rafmacalaba/datause-encoder-data huggingface dataset:
Table 2: Summary statistics of the variables involved in the plant level analysis.

||(1)<br>N|(2)<br>mean|(3)<br>sd|(4)<br>min|(5)<br>max|
|---|---|---|---|---|---|
|Industry number of robots (1000s of workers)|55,417|0.181|0.676|0|4.802|
|OECD-average industry number of robots (1000s of workers)|46,467|3.257|5.474|0.171|54.95|
|Innovation-intensity index|55,417|0.210|0.268|0|1|
|Share of secondary education workers|55,417|0.611|0.361|0|1|
|Share of primary education workers|55,417|0.375|0.368|0|1|
|Share of tertiary education workers|55,417|0.0135|0.0486|0|1|
|Principal component of: secondary, size, foreign capital, openness|55,413|-1.30e-09|1.298|-1.418|9.513|
|Downstream exposure to robots (1000s of workers)|42,773|0.108|0.363|0|4.234|
|Real investment in machinery and equipment (log)|55,417|1.659|3.467|0|19.03|
|TFPQ (log)|55,417|-7.015|15.94|-51.91|25.99|
|Real marginal cost (log)|55,417|2.245|1.732|-2.644|6.831|
|Real markup (log)|55,417|2.625|1.534|-0.792|7.168|
|Real value added (log)|55,417|12.58|1.828|5.925|22.85|
|Real revenue (log)|55,417|13.64|1.890|6.961|23.42|
|Exports (share of revenue)|55,417|0.118|0.295|0|1|
|Imports (share of revenue)|55,417|0.0383|0.143|0|1.009|
|Labor share in value added|55,417|0.615|2.616|0.000136|481.2|
|Gross surplus (share of renveue)|55,417|0.385|2.616|-480.2|1.000|
|Employment (log)|55,417|3.933|0.934|2.197|9.458|
|Real average wage (log)|55,417|7.588|1.107|0.131|16.43|
|Production employmeent (log)|55,417|3.735|0.952|0|8.821|
|Real average production wage (log)|55,417|7.533|1.139|0|16.34|
|Non-production employment (log)|55,417|1.609|1.428|0|8.827|
|Real average non-production wage (log)|55,417|6.253|3.402|-2.913|16.59|
|Real expenditure on domestic inputs (log)|55,417|12.68|2.114|2.772|22.59|
|Real expenditure on services (log)|55,417|1.563|3.588|0|19.70|

The industry-year effects are based on the IFR industrial classification, thus capturing any industry-specific, time varying shocks, which may be related to both the outcome variables and the adoption of robots, such as changes in international trade patterns, demand or supply shocks. This mitigates the concern that exposure to robots in an industry may be endogenous to the outcomes of plants operating in that industry. At the same time it also absorbs the variation of _ETRi,t_ , which therefore cannot be estimated.

Estimates of _γ_ 1 in (3) quantify the differential impact of robots’ adoption within an industry in a given year across plants with different degrees of exposure to robots.

The inclusion of a rich set of time invariant and time-varying fixed effects in (3) should minimize the potential bias afflicting estimates of _ETR_ . However, it might still be the case that the _interaction_ of industry characteristics and their share of routine labor might be correlated to unobserved variables having an impact on plants’ outcomes. In particular, one concern is that the share of routine labor might be systematically correlated 12
0 **c) 0 Cz** 2 M co **0 0**

