rafmacalaba/data-use-mentions-extended huggingface dataset:
from a decline of _≈_ 15% to an increase of _≈_ 570% for high-density days. For low-density days, it ranges from a decline of 100% (bounded at zero days) and an increase of _≈_ 320%. To examine how our projected impacts change over this large uncertainty range, we estimate average impacts for every combination of changes within the bounds of our linear extrapolation – for high density days from a 15% decline to a 570% increase and for low density days a 100% decline to a 320% increase.

In contrast to the lack of research projecting the number of smoke days under climate change, there is a substantial literature that predicts changes in burned area, biomass consumed by wildfire, and increases in particulate pollution due to wildfires. Ref.<sup>14</sup> reviews several of the pre-2009 estimates of changes in burned area for the Western U.S. and Canada. On average, the reviewed studies predict a 65% increase in the continental U.S. and western Canada by mid-century relative to the 2010s. This is much less than the observed increase (roughly 200%) in the United States from 1985 to 2020.<sup>1</sup> In general, the newer papers reviewed predict larger increases by 2050. Ref<sup>14</sup> suggests an increase in burned area of 160% across the whole Western U.S. Other recent work suggests increases of up to 1000% in the Rockies, increases of 78-175% in the Pacific Northwest and the Rockies, and increases of burned area of between 75-310% and biomass consumed of 127-410% in the Pacific Northwest.<sup>15,11,35</sup> Ref.<sup>1</sup> reports annual area burned in the United States from 1985 to 2020. Extrapolating the linear trend from that data results in a 235% increase by mid-century relative to the average from 1985 to 2020
Table D.17. Workers by Economic Activity

||Share of Work|ers by Econo|mic Activity|Share of Agricultural Workers|
|---|---|---|---|---|
||Agriculture|Industry|Services|Growing Cereals and Fruits|
||(1)|(2)|(3)|(4)|
|Guerrilla control|0.0465***|-0.0261***|-0.0203**|0.0456***|
||(0.00985)|(0.00559)|(0.00878)|(0.00944)|
|Observations|3,636|3,636|3,636|3,636|
|Bandwidth (Km)|2.266|2.266|2.266|2.266|
|Dependent mean|0.194|0.227|0.579|0.160|

_Note:_ The table presents the results of equation 1 for the share of workers in each economic activity. The information was calculated from the Census 2007 and using ISIC v4 to classify each occupation. The unit of observation is the census tract. Controls not shown include a linear polynomial of the distance to the boundary of guerrilla territory, its interaction with whether the tract was under guerrilla control or not, and up to 400 fixed effects representing the closest evenly spaced break in the guerrilla-controlled boundary. The estimates use triangular kernel weights. Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1, _†_ p _<_ 0.15.

Table D.18. Inequality of Income at the Canton Level

|||||_Real Per Capita Income_|||
|---|---|---|
Fifth, even though crisis countries had stronger information disclosure requirements, the incentives for the private sector to monitor bank risks were weaker in these countries. At the start of the crisis, all crisis countries were required to produce consolidated accounts covering all bank and any non-bank financial subsidiaries. Crisis countries were more likely to require banks to disclose risk management procedures, capital ratios, transactions with related parties, and the scope of consolidation. In over 95 (79) percent of crisis countries, issuance of bonds (commercial paper) received a credit rating, whereas this happened in 47 (32) percent of non-crisis countries. While all crisis countries had an explicit deposit insurance scheme before the crisis erupted, this was in effect in about 50 percent of the non-crisis countries before the crisis and in 72 percent by the 2011–12 BRSS. According to the 2007 survey, in a larger share of crisis countries (11 percent in crisis versus 2 percent in non-crisis), the deposit insurance agency had the power to insure liabilities beyond any explicit deposit insurance scheme.

Overall, while these tests do not imply causality, they nevertheless provide suggestive evidence that crisis countries suffered from greater weaknesses in their bank regulations and supervision frameworks.

