rafmacalaba/data-use-mentions-extended huggingface dataset:
)d|
|Finland|18.0|87|39.7|18.7|5.2|
|Ghana|1.0|6|NA|60.0|50.8|
|Mexico|6.0|41|1.1|18.9|17.0|
|Philippines|7.7|79|16.6c|23.1|21.7|
|Spain|8.0|88|43 <sup>.</sup>2<sup>d</sup>|5.7|1.4|
|Sweden|24.0|145|_58.5_|10.8|7.4|

_Source:_ a. The product of an index of how well the legal framework protects secured creditors and a law and order index. Creditor right index taken from Porta et al (1997) and law and order index taken from International Country Risk Guide, various editions. The index ranges from 0 to 24 with 0 as the lowest and 24 as the highest score. d. author's calculation. b. IMF, International Financial Statistics. Private sector claims and bond market capitalization are shown at the onset of the financial crises in these countries. c. 1983. D. 1990.

Table 5 presents an overview of the main characteristics of the AMCs established including its narrow objective, type of assets transferred-real estate assets or assets or loans to politically motivated loans-the independence of the agency, legal superpowers and funding resources. The table highlights that the success of the AMCs in developing countries was mainly hindered by the following factors. Firstly, AMCs in developing countries mostly received non-real estate, state-owned enterprise assets, or assets reflecting political connections. All of these types of assets tend to be harder to restructure especially for a government agency. Secondly, many AMCs in developing countries had to resolve large amounts of banking system assets and received assets regardless of their size. Thirdly,
# **1 Introduction**

There is widespread evidence of both a wage and an employment premium in exporting vis-`a-vis non-exporting firms (Bernard and Jensen, 1999; Bernard, Jensen, Redding and Schott, 2007). This paper investigates differences the _composition_ of employment of exporting firms. In particular, it asks whether exporting firms hire more engineers relative to blue-collar workers than non-exporting firms. This can happen because firms produce goods of varying quality and exporters tend to produce higher quality goods. As in the literature, more productive firms become exporters (Melitz, 2003). In our framework, goods are produced with tasks using assignment rules (Acemoglu and Zilibotti, 2001; Acemoglu and Autor, 2011; Costinot and Vogel, 2010). Production involves different tasks, such as managing, output and design, and maintenance services. Exporters and non-exporters fill managerial tasks with skilled workers and fill maintenance services tasks with unskilled workers. However, output and design can be performed by engineers or by blue-collar workers. Engineers can deliver higher quality goods than blue-collar workers because they are more efficient in input supervision, assembly, and general product attributes that determine quality (Acemoglu and Zilibotti, 2001; Bastos, Silva and Verhoogen, 2014; Bernard and Jensen, 1997; Brambilla, Lederman, and Porto, 2012; Bustos, 2014; Caron, Fally and Markusen, 2014; Kugler and Verhoogen, 2012; Verhoogen, 2008; Yeaple, 2005). For given factor prices, more productive firms can afford to enter export markets, choose to produce on average higher quality products, and hire on average a higher share of engineers relative to blue-collar workers than less productive, non-exporting firms.

To test these predictions, the empirical analysis exploits detailed information of firms’ demand for different tasks contained in the Chilean Encuesta Nacional Industrial Anual (ENIA). The results from an instrumental variable estimator show that Chilean exporters indeed utilize a higher share of engineers over blue-collar workers than non-exporting firms. The results provide causal evidence of the skilled
# **1. Introduction**

While crises are periods of intensified adjustment, firm-level evidence on the impact of crises on resource allocation is limited. Moreover, their impact is theoretically uncertain. The idea that crises may accelerate the Schumpetarian (1939) process of creative destruction by “cleansing” out unproductive arrangements and freeing up resources for more productive uses features prominently in macro-models (see e.g. Hall, 1995, Caballero & Hammour, 1994, 1999 and Gomes et al., 1997). On the other hand, some recent papers suggest that, rather than being cleansing, crises “scar” the economy and undermine long-run productivity growth by exacerbating market imperfections and destroying productive firms (see Barlevy, 2002, and Ouyang, 2009). Which view is correct matters for policy, since at issue is whether or not there is a tradeoff between minimizing the short-term impact of crises and maximizing long-run growth prospects; if crises are cleansing policies to dampen short-term impacts may obstruct long-run recovery and be not only costly, but also counterproductive. By contrast, if crises are scarring, policies to minimize short-term impacts are consistent with maximizing long-run growth prospects (see Paci et al., forthcoming). Given the critical role of the reallocative process in facilitating recovery and growth, and the paucity of available firm-level studies on the impact of crises on resource allocation, empirically discriminating between these competing paradigms is important.