**0**

# **Table 1. Degrees of Informality**

||Formal Sector<br>Enterprises|Taxicabs|Kiosks|Boot repair<br>("black<br>market" and<br>street)|Selling used<br>parts/tools<br>(tech. mkt)|Selling<br>individual<br>cigarettes,<br>soft-drinks<br>or chewing<br>gum (street)|
|---|---|---|---|---|---|---|
|Registration as an organization<br>required|yes.companies and<br>partnerships must<br>register by law|no|no|no|no|no|
|Registration with tax authorities<br>required|yes|yes|yes|yes|yes|yes|
|Percentage registered with tax<br>authorities|- _100%_|74% of full-time drivers,<br>20% of part-time drivers|100%|63%|31%|0%|
|Percentage paying more tax under|0%|7%|76%<br>|0%<br>|69%|60%<br>|
|_the informal Sector Income Tax_<br>than they would under the<br>_Citizen 's Income Tax/ Economic_<br>_Entities Income Tax_|_0%_|_5%_|48%|0%|31%|4%|
|Other registrations required|many, depending on<br>the nature of the<br>activity|all registrations<br>associated with vehicles.<br>permission for inter-<br>aimag travel|permission for<br>land use|none|none|none|
|Applicable regulations|many, depending on<br>the nature of the<br>activity|technical inspections|city health and<br>sanitation<br>regulations|none|none|none|
|Governed by provisions of the<br>_Labor Law_|yes|no|no|no|no|no|

_Source:_ Informal sector surveys conducted by the author.

***** excludes moonlighters; items in italics are reasonable<sup>_a priori_assumptions.</sup>
allows us to test whether the pessimism generated by our earlier analysis of PCs for monopolies applies to PCs with competitive firms as well.

The disadvantage of using Chinese PCs for our sample is that it raises questions about the applicability of our results to PCs in other countries. However, since we are interested in contracts between government and state enterprises, the differences between China and other countries are less important than they would be if we were drawing conclusions for private firms. Studies of SOEs (such as World Bank 1995) suggest that the situation of SOEs in developing market economies closely resembles that of state enterprises in China (although not township and village enterprises) and other transitional economies. In both cases, governments intervene widely in operations, extend protection from competition and bankruptcy, and provide subsidies and debt relief. Since our study of PCs in China extends an earlier analysis of PCs in six market economies (Ghana, India, Korea, Mexico, the Philippines, and Senegal), radically different findings could suggest that China's special circumstances might be the cause. This was not the case, however. Finally, we postulate that the productivity effects of PCs are a function of how well the contracts address problems of information asymmetry, incentives, and commitment, contractual features which, judging from the literature on information economics, are the most important generic elements in characterizing contracts and country circumstances. At the same time we have attempted to control for as many aspects of the unobservable as we can, such as technology differences, competitive environment, etc., which should reduce the influence of factors which might be special to China in our results.

4
_Page 17_

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likely to attend them. This is primarily because of the GS role in selecting BPL beneficiaries which the poor, disadvantaged families are more likely to benefit from. However, Besley Pande and Rao (2005) also show that this extreme form of selection is less acute in villages with higher literacy levels, where GSs have more representative participation.

Table 1 shows that a third of the attendees, on average, are women and 37 per cent are SCs. However, women and SCs do not speak much at the meeting. The “indicator” variable has a value of 1 when any person in a category speaks in a GS, while the “intensity” variable is the time that any person in that category speaks as proportion of the total length of the gram sabha<sup>8</sup> . With this metric we see that 68% of GS had at least one woman speak, but women spoke on average for 9% of the GS’s length. Similarly 60% of GS had at least one SC person speak, but they spoke, on average, for 11% of the time.<sup>9</sup> The typical GS meeting begins with a presentation by the president or the secretary of the GP. This is followed by a public discussion open to all participants during which, typically, villagers mention their demands and grievances, and the secretary or a member of the GP responds to them. These discussions generally center on routine problems, which are summarized in Table 1. The discussions are dominated by issues related to drinking water and village roads, followed by education, electricity, housing, and health. Concerns about employment and agriculture feature less prominently. Discussions also address such complex problems as the legitimacy of

> 8 Strictly speaking it is the proportion of the number of lines in the transcript spoken by the category divided by the total number of lines in the GS transcript.

> 9The SC data are imperfect because we were able to identify SC speakers in only about a third of the sample, which may result in some significant downward bias. Also, the data do not allow us to identify other discriminated castes, like the “backward castes” or BCs.

