rafmacalaba/datause-encoder-data huggingface dataset:
investments in digital solutions and product innovation persists (Figure 18, panels b and c).<sup>28</sup> Smaller firms possibly face larger barriers to invest in digital or product innovation due to the lack of complementary inputs (i.e., managerial skills) or access to finance.

These results suggest that the benefits of the digital acceleration and opportunities to pivot productmix may be accessible only to larger firms or selected groups which have both access to finance and the required managerial skills to take advantage of them. While this might widen the inequality across firms, in the long-run it may also lead to a possible reallocation of market shares towards more productive firms. This is not surprising because more productive firms have a higher tendency to adopt digital technologies and they also appropriate larger benefits from such adoption because they have complementary capabilities (e.g. technical, managerial, and organizational) (Sorbe et al., 2019; Gal et al., 2019).<sup>29</sup> BPS data also confirms that the probability of adopting digital technologies tends to increase with productivity, although the differences are slight and not significant (Figure C15). Thus, the COVID-19 shock may end up reallocating resources and market share from low productive firms to those with higher productivity.

> 28We show only wave 2 data for product innovation because it is not exactly comparable with wave 1. It should be noted that the gap between large and micro or small firms in the aftermath of the COVID-19 crisis, which ranges between 7 and 9 percentage points, is actually lower than the typical gap found in innovation surveys during normal times in the OECD countries where the average innovation (product, process or organizational) gap between large firms and SMEs is 24 percentage points (OEC, 2021).

> 29In the context of the COVID-19 crisis, Riom and Valero (2020) find path dependence in digitalization, that is, firms that had already invested in digital technologies were more likely to adopt more of these technologies.

24
Agricultural occupations account for the vast majority of highly exposed occupations, comprising approximately 38.2% of total respondent’s occupations in the 47 countries for which occupational data are available. Unskilled manual labor accounts for 7.77%, and the army makes up 0.22% of responses.

# _Limiting to Non‐Agricultural Occupations_

Rural‐urban migration and transition out of agriculture are key features of economic development and demographic transition, trends that are likely to continue in many developing economies. As such, one might want to know how occupational exposure bias operates in contexts in which agriculture is not the primary source of employment for the economy. To do this, we redefine the “exposed occupation” dummy to exclude agricultural occupations, focusing primarily on unskilled manual labor occupations.

In a logit regression of this non‐agricultural exposure dummy on household wealth percentile, we find that 16 of the total 47 countries to exhibit a significant negative relationship; whereas 23 exhibit a significant positive relationship. This suggests that in just over half of the sampled countries, there is a positive relationship between wealth and likelihood of working in unskilled manual labor.<sup>29</sup> Detailed regression results are presented in the Appendix, Table D.

There are ex ante reasons to suspect that these results may exhibit heterogeneity based on a country’s development level. In very poor countries, unskilled manual labor may be a desirable alternative to agriculture, suggesting a positive relationship. In relatively more developed countries, unskilled manual labor is less desirable compared to skilled work, suggesting a negative relationship.

As a first pass at exploring this intuition, we group countries by their World Bank income level classifications: low, lower‐middle, and upper‐middle.<sup>30</sup> Out of the 39 countries in our sample which feature some statistically meaningful relationship between wealth and (non‐agricultural) occupational exposure, 17 are classified as low‐income, 17 as lower‐middle‐income, and 5 as upper‐middle‐income.

Among the low‐income countries, 82% feature a positive relationship between household wealth and non‐agricultural occupational exposure, whereas only 41% and 40% of lower‐middle‐income and upper‐middle‐income countries exhibit positive relationships respectively. This suggests that structural factors may be important to consider in future work that seeks to model the distributional welfare impacts of heat stress over the long term.

> 29 Only a handful of countries made use of the “army” category in their occupational reporting, so non‐agricultural exposure is accounted for almost entirely by unskilled manual labor.

> 30 The World Bank also provides a high‐income category, but no such countries are present in our data set.