This paper uses plant-level manufacturing census data from Indonesia to examine the impact of the East Asian crisis on the process of creative destruction. Aggregate productivity growth decompositions help assess macro-level trends in the contributions of entry, exit and reallocation to productivity growth during and after the crisis. These decompositions are a prelude to firm-level analysis of exit and employment growth patterns, which assesses whether and how firm dynamics during the crisis were different from those operating in pre- and postcrisis periods. Thus, we examine both the impact of the crisis on resource allocation and the reallocative process itself.

2
|South Africa|3|
|New Caledonia<br>|2|Indonesia|3|Greenland|1|Mexico|1|India|2|Nauru|3|

_Source:_ Authors based on World Bank’s AFE Energy Transition Material (ETM) reserves data.

# **The IRA is expected to boost demand for critical minerals in the US, especially for green technologies,**

**benefiting eligible exporters** . In the short-term, established producers of raw and processed critical minerals for EV batteries with a US FTA, like Mexico, are expected to see increased exports. Other top exporters to the US, like Brazil and China, could lose market access without an FTA as EV credits are contingent on a large share of the minerals in the battery being sourced from a country with an FTA. In the medium-term, as new mining enters operation, countries with large reserves of critical minerals may enter the US market or increase their exports of critical minerals for EV batteries if they secure an FTA. For instance, African countries with zinc and manganese reserves that secure an FTA may benefit from US diversification efforts. Similar short and medium-term impacts are expected for countries that export raw and processed critical minerals for other technologies (besides EV batteries) but without FTA membership affecting demand as only minerals for batteries require FTA membership for eligibility to credits. In the short term, the developing countries with mining sectors most exposed to the increased US demand are South Africa, China, and Russia. However, the exposure of these countries’ mining sectors and economies is not high as their raw mineral exports to the US do not represent a large share of their total exports of raw and processed minerals to the world and of their GDP.

**China's green energy transition is also anticipated to drive demand for critical minerals from developing countries.** Rising EV production will increase the need for battery materials like lithium, nickel, and cobalt.<sup>24</sup>
_Appendix 2: Institutional environment indicators in the East Caribbean States_<sup>_6 0_</sup>

# **Statistical Table 1 Rule credibility indicator and components (East Caribbean)**

||Record<br>managemen|Internal<br>t audit|Performance<br>appraisal|<br>Project<br>evaluation|Rule<br>Credibility|
|---|---|---|---|---|---|
|Antigua and Barbuda|8.4|3.5|4.3|3.7|_5.0_|
|Grenada|8.8|3.6|9.3|6.1|6.9|
|St. Kitts and Nevis|7.1|6.5|5.6|_5.0_|6.1|
|St. Lucia|8.6|5.5|8.1|4.1|6.6|
|St. Vincent And Grenadines|8.9|6.9|4.2|_5.1_|6.3|
|East Caribbean|8.6|5.2|6.6|4.7|6.3|

# **Statistical Table 2 Policy credibility indicator and components (East Caribbean)**

||Consistency|Policy<br>coordination|Micro-<br>management|Policy<br>Credibility|
|---|---|---|---|---|
|Antigua and Barbuda|4.1|_5.3_|4.2|4.5|
|Grenada|3.7|5.4|3.8|4.3|
|St.Kitts andNevis|7.3|6.9|3.3|5.8|
|St. Lucia|6.0|6.4
The estimated regression coefficient on storm intensity from the US analysis and the estimated coefficients o income and population density from the international regression are then used to predict the damages that would be caused by each storm in the generated data set. The global data set of 17,000 storms for both current and future climate is a rich data set that describes the expected value and distribution of tropical cyclone damages. The comparison of the results across the four climate models also provides an additional sense of the uncertainty of the estimates with respect to climate. We use both a minimum pressure damage model and a maximum wind speed model to reflect some of the uncertainty in the damage function. In each case, the damages to each country are estimated and then aggregated.

The analysis first calculates the damages to each country from tropical cyclones with the current economic baseline and current climate. The damages are then predicted for the future using projected populations and economies in 2100. This analysis reveals how much baseline damages will increase because of higher incomes and populations given the current climate.