18
They analyze 30-year periods from 1971 to 2000 in the 20th century, and 2071 to 2100 in the simulations under the IPCC’s A2 scenario.<sup>25</sup> All sixteen countries exhibit substantial increases in the occurrence and magnitude of extreme hot events, with the occurrence of the present 30-year-maximum event increasing more than 2700% in parts of the northern Mediterranean, and the magnitude of the 30-year-maximum event increasing 1000% to more than 2250% in much of central Africa. Most countries also display increases in the occurrence and magnitude of extreme dry events, with peak changes of greater than 800% and 60% (respectively) occurring over Mediterranean Europe.

The magnitude and spatial heterogeneity of changes in climate volatility suggest that the impacts on poverty could also be large and heterogeneous. Among the 16 countries they analyze, those with the highest shares of populations entering poverty due to these extreme events include Bangladesh, Mexico, Mozambique, Malawi, Tanzania, and Zambia. For example, in Malawi and Zambia, simulated grains productivity declines of about 75 percent cause the poverty headcount to increase by about seven percentage points relative to their total populations. There is also tremendous heterogeneity in the poverty vulnerability across different segments of the population (differentiated by primary income source). As in Hertel et al. (2010) the analysis also reveals that the most vulnerable group is the urban wage-labor dependent stratum. While the urban labor group contributes modestly to total poverty in the sample of 16 countries analyzed here, it appears to be highly vulnerable to extreme climate events (e.g. in Malawi, the poverty rate for this group doubles). Zambia and Mexico also show high vulnerability among this group. The source of vulnerability of the urban poor is their extreme exposure to food price increases (with food being a major expenditure, their group’s consumption falls with rising prices, pushing them below the poverty threshold of consumption). Agricultural households, on the other hand are much less exposed. While they are generally hurt by the adverse productivity shock as consumers, they tend to benefit from the higher food prices as producers.

Given that the shares of developing countries populations living in rural areas are projected to decrease by more than one third between 2010 and 2050 (United Nations 2009),

> 25 As previously discussed, the A2 SRES Scenarios might not truthfully represent the expected GDP and population growth, and the consequential emissions path. As a result, it is an extreme scenario which overestimates the negative impact that climate change will have on poverty reduction efforts.

20
Table 4: Perception of norms is associated with women’s use of the reserved space

|Panel A: Unconditiona|l||||
|---|---|---|---|---|
||Usually chooses reserved space||||
||(1)|(2)|(3)|(4)|
|D-Score on Provokes Advances IAT|0.179||||
||(0.127)||||
|First order belief: women in public space more likely to accept advances||0.081<br>(0.088)|||
|Second order belief - women: women in public space more likely to accept advance|||0.170<br>(0.078)||
|Second order belief - men: women in public space more likely to accept advances||||0.227|
|||||(0.112)|
|Observations<br>Panel B: Conditional on perceived proba|143<br>bility of harassment|155|155|155|
||Usually chooses reserved space||||
||(1)|(2)|(3)|(4)|
|Expect any physical harassment in public space|0.447|0.516|0.471|0.516|
||(0.089)|(0.086)|(0.087)|(0.090)|
|Expect frequent physical harassment in public space|0.021|-0.008|-0.037|-0.048|
||(0.109)|(0.107)|(0.110)|(0.107)|
|D-Score on Provokes Advances IAT|0.164<br>(0.125)||||
|First order belief: women in public space more likely to accept advances||0.080<br>(0.087)|||
|Second order belief - women: women in public space more likely to accept advance|||0.151<br>(0.083)||
|Second order belief - men: women in public space more likely to accept advances||||0.229<br>(0.107)|
|Observations|143|155|155|155|
|Sample mean Y _|_ X = 0||0.460|0.392|0.286|

_Note:_ Unit of observation is an individual. Subsample of female platform survey respondents who were randomly allocated both harassment probability and attitude questions. All estimates include controls for employment, age, education, and platform fixed effects. Sampling weights are applied to platform survey observations. Robust standard errors in parentheses.
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24
## [ _Insert Table 6 around here_ ]

Overall, the double-hurdle estimation suggests that certain debt characteristics – in particular, a bond backed by future-flow receivables, SPVs, and collateralized bond – raise the probability of piercing the sovereign ceiling and increase the distance above the ceiling. The magnitude of the effect of future-flow securitization structures is the largest compared to SPVs and other collateralized bonds. Other debt characteristics, country-level variables, and global variables serve the role of useful controls but are in most cases themselves not robustly related to the sovereign ceiling.