31
|**Table 13c. Impact of Partial Fo**<br>|**reign Direc**<br>Overall<br>_change from b_|**t Investment**<br>_ase year, %_|**Liberalizati**|**on in Servic**|**es**||||
|---|---|---|---|---|---|---|---|---|
||average|Northwest|Central|South|Volga|Urals|Siberia|Far East|
|**Social Welfare Indices (ev/GDP)**|||||||||
|sigma=0|1.1|1.8|1.4|1.4|0.8|1.0|1.1|1.8|
|sigma=1|2.0|2.6|2.1|2.0|1.6|1.7|1.5|2.3|
|sigma=4|2.3|2.9|2.4|2.4|2.1|2.2|1.8|2.5|
|sigma=+inf|2.4|3.0|2.5|2.5|2.2|2.4|1.9|2.6|
|ev/C|4.4|5.7|4.1|4.5|4.2|6.0|3.3|5.1|
|**Aggregate trade**|||||||||
|Regional terms of trade (% change)|2.1|3.2|2.3|1.4|1.8|0.6|0.6|2.1|
|Regional exports (% change)|0.9|1.5|1.5|0.8|1.2|-0.3|0.2|1.1|
|Real exchange rate (% change)|1.8|2.3|2.1|1.9|1.8|1.3|1.0|1.8|
|International exports (% change)|7.0|10.4|12.8|8.6|10.5|1.5|6.9|7.9|
|**Return to primary factors**(% change)|||||||||
|Unskilled labor|2.0|2.9|2.1|2.2|1.6|2.2|1.4|2.3|
|Skilled labor|2.4|3.1|2.4|2.4|2.1|2.6|2.0|2.7|
|Aggregate capital earnings|2.6|3.1|2.8|2.6|2.6|2.0|1.8|2.6|
|Regional mobile capital|4.8|7.5|6.2|4.5|4.9|3.0|4.0|5.8|
|Energy sector resources|0.6|2.8|9.2|2.3|2.5|-0.6|2.6|3.5|
|Specific capital in domestic firms|-12.1|-14.2|-13.4|-12.2|-14.0|-6.5|-8.8|-13.0|
|Specific capital in multinational firms|43.1|29.2|30.0|55.2|66.9|66.5|66.3|57.2|
|**Factor adjustments**|||||||||
|Unskilled labor (% changing sectors)|2.1|2.6|2.0|1.5|2.3|1.8|2.3|2.3|
|Skilled labor(% changingsector)<br>Source: Authors' calculations.|2.1|2.6|2.1|1.5|2.0|2.2|2.2|2.4|

50
**Table 2a U.S. AVE protection against goods by suppliers, 2007** <u>(percent)</u>

||India|US|NAFTA|EU27|LAC|Japan|China|MENA|ASEAN|SAFTA|SSA|ROW|Total|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Agriculture|1.0|n.a.|0.3|3.7|2.5|1.4|1.3|0.9|2.5|3.7|2.0|6.4|1.6|
|Processed agriculture|2.2|n.a.|2.0|3.3|3.9|4.0|3.5|4.3|2.3|0.8|5.6|4.7|3.1|
|Coal, oil, gas and other mining|0.0|n.a.|0.0|0.0|0.0|0.0|0.2|0.0|0.0|0.0|0.0|0.0|0.0|
|Textiles and apparel|9.1|n.a.|0.0|8.2|3.9|6.3|10.1|5.4|12.1|10.6|0.5|10.1|8.5|
|Other light manufacturing|3.8|n.a.|0.0|3.1|1.7|0.6|6.2|0.8|3.2|3.9|0.3|1.7|3.6|
|Paper and mineral products|1.5|n.a.|0.0|2.6|1.4|1.4|2.5|0.6|1.0|1.6|0.1|1.2|1.2|
|Chemical, rubber and plastic products|0.7|n.a.|0.0|1.2|0.4|1.9|2.7|0.4|0.9|0.3|0.0|1.5|1.1|
|Iron, steel and metal products|0.1|n.a.|0.0|1.3|0.3|1.7|1.9|0.3|0.5|0.8|0.0|1.0|0.7|
|Petroleum and coke products|0.7|n.a.|0.0|1.3|0.5|1.1|0.1|0.8|0.6|0.0|0.0|1.0|0.7|
|Transport equipment|0.0|n.a.|0.0|0.9|0.2|1.2|1.5|0.1|0.2|0.0|0.0|1.1|0.6|
|Electronics and machinery|0.2|n.a.|0.0|0.8|0.1|0.9|0.9|0.0|0.2|0.1|0.0|0.7|0.6|
|Other manufacturing|0.0|n.a.|0.0|1.3|0.0|1.7|1.1|0.0|0.3|0.3|0.0|1.4|0.8|
|Total|2.7|n.a.|0.1|1.4|0.9|1.2|2.8|0.3|2.4|9.6|0.1|1.6|1.3|

