those whose HDI scores are higher or lower. We interpret results on HDI variation with caution, as the gridded HDI data by Kummu et al. (2018) display very limited subnational variation.

# **4.3 ‘Strategic’ location choice**

In this section, we explore the notion that location choices for polluting power plants are not made at random. Case study evidence, especially from the U.S., documents that low-income and ethnic minority communities are disproportionately exposed to air pollution. We analyze the relationship between income and air pollution for over 1.3 million unique locations (pixels) around over 3,800 coal plants. We find that power plant locations are indeed not evenly distributed across income groups. In the proximity of coal plants, downwind areas are systematically poorer than upwind areas. Poorer communities are disproportionately exposed to toxic pollution from coal plants.

This pattern could be explained by several factors: When deciding plant locations, operators may strategically select spots where prevailing wind directions mean that pollutants are carried to lower-income communities. These communities are likely to have lower bargaining power to oppose such siting decisions. This regressive environmental burden may be further aggravated by spatial sorting once the plant assumes operations. As environmental quality (including air quality) is priced into land values, high-income households are able to sort into cleaner upwind areas, while low-income households are more likely to move into cheaper, more polluted areas.

To assess systematic inequalities in pollution exposure, we conduct a single difference analysis designed to test if plants’ downwind directions are associated with lower income levels. We estimate the following model: where the sample is at the plant-pixel level. We use the year when plants started to operate to code dependent variables, where start years differ across plants. In the first stage, we use SO2 at recipient pixel _j_ near plant _i_ . Our sample consists of pixels that are located within a 0.5-degree radius around plants, and located in up- or downwind locations. Pixels in crosswind directions are dropped. If the prevailing wind direction at a plant has a reference angle of 0<sup>_◦_</sup> , upwind pixels are defined as those located within -45<sup>_◦_</sup> to 45<sup>_◦_</sup> around plants, while downwind areas are located within 135<sup>_◦_</sup> to 180<sup>_◦_</sup> and -180<sup>_◦_</sup> to -135<sup>_◦_</sup> .

_Downwindij_ is a binary variable that is one if pixel _j_ is in the downwind direction of plant 16
# _Table 2 Summary_ _<u>Statistics of Vietnam’s Workers aged 15-59 from the VHLSS 2004</u>_