# **IV.   REGULATORY CHANGES IN THE CONTEXT OF THE GLOBAL FINANCIAL CRISIS**

By comparing responses to the 2011-12 and 2007 surveys, we are able to provide the first documentation of recent regulatory changes in the context of the global financial crisis. Overall, the survey responses underscore the evolutionary, slow nature of regulatory and supervisory changes. It does not appear that the recent global financial crisis caused a major and sudden change in regulatory frameworks around the world. For example, 85 percent of “yes” or “no” responses remained unchanged between the 2007 and 2011–12 surveys (i.e., “no” remained “no”, “yes” remained “yes”). Similarly, most of the quantitative indicators showed relatively 10
and identify two key dimensions of heterogeneity for our core set of results in our sample. The first is GDP per capita (e.g., Carleton et al. (2022)). The second is firm size, measured as the number of permanent, full-time employees ( _Emp_ ), which we consider a stable long-term proxy for firm quality. Modeling these two dimensions of heterogeneity allows us to test the hypotheses that the economic effects of climate change mostly materialize in poorer regions and that small firms may face more constraints in adapting to climate change. Including additional dimensions of heterogeneity beyond GDP and firm size does not change the relationship between temperature differences and firm performance, suggesting that GDP and firm size parsimoniously capture first-order heterogeneity (see Figure A1).

We flexibly model each weather variable using a third-order polynomial, which allows the data to drive the observed relationship between weather deviations and firm performance while preserving statistical power.<sup>3</sup> We fully saturate the model by interacting each term in the polynomial with the our two dimensions of heterogeneity. Following the recommendation of Abadie et al. (2023), we cluster our standard errors at the strata level (i.e., by subnational regions, sectors, and firm size categories). Indexing firms with _i_ , sectors with _j_ , countries with _c_ , fiscal years with _t_ , and strata with _s_ , we estimate Here, _CDiffijcst_ is the difference between the relevant climate variable for firm _i_ in year _t_ (when the Enterprise Survey was conducted) relative to that climate variable in firm _i_ ’s location from 1980–2008. The variable log( _GDPct_ ) is the logarithm of GDP for country _c_ in the year we observe firms in the Enterprise Survey and _Empijcst_ is the number of employees working at firm _i_ in year _t_ . _CHistijcs_ is the historical average of the relevant climate variable from 1980–2008, _γjct_ is a sector by country by fiscal year fixed effect, and _εjsc_ is the error

> 3Figure A2 shows that
49 (GHED), and aggregate consumption is obtained from the World Bank World Development Indicators data set.

These external checks led to the dropping of some survey ‘families’ (notably WHO’s WHS and MCSS survey families) and some survey adaptations for some countries (e.g. LIS for the USA), because some or all of the above flags were raised consistently.

Our internal consistency checks involve examining the full set of datapoints available for each country. When different datapoints were available for a given country-year combination (for example when a datapoint is derived from the raw data, and when there is also another datapoint derived from an adaptation of the raw data set), longitudinal consistency across the different datapoints was favored. For example, in the case of Mexico, the LIS-based estimates were used for all years, since they were the best in terms of the external flags for the vast majority of years, even though for some years they were not the best.

# <u>Illustrations using the financial protection data</u>

Figure 12 shows trends in catastrophic expenditures (using the 10% threshold) in selected countries, all of which have enacted reforms aimed at expanding and deepening health insurance coverage (Wagstaff et al. 2016). In both Indonesia and Georgia, the incidence of catastrophic expenditures has been rising, but the rise has been far more pronounced in Georgia, and the base was higher as well. Despite the recent rise in Indonesia, the rate at the end of the series is still lower than that in the other three countries. In both Mexico and the US, the incidence of catastrophic expenditures has come down, in Mexico’s case the drop apparently happened somewhat after the ‘Seguro Popular’ reform, while in the US the drop apparently began somewhat before the Affordable Care Act or ‘Obamacare’ reform; the drop in Mexico has been more pronounced.
,m<br>(5)|
|---|---|---|---|---|---|
|Postm*BankExposureGovb,Mar16|0.01|1.26*|-1.18***|-0.02|1.23**|
||(0.03)|(0.71)|(0.37)|(0.31)|(0.60)|
|Observations|468|417|456|468|468|
|R-squared|1.00|0.96|0.94|0.95|0.76|
|Bank FE|Yes|Yes|Yes|Yes|Yes|
|Month FE|Yes|Yes|Yes|Yes|Yes|

52
65.00|59.22|66.04|
|Bricks|51-73|47.17|70.07|45.08|
|Iron and Steel|53.86|58.02|72.50|57.31|
|Radio and TV Receivers|62.17|64.80|68.75|63.23|
|Motor Vehicles|34.21|68.86|59.86|66.68|
|Weighted<br>Average<br>17 industries|57.61|72.78|67.23|71.32|

