rafmacalaba/data-use-mentions-extended huggingface dataset:
detail. This matters since there have been significant increases in green patenting over the 2006-2010 period. The paper also examines different frontier innovation patterns by level of technological sophistication of countries, and by extent of cross-country collaboration. As a proxy for patterns of broader green catch-up innovation, we analyze data on trade in environmental products. And a deeper analysis of trade data allows us to explore the extent to which developing countries export products ̳similar‘ to environmental products in terms of required inputs or technologies, as an indicator of their capability to start producing greener products. Finally, the paper also reports findings from an exhaustive survey of green Base-of-Pyramid (BoP) innovations to meet the needs of poor consumers.

In examining appropriate mixes of policies to foster green innovation, the paper combines insights from the latest relevant policy literature, available data on policy actions and firm-level responses, and selected case studies. These are organized around three complementary policy areas, namely to promote frontier innovation, to promote catch-up innovation, and to develop absorptive capacity. The paper makes the case for a portfolio of green innovation policies that include policy instruments to address the two complementary knowledge and environment-related market failures: both supply-side ̳technology-push‘ elements that reduce costs of knowledge creation and adoption as well as demand-side ̳market-pull‘ elements that increase net revenues from sales of greener products. While it is not premature to put a green twist on generic innovation policies, for instance that prizes are a preferred policy instrument over patents from a number of dimensions to promote the creation and diffusion of green as opposed to nongreen technologies, there is clearly still considerable progress to be made in ranking green innovation 2
of countries into “distorted” and “efficient” based on the crude measure of GDP per capita. I choose the cutoff to be the GDP per capita in Cyprus. After removing countries with insufficient data, the list of “efficient” economies includes Austria, Belgium, Finland, France, Germany, Iceland, Italy, Luxembourg, Netherlands, Norway, Spain, Sweden and Switzerland. The list of “distorted”

> 11The AMADEUS database (Analyze Major Databases from European Sources) is published by Bureau van Dijk Electronic Publishing.

30
firms in the estimation (Appendix Table 3). For this regression, we use a different measure of risk. The risk of banked firms is based on real and financial data, while that of unbanked firms is calculated using only real data. To simplify, we report only results with industry and municipality fixed effects. The selection results are also robust to using the ex-ante spread as a simple and direct measure of risk instead of using the predicted default probability from our estimated default probability model (Appendix Table 4).

# **5.3 Dynamic Lockdowns and Program Selection**

The result that firms with both positive and negative variations in sales during the pandemic are more likely to apply and be approved for credit indicates that credit flows across the board regardless of how the pandemic affects firms. To further investigate this issue, we analyze credit distribution to firms in municipalities under lockdown and those in contiguous municipalities with free circulation. To do so, we exploit the dynamically staggered imposition by the sanitary authorities of mandatory lockdowns across municipalities (as explained in Appendix A). These measures aim to contain the coronavirus as it spreads over time across the country and generates a spatial heterogeneity of exposure to social distancing measures. Appendix Figure 1 presents the map of municipalities according to their overall lockdown status and shows substantial geographical variation. Appendix Figure 2 presents the weekly evolution of the cumulative number of municipalities under lockdown.

At the firm level, the lockdowns produce a plausibly exogenous source of variation of sales since the sales of firms in municipalities affected by the sanitary measures decline substantially (Briones et al., 2021). We thus define the treatment event as the week in which a municipality enters a mandatory lockdown. Treated firms are in municipalities that enter a lockdown at any point during May to July of 2020. Control firms are those in adjacent municipalities that remain open during the same period. The underlying assumption is that firms in adjacent municipalities are similar in terms of risk, firm attributes, and exposure to the pandemic and only
%|
|TOTAL ABOVE|62.9|61.5|77.8|90.3|98.6|122.2|168.3|228.7|87%|
|Imports of parts as percent of exports|130|117|117|110|99|100|82|87|-13%|
|Share in EU external imports|0.09%|0.09%|0.09%|0.12%|0.13%|0.17%|0.24%|0.29%|75%|
|Share in Bulgaria’s EU-oriented exports|2.9%|2.6%|3.1%|3.8%|3.5%|3.9%|5.1%|5.4%|37%|

Source: Derived from EU trade data as reported to the UN COMTRADE database.

Second, the 2001-03 period witnessed significant acceleration of exports of both parts and other engineering products together with parts. This has not been because of appreciation of Euro vis-à-vis US dollar, as the increase in the value of exports of 87 percent in 2003 over 2001 accompanied a similar increase in Bulgaria’s share in EU-external imports increasing 75 percent over this time. These products have driven the recent expansion in Bulgaria’s EU-oriented exports, as their share in Bulgaria’s exports increased from 3.7 percent in 2000-01 to 5.4 percent in 2003.
involving non-Russians, because of not capturing other forms of migration, should be viewed with the caveat that the share of migrants is likely underestimated in the survey and that a more fine-tuned definition of migrants through better surveys may render rather different results.