# **Table 2b India AVE protection against goods by suppliers, 2007** <u>(percent)</u>

||India|US|NAFTA|EU27|LAC|Japan|China|MENA|ASEAN|SAFTA|SSA|ROW|Total|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Agriculture|n.a.|26.5|56.0|32.1|44.3|13.7|33.6|19.8|20.6|25.5|23.4|59.4|33.9|
|Processed agriculture|n.a.|54.1|72.4|90.1|50.5|42.4|37.1|52.4|97.6|14.4|50.4|55.4|78.8|
|Coal, oil, gas and other mining|n.a.|13.1|9.4|12.7|5.7|13.1|15.3|10.1|11.0|9.9|10.0|18.2|10.6|
|Textiles and apparel|n.a.|15.0|15.9|15.4|14.6|15.0|17.0|15.3|15.0|10.2|15.5|15.3|15.8|
|Other light manufacturing|n.a.|14.8|14.7|14.3|11.4|14.8|14.9|11.8|13.9|5.8|10.8|12.8|13.4|
|Paper and mineral products|n.a.|13.7|11.6|14.4|13.6|15.1|14.3|14.9|13.0|5.1|13.3|14.6|13.8|
|Chemical, rubber and plastic products|n.a.|10.8|13.6|14.8|15.2|14.5|14.3|12.8|15.6|6.3|15.4|14.1|13.8|
|Iron, steel and metal products|n.a.|16.8|17.5|16.9|18.8|18.2|16.4|15.5|16.8|9.8|15.3|15.5|16.0|
|Petroleum and coke products|n.a.|15.0|13.9|14.8|11.2|15.0|15.0|13.6|13.3|14.1|13.3|14.5|13.8|
|Transport equipment|n.a.|4.9|13.0|11.2|12.1|29.9|15.4|12.3|17.8|2.0|25.5|16.1|10.4|
|Electronics and machinery|n.a.|9.7|8.3|12.3|12.7|13.2|7.6|7.2|6.5|2.8|10.0|11.4|9.9|
|Other manufacturing|n.a.|15.0|15.2|15.1|11.5|15.1|13.7|15.0|13.6|6.5|14.0|15.0|14.8|
|Total|n.a.|9.9|19.4|13.8|12.3|16.3|11.7|11.2|23.3|12.1|11.5|16.5|13.8|

_Source_ : GTAP 8 Database. Notes: n.a. = not applicable.

11
_Figure 6.2: Percentage (%) change reduction in fossil fuel emission from tax on fuel and kerosene compared to base case_ <!-- Start of picture text --> Percentage change reduction in fossil fuel emission from tax on fuel and<br>kerosene compared to base case<br>2018 2022 2026 2030<br>(5.0 USD/ton) (13.3 USD/ton) (21.7 USD/ton) (30.0 USD/ton)<br>0.0%<br>-1.0%<br>-2.0%<br>-3.0%<br>-4.0%<br>-5.0%<br>-6.0%<br>DTAX STAX SAVINGS CORPOR TRANS<br>-0.3%<br>-0.4%<br>-0.5% -0.5% -0.5%<br>-1.1%<br>-1.3%<br>-1.7% -1.7% -1.7%<br>-3.2%<br>-3.5%<br>-4.0%<br>% change from base case emission -4.1% -4.1% -4.1%<br>-4.3%<br>-5.3% -5.3% -5.3%<br><!-- End of picture text --> 15
downgrades to downgrades of public enterprises, banks and private sector entities, by raising their funding costs and potentially triggering covenants (Augustin et al., 2016; Chow, 2015; Williams, Alsakka and ap Gwilym, 2013). The deterioration in balance sheets of public enterprises and banks could negatively feed back to the sovereign via credit guarantees or other financial supports, augmenting their sovereign-corporate linkages.<sup>3</sup> Since the relaxation of sovereign ceiling policy, corporate ratings that “pierce” the sovereign ceiling has become more common (Almeida et al., 2017), and bonds issued by such sub-sovereign entities could be more sensitive to changes in the sovereign rating.