||||ByGe|nder|By|Skill|
|---|---|---|---|---|---|---|
|Ownership||Total|Male|Female|skilled|unskilled|
|Total|Number of Obs.|21022|10599|10423|3981|17041|
||Share of female (%)|.50|||.46|.51|
||Age|35.0 (11.7)|34.8 (11.8)|35.3 (11.6)|35.0 (10.7)|35.0 (11.8)|
||Education years|7.7 (3.7)|8.0 (3.6)|7.3 (3.8)|12.8 (1.5)|6.5 (3.0)|
||hourly wage<sup>*1</sup>(VND 1,000)|5.3(4.7)|5.5 (5.1)|4.9 (3.9)|7.4 (6.3)|4.2 (3.0)|
||Hours worked per week<sup>*2</sup>|34.8 (15.8)|34.9 (15.4)|34.7 (16.3)|38.6 (15.8)|33.9 (15.7)|
|Foreign|Number of Obs.|278|106|172|134|144|
||Share of female (%)|.62|||.57|.67|
||Age|27.5 (7.8)|30.2 (8.7)|25.8 (6.7)|27.5 (7.8)|27.5 (7.9)|
||Education years|10.4 (3.2)|10.8 (3.2)|10.2 (3.1)|13.1 (1.8)|7.9 (1.9)|
||hourly wage (VND 1,000)|6.8 (6.9)|9.0 (9.7)|5.4 (3.7)|8.9 (9.2)|4.8 (2.2)|
||Hours worked per week|45.1 (11.7)|44.8 (10.3)|45.4 (12.5)|44.1 (11.7)|46.1 (11.7)|
|State|Number of Obs.|2291|1281|1010|1581|710|
||Share of female (%)|.44|||.46|.39|
||Age|37.7 (10.1)|38.8 (10.0)|36.2 (10.1)|37.3 (9.8)|38.4 (10.8)|
||Education years|11.9 (3.2)|11.9 (3.3)|11.9 (3.0)|13.8 (1.8)|8.2 (2.3)|
||hourly wage (VND 1,000)|7.1 (5.8)|7.4 (6.6)|6.8 (4.5)|7.9 (6.2)|5.4 (4.1)|
||Hours worked per week|41.2 (11.5)|41.5 (11.3)|40.9 (11.7)|40.8 (10.9)|42.2 (12.7)|
|Private|Number of Obs.|945|586|359|328|617|
||Share of female (%)|.38|||.41|.36|
||Age|30.2 (10.3)|31.6 (10.7)|27.8 (9.0)|30.7 (10.0)|29.9 (10.4)|
||Education years|9.2 (3.4)|9.2 (3.4)|9.3 (3.5)|12.8 (1.5)|7.3 (2.5)|
||hourly wage (VND 1,000)|5.1 (4.6)|5.6 (5.1)|4.3 (3.5)|6.4 (6.2)|4.4 (3.2)|
||Hours worked per week|42.2 (14.0)|42.5 (14.6)|41.8 (12.8)|42.6 (13.3)|42.1 (14.3)|
|Informal Wage|Number of Obs.|3109|2123|986|301|2808|
||Share of female (%)|.32|||.27|.32|
||Age|31.2 (10.8)|31.2 (10.5)|31.3 (11.2)|30.7 (10.4)|31.3 (10.8)|
||Education years|6.6 (3.4)|7.0 (3.3)|5.9 (3.5)|12.1 (.67)|6.1 (3.0)|
||hourly wage (VND 1,000)|3.9 (2.7)|4.3 (2.9)|3.1 (2.0)|5.0 (3.6)|3.8 (2.6)|
||Hours worked per week|35.1 (14.6)|36.1 (13.5)|33.0 (16.5)|36.0 (15.6)|35.0 (14.5)|
|Self-employed|Number of Obs.|14399|6503|7896|1637|12762|
||Share of female|.55|||.49|.56|
||Age|35.9 (11.9)|35.5 (12.2)|36.2 (11.6)|35.1 (11.2)|36.0 (12.0)|
||Education years|7.1 (3.4)|7.4 (3.3)|6.8 (3.4)|12.2 (.78)|6.4 (3.0)|
||hourly wage|n.a.|n.a.|n.a.|n.a.|n.a.|
||Hours workedper week|33.0(16.4)|32.3(16.1)|33.5(16.6)|35.6(19.5)|12.7(15.9)|

Notes: See the text for the definition of each ownership category. The standard deviations are between parentheses.

*1 Average hourly wage is calculated dividing annual income from first job by hours worked for 6623 individuals who have wage employment as first job. The wage includes bonus/award, social allowances, and trip subsidy.<sup>*2</sup> Hours worked per week are computed by combining the hours worked for the first and second jobs.

.

29
# **Figure 2 – Probability of Participation by Age Cohort and Marital Status**

<!-- Start of picture text --> Urban Rural<br>Cohort of age Cohort of age<br>Urban Rural<br>Cohort of age Cohort of age<br>Not married Married Not married Married<br>.6 .6<br>.4 .4<br>.2 .2<br>Probability of Participation Probability of Participation<br>0 0<br>15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 >=70 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 >=70<br>.6 .6<br>.4 .4<br>.2 .2<br>Probability of Participation Probability of Participation<br>0 0<br>15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 >=70 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 >=70<br><!-- End of picture text --> Source: LFSs 2007-2011. Values are estimated as marginal effects based on equations (2) and (4) in Table 2 and conditional on age cohorts and marital status. Bars over points represent 95% confidence intervals.

18
AIJ programs.<sup>10</sup> Both features help make comparisons among AIJ projects feasible. Even so, as discussed below, differences among the national programs emerged, giving rise to differing incentives and procedures.

# **3. Related Studies**

In the next section we develop a conceptual model of how project investments came about under the AIJ program. Before doing so, it is useful to briefly review results from studies that discuss theoretical and practical motivations for carbon project investments and the country-specific processes by which AIJ project were approved.