# **5. Robustness check**

In the analysis above, a large sample of sub-sovereign bonds was used to estimate the influence of sovereign ratings. However, as we only observe ratings when sub-sovereign bonds were issued, the sample may differ from the population of all bonds. In this section, we address the issue of possible sample selection bias using a two-step Heckman selection model to check the robustness of our baseline results.

Our identification strategy relates to Francis, Aykut, and Tereanu (2014) which reshaped loan and bond-level data into country-quarter panel to correct the selection bias by estimating the probability of corporate bond/loan issuance in the first stage using the average bond-level characteristics as an exclusion restriction. For running the selection regression, we similarly need to collapse the bond-level dataset at higher unit, at industry level for instance, to have observations for each month and country where a borrower did not issue any bonds. We decide to categorize bond issuers into eight industry types (based on their ownership type (public vs. private), industry (financial vs. non-financial), and the governing law they used for issuance, and take average sovereign and sub-sovereign ratings for each country-industry-month pairs to build a balanced panel. Should at least one firm in the same industry for each country issue bonds in a particular month using the same governing law, we observe the distance between the sovereign and sub-sovereign ratings for each country-industry-month pairs.

In several of the emerging market countries covered in our sample, only a few foreign currency bonds were issued during 1990-2013. For this reason, we restrict our sample to 22 emerging 18
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# Binding or non‐binding and all‐inclusive costs

Independently of the type of benchmark used, a key question is _whether the caps are binding_ in a sense that they are set at levels below market rates. In countries, where the primary aim of the restriction is to prevent usury this is usually not the case and ceilings are fixed at very high levels that affect only extreme pricing but leave the core market to operate with minimal implications. In the European Union, for example, caps are generally set at a substantial percentage above the average market rate for certain product groups, ranging between 33% (France), 50% (Italy) and 100% (Germany).<sup>12</sup> In contrast, when interest rate caps are used as a policy instrument to achieve certain socio‐economic goals, such as lower cost of credit or more competition in the banking sector, ceilings are set below market rates. Such _binding constraints_ amount to direct price setting and can alter the structure of the market if effectively enforced (FSDT Kenya, 2016). Recent examples include Bolivia, Zambia and Kenya or Cambodia where caps were fixed substantially below prevailing average lending rates (see country case studies below for more details).

A further important differentiation is whether stated caps only apply to nominal interest rates or _whether they also apply to fees and commissions related to the loan_ . If the regulation seeks to cap those as well this can be either done by directly specifying limits on non‐interest costs of a loan or by setting caps on the annual effective rate (APR), which includes the interest rate and all fees and charges. The first approach is, for example, taken by South Africa, which publishes a comprehensive list of maximum fees applicable to different types of credit in addition to the respective interest rate caps.<sup>13</sup> In India, MFIs are restricted from charging any fee other than a processing fee of 1% of the loan amount.<sup>14</sup> Caps on effective rates are used in Thailand, where the 28% ceiling includes interest rates and fees. Other examples of countries explicitly targeting effective rates are El Salvador and Morocco. Effectively limiting the level of non‐interest costs, however, is challenging as banks have strong incentives to come up with new and “creative” solutions and to find loopholes in regulations in order to re‐ coup lower returns stemming from interest rate caps. Examples for this are the bundling of services, such as adding mandatory credit insurance to loan contracts.

> 12 Iff/ZEW, 2010.

> 13 www.saica.co.za/Portals/0/Technical/LegalAndGovernance/39379_gon1080_Fees.pdf

> 14 https://rbidocs.rbi.org.in/rdocs/notification/PDFs/43BF010714FSC.PDF

17