Despite a growing literature on sovereign-corporate linkages, there is very little evidence on how far the ratings of sub-sovereign bonds would be generally positioned _above_ or _below_ the corresponding sovereign rating. In particular, as the sovereign ceiling policy has recently been relaxed, further empirical analysis is needed to identify key factors that determine the strength of sovereign and sub-sovereign rating linkage when bond issuers are constrained or unconstrained by the sovereign ceiling.

This paper revisits the effect of sovereign creditworthiness on sub-sovereign ratings using data on foreign currency bonds issued by sub-sovereign entities in 47 emerging markets and developing economies (EMDEs) during 1990-2013. We use censored and double-hurdle regressions to estimate relative contributions of each factor to determine the “distance” between sovereign and sub-sovereign ratings, separately for bonds rated at or below the sovereign ( _bounded bond ratings_ ) and above the sovereign ( _non-bounded bond ratings_ ). We also merge the bond-level dataset with balance sheet data from Bloomberg to estimate the importance of firm’s financial situation in affecting the distance for a subset of matched debt issuers.

The results shed light on the asymmetry in factors which determine the distance between bounded and non-bounded bond ratings. For bounded bond ratings, their distance is mainly determined by bond issuers’ ownership structure, size of their assets, and sector of issuer. The distance tends to be narrower as the country’s trade linkages become stronger and global

> 3 In related work, Williams et al. (2013) and Huang and Shen (2014) found that the impact of sovereign credit ratings on bank credit ratings is significant but varies by countries’ macroeconomic and institutional factors. Correa et al. (2014) found that a downgrade of sovereign credit rating has a large negative effect on bank stock returns for those banks that would receive stronger support from their governments. Ağca and Celasun (2012) also outline channels through which higher public external debt (which is typically associated with lower sovereign credit ratings; see Cantor and Packer 1996 and S&P 2013) can raise corporate default premia.

3
11 political freedom are not perfectly correlated, but the correlation is expected to be highly positive. For the sample of 89 countries for which data are available for 2000, the correlation between the negative of the corruption perception index and the Gastil index is 0.66.<sup>14</sup> The fact that the correlation between the Gastil index and the two available proxies, albeit strong, is far from being perfect should not be viewed as disappointing since the Gastil index itself is an imperfect discrete measure of the underlying concept of democracy.

The equation to be estimated is thus: Since this study focuses on the long-term determinants of the degree of fiscal decentralization rather than on explaining short-term deviations from the mean, equation (1) is estimated by applying the _between_ panel estimator that exploits the cross-sectional variation of the time averages for each region.<sup>15</sup>

**2.4. Data** . To test the five hypotheses, we assembled a new dataset on Russian regions. The data cover the period 1994-2001 and 86 Russian regions (the cities of Moscow and St. Petersburg are excluded as incomparable with the rest of the regions for the purpose of the analysis; and Chechnya is excluded due to data unavailability).<sup>16</sup> The data are taken mostly from official Russian statistical yearbooks and the Ministry of Finance database and augmented from other sources listed in Appendix 1. The data for certain years and regions are unavailable, for

> 14 Higher values of the corruption perception index compiled by Transparency International correspond to lower corruption and hence the negative of the index is taken. 15 See Wooldridge (2002) for details.