The second analysis estimates the impact of climate change on tropical cyclone damages. Climate change impacts are calculated as the tropical cyclone damages with a 2100 climate minus the tropical cyclone damages given the current climate, both estimated using future income and population.

The next section of the paper describes the methodology in more detail. The empirical findings of the paper are then reviewed in Section III. The paper concludes with a review of the major findings and a few policy observations.

# II. Theoretical Methodology

The economic damage ( _D_ ) from each tropical cyclone is the sum of all the losses caused by it. In this analysis, we focus primarily on lost buildings and infrastructure. The economic damage of capital losses is the present value of lost future rents. This should be equal to the market value of the building. Note that the market value of capital is often less than the replacement cost.

In order to model tropical cyclones, it is critical to recognize that they are rare
Singapore|1996<br>1992|1<br>1|||||||
|South Africa|2009<br>1998<br>1993|5<br>36||1<br>3|5||3||
|Sri Lanka|2010<br>1995|4<br>23|||||||
|Taiwan,China|2010||||1||||
|Thailand|2007<br>1995<br>1991|3<br>11<br>36|6|1<br>2|8<br>12|2**|3||
|Tunisia|2011<br>1994|54|||||2|22 *|
|Uganda|1995|3|||||||
|Uruguay|2012<br>1998<br>1992|3||1|1|4**|31||
|Venezuela|2008<br>1996|24 *<br>2|3 *<br>3|1 *|||13||
|Zambia|1996||1||||||
|Zimbabwe|2011<br>1995|23||||100***|||

Notes: Frequency ratio of percentage in HS 2-digit products affected by each NTM.

All countries report several product categories—usually 15-30% of tariff lines as subject to licensing and/ or prohibitions for health, sanitation, safety or defense reasons. Only licensing and/or prohibitions for economic reasons are reported here. * in percent at HS 6-digit level; ** import monitoring; *** exchange control Sources: GATT TPR (1989-1994); WTO TPR (1995-2012); UNCTAD/World Bank NTM database.

16
br>**primary**|**Secondary**|**Further**<br>**education**|_No. of Obs._|
|---|---|---|---|---|---|---|
|Proportion of adult ruralpopulation(%)|||||||
|No occupation|3.7|3.6|1.9|2.3|0.0||
|Agricultural sector|88.3|77.1|58.7|21.3|0.0||
|Non-farm enterprise|5.5|6.7|15.0|2.4|0.0||
|Non-farm wage worker|2.4|4.9|17.5|17.1|38.5||
|At school|0.0|7.7|6.9|57.0|61.5||
|_Number of observations_|_3965_|_8158_|_361_|_740_|_16_||
|Mean Wage for non-farm wage workers(Rwf)|||||||
|Primaryoccupation|52.1|78.9|159.8|193.8|646.3|_1523_|
|Secondary occupation|75.0|107.9|272.1|217.5|2674.7|_296_|
|Mean Wage for non-farm self-employed(Rwf)|||||||
|Primaryoccupation|106.8|269.1|335.7|555.1|1630.6|_632_|
|Secondaryoccupation|112.8|187.8|296.4|940.6|164.8|_412_|

Generally, the presence of barriers to entry in specific sectors as well as limited overall economic activity may lead to a concentration of employment in only a few
_7_ The simple elasticity between the annual number of non-cash payments per person and real cash holdings per person is -.68, so a 10% reduction in cash holdings is associated with a 6.8% rise in non-cash transactions. A deeper analysis of these cross-country payment effects within a standard demand function framework suggested that while cash and non-cash payment instrument use are negatively related, the implied substitution between them is due more to differences in use across countries than it is to changes in use over the 1987-93 time period, a result that also applies to the substitution among the five non-cash instruments as well.

**Table 1: Correlation matrix for per person use of payment instruments** **_(14_** _developed countries. 1987-93)_

||_Paper_<br>_Ciro_|_Electronic_<br>_Giro_|<br>_Credit_<br>_card_|_Debit_<br>_card_|_Cash_|
|---|---|---|---|---|---|
||_(1)_|_(2)_|_(3)_|_(4)_|_(5)_|
|Check|-.37|-.39|.81|-.11|-.42|
|Paper Giro||-.09|-.23|.30|-.14|
|Electronic<br>Giro|||-.50|.12|.19|
|Credit<br>card||||-.17|-.38|
|Debit card|||||-.39|

_Source: Computed from time-series, cross-section data from Bank for International Settlements (May 1989, December 1993, July 1994, December 1994, and May 1995)_