# **III. Methodology**

The study of well-being using subjective indicators has grown significantly in recent years. Subjective measures of well-being go from life satisfaction (sometimes called happiness) to job satisfaction, self-assessed health and other indicators of access to basic needs. These measures complement, or contrast, the more conventional estimates of well-being that use objective measures such as GDP per capita, labor incomes or consumption-based poverty measures. The quality of these indicators of subjective well-being can help to discern important aspects of economic behavior and policy (Kahneman & Krueger, 2006). Moreover, many of these studies explore the connection between subjective indicators of well-being (i.e., job satisfaction, life satisfaction, self-reported health) and specific labor market status such as unemployment, selfemployment, commuting to work, volunteer work or informality.<sup>20</sup> Most of these studies test the 20 For a review of the literature <u>on the Economics of Happiness see (MacKerron, 2012).</u> Informal Employment and Worker’s Wellbeing in Russia 16/67
>4 4<br>2 2<br>0 0<br>Health Education Public LAC small EAP small Other small<br>investment states states states<br><!-- End of picture text --> _Sources_ : GFS (database); International Monetary Fund; WDI (database); WEO (database); WHO (database); World Bank.

_Note_ : EAP = East Asia and Pacific; EMDEs = emerging market and developing economies; LAC = Latin America and the Caribbean.

A. Panel shows the share of major components of total spending for 19 small states and 81 other EMDEs for the period 2011-22.

B. Average net interest payments as a percent of GDP for 32 small states and 110 other EMDEs.

C. Average expenditures on health, education, and public investment as a percent of GDP in 31 small states and 116 other EMDEs for the period 2011-21.

D. Average expenditures on health and education as a percent of GDP for the period 2011-21. EAP small states include 10 economies, LAC small states include 11 economies, and other small states include 10 economies.

42
,766<br>13,009<br>11,48910,516 9,979<br>6,304<br>5,460 5,076<br>4,244<br>3,136<br>2,569 2,240 2,220 1,861<br>898<br><!-- End of picture text --> <!-- Start of picture text --> 1,861<br>898<br><!-- End of picture text --> <!-- Start of picture text --> EstoniaLithuania<br><!-- End of picture text --> <!-- Start of picture text --> Latvia<br><!-- End of picture text --> <!-- Start of picture text --> PolandBulgaria<br><!-- End of picture text --> <!-- Start of picture text --> Romania Turkey<br><!-- End of picture text --> <!-- Start of picture text --> Albania<br><!-- End of picture text --> Source: Trade as reported to the UN COMTRADE database and population data from WDI.

In fact, Turkey ranks very high in terms of trade openness and integration into global markets if variables determining a country’s dependence on foreign trade are taken into account. Its actual trade relative to GDP in 2003 exceeded the level generated by an empirical model developed for 149 countries. The model regresses openness, defined as the share of goods in GDP, on the country’s population, distance to major markets and GDP per capita (World Bank 2005). Its results are theoretical or potential shares of goods exports and imports in the GDP. If the potential (predicted) share is below its actual share, then a country ‘under-trades.’ If
sub-Saharan Africa (excluding Nigeria). Table 4b shows the level of tariff ceiling bindings and other duties and charges for all agricultural products. As indicated by the tariff comniitments, many least-developed net-food importing countries were extremely cautious in their own liberalization commitments under the Round. Except Nigeria, which committed to reduce its rate of protection on wheat and coarse grains to 150 percent (from 190 and 452 percent in the 198292 period), very few commitments to reduce applied protection were offered. The data also indicate that these countries left themselves significant room to raise applied protection, given the very high levels of tariff bindings.

At the same time, the UR agreement is unlikely to burden least-developed and net-food importing countries with many new obligations. This is because many of these countries made no substantial liberalization commitments on border protection either in other sectors such as industry or services. Moreover, many of the general exemptions for development and balance-of-payments support remain available for them to legitimize trade restrictions.

The tariff data are derived from the TRAINS, WTO IDB database and from the details of the PSE and CSE database developed by the USDA. Where PSE and CSE data are not available for some commodities, they are set equal to zero.

# F. ESTIMATES OF WELFARE IMPACTS

The welfare effects of world price changes resulting from the URA for 14 countries based on the comprehensive measures (terns-of-trade and distortion effects) are shown in Table 5. In addition, the first-order terms-of-trade impacts estimated for the 54 least-developed countries are shown in Table 6. Estimates based on the baseline price changes (scenario II) and those based on the largest predicted price changes (scenario IV of Goldin et al) are presented.1 5 All estimates represent percent changes at the end of the implementation period.