> 16 The data before 1994 are unreliable due to hyperinflation and poor coverage (e.g., GRPs were not computed). The period after 2001 appears to be characterized by much stronger centralization, particularly in the political sense, so that the degree of fiscal decentralization within a region can no longer be thought of as chosen almost entirely at the discretion of regional authorities.
transfers showed an increasing trend, experiencing a 220% increase, while the expenditure level increased too, though at a lower rate (181%).

In particular, during year 2010 and 2014, the financial result was significantly positive climbing up to its top level in 2014 (1.3% of the GDP). These positive results presented in Figure 12 depend greatly from transfers made by the State, which account for 40% of total pension expenditures. Without these public resources, the IESS would have had a sustained deficit (on average equal to -0.15% of the GDP).

Figure 12. Resources, Expenditures and Financial Result 2003 - 2017 (in GDP %) <!-- Start of picture text --> 5%<br>4%<br>3%<br>2%<br>1%<br>0%<br>-1%<br>-2%<br>-3%<br>-4%<br>2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>Expenditure Public Transfers (General revenues)<br>Own resources Capital Income<br>Contributions Financial Result<br>Financial Result without public transfers<br>% of GDP<br><!-- End of picture text --> Source: author elaboration based on IESS Note: Public transfers correspond to transfers made by the State for 40% of total expenditures on pensions.

A second term begins from year 2015 on. Two important changes have affected the composition of resources to the contributory pension system managed by the IESS. On the one hand, the public sector financing chance was eliminated: 40% of total pension expenditures. On the other hand, as mentioned before, contribution tax rates were reduced. These changes implied a significant reduction in income sources. In order to finance the undertakings assumed for pensions, the IESS resorted to 20
# TABLE 8

|DOMESTIC<br>LIABILITIES|TOTAL STOCK OF|RATIO|
|---|---|---|
|OF THE CENTRAL<br>BANK|DOMESTIC<br>MONEY(m2)|_(1)1(2)_|
|(1)|(2)||
|DE85<br>8581|10033|0.86|
|JA86<br>8365|10538|0.79|
|FE86<br>8366|10971|0.76|
|MA86<br>9443|11517|0.82|
|AP86<br>9319|12079|0.77|
|MY86<br>10214|1'i44|0.78|
|JN86<br>10863|13840|0.78|
|JL86<br>11447|14675|0.78|
|AG86<br>12351|15336|0.81|
|SE86<br>12532|15876|0.79|
|OC86<br>11615|17288|0.67|
|N086<br>13184|18176|0.73|
|DE86<br>14015|20120|0.70|
|JA87<br>14867|21126|0.70|
|FE87<br>14978|21964|0.68|
|MA87<br>15593|23011|0.68|
|AP87<br>16619|24097|0.69|
|MY87<br>17286|25765|0.67|
|JN87<br>18082|27679|0.65|
|JL87<br>19717|29898|0.66|
|AG87<br>21095|31530|0.67|
|SE87<br>21568|34314|0.63|
|0C87<br>24881|38811|0.64|
|N087<br>26139|42602|0.61|
|DE87<br>30274|48075|0.63|
|JA88<br>31864|52343|0.61|
|FE88<br>33752|56984|0.59|
|MA88<br>42924|65494|0.66|
|AP88<br>48575|74191|0.65|
|MY88<br>58660|85827|0.68|
|JN88<br>71448|102730|0.70|
|JL88<br>92428|120617|0.77|
|AG88<br>117793|150250|0.78|
|SE88<br>143378|175167|0.82|
|OC88<br>155276|193011|0.80|
|N088<br>174917|215552|0.81|
|DE88<br>200514|259270|0.77|
|JA89<br>221780|287405|0.77|
|FE89<br>243199|315141|0,77|
|MA89<br>277949|368864|0.75|

(*) Millions of Current Australes. End of month data. Source: M2 data from FIEL; Remunerated CB liabilities from IBRD; Non Remunerated Monetary Base from IFS.