**_Paymentprice elasticities._** Own prices, which are small in magnitude, vary little over time, and may not depend on incremental use,
DEA tends to dominate the methodologies. There are however many methodological debates among port specialists and efficiency specialists. For instance, Park and De (2004) using data on 11 Korean ports for 1999 compare all the methods available and suggest a new 4 stages DEA to increase the robustness of the results. There is also a more conceptual debates on the number of port activities that need to be covered by efficiency studies. The matter is not a simple one since many of the activities are competitive while a few, more obviously the port infrastructure, are not. Moreover, while it is obvious to all that there is a complementarity between all activities, there is no uniform view on how to address it in the context of efficiency measures or even whether it needs to be addressed. For instance, some argue that all equipment, including equipment owned and operated by independent competitive providers, must be included in the assessment of the performance of the port authorities responsible for the basic infrastructure. Many more disagree with this vision arguing instead that there is an interest in assessing the performance of the regulated monopoly independently of the performance boost or penalty that independent competitive providers may generate. At best, when information is available, the competitive segments of the port should enter as environmental variables.

In practice, the response to the debate is generally settled by data availability. In general, the focus of the assessments in ports tends to be the production side rather than the cost side mainly because of data constraints. But these constraints are so binding that they also drive other choices. They explain why most articles focus on _Cobb-Douglas_ rather than _Translogs._ Most studies also focus on data panels than time series. There are however a few very real policy aspects that cannot be ignored. For instance, most studies explicitly recognize the multiproduct nature of the port business, although a few focus on aggregate output measures. The most common is to see a disaggregation between general, dry-bulk, liquid-bulk and containerized cargo. On the input side, labor and capital
.00128|-0.00044||-0.000413|-0.000332|||||
||(0.000576)***||(0.000577)**|* (0.000802)|(0.000342)||(0.000346)|(0.000464)|||||
|Share of hhlds with computer||-0.182|-0.202|-0.215||-0.155|-0.176|-0.193|||||
|||(0.133)|(0.135)|(0.140)||(0.0869)*|(0.0820)**|(0.0834)**|||||
|IDN exports per worker *||||-0.00338||||-0.0003|||||
|Share of hhlds with computer||||(0.00253)||||(0.00146)|||||
|Observations|3,123|3,123|3,123|3,123|3,123|3,123|3,123|3,123|||||
|R-squared|0.919|0.919|0.919|0.919|0.782|0.782|0.782|0.782|||||

Source: Authors’ calculations based on Sakernas 2005-2010 and 2011-2015, Susenas 2005-2010 and 2011-2015, 2015-2016 Philippines STEP survey, and UN Comtrade database 2005-2010 and 2011-2015.

Notes: IV regressions. IV: IV: Global demand for foreign products excluding Indonesian products. Indonesian exports per worker in thousands of USD at 2010 prices and global demand for foreign products in millions of USD at 2010 prices. ICT variable at the province level is the share of households with
“CR5” denotes the concentration ratio of the top 5 investing firms. Countries averaging fewer than 5 foreign investors are excluded from these calculations. “HHI” refers to the Herfindahl-Hirschman concentration index, calculated using the “CR5” sample. “log(# MNEs)” represents the natural logarithm of the number of foreign investors, and “log(K)” denotes the natural logarithm of the average capital expenditure. “Uncertainty” is based on the “T3” variable from the World Uncertainty database (Ahir et al. 2022), which counts the occurrence of the word “uncertainty” from Economist Intelligence Unit (EIU) reports for 143 countries since the 1950s. A higher value indicates greater uncertainty. Uncertainty in year _t_ is approximated as the logarithm of the average number of uncertainty-related words in EIU reports over the three years preceding the investment (standardized to have a mean of zero and a standard deviation of 1). The analysis includes country fixed effects and (world) region by year fixed effects. Countries are classified into 7 regions based on World Bank classifications. Destination-specific control variables include the logarithms of population and GDP (source: Conte et al. (2022)) and the average exchange rate over the three years preceding the investment (national currency/USD, Penn World Tables, mark 10.1, Feenstra et al. (2015)). Standard errors are clustered at the destination country level. Significance levels: *** _p <_ 0 _._ 01, ** _p <_ 0 _._ 05, * _p <_ 0 _._ 1.