After matching the producta in the sample, we had to make volume and unit value adjustments to the 1977 US census figures, in order to make them comparable with those for Brazil and Mexico which are for our preferred benchmark year 1975. The volume adjustments for the USA from 1977 to 1975 were derived from the 1982 US Industrial Outlook, in which gross value of output is shown at constant 1972 UJSS for separate product groups. These ratios (see first column of table 3.6), were applied to the 1977 US Census figures. The resulting 1975 figures at 1977 prices were compared with the product group figures for 1975 at 1975 prices derived from the Annual Survey of Manufactures 1975-1976 (ASM). From this latter confrontation we derived our unit value indices for 1975 relative to 1977, which are presented in the second column of table 3.6.

Fabricant (1940, p. 364-6) suggested a 40 per cent minimum coverage ratio.
Substantial quality improvements were only possible from reallocation in systems where sufficiently high-competence providers were available _and_ sufficiently busy postings existed.

In a benchmarking exercise ( **Supplemental Appendix Figure 6** ), we contextualize the size of the potential quality improvements from reallocation by using the SDI data to simulate instead the impact of training existing nurses and para-professionals in each country to the same level of medical knowledge as doctors in the same country. Specifically, we replaced the competence of those providers with a random draw from the competence distribution of doctors, again without changing any caseloads or postings, and then calculated the potential improvement in quality of care.

In countries where reallocation offered substantial improvements, we estimated that “upskilling” would typically be about half as effective as reallocation – either because doctors already made up a substantial share of observed providers, or because doctors were not reliably more competent than other provider cadres.<sup>9</sup> In other countries, reallocation was relatively ineffective because the availability of high-competence providers was very low. In these countries – namely, Madagascar, Mozambique, Niger, Sierra Leone, and Togo – increasing workforce training to the level of local doctors was equally ineffective, because even the most competent providers still had low rates of correct management in the vignettes. In such contexts, where systems of medical education and retention have failed to produce a minimally competent workforce, there appear to be few options within the existing system that would offer viable avenues to quality improvement.

# **5 Discussion: Unequal Burdens, Unequal Outcomes**

Organizations such as the World Health Organization have repeatedly argued that SubSaharan African countries face severe health worker shortages and that, as a consequence, health facilities are overcrowded and providers are routinely overburdened. A new literature on quality and availability suggests that the picture is more complicated. Studies that count the number of actual patients and medical staff at health care facilities show that health care providers, on average, are not overloaded and in fact have substantial excess capacity.<sup
tables were used by OECD and WTO to release their first set of Trade in Value-Added (TiVA) indicators in January 2013.

- In order to cover additional countries where no official input-output tables are available, other projects use alternative sources and estimate missing data. The Global Trade Analysis Project (GTAP) database includes social accounting matrices which were used, for example, by Koopman, Powers, Wang and Wei (2011) to provide measures of trade in value-added terms. The forthcoming World Bank dataset of trade in value added is also based on GTAP. Developed by Francois, Manchin, and Tomberger (2012), it covers about 100 countries that allow exploiting social accounting matrices spanning intermittent years from 1992 to 2007 to construct country-specific measures of the direct and indirect contribution of goods and services to the value added contained in a given country’s domestic production and exports. Specifically, the dataset contains two matrices, a domestic value added table and an export value added table, which allow the identification of the value added contribution of particular inputs to sectors that either sell the final good to the domestic market or export it.

- Lastly, inter-country and industry information can be found in the Eora global multi-region input-output (MRIO) tables produced by the University of Sydney and funded by the Australian Research Council. The Eora MRIO brings together a variety of primary data sources which are combined in a single dataset using interpolation and estimation techniques to provide a contiguous, continuous dataset for the period 1990-2010. Because the project focuses on environment issues, there are important deviations from observed trade flows and GDP and the tables are balanced to match principally data from large economies. The Eora MRIO was used by UNCTAD to produce trade in value-added indicators in a report released in February 2013.