30
- 16 -

If Central Bank credit expansion is financed by increasing reserve requirements of the commercial banks, this could lead to reduced credit availability and higher domestic interest rates, and hence adversely affect economic activity. Excessive reliance on borrowing would unnecessarily raise interest payments in the future, and hence would result in a further increase in the public sector deficit. Hence, the deficit should be reduced to levels which could be prudently financed by net disbursements of external loans, mostly obtained at concessional terms. Based on IBRD projections, this level is projected at about 1.51-2Z of GDP for 1988-90. The reduction in deficit could be achieved through a combination of revenue increases and expenditure restraint. Specifically, raising tax collection effort, increasing public utility prices should provide the bulk of savings effort in the short-term. Obviously, any serious medium-term effort should focus on expenditure reductions through elimination of low-priority programs, divestiture of public enterprises and reduction of public employment.

Table 10: Banking System Financing of the Public Sector As End of Year. 1983-87 (millions of t)

||1983|1984|<br>1985|1986|<br>1987|
|---|---|---|---|---|---|
|Central Bank CreditT||||||
|To Public Sector|619.9|650.9|698.3|622.8|751.3|
|To Central Government|530.6|576.9|622.5|628.7|721.3|
|To Rest of the Public Sector|89.3|<br>74.0|<br>75.8|34.1|30.0|
|Commercial Bank Credit:||||||
|To Public Sector|349.8|405.9|387.2|491.8|537.9|
|To Central Government|371.2|449.2|517.6|676.7|691.1|
|To Rest of the Public Sector|-21.4|-43.3|-130.4|-184.9|-153.2|

# Source: IMF.

# E. Budaet for 1988

56. Table 11 indicates the budget for 1988. Essentially, this budget perpetuates the existing problems inherited from previous years. Congress introduced a large number of modifications to the proposed budget prepared by the Executive branch of the Government. These measures reduced the proposed budgetary allocations by about tl53.0 million, or about 1.7? of GDP, with about two-thirds of it expected to be financed by external sources. Congress also changed the composition of proposed expenditures by increasing allocations for some programs, while scaling down others. For example, allocations for defense were increased from the proposed amounts, while some investments with concessional financing were reduced. Also
Nevertheless, this result is consistent with related research investigating the global proliferation of antidumping in particular. First, the United States is not only one of the largest users of trade remedies worldwide. Zanardi (2004) notes that between 1991 and 2002, exporting firms from the United States were the third-most _investigated_ producers worldwide in foreign antidumping investigations, as well as ranking third in number of instances of being _hit_ with trade remedy measures (antidumping duties and price undertakings) worldwide, behind only China and Korea. Finally, Prusa and Skeath (forthcoming) examine the pattern of antidumping filings across the world and find evidence at the bilateral level of retaliation activity that is consistent with the indirect evidence described here, while Feinberg and Olson (2004) use the worldwide antidumping filing data to find evidence of such a relationship across 2-digit industries as well.<sup>28</sup>

# **5.2.3 Estimates for inclusion of countervailing duties, the injury determinants and without the**

# **control for selection bias**

The rest of table 6b presents a number of initial robustness checks to our analysis. First, specification (2) adds in the additional observations for U.S. countervailing duty measures and their WTO challenges. Even after including these trade remedy observations as well, the basic pattern of qualitative results is unchanged.

In specification (3) of table 6b, we assess the importance of determinants expected to affect the injury decision in the U.S. trade remedy investigation (as described in table 6a) to check the robustness of our results. Inclusion of these variables in the second stage does not affect the qualitative pattern of results, and these variables are not of the expected sign and are not statistically significant. Part of the explanation for their poor performance is likely due to the fact that some countries are not challenging the injury investigation that took place in year _t_ , but instead may be challenging a sunset or administrative review or a dumping or subsidy determination.

Finally, in specification (4) of table 6b, we re-estimate the probit regression equation of specification (1) _solely_ , i.e., without the selection equation. The sign and size of the estimates are virtually unchanged from the earlier specification, suggesting that selection bias is not likely affecting the estimation in this particular application.

> 28 Martin and Vergote (2004) develop a game theoretic model with private information between governments and industries in which reciprocal antidumping and the sort of “vigilante justice” described here occur on the equilibrium path.

20