The results are summarized as follows. First, as expected, the small world price changes applied to a small proportion of total expenditures result in relatively small (in proportion to total GDP) changes in welfare in many of the least-developed countries studied. Only in a few
, we use the values measured in 1980, which is the first year of our sample.

Column (1) of Table 8 runs our preferred specification for all countries from column (3) of Table 3 on the subsample of countries for which we have data on the Legal System and

> 27 Efficiency-maximizing managers would abandon of their own volition those exports that use the available resources suboptimally. However, exporting activities might generate substantial managerial perks that would induce self-interested managers to keep alive also those exports that they know to be inefficient. Jaud, Kukenova and Strieborny (2018) present a formal model alongside these lines.

27
25 heaping at 1 month (Table C.5 of the 2008 DHS report). Nonetheless, to test for sensitivity to misreporting, we also ran the postneonatal analysis using an alternative definition, examining only deaths that took place during at age 2-12 months. These results (not shown) are similar to those for postneonatal mortality presented in the body of the paper.

# **Data description and imputation procedures – Malaria prevalence data**

The data on malaria risk in the DHS clusters were kindly provided by the Malaria Atlas Project funded by the Wellcome Trust, UK. The data stem from a map of infection and malaria risk distribution in Kenya in 2009. The map was created within a Bayesian geostatistical spatialtemporal framework using data on Plasmodium falciparum parasite rates in Kenya in 1975-2009. For further details on methods and data used for the map of malaria risk in Kenya in 2009 see Noor, Gething et al (2009).

Using the GIS information on the location of clusters in Kenya DHS surveys, a malaria risk measure was attributed to each cluster. The measure of malaria endemicity is the posterior predicted mean parasite prevalence (mean PfPR) among children aged 2-10 years. Since the data on malaria prevalence are available for the year 2009, we have a good malaria risk measure for observations from the DHS 2008-2009 and only an approximation for observations in the DHS 2003.<sup>18</sup> The GIS information is missing for one cluster in the DHS 2003 and for one cluster in 2008, leading to the exclusion of the affected observations from the analysis (concerns 20 observations).

Based on a recommendation by Robert Snow from the Malaria Atlas Project, we created a categorical variable on malaria risk. Originally, 5 malaria risk zones were recommended: very low transmission hard to distinguish from zero (mean PfPR lower than 0.1%), low risk (mean PfPR higher than 0.1% and lower than 1%), intermediate low risk (mean PfPR between 1 and 4.9%
imported intermediate inputs across firms. This export-import complementarity, where access to export markets allows firms to pay the fixed costs of importing intermediate inputs and thus improve productivity has largely been ignored in the export-productivity literature. The export-technology complementarity has been emphasized in theoretical models such as Melitz and Costantini (2007) where access to larger markets allows firms to pay the fixed cost of R & D. I examine the relative contributions of these two channels to productivity improvements of export starters.

I use CMIE Prowess, a panel data set of manufacturing firms from India. I first recover the parameters of the production function by explicitly including imported intermediate inputs and investment in R & D within the production function of the firm while also taking into account heterogeneity in firm prices. I use proxy methods as suggested by Ackerberg et al. (2006) to

> 3Foster et al. (2008) use data on physical quantities of a few homogeneous industries in the United States and find a negative correlation between price and efficiency. More productive firms charge lower prices and there is wider variation across firms in physical productivity as compared to revenue productivity.

> 4Smeets and Warzynski (2013) estimate value added production functions using firm price deflators and find that these yield higher trade premia in Danish firms as compared to revenue based measures.

> 5While most empirical work ignores price variation, there are a few exceptions. They control for unobserved prices by modeling demand under the assumptions of single product monopolistically competitive firms. Aside from the fact that they usually assume that firms produce only a single product, monopolistic competition implies a optimal price where prices are a constant markup over marginal costs where the markup ratio is determined by the elasticity of demand in the industry and is assumed to be the same for all firms in the industry. I observe in the data that a large proportion of the firms are multiproduct firms. The advantage of directly observing product level revenue and price data is that I can directly control for price heterogeneity without making these assumptions.

3
decades, Bhutan has also achieved broad-based and inclusive poverty reduction. As noted by NSB and World Bank (2014), between 2007 and 2012 the proportion of the population with consumption below the official poverty line dropped by half and Bhutan was able to practically eradicate extreme poverty. Multidimensional poverty indices and indicators related to education and health outcomes have also seen significant improvements.

