rafmacalaba/datause-encoder-data huggingface dataset:
**Table A17: Firm-level regression – likelihood of exit**

|Year, industry and location controls<br>Clustered at sector (4-digit NACE) and year|**(1)**|**(2)**|**(3)**|**(4)**|
|---|---|---|---|---|
|**Independent variables**|**exit=**�<sup>1 if yes</sup><br>0 if no|**exit=**�<sup>1 if yes</sup><br>0 if no|**exit=**�<sup>1 if yes</sup><br>0 if no|**exit=**�<sup>1 if yes</sup><br>0 if no|
|**surge/slump (surge=1, slump=0)**|0.0106***<br>(0.00334)|-0.00313<br>(0.00212)|0.00437<br>(0.00292)|-0.00345<br>(0.00433)|
|**ln(value added per worker)**|-0.00566***<br>(0.000345)|-0.00538***<br>(0.000331)|-0.00531***<br>(0.000428)|-0.00560***<br>(0.000449)|
|**ln(current assets)**|0.000845**<br>(0.000365)|0.000304<br>(0.000318)|0.000356<br>(0.000420)|0.000981**<br>(0.000482)|
|**small (15-99 employees)**|-0.00436***<br>(0.00125)|-0.00376***<br>(0.00126)|-0.0196***<br>(0.00174)|-0.0210***<br>(0.00173)|
|**medium (100-249 employees)**|-0.00945***|-0.00854***|-0.0217***|-0.0249***|
||(0.00184)|(0.00185)|(0.00260)|(0.00260)|
|**large (250+ employees)**|-0.0171***<br>(0.00205)|-0.0165***<br>(0.00206)|-0.0308***<br>(0.00288)|-0.0358***<br>(0.00287)|
|**age**|-0.000492***<br>(3.32e-05)|-0.000385***<br>(3.21e-05)|-0.000836***<br>(4.46e-05)|-0.000645***<br>(4.09e-05)|
|**surge/slump × ln(value added per worker)**|-0.00202***<br>(0.000597)|-0.00298***<br>(0.000538)|-0.00434***<br>(0.000712)|-0.00381***<br>(0.000792)|
|**surge/slump × ln(current assets)**|-0.00165***|||-0.00250***|
||(0.000502)|||(0.000857)|
|**surge/slump × age**||0.0154***<br>(0.00149)||0.0365***<br>(0.00228)|
|**surge/slump × small**|||0.00182|0.0128***|
||||(0.00240)|(0.00269)|
|**surge/slump × medium**|||-0.00352<br>(0.00356)|0.0154***<br>(0.00433)|
|**surge/slump × large**|||0.00124<br>(0.00346)|0.0259***<br>(0.00471)|
|**Constant**|0.0535***|0.0549***|0.0803***|0.0773***|
||(0.00423)|(0.00407)|(0.00466)|(0.00477)|
|**Observations**|357,252|357,252|256,544|256,544|
|**R-squared**|0.016|0.016|0.019|0.021|

Note: Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Note: Microenterprises is the omitted category and absorbed by the constant. Source: Author’s calculations using RUSLANA 32
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Table 1. Summary of main groups of explanatory factors used in the analysis

|Group|Definition|Examples|Type|
|---|---|---|---|
|Market<br>characteristics|The technological and<br>competitive structure of<br>the market.|· Number of potential<br>suppliers<br>· Technological complexity|Structural|
|Tender<br>specifications|The conditions for<br>bidders to participate in<br>a tender.|· Length of advertising<br>bids<br>· Conditions of<br>participation|Directly policy<br>influenceable|
|Buyer<br>characteristics|Level of administrative<br>quality of the buying<br>office or agency|· Average time taken for<br>evaluating bids<br>· Buyer office location|Directly policy<br>influenceable|
|Bidder/supplier<br>characteristics|Company productivity<br>and capacity|· Company size<br>· Company headquarters<br>location|Indirectly policy<br>influenceable|
|Tender<br>outcomes|Intermediate and final<br>results of the tendering<br>process|· Number of bids<br>submitted<br>· Contract start date|Indirectly policy<br>influenceable|

_Source: de Oliveira et al, 2019_ Market or product group characteristics encompass those factors which describe the technological and competitive structure of the market in which the items are purchased. Most of these variables are in effect structural givens from the perspective of the analysis as they tend to be changing slowly or policy can only influence them at high cost. Procurement markets can be defined as a combination of product codes and geographical codes (Fazekas & Tóth, 2016) which capture fundamental differences, among others, by product complexity and technological characteristics, geographical characteristics (e.g. remoteness), and market concentration. For 9
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# 4 Conclusion

This paper has studied the association between capital and labor incomes at the top of the distribution using tax return data. This helps to understand the driving forces behind the rise in the top 1% income share, that has been documented before (Piketty and Saez, 2003). We nd that capital and labor incomes have become more closely associated between 1985 and 2006, such that top capitalists and top earners are increasingly the same people. This rising association has contributed to the well-known increase in the top 1% income share, exacerbating the eects of rising inequality within capital incomes and earnings. In contrast, the 20 years leading up to 1985 saw a tendency towards a declining association, thus resulting in a U-shaped pattern. The association is asymmetric in some parts of the distribution, as a top earner is almost guaranteed to also be among the richest fth of capitalists, while a sizable share of top capitalists fall into the bottom four-fths of earnings. The association is more symmetric for richer quantiles, such as the top 5%. Our conclusions are robust to alternative treatments of negative incomes and capital gains, and how prots from self-employment and closely-held businesses are split between capital and labor.