All these databases allow for the estimation of the source(s) of value (domestic vs. foreign and/or by country and industry) that is added in producing goods and services for export. They recognize that growing global value chains means that a country
Madagascar**|**Mozambique**|**Sudan**|**Tanzania**|**Uganda**|
|---|---|---|---|---|---|---|---|---|---|
|HAI rate (% of<br>admissions)|12.0%|12.0%|17.0%|12.0%|12.0%|12.0%|12.0%|12.0%|12.0%|
|Total inpatients<br>peryear|1,779,400|2,760,000|2,976,000|3,200,000|888,000|3,607,172|1,874,960|1,630,442|2,069,310|
|Estimated<br>number of HAIs|213,528|331,200|505,920|384,000|106,560|432,861|224,995|195,653|248,317|
|AMR rate (% of<br>HAIs)|50.0%|50.0%|50.0%|50.0%|50.0%|50.0%|50.0%|50.0%|50.0%|
|Estimated<br>number of AMR<br>infections|106,764|165,600|252,960|192,000|53,280|216,430|112,498|97,827|124,159|

Note: Figures in red are estimated based on 4 admissions per 100 population.

For more detailed future analysis, breakdowns between different hospital wards would be possible if specific HAI rates are available. In Kenya, for example, admissions data are commonly provided
In this paper, we aim at filling this gap in the literature and document the link between political connections and access to credit using a large sample of firms from Bulgaria, Hungary, Romania, the Russian Federation, Serbia and the Slovak Republic observed over the period 2008 _−_ 2013. Using these data, we show that connected firms, in comparison to their unconnected peers, have higher levels of leverage, are less profitable, have lower marginal productivity of capital, and do not invest more. A major finding is that political connections appear to ease credit constraints. In addition, we find evidence that this privileged access to credit generates distortion in the allocation of capital as connected firms which benefit from easier credit tend to be less productive. In fact, it seems that credit constraints are reduced disproportionally more for the least efficient and least profitable firms, leading to clear welfare losses for these economies.

The paper is organized as follows: the next section describes the data and how we identify and measure political connections. Section 3 provides an initial assessment of the differences between connected and unconnected firms in terms of their leverage, profitability, and investment activity. Section 4 documents that availability of cash flow is less crucial for investing in the case of connected firms, demonstrating that they have easier access to credit than comparable unconnected firms. Section 5 shows that these connections lead to capital misallocation and welfare losses. Some final remarks follow in section 6.

# **2 Measuring political connections**

We implement our empirical strategy using two sources of data. First, we rely on a proprietary database that documents in detail an exhaustive list of politically exposed persons (PEPs). The data are primarily used by private institutions to undertake due diligence. In the PEP data set, the definition of politically exposed person depends on the classifications by intergovernmental bodies such as Financial Action Task Force (FATF) or legislation such as the European Union’s Anti-Money Laundering Directive. More importantly, for our purposes, a wide range of information
# **Figures and tables**

Table 1: Data availability on tax records

||Observations|Individual yearly|||Witholding f|orms|||Other sources|Undistributed|
|---|---|---|---|---|---|---|---|---|---|---|
||Tax data|tax flings|Wages|Services|Dividends|Interest|Capital Gains|Rental|Third-party info|Corporate income|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|2003|38,778|24,260|0|0|0|0|44|0|0|13,639|
|2004|50,126|25,445|0|0|0|0|165|0|175|23,354|
|2005|58,136|26,556|0|0|0|0|229|0|302|29,676|
|2006|62,439|28,159|0|0|0|0|200|0|290|21,957|
|2007|67,808|31,695|0|0|0|0|281|0|356|34,440|
|2008|71,471|33,342|0|0|0|0|371|0|255|35,395|
|2009|73,549|34,024|0|0|0|0|301|0|2,384|34,800|
|2010|80,770|37,363|464|399|16|68|355|103|5,783|38
50<br>VIX implied volatility<br>40<br>30<br>20<br>10<br>0<br>Jan-07 Jul-07 Jan-08 Jul-08 Jan-09 Jul-09<br>Source: Datastream<br>(c) VIX market volatility index, 2007–10<br><!-- End of picture text --> Figure 1: Three-month LIBOR-OIS and TED spreads (top panel), five-year CDS spreads on U.S. banking sector index (middle panel), and VIX implied volatility of S&P 500 index options (bottom panel), January 2007 through December 2010. The charts show, for both the initial (light shaded area) and acute (dark shaded area) phases of the crisis, that there was severe difficulty in obtaining liquidity (interest rate spreads), significant concerns over bank solvency (credit default swap spreads), and that the volatility of returns on asset markets was elevated relative to non-crisis periods (market volatility index).