With regard to Bhutan’s labor market, overall conditions are more favorable in the country than in most other South Asian economies. For instance, data from MoLHR (2015) show that in 2014 Bhutan’s overall unemployment rate stood at 2.6 percent. Besides, the gaps between male and female labor force participation and employment rates have long been much narrower than in many other South Asian economies. While the overall education level of Bhutan’s labor force continues to be comparatively low, it has been improving rapidly due to a trend toward almost universal enrollment in primary education, increasing enrollment rates in secondary education, and an ever greater emphasis on tertiary education.

At the same time, Bhutan faces a number of significant labor market challenges and bottlenecks. These include high levels of informality and underemployment (in particular in rural areas), a limited formal social protection system and relatively elevated rates of unemployment for certain parts of the labor force, especially for young, well-educated city dwellers. Specific labor market challenges relate to the interplay between the public and the private sector. A vibrant private sector has yet to emerge in Bhutan. As documented in MoLHR (2015), the country’s public sector comprises almost 50 percent of employment outside agriculture. It also employs two-thirds of workers with tertiary education. In addition to that, the public sector is widely perceived as the preferred employer, including among educated youth who aspire to white-collar jobs. In a recent survey among unemployed youth in Bhutan, 50.3 percent of respondents said they would prefer to work for the government and another 32.4 percent aspired for a job in a state-owned enterprise (
Other results accord with intuition; exporters fared significantly better during the crisis, whereas large firms shed more labor, in part because such firms were less likely to go out of business (our regressions are conditional on firm survival). Government firms also appear to have shed less labor.

# **7. Conclusion**

While crises are recognized to be periods of intensified adjustment and aggregate studies suggest that firm dynamics are a key determinant of the depth and duration of crises, firm-level evidence on their impact on the efficiency of resource allocation is scant. Perhaps because of the paucity of the empirical evidence, there is an active debate as to whether or not crises have a silver lining by improving resource allocation. On the one hand, a host of macroeconomic models are predicated on the idea that the additional competitive pressure induced by the crisis will hurt inefficient producers disproportionately. They predict that the crisis will “cleanse” out unproductive firms and reallocate resources towards more efficient producers. On the other hand, a series of recent papers point out that, in the presence of market imperfection, these conclusions may be overturned and that crises may “scar” the economy by driving productive firms out of business.

Using Indonesian manufacturing census data from 1991-2001 to examine the impact of the East Asian crisis on resource allocation, this paper rejects the hypothesis that the crisis unequivocally improved the reallocative process. Decompositions of aggregate productivity growth reveal that firms that suffered the largest productivity losses also suffered the largest reductions in market share. In addition, market share reallocation between firms contributed more positively to average productivity than during non-crisis times, although the magnitude of this contribution was modest and only positive in absolute terms in 1998. However, the correlation between productivity and employment growth did not strengthen, which is concerning given the excessive amount of job reallocation taking place during the crisis. More 29
balance, and also takes the form of an exponential function depending on variable renewable energy E shares and parameterized to the Ueckerdt _et al._ data.

- In the MESSAGE model (Messner and Strubegger, 1995, Riahi _et al._ , 2012), region- and sharedependent residual load duration curves (Ueckerdt _et al._ , 2017) are used to parameterize how flexibility of the residual non- variable renewable energy system, variable renewable energy curtailment, and wind and solar PV capacity values change with increasing variable renewable energy share (Johnson et al., 2017--in this issue). These equations are translated into step-wise linear functions that describe the contribution of variable renewable energy to capacity adequacy and system flexibility constraints, where increasing deployment requires more firm (backup) capacity and increasing flexibility from the non- variable renewable energy portion of generation. In addition, electricity storage and hydrogen electrolysis technologies are included as options for repurposing both variable renewable energy and non-variable renewable energy production that would otherwise be curtailed. Thermoelectric technologies are represented in two modes of operation, baseload and flexible, to better account for the cost, efficiency, and availability penalties associated with flexible operation and the consequences of variable renewable energy deployment for non-variable renewable energy plant utilization. Since MESSAGE is a least-cost optimization model with perfect foresight, the additional electricity system requirements for integrating variable renewable energy endogenously influence investment decisions within the power sector.

- The energy-economy-climate model REMIND (Luderer _et al._ , 2013, Luderer _et al._ , 2015) is a Ramsey-type general equilibrium growth model of the macro-economy in which inter-temporal global welfare is maximized, combined with a technology-rich representation of the energy system. Its power sector implementation is based on the region-specific residual load duration curves developed in Ueckerdt _et al._ (2017), which capture the effects of adding wind and solar power to the power sector on a) capacity adequacy, b) dispatch, c) storage and d) curtailment. The residual load duration curves are represented by four