The reversal from declining to increasing association coincided with a strong fall in the top marginal income tax rate in the US (dotted line in Figure 3). The top marginal rate declined from 91% in the early 1960s to 28% in 1986, and remained below 40% for the rest of the period. Lower taxes at the top raise the reward to bargaining more aggressively for higher pay, and therefore may explain the rapid rise in (gross) salaries at the top, which account for a large share of the increase in top income shares (Bakija et al., 2012; Alvaredo et al., 2013; Piketty et al., 2014).<sup>26</sup> Lower taxes may also account for the increasing association: When top marginal tax rates are low, tax units can save a greater share of their wages, thus accumulating more capital income over time.<sup>27</sup> This explanation assumes high saving

> 26The decline in top marginal tax rates is not the only possible explanation for these patterns (also see Alvaredo et al., 2013). Other explanations may include a superstar theory together with a globalized economy (Atkinson, 2008), the spread of performance-based pay (Lemieux et al., 2009) and the role of the nancial industry (Kaplan and Rauh, 2010; Philippon and Reshef, 2012).

> 27Saez and Zucman (2016) discuss the eect of increasing top incomes and high savings rates for wealth

18
O*NET (2010) O*NET® Green Task Development Project. The National Center for O*NET Development.

O*NET. (2013) Greening of the World of Work: O*NET® Project’s Book of References. The National Center for O*NET Development.

ONET Green Tasks Statmenets excel https://www.onetcenter.org/dictionary/20.1/excel/green_task_statements.html Skills approach Burning Glass Technologies (2021). After the Storm. The Jobs and Skills that will Drive the Post-Pandemic Recovery ESCO. Skill/Competence S.3.3.2 follow environmentally-sustainablework practices http://data.europa.eu/esco/skill/a992f345-7c06-4982-8fc9-5fab55e316af ESCO. Skill/Competence S.3.3.2 complying with environmentalprotection laws and standards http://data.europa.eu/esco/skill/S3.3.2 Excel shared by Burning Glass with a list of green job titles terms

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there may be a limit on how many hours of work a worker can miss and thus additional caregiving would have to be shared between household members.

Table 5: Exploring attenuation behavior

||||Hour<br>|s worked<br>|||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|PM 2.5|-0.185***|-0.190***|-0.175***|-0.132*|-0.199***|-0.195***|
||(0.039)|(0.063)|(0.043)|(0.070)|(0.058)|(0.042)|
|PM 2.5 _×_ worker|0.025|0.028|-0.004|-0.058|0.022|0.062|
|characteristic|(0.062)|(0.068)|(0.083)|(0.069)|(0.084)|(0.078)|
|Worker|Is house-|Is female|Under|Complete|Indep.|Has job|
|characteristic|hold head||25 years|secondary|worker|contract|
|Household has<br>susceptible indiv.|yes|yes|yes|yes|yes|yes|
|Observations|2,167|2,167|2,167|2,167|2,167|2,167|
|R-squared|0.422|0.422|0.422|0.423|0.430|0.422|

Notes: Robust standard errors in parentheses. Standard errors are clustered at the municipality level. * denotes significant at 10%, ** significant at 5% and *** significant at 1%. All regressions include household, week and municipality-by-year fixed effects, and the same individual and household controls as the baseline specification (see notes of Table 2) plus interactions of PM 2.5 with workers’ characteristics. Sample includes only workers in households with susceptible individuals.

An alternative explanation is that households might have other, more effective, attenuation strategies. For example, workers could use their vacation or sick days. In that case, air pollution would reduce hours worked, but have a smaller, or even negligible, effect on income. This strategy would be limited, however, to workers with such job benefits. We indirectly examine this alternative explanation by studying the effect of air pollution on earnings.

To do so, we estimate the baseline regression (1) using the log of monthly earnings as outcome variable.<sup>47</sup> To capture possible heterogeneous effects by access to employee benefits, we distinguish workers with and without job contracts. We consider a worker to have a job contract if she is a dependent worker with an indefinite or fixed-term contract. Only workers

> 47Note that, in contrast to labor outcomes, the period of reference for earnings is the 4 weeks prior to the interview (i.e., weeks _t_ to _t −_ 3). Thus the reference period for our measure of exposure to PM2 _._ 5 is weeks _t −_ 1 to _t −_ 4. Given the length of the period, using average PM2 _._ 5 would mask episodes of high pollution. For that reason, we use instead the share of weeks in the reference period in which average PM2 _._ 5 exceeded the U.S. 24-hour standard. We also use month instead of week fixed effects, and relax the model by using household plus year fixed effects without municipality-by-year fixed effects. Results adding municipality-by-year fixed effects are similar but noisier (see Table B.7 in Appendix).

24
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