However, eliminating source countries one by one from the descriptive regression associating uncertainty with investment concentration at the destination does not yield major variation in the coefficient (see Tables B8 and B9). This result points to destinationspecific variation as the main driver of the pattern. Controlling for home-country concentration (see Tables B10 and B11) and disaggregating industry-level concentration (see Table B12) confirms the robustness of our
<!-- Start of picture text --> Number of Exporting Firms,<br>Egypt, 2017<br>— Tt.Swe<br>~<br>, se<br>Nesabor of Femse :<br>LJLJ 25000-2500- 5000 A A<br>Own elaboration using Enterprise Survey<br><!-- End of picture text --> <!-- Start of picture text --> Employment<br>Egypt, 2017<br>‘ ae S ae. =<br>584 a) \<br>mm<br>F<br><<br>11 5<br>17 Jes<br>2<br>27<br>(391,021,686,673] %<br>|_|}|_] (44,321,174,593}(4,050,44,321} ‘ y<br>Oni sabicremnspiinh UPS dak<br><!-- End of picture text -->
- From the end of the 1980s, government drastically cut its involvement in pesticides trade through government-managed cooperatives, leaving trade at all levels to private dealers.

- Government from 1994 removed pesticide price controls, which had become unwieldy due to rapid inflation.

Several other features of Turkey's current pesticide registration process favor new technology and competition. Government accepts toxicology data from other countries, which reduces time and expense for companies to introduce new formulations. To register competing brands for old offpatent formulations that are already in the market, government asks only for chemical analyses of new brands, which favors competition and low prices for me-too or commodity products. Also, government has from 1971 de-registered (banned) many chemicals that are highly toxic, persist in the environment, or have chronic health or reproductive impact.

# **2.4** Fertilizers

From 1971, government stopped the small amount of private trade previously allowed, restricting fertilizer import and domestic trade to Agricultural Supply Organization (DONATIM or TZDK) and the Sugar Corporation, with government-controlled cooperatives dominating retail trade. While government dominated trade, a mix of public and private companies generally supplied over twothirds of domestic demand along with some exports.

With general import trade liberalization from 1980, the fertilizer industry gained improved access to raw materials and spare parts, allowing higher capacity utilization and production (State Planning Organization 1988, p 11). However, government continued to control prices and trade at all levels, so that improvements for producers had little impact at the farm level.

From July 1986, government replaced its monopoly in fertilizer trade with a public-private oligopoly, allowing the handful of fertilizer producers along with government-controlled cooperatives and their agents to enter international and domestic fertilizer trade at all levels. From 1988, government further opened domestic fertilizer trade to anyone with TL 1 billion capital, leaving import trade in oligopoly hands. From September 1994, government removed this trade barrier as well, opening fertilizer imports to any company with TL 1 billion capital. At the same time, anticipating customs union
’s per capita GDP reported between 2006 and 2015. Explicitly, a 10 percent increase in mobile phone penetration in the region increases by 1.2 percent the GDP per capita. Contrary to these results, the authors find that internet penetration has not contributed to GDP per capita growth during 2006-2015. The authors claim that this could be due to the low internet penetration in the region, high technology illiteracy, or limited technological infrastructure, among others.

The literature on causal effects is sparser but, to a large extent, it points at positive causal effects of ICT adoption on growth. Castaldo et al. (2018), use General Method of Moments— Instrumental Variable (GMM-IV) to find a positive and statistically significant causal effect between fixed broadband and economic growth in 23 OECD countries between 1996 and 2010, even after controlling for countries’ initial endowments (measured as fixed capital formation). Similarly, based on panel co-integration and Granger causality tests, Arvin and Pradhan (2014) claim a causal relationship between broadband adoption and GDP growth using data for 19 countries—developed and developing—between 1998 and 2011. Furthermore, the study finds a bidirectional relationship between GDP growth and broadband penetration only in developed countries. In contrast, in developing countries, the authors find that economic growth has a causal effect on broadband penetration. Gruber et al. (2014) use simultaneous equations models to conclude that fixed broadband adoption has had a significant positive causal effect on GDP growth in 27 countries in the European Union from 2005 to 2011. The authors argue that the effect is larger in high adoption countries. More recently, Calderon and Cantu (2020) examine the impact of digital infrastructure on the development of Sub-Saharan Africa, by comparing data from 177 countries between 1990 and 2019. The authors use a principal component analysis (PCA) and general method of moments with instrumental variables (GMM-IV) system estimator to find a positive and significant causal impact of digital infrastructure 7