7
40

# Annex 1: List of Variables

The Database on Political Institutions (DPI) is a large new cross-country database of political institutions that covers 177 countries over 21 years, 1975 - 1995. This database was prepared by Thorsten Beck, George Clarke, Robert Cull, Alberto Groff, Philip Keefer, and Patrick Walsh of the Development Research Group of the World Bank. When using this database, please cite the current paper.

## General Variables

#### Country Code COUNTRYC

International Financial Statistics code IFS Year YEAR

## Chief Executive Variables

Direct Presidential (0); strong president elected by assembly (1); Parliamentary (2) SYSTEM How many years in office YRSOFFC Is there a finite term in office? (1 if yes, O if no) FINITTRM Years left in curent term YRCURNT If there are formal restraints on his term (NA if not), can he serve multiple terms? MULTPL? Is Chief Executive a military officer? MILITARY Is defense minister a military officer? DEFMIN , President got what % of votes in the 1st/only round? PERCENT1 President got what % of votes in the final round? PERCENTL Party of chief executive has been how long in office PRTYIN

### Chief Executive’s Party

; Name of party, if any EXECME Right (R); Left (L); Center (C); Not applicable (0) EXECRLC Nationalist (1 if yes) EXECNAT Rural (1 if yes) EXECRURL Regional (1 if yes) EXECREG Religious (1 not specified, CH:Christian, CA: Catholic, IS: Islamic, HD: Hindu, BD: Buddhist, jw: Jewish, O: otherwise) EXECREL Time since formation under this name EXECAGE Does party of executive control all relevant houses? ALLHOUSE In systems with both non-ceremonial PM and President, what is the party affiliation of the one not called Chief Executive? NONCHIEF og, Party Variables in the Legislature | Herfndahl Index for Govt. HERFGOV .
8||12.1|4.5|
|**Pacific**|33.5|48.4||13.0|5.1|
|**South**|38.4|44.4||12.0|5.2|
|**Center-North**|36.5|46.4||12.0|5.1|
|**Center**<br>Source: Census 19|35.2<br>80,1990,and 2000.|46.9||12.5|5.4|

Fecundity, the number of children per mother, dropped from 2.8 in 1980 to 2.6 in 2000 in rural Mexico (Table 2.4). In the poor South region the fertility rate is still higher than in Mexico as a whole and the Capital region, namely by 0.3 percentage points and 0.7 percentage points, respectively. Women’s increased participation in the labor market (by 3.3 percentage points see Table 2.5) may have been an important factor contributing to the reduction in the fertility rate and drop in the dependency rate. The female labor force participation rate is still lower in the South region (24.2 percent) than in the Capital region (35 percent) and therefore the high fertility rate in the former could be an important factor explaining this difference. Moreover, lack of access to childcare facilities etc. may also play a roll in the large difference in labor market structure among regions.

6
# **Appendix: Exchange Rate Regime Classification**

The starting point is the IMF's quarterly database on exchange rate regimes, which encompasses a total of **10** categories, based on officially reported exchange arrangements for the period **1975-1996.** We transform the IMF database to a monthly basis, complementing the original source with information contained in Cottarelli and Giannini **(1997).** Finally, the classification is extended until March **1999,** using information from IMF reports and publications, including the Exchange Rate Arrangements and Restrictions and the International Finance Statistics, **1998** and **1999.** For the countries used in the individualcountry estimates, we extend the classification until December **1999.** In addition to the original classification, we construct new categories to account for the specific currency to which some fixed regimes are pegged.

For most experiments in the paper, we condense the ten categories in the original source into three broader exchange rate regimes: fixed (pegs), intermediate (limited flexibility, crawls, bands, managed floating), and flexible (free-floating). Specifically, pegged regimes include: peg to the **US** dollar, peg to the French franc, peg to other currencies (comprising Indian rupee, South African rand, British pound, and Deutsche mark), peg to SDR (IMF basket), and basket pegs (including the so-called Bretton Woods basket peg). In turn, intermediate regimes include: limited flexibility with respect to a basket, limited flexibility with respect to a single currency, limited flexibility with respect to a cooperative arrangement (including the European Monetary System), managed floating, crawling pegs, and crawling bands. The full details are given in the Appendix Table.

**23**