Among related studies, the largest group comprises studies that estimate the potential benefits of the Kyoto Protocol’s flexibility mechanisms. Without exception, the studies find that the costs of reaching greenhouse reduction goals are greatly reduced by rules that allowed spatial and temporal flexibility. By way of example, model results by Bernstein, Montgomery and Rutherford (1999) suggest that flexible trading rules could reduce the price of carbon permits – which can be seen as the marginal cost of emission reductions – by a factor of seven in the European Union and by a factor of sixteen in Japan.

Generally, the numeric studies focus on incentives created by differences in average abatement costs among countries and model the resulting demand for permits arising from project investments. Benefit measures are calculated by comparing predictions of market-clearing prices for emission permits under alternative scenarios.<sup>11</sup> Most often, the mechanism by which project investments are approved is not explicitly developed, although in some cases transaction costs are expected to differ according to the types of partnerships formed.<sup>12</sup> Even so, there is an implicit assumption in most studies documenting the benefits of flexibility that project investors will be free to pursue least-cost opportunities.

As experience emerged about project-based investments from pilot programs, a small group of case-based studies examined the self-declared motivations of early private sector

> 10 Convention participants assigned an advisory committee, the Subsidiary Body for Scientific and Technological Advice (SBSTA) to establish reporting guidelines and to compile and publish the reports on an on-going basis. 11See Nordhaus and Boyer (2000) and Weyant (2004) for reviews of modeling approaches and results, as well as citations in Stevens and Rose (2002). Painuly (2001) reviews numerical models that address project-based investments in developing countries Muller and Mestelman (1998) review related laboratory-based experiments.

> 12 See the discussion and references in Woerdman (2001).

6
_20_ served and unobserved factors. In this section, we perform heterogeneity analysis by firm size, industry subsamples, income groupings, and world regions. For the heterogeneity analysis by firm size, we split the sample into three groups in line with the predominant literature: small enterprises (5-19 workers), medium-sized enterprises (20-99 workers), and large enterprises (100+ workers). For the heterogeneity analysis by industry classification, we use the broad industry classification of manufacturing and services. To examine the heterogeneity by income, we use the 2023 World Bank country classification of income into low, middle, and high.

# Firm Size

Table 3 presents the results of the estimation of the effect of the deviation in WBT on log sales per worker for each sub-sample by firm size. The results are mixed. Although we do not detect a statistically significant negative effect on mediumsized firms located in the two hottest climate zones, there are strong and negative impacts on both small and large firms in the hottest climate zone. For small firms, a unit increase in WBT from the long-term average in the _Zone_<sup>_Q_</sup> (25<sup>4</sup> _._ 7<sup>0</sup> _C,max_ )<sup>group</sup> reduces productivity by 33.7 percent, compared to the reference group (coldest region). The productivity loss for large firms in the same WBT zone is almost three times that for small firms. Large firms experience significantly large productivity losses in all the temperature categories, with the largest losses for firms in the hottest climate zone. The large and negative effects on the productivity of large firms are evident in the various specifications and suggest that the general impact that we observed earlier was driven largely by these negative effects on large firms.

There are several plausible explanations, for why large firms are more prone to such effects compared to small or medium sized firms. First, large firms tend to be particularly concentrated in the manufacturing sector. About 69 percent of large firms are in manufacturing compared to 60 percent of medium-sized firms and 46 percent of small firms. Within the large manufacturing firms, a significant share is in metals and metal products; chemicals, plastics, and rubber; and textiles, garments, and leather. Among small firms, a large share is in retail, and accommodations; food and beverages, hospitality, and other services. Table 9 in the appendix presents a tabulation of the various activities in each size classification. Some of these activities might not require major physical labor that could put the human body at risk of heat stress and exhaustion during hotter days. The main reason for the distinct effects we observe across firm sizes seems to arise from the differences in exposure to excess heat across various industries, with varying effects on workers depending on work intensity. We discuss this below in the context of the heterogeneity by sector.

In addition to the different sets of activities, smaller firms